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How to Get a Data Science Internship in 2024

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Data science is a competitive field as it remains a preferred career prospect; in fact, the employment rate is expected to grow 36% by 2031. To successfully land a job, you must prove you know how to manage the responsibilities. Having practical experience on your resume makes you stand out to employers as a capable candidate who understands how to work with real-life data. And a way to gain experience is through internships.

This article shares advice on how to get a data science internship as a student or someone transitioning from a different field.

Table of Contents

• Where to Look for Data Science Internships
• What Skills Do You Need for Your First Data Science Internship?

Do You Need Experience to Get a Data Science Internship?

How to write a resume and cover letter for a data science internship, how to build a portfolio for your data science internship, how to build your personal brand to land a data science internship, what to expect as a data science intern, how to get a data science internship: next steps, where to look for data science internships.

More companies are extending internship opportunities for aspiring data scientists to gain relevant industry skills in a real-life setting. Here are some sites you can start your search.

Browse Online Job Boards

Companies that hire data science interns will often advertise the opportunities on online job boards, including:

You can also browse specific data science job boards. Some noteworthy ones include KDNuggets , DataJobs , and iCrunchData .

Large companies like Meta, Google, and Amazon also offer data science internship programs they advertise on their websites. But keep in mind that they’re highly competitive. Of course, that shouldn’t deter you from applying because work experience in a prestigious company will look great on your resume.

University Boards

Remember to consider your institution's job boards if you're a student. Sometimes firms announce specific job openings exclusively through the university portal. Only a few pay attention to this source—so keeping an eye on it increases your chances.

Be proactive and contact intriguing startups. Generally, these firms are focused on setting up their business; they usually are looking for fresh perspectives and won’t have as many internship queries as bigger companies.

As a data science intern in a startup, you’d have more varied tasks than in a more established company with advanced processes. Working within such a team would be great for your personal development, and you’ll gain valuable experience working in a dynamic environment.

Data Science Conferences

The benefit of visiting data science events is that you network with professionals in the field. If you talk to the right people, you can expand your connection list and invite work experience opportunities. It’s a time-efficient process, and you can make a good impression by showing off proactive motivation.

Data Science News

Suppose your future employer asks you about the latest data science article you read. You don’t spend much time browsing the internet for data science news, but you open your email once or twice a week and read the newsletters from such websites as  SmartDataCollective  and  KDNuggets . The names of these well-recognized platforms are memorable, leaving an incredible first impression. And, of course, the more you read, the more up-to-date you’ll be—and the more bonus points you score with your future employer.

What Skills Are Necessary for Your First Data Science Internship?

While you don’t need professional experience to become a data scientist intern, most employers look for individuals with domain knowledge, capabilities within the data science sphere, and desirable transferrable skills.

Let’s look at what will make you stand out.

Technical Skills

Your responsibilities as an intern will be part of the overall data science workflow—so you must be prepared to work with industry-standard tools and techniques. Our research into the data scientist job outlook showed that the most in-demand tools in the filed are Python and SQL . Other frequent requirements for internship candidates that appear in job postings include the following:

• Mathematical and analytical capabilities for data analysis and data manipulation
• Spreadsheet and functions proficiency for working with datasets in Excel

Occasionally, some ads list machine learning and business acumen, but these are more advanced domains you’ll learn as you grow into your role. Nevertheless, understanding them as early as possible is a good idea.

An excellent way to build your skills if you’re not from an academic data science background or have an adjacent degree is to take online courses and earn certificates for your resume. We recommend our data scientist career track , which starts from the building blocks of data science and introduces more advanced topics as you go.

Remember, your skills don’t need to be perfect, but having a good grasp of the theory and tools will impress your employers and give you a competitive advantage in the job market.

Soft Skills

Employers also look for essential soft skills when hiring data science interns, including:

• Strong communication skills (written and verbal)
• The ability to translate complex technical topics to non-tech stakeholders

Finally, being proactive and showing a desire to learn will leave a good impression on your internship supervisors and open opportunities to expand your toolkit into broader data science techniques.

You don’t need formal experience to acquire a data science internship—after all, that’s why you’re doing it in the first place. An intern’s fresh perspective brings new and innovative ideas to any team. “Companies can use this perspective to their advantage by working closely with interns to develop and test new hypotheses,” says Eric Frenkiel (co-founder and CEO of database startup MemSQL).

Employers often require you to be a student or have higher education (bachelor’s or master’s degree)—typically in data science , computer science, statistics, or an adjacent degree in the industry they’re working in.

If you’re switching careers from an unrelated field, check out our guide on How to Become a Data Scientist with No Experience .

The key to landing an internship in data science lies in a well-presented application. After all, your resume and cover letter will be at the fore of potential employers—so make it count.

The sooner you start building your data science resume , the better. You need to ensure it’s up-to-date and includes previous projects¾we’ll talk about those in the next section.

Tailor your application to the company you’re applying for an internship with, which should include the following:

• Customize your resume to fit the job spec—but only if you meet the requirements. (Lying on your application is bad practice.)
• Include keywords from the posting in your cover letter to show you understand what the company needs from interns and demonstrate you have what it takes.
• Highlight your technical capabilities and how you’ve earned them, including your formal education and certifications.
• Emphasize the transferrable skills that make you a good fit for the role—even if you’ve developed them in a non-data science-related capacity.

Employers may not pick a generic resume and cover letter from the vast pile, but a customized one will ensure you stand out from the other applicants. Suppose you'd like to learn how to tailor your cover letter for success. In that case, you can read our dedicated guides on the topic:

• How to Write a Winning Data Science Cover Letter
• How to Organize a Data Science Cover Letter
• How to Format a Data Science Cover Letter
• Data Science Cover Letter Dos and Don'ts

Another tip is to be aware of appropriate interview etiquette to have a better chance of landing the data science internship. Practice possible interview questions with your friends—making you feel more confident and well-prepared.

A robust portfolio is an excellent way to land temporary work experience or a permanent position. Employers like to see you know how to implement your skills in real-life situations. And projects are a great way to prove that.

So, where should you start? The following addresses the best ways to work on data science projects while studying or upskilling.

Enter Data Science Competitions

Kaggle is one of the best platforms to build and share your data science portfolio. You gain hands-on experience with real-life datasets by entering Kaggle competitions . You’ll also learn data cleaning techniques and practice analyzing data and model creation—skills that make you a desirable data science intern.

Post on GitHub

Some hiring managers look at the applicant’s GitHub profile to better understand what they’ve built and how they made it. GitHub is where you can upload your code and interact with data scientists and  machine learning engineers . An active account on the platform is a powerful signal that you wish to enter the field and can help you build credibility.

Freelance Work

Another great idea is to pick up side projects. With platforms like Toptal and Upwork, you can sign up as a data science freelancer and work with real-world clients on various tasks. You can then add these projects to your portfolio and discuss them in your application.

It might be initially challenging to land freelance work—especially if you’re at a data science internship level—but when you do, you’ll gain valuable work experience and learn a lot along the way.

Personal branding is your online appearance and what you want hiring managers to know about you. This helps make an even more lasting impression on future employers because they’ll learn what you’re passionate about and what you engage with outside of your application. In other words, personal branding shows more of your personality.

We suggest creating a professional LinkedIn profile. Unlike your resume, this allows you to describe all your projects and work experience with companies in depth.

Employers search for people on LinkedIn often, so your objective as a future data science intern is to show up in the search. For this reason, consider including relevant keywords in your profile.

In addition, LinkedIn helps you gain insights into industry trends or how you compare with other aspiring data scientists. It can also strategically cultivate your network and build your brand.

Data science internships are a unique opportunity for those who wish to gain hands-on experience working with data at a fast-growing company.

Many students and recent graduates have difficulties entering their first job because they’re not used to working with messier and more complex data than they experienced while studying.

Why does that happen? As a student, many of the datasets you encounter are carefully preprocessed by the course instructor—so they’re cleaner than actual real-world ones. This is one of the benefits of taking an internship: to learn how to manage messy data.

Those who have just entered the field have high expectations about the job. Still, it’s improbable that you’ll be tasked with creating a machine learning algorithm immediately because 90% of machine learning is preprocessing, and 10% is modeling.

To summarize, expect messy, raw data and all that comes with this alluring chaos—i.e., the hands-on experience and unparalleled exposure to skilled data scientists that will help you along the way.

As an intern, you’ll be on a team of professionals who solve business problems to create value. We’ve mentioned machine learning, but a more probable workload scenario would involve the following:

• Conducting analyses
• Producing reports
• Building creative data visualizations
• Molding data into a narrative

You’ll be asked to devise metrics, design randomized controlled experiments, and tackle hard, open-ended problems .

This might sound overwhelming for the novice data scientist, but you won’t be alone. You’ll work closely with engineers, product designers, and product managers who will offer support and guide you through the internship.

The benefits of a data science internship are countless—from working with professionals in the field to building your portfolio and receiving excellent mentorship and networking opportunities. You’ll learn from professional data scientists and demonstrate you’re one step ahead of your peers.

Check out The Ultimate Data Science Career Guide (our all-encompassing resource) for more career guidance and advice on how to break into data science.

And if you’re looking for how to improve your chances of getting a data science internship, the 365 Data Science program offers self-paced courses by prominent industry professionals who leverage their experience to help you break into data science. Begin by mastering the data science fundamentals, build your coding skills in Python and SQL, level up to more advanced machine learning methodologies, and gain invaluable business domain knowledge. Our data scientist career track equips you with the necessary toolkit to confidently pursue your dream job with the right qualifications.

Sign up below to start your journey.

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Thesis/Capstone for Master's in Data Science | Northwestern SPS - Northwestern School of Professional Studies

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Capstone and thesis overview.

Capstone and thesis are similar in that they both represent a culminating, scholarly effort of high quality. Both should clearly state a problem or issue to be addressed. Both will allow students to complete a larger project and produce a product or publication that can be highlighted on their resumes. Students should consider the factors below when deciding whether a capstone or thesis may be more appropriate to pursue.

A capstone is a practical or real-world project that can emphasize preparation for professional practice. A capstone is more appropriate if:

• you don't necessarily need or want the experience of the research process or writing a big publication
• you want more input on your project, from fellow students and instructors
• you want more structure to your project, including assignment deadlines and due dates
• you want to complete the project or graduate in a timely manner

A student can enroll in MSDS 498 Capstone in any term. However, capstone specialization courses can provide a unique student experience and may be offered only twice a year. 

A thesis is an academic-focused research project with broader applicability. A thesis is more appropriate if:

• you want to get a PhD or other advanced degree and want the experience of the research process and writing for publication
• you want to work individually with a specific faculty member who serves as your thesis adviser
• you are more self-directed, are good at managing your own projects with very little supervision, and have a clear direction for your work
• you have a project that requires more time to pursue

Students can enroll in MSDS 590 Thesis as long as there is an approved thesis project proposal, identified thesis adviser, and all other required documentation at least two weeks before the start of any term.

From Faculty Director, Thomas W. Miller, PhD

Capstone projects and thesis research give students a chance to study topics of special interest to them. Students can highlight analytical skills developed in the program. Work on capstone and thesis research projects often leads to publications that students can highlight on their resumes.”

A thesis is an individual research project that usually takes two to four terms to complete. Capstone course sections, on the other hand, represent a one-term commitment.

Students need to evaluate their options prior to choosing a capstone course section because capstones vary widely from one instructor to the next. There are both general and specialization-focused capstone sections. Some capstone sections offer in individual research projects, others offer team research projects, and a few give students a choice of individual or team projects.

Students should refer to the SPS Graduate Student Handbook for more information regarding registration for either MSDS 590 Thesis or MSDS 498 Capstone.

Capstone Experience

If students wish to engage with an outside organization to work on a project for capstone, they can refer to this checklist and lessons learned for some helpful tips.

Capstone Checklist

• Start early — set aside a minimum of one to two months prior to the capstone quarter to determine the industry and modeling interests.
• Networking — pitch your idea to potential organizations for projects and focus on the business benefits you can provide.
• Permission request — make sure your final project can be shared with others in the course and the information can be made public.
• Engagement — engage with the capstone professor prior to and immediately after getting the dataset to ensure appropriate scope for the 10 weeks.
• Teambuilding — recruit team members who have similar interests for the type of project during the first week of the course.

Capstone Lesson Learned

• Access to company data can take longer than expected; not having this access before or at the start of the term can severely delay the progress
• Project timeline should align with coursework timeline as closely as possible
• One point of contact (POC) for business facing to ensure streamlined messages and more effective time management with the organization
• Expectation management on both sides: (business) this is pro-bono (students) this does not guarantee internship or job opportunities
• Data security/masking not executed in time can risk the opportunity completely

Publication of Work

Northwestern University Libraries offers an option for students to publish their master’s thesis or capstone in Arch, Northwestern’s open access research and data repository.

Benefits for publishing your thesis:

• Your work will be indexed by search engines and discoverable by researchers around the world, extending your work’s impact beyond Northwestern
• Your work will be assigned a Digital Object Identifier (DOI) to ensure perpetual online access and to facilitate scholarly citation
• Your work will help accelerate discovery and increase knowledge in your subject domain by adding to the global corpus of public scholarly information

Get started:

• Visit Arch online
• Log in with your NetID
• Describe your thesis: title, author, date, keywords, rights, license, subject, etc.
• Upload your thesis or capstone PDF and any related supplemental files (data, code, images, presentations, documentation, etc.)
• Select a visibility: Public, Northwestern-only, Embargo (i.e. delayed release)
• Save your work to the repository

Your thesis manuscript or capstone report will then be published on the MSDS page. You can view other published work here .

For questions or support in publishing your thesis or capstone, please contact [email protected] .

The Ultimate Guide to Landing a Data Science Internship

Looking to break into the field of data science? Find out how to land your dream internship with this ultimate guide.

As a beginner in tech, you’ve probably found it challenging to get the right data science internship. Questions about which firms to apply to and steps to take plague newcomers to the tech industry. Unfortunately, this worry isn’t out of place because internships can make or break your data science career.

Knowledge is power, so knowing a few strategic steps can land you your first data science internship in no time. Here, you’ll learn where to search for internships, requirements, and solid tips for getting a data science internship. Keep reading to discover more.

What Is a Data Science Internship?

A data science internship is any program where a beginner in data science can get hands-on experience, practice their skills, and clearly understand the scope of the field. It usually lasts around three to four months, but some may run for up to a year, depending on the organization.

As a data science intern, you’ll work with more seasoned professionals to fetch, analyze, and collate data and make professional reports on your findings. These activities eventually culminate in valuable industry experience relevant to employers, besides volunteer work or actual jobs.

Skills Required for a Data Science Internship

Before starting your search, there are a few skills you must have locked in place. Having these skills prior to sending out applications gives you a higher chance of landing a data science internship.

In addition, most companies will require that applicants have some background knowledge, and a few may give quizzes before admitting you. So we’ll see a few of them below.

1. Knowledge of Programming and Scripting Languages

Although programming isn’t a core part of data science, it can help you visualize and manage large, unstructured data. Python is the most commonly used data science programming language, but you can go further with R.

Other data science languages include Julia, Matlab, Java, SAS, and C++. However, remember, you’re applying as an intern; no one expects you to know everything from the start.

2. Knowledge of the Core Data Science Tools

Writing codes and learning algorithms will help automate some processes and arrange data, but that’s not the only necessary skill for a data science internship. You’ll also need to make charts, predict models, and properly analyze your data. This is where your data science tools come in.

Thanks to technology, data scientists now have options for collecting, collating, cleaning, and transforming data. One of these options is Microsoft’s Power BI, a ground-breaking software that transforms bulk data into aesthetic charts and dashboards. For equally useful alternatives, go for Excel or Tableau.

3. Statistics

While it may look daunting, especially if you’re self-learning, statistics isn’t an unscalable hurdle. A background in statistics will enable you to relate to and analyze your data more effectively. Some key concepts in data science, like logistic regression and clustering, are built on statistics and mathematics.

A fundamental understanding of the subject boosts your chances of bagging data science internships and paints a clearer picture of your career path. Note that you can start your data science career without a degree in statistics, so don’t let that worry you.

Tips for Getting Your Data Science Internship

What are the next steps to take to ensure you land choice internships? Learning many skills won’t do you much good if you’re not using them, so study these pointers below.

1. Work on Personal Projects

Practice, as they say, makes perfect, and this also applies to data science. To get selected for good data science internships, you need something to show in your resume or portfolio, creating the need for personal projects.

Many tutorial sites and courses have built-in projects or assignments that you can showcase. So rather than waiting till you’re at the finish line of your learning journey, build projects with any new knowledge you acquire. This way, you’ll have a variety of work to present when you’re ready to begin your search.

2. Create an ATS-Compliant Resume and Cover Letter

Once you have your projects ready, creating your resume follows. At first glance, this may appear easy, but seemingly minor mistakes could cost you later. One such mistake is failure to learn how to write an ATS-friendly resume .

Although this isn’t strictly data science-related, it’s no less important. Making your resume ATS-compliant ensures that it stands out amongst several similar ones. Furthermore, you want to tailor your cover letter to suit the specific internship, rather than copying a random sample off the internet.

3. Build Your Network by Attending Data Science Events and Tech Workshops

Your network is your net worth, even in the tech world. Besides your applications, referrals and recommendations are another great way to land data science internships.

An extensive network skyrockets your prospects of getting that lucky recommendation, and attending events is a reliable method of building your network. There’s no rule against attending non-tech events, but you would benefit more from a network of people in your niche or related fields.

4. Reach Out to Startups

In the tech community, there’s a general misconception that startups don’t need data scientists. However, this is both false and limiting. Apply to more established firms, but don’t hesitate to inquire about available data science internships at smaller companies and startups around you.

5. Actively Utilize GitHub and Kaggle

GitHub and Kaggle are like the equivalents of Facebook and Instagram for data scientists. With GitHub, you can store any codes you write and share them with hiring managers and interviewers.

On the other hand, Kaggle lets you write and share your Python and R codes, take courses on analysis, and win amazing prizes! Both sites have their pros and cons, so it’s up to you to choose which to use. Whichever you stick with, frequently push your codes and build an impressive presence.

Where to Find Data Science Internships

With your skills in the bag, a solid resume and portfolio, and a good network, you’re now eligible for data science internships. Here are a few sites to begin your hunt.

1. LinkedIn

Several firms and hiring managers post available internships and job opportunities on their LinkedIn pages. To take advantage of this, ensure you follow people in the data science or tech niche.

Also, like any social networking site, LinkedIn is a personal advertisement site that every professional can leverage. Tailoring your posts around data science and showcasing your learning progress can generate interest from companies looking to offer data science internships.

2. Handshake

This site is more targeted at college students, so if you're an undergraduate, this one’s for you. Simply sign up, search for “data science internships,” and apply to any that pique your interest. The best part about Handshake is you don’t need previous experience, making it perfect for a beginner. Other popular websites for finding internships include Indeed and Glassdoor .

3. University Job Boards

If your institution has a job posting platform, then this could be beneficial in your search for data science internships. Also, studying a related course gives you a slight edge; feel free to contact and make inquiries from your professors and instructors.

Land Your Dream Data Science Internship With These Steps

By following the steps above, you can land the best data science internships that’ll grow your career in leaps and bounds. Proper training is the first step to a successful career, and there’s a world of them for beginners, depending on your preferences. Remember, they don’t have to be physical; virtual data science internships also count.

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Unveiling the Value: A Data Science Internship Experience

A data science internship during graduate school is like a turbo boost for learning. It’s where theories meet the real world and you get your hands dirty with actual data.

Sebastian Carmona Angel, a UMBC Data Science graduate student, recounts his enlightening Uber internship journey. This firsthand account highlights the importance of internships in shaping a successful data science career.

The Transition from Petroleum Engineer to Data Science

Sebastian’s journey began in the field of petroleum engineering, but the winds of change blew in with the onset of COVID-19. With newfound free time, he immersed himself in Python and various data-related courses. This experience led him to his first role as a data scientist, setting the stage for his decision to pursue a master’s degree at UMBC.

UMBC’s Data Science Program: A Catalyst for Career Growth

Sebastian chose UMBC’s Data Science Program for its emphasis on hands-on learning and the opportunity to develop a comprehensive portfolio. He valued practical experience for employers, seeking a program with tangible projects to showcase his skills.

The Uber Data Science Internship: An Immersive Dive into AB Testing

During his internship at Uber, Sebastian joined the experimentation team, where he worked on crucial projects centered around AB testing. He assessed data pipelines and provided real-time insights for internal users to manage experiments. This experience honed his technical abilities and broadened his understanding of how data science drives product development.

Sebastian’s Uber internship provided not just technical skills, but also valuable insights into tools like Python libraries for dashboards.

Diversity and Inclusion: Enriching the Learning Experience

One of the standout features of UMBC’s Data Science Program is its commitment to diversity and inclusivity. Sebastian highlights how interacting with peers from diverse backgrounds enhanced his learning experience. This global perspective has proved invaluable in his professional journey.

Career Center Support: A Bridge to Success

UMBC’s Career Center played a key role in refining Sebastian’s skills, leading to his Uber internship. The center’s guidance proved instrumental in positioning him as a competitive candidate in the job market.

The Value of Hands-On Learning

Sebastian’s internship experience at Uber serves as a testament to the immense value of hands-on learning in data science. UMBC’s Data Science Program emphasizes practical projects and diversity, preparing professionals for success in this evolving field. By sharing his story, Sebastian offers aspiring data scientists a roadmap to success, emphasizing the transformative power of internships in shaping a thriving career.

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Data science internal internship in higher education at brandeis university.

Top performing students can help drive innovation and increase operational effectiveness when it comes to data scientific applications in higher education. The Data Science Internal Internship (DSII) in Higher Education at Brandeis University explores this possibility by linking relevant COSI faculty, top performing undergraduate students, and willing administrators across the university open to allowing students to bring data scientific solutions into existing work-flows.

The project supports innovation in university administration while giving DSII students the opportunity to acquire practical experience in data science applications, get paid for their efforts, leave a noteworthy legacy of contribution to their  alma mater , and bolster their preparedness for making a positive impact in the world through technology.

The DSII creates partnerships between undergraduates in data science and administrative offices, oversees the experiential learning of participating students, and tracks the impact of bringing data science, machine learning, and artificial intelligence into higher education operations. It grew out of Dr. Liebowitz’s and Professor Tim Hickey’s data analytic research on the higher education workforce. For more context and background,  see the article about the Brandeis Data Science Internal Internship project in the July-August 2023 edition of Trusteeship Magazine , and also the article in Brandeis Stories . 

Examples of Project Partnerships Between DSII Interns and Administrative Mentors

Some of the examples below represent on-going innovation in data scientific applications by multiple generations of DSII students. Others are of work already completed and designed to update automatically. Others still are of successful prototypes built by students that are now poised to become fully integrated by full-time staff or technical consultants into existing workflows.

Campus Planning and Operations

• Improving response to daily building alarms : DSII Intern built machine-readable dataset of alarms from all campus buildings, then created analytics to pinpoint faulty alarms, missing sensors, and disproportionately high percentages associated with specific buildings. These analytics help increase the capacity of administrators to rely on data-informed decision-making to allocate scarce staff resources to response and repair.
• Enabling geographic view of utilities infrastructure : DSII Intern designed the schema necessary to create a spatial data platform for viewing all buildings, infrastructure, and utilities components on a campus map. The prototype geographical information system includes precise details of sewer pipes, manhole covers, and electric submeters, all aligned with location-specific measures of deferred maintenance, enabling administrators to more effectively evaluate potential investments (“does it make sense to install a bathroom on this spot in this field?”) and troubleshoot building failures (“which water pipe might be responsible for that manhole spewing so much steam?”)
• Measuring and predicting electricity use : DSII Interns engineered the integration of data from multiple current and historical sources to create a machine-readable dataset of electricity usage by campus building. Interns designed and built a dashboard that allows administrators to analyze usage levels and patterns for each building across seasons (winter, spring, summer, fall) and functions (residence hall, science building, gym, etc). Interns also began building predictive tools for better estimating energy usage by building as well as to highlight relevant anomalies deserving of strategic attention. For senior thesis in Computer Science based on this work,  please read here .
• Transportation Logistics : DSII Intern created the data scientific platform, including regular feeds of machine-readable data, and user-friendly dashboards, to analyze and visualize ridership data of university campus shuttle service. This data visualization allows administration to identify what day and time vehicles are reaching maximum capacity, as well as identify underutilized stops and times.  This work also generates general trends such as ridership by day, time, and stop as well as highlighting vendor on-time/early/late arrival performance by stop. This is transforming the capacity of administrators to more effectively and efficiently align campus transportation resources to community needs. 

Dean of Arts & Sciences

• Analyzing trends in course enrollments and instructor workloads : DSII Interns engineered and integrated datasets to make it possible to build dashboards of historical and current data on undergraduate course enrollments and instructor workloads. The dashboards now make it possible to disaggregate the data by academic department, instructor, undergraduate major, and individual course. Experimental analytics by Interns based on this new data platform demonstrate the possibility of predicting enrollments in required and high traffic courses, a functionality that, once formally implemented, enables administrators to more effectively plan and allocate academic resources.

Graduate School of Arts & Sciences

• Analyzing trends in course enrollments for 100+ level courses across the university’s graduate programs : DSII Intern will leverage the data engineering and dashboard design expertise of Interns working in the Dean of Arts & Sciences to build out a dashboard of course enrollment trends and analytics specific to graduate school courses. As with other DSII projects that introduce dashboard functionality to existing work-flows, this will require integration of multiple data sets from various sources. Accomplishment of the goals of this project will bring dramatically increased efficiencies to administrators responsible for assessing and allocating academic resources at the graduate level.

Human Resources

• Launching a Human Capital Management dashboard of  Organizational metrics : DSII Interns began by assembling a prototype HR dashboard, then integrating the data streams and analytics necessary to create metrics showing turnover rates, time-to-fill for open positions, degree of diversity in recruitment pools, and completion rates for performance reviews. Interns also helped collect feedback from a pilot group of users across the university to improve the design and usability of the dashboard. Interns are now creating predictive analytic tools that, once formally implemented through the HR dashboard, will help senior leaders foresee and manage workforce related challenges and opportunities. In addition, current Intern developed a user-friendly encoder for HR admins to use when needing to anonymize sensitive HR data. 

Institutional Advancement

• Building predictive models of charitable giving and of bequests that are customized to Brandeis alumni, donors, and friends : DSII Interns brought data scientific analyses to an entire corpus of Brandeis alumni and fundraising data, including current and historical data dating back to the founding of the university in 1948, enabling fundraising admins to identify key characteristics of those most likely to make major gifts or to leave bequests to the university. Interns are relying on these initial analyses to develop tools for data-informed guidance to administrators making resource allocation decisions regarding the cultivation of potential donors.

Information and Technology Services

• Expanding data analytic client services capacity : DSII Interns develop trustworthy and reliable expertise in report generation through Workday (the university’s new cloud-based enterprise management system) which helps administrators broaden data scientific support across the university. For example, DSII Interns in ITS were instrumental in helping to roll out the Human Capital Management dashboard.
• Experimenting with potential improvements to Workday implementation : DSII Interns in ITS help specify potential solutions to data-management challenges that emerge in the Workday implementation process. This has included identifying opportunities for improvement in the Workday Student module, with technical descriptions of functionalities that need to be activated in order to better serve the Brandeis student community.
• Speeding up deployment of data management innovations : DSII Intern was able to create an automated solution to the NSF’s required annual Higher Education Research and Development (HERD) survey thanks to application of innovative data management tool Prism, which is specifically aimed at enabling the Workday ERP to effectively integrate non-native data. Full-time staff resources had not yet been available to deploy Prism for the NSF HERD survey. 
• Security, Access, and Collaboration Among DSII Interns : Intern in ITS is creating new infrastructure for governance check-lists related to on-boarding and off-boarding of DSII projects, including dynamic inventory of datasets being used, software tools being deployed, and details of differentiated access being granted by administrators. This automated system, once implemented, will enable security-conscious distribution and sharing of DSII-generated tools across university divisions.

Office of Investment Management

• Increasing efficiencies in information retrieval to support the investment decision process: Intern launched experimentation with varieties of language-based data scientific tools to search voluminous and multifaceted documents containing investment-relevant information. The results of this experimentation customized to the Brandeis Investment Office will be used by management to support workflow changes aimed at dramatically cutting down on the number of hours needed to identify key insights, trends, and relationships necessary for investment decision-making. 
• Enabling strategic understanding of Leaves of Absence (LOA) taken by undergraduates : Intern engineered and integrated multiple sources of unstructured data into a machine-readable dataset relevant to LOA students, including reasons for the leaves, length, graduation rates, academic majors, and demographic characteristics of the LOA students. Creating a dashboard based on this dataset has now made it possible for administrators to begin analyzing patterns and implications of LOA trends for potential interventions to help improve student outcomes. Intern also automated the process for regularly integrating data and updating analytics based on information from new generations of students.
• Analyzing bias in student course evaluations : Intern created a machine-readable data analytic platform capable of identifying demographic bias driving student responses in course evaluations. This required exploration and application of appropriate statistical techniques for the complex social science analysis of biased behaviors. Preliminary results show surprisingly low to no discrimination from Brandeis students based on the gender of instructors. Results for potential race and ethnicity bias are inconclusive, so far, due to insufficient data. 

Office of Research Administration

• Visualizing trends in sponsored research and predicting likelihood that any one particular grant proposal will be funded : Thanks to the dashboard built by DSII Interns, experts in research funding at Brandeis are now able to more easily share trends with senior academic leaders about the percentage of grant proposals that are successfully funded, with break-outs by department, principal investigator (PI), faculty rank of PI, and individual granting agency. Interns are also working on predictive tools that will help make it possible for administrators to identify not only the likelihood of a proposal’s success, but also the specific characteristics of the proposal that might increase or decrease its likelihood of success.

Office of Technology Licensing

• Identifying key features of federally funded grants that are likely to result in successful development of patents : Intern is engineering, parsing, and linking datasets of federally funded research projects together with US Patent Office data to help specify the profile of grants that result in patents. Once applied to the universe of federally funded projects at Brandeis, this exploration will help highlight those research projects on campus with translational potential. 

Student Affairs

• Creating interactive access to outcomes data and to analytics that answer administrators’ Brandeis-relevant questions from the National College Health Assessment (NCHA) survey : DSII Intern transformed NCHA data into format capable of supporting user friendly dashboard. This now makes it possible for administrators to better understand risks associated with student wellness and to design programs and interventions targeted to address these data-informed insights. DSII Intern automated the process for updating the dashboard with new results from the NCHA. For more detail on this DSII project, see section below on “Measurable Impact.”

The DSII is co-sponsored by participating administrative offices, the Department of Computer Science, and the Office of the President.

Measurable Impact

We are beginning to explore various ways to measure the impact of the DSII concept. Our sense is that each DSII Intern-Admin partnership will suggest somewhat different ways of measuring impact.

For example, in the case from Student Affairs, below, the administrator reported a 99% reduction in time needed to access relevant analytics, thanks to the dashboard built with the DSII student. Measuring the amount of time an administrator is dedicating to getting questions answered seems like a meaningful measure of impact.

Another example we see from the Student Affairs case: counting the numbers of program modifications, or new programs and solutions that administrators are able to implement as a result of the data scientific tools and techniques introduced by the DSII, also seems like a path worth pursuing. The case study below describes one such program modification resulting from analytics that would not otherwise have been evident to Student Affairs staff had it not been for the dashboard built through the DSII.

Our explorations of impact measurement will also include the perspective of the DSII’s educational experience. We know that in the Student Affairs case, the Intern faced the responsibility of quickly and effectively teaching themselves “plotly” and “dash” (python-based app design tool that is open source) to build the requisite dashboard. The Intern acquired these skills while learning to support an intensely iterative process with their administrative mentor, acquiring domain knowledge and testing software development progress along the way. So, a combination of mastering new technical skills, self-directed learning, and organizational leadership was required to get the job done in this case. We will be designing a survey to help capture and measure this type of educational experience.

Case Study of Impact: Student Affairs As part of its responsibility for supporting and improving student well-being on campus, the Division of Student Affairs conducts the National College Health Assessment (NCHA) of the American College Health Association (ACHA) on the Brandeis campus. The value of participating in the survey is that Brandeis Student Affairs is able to receive and analyze completed data to better inform program design and implementation plans regarding student wellness. Unfortunately, student affairs professionals across the country have been constrained in their ability to leverage the NCHA data in support of wellness objectives because of the format of the data: the ACHA manages and delivers the survey through SPSS, a statistical software package introduced 55 years ago which lacks the capability to support quick access to key insights with the greatest relevance to decision-making. The DSII Intern converted the SPSS data into formats that are machine-readable by modern programming languages like python. This made it possible for the DSII to build a dashboard for easy interactivity with the data. The result is a dramatic reduction in the time needed to access key findings in the data, which in turn dramatically raises the capacity of Brandeis Student Affairs professionals to pull practical relevance out of NCHA data. The application of data scientific techniques to NCHA data at Brandeis has also made it possible to conduct analyses that would not otherwise be possible: these include most importantly the disaggregation of data across multiple groupings, such as race, gender, academic program, family background, and year of graduation. Take alcohol consumption as a student wellness issue of obvious importance that needs measuring, monitoring, and guidance on a college campus: student affairs professionals are naturally concerned about risky behaviors involving alcohol among first-year students, since the inexperience of first-years suggests that they stand to benefit most from extra guidance and education. Yet, the data scientific analyses of the NCHA data at Brandeis, which could be disaggregated by class year, demonstrated that it is juniors and seniors who are most engaging in dangerous behaviors involving alcohol. As a result, Brandeis professionals are now redesigning student wellness materials to incorporate the extra support needed by upper-class students. DSII Intern automated the process by which new rounds of NCHA survey data will be updated into the interactive dashboard. The creation of the NCHA dashboard promises to generate many examples of positive impact.

Details of research we are launching based on DSII project on “reverse mentoring” and on “scrum and agile methodologies” are forthcoming.

Intern Finalist Selection Committee

• Tim Hickey , Professor, Undergraduate Advising Head, Computer Science 
• Jessica Liebowitz , Research Scientist, Computer Science
• Olga Papaemmanouil , Professor, Computer Science, and Senior Associate Dean of Academic Affairs
• Pito Salas , Professor of Practice, Computer Science
• Nianwen Xue , Professor and Chair of Computer Science, ex officio
• Antonella Di Lillo , Associate Professor, Computer Science

Co-Directors

Jessica Liebowitz  is a Research Scientist in Computer Science at Brandeis, where she has helped launch the Data Science Internal Internship (DSII). This project creates partnerships between undergraduates in data science and administrative offices across the university. It oversees the experiential learning of participating students, and tracks the impact of bringing data science, machine learning, and artificial intelligence into higher ed operations. It grew out of Dr. Liebowitz's and Professor Tim Hickey's data analytic research on the higher ed workforce.

Dr. Liebowitz holds a PhD from Harvard University, BA from Yale University, and Honorary Doctorate from Middlebury College.

Timothy J. Hickey  is a Professor of Computer Science whose current work focuses on educational technology, brain-computer interfaces and game-based learning. His specialties include analysis of algorithms, logic programming and parallel processing, symbolic manipulation, and groupware.

For information on applying to be a DSII Intern, or becoming an administrative partner, please contact  Sandra Iriskic , Executive Administrator.

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Data science thesis internship

Business studies and/or management science, engineering and/or technology, mathematics and/or informatics.

• Internship details

General information

Dashmote is an AI technology scale-up headquartered in Amsterdam, The Netherlands. With the goal of bridging the gap between images and data, we are working to bring AI-based solutions to marketers at clients like Heineken, Unilever, Philips, L’Oreal, and Coca-Cola. We add value in areas such as Location Analysis, Trends Analysis, and Marketing Intelligence.

Today, our company has offices in Amsterdam, Shanghai, Vienna, and New York. Over the past few years, our teams have solved a wide variety of cases, such as analyzing beer drinking and hairstyle trends by utilizing our Visual Recognition Tools, as well as identifying prospective leads by generating intelligence dashboards derived from Visual Content Analysis.

Role Description

You are an enthusiastic student with a passion for data and analytics and is looking for a hands-on thesis internship opportunity at a growing tech scaleup. You are able to analyze big data sets, combining your technical input with A.I. sourced information and build visualizations to effectively communicate strategic data insights with customers.

Some of the typical responsibilities are:

Working with external stakeholders to define the objective of the analysis

Obtaining data from multiple disparate sources such as structured, semi-structured, and unstructured sources

Using statistical analysis techniques to understand patterns in large volumes of data, identify relationships and data clusters, detect data anomalies, and classify data sets

Develop machine learning algorithms (supervised and non-supervised algorithms) based on large databases (consumer profiling, clustering and such)

Keep improving our process and workflow. We're in a growing stage and therefore very open to discover and use new technologies and explore how we can continuously improve our way of working.

Our (current) tech stack:

Python and R for project development

PowerBI for visualization

GCP and AWS for cloud storage

Serverless and Docker for API deployment

SQL and NoSQL for database management

Requirements

• Final year student in a quantitative field such as Data Science, Artificial Intelligence, Statistics, Economics or relevant area

Experience with programming languages such as R, Stata, Python, SQL and visualization tools  

Good understanding of data mining techniques and data preparation

Prior internship/project experience in data science is a plus point

What's in it for you?

Great office location in Rokin, Amsterdam (5 minutes walk from Dam Square and 15 minutes walk from Amsterdam Central station)

Working with an international team that truly values your contribution

An awesome culture of responsibility and freedom to turn your ambition into reality - regardless of your role and level

Exciting work atmosphere with no shortage of snacks, drinks, birthday treats, and social events

At Novo Nordisk, we are committed to your development. You will have the opportunity to kick-start your career by working closely with experienced industry professionals and gaining valuable, hands-on, full-time work experience. You will be immersed in the environment of a global pharmaceutical company and will contribute to the line of business by providing sustainable and meaningful project work. We offer internship opportunities in various lines of business in our Denmark headquarters office and in affiliate offices around the world. 

If you want to be considered for future internships, sign up for our Talent Pipelines. We fill most of our internships through Talent Pipelines. By signing up you will automatically be considered for all internship positions at Novo Nordisk which match your educational background and interests.

You can apply for our Talent Pipeline twice a year: in February and September. We evaluate the applications on an ongoing basis. So long as you meet the basic requirements, you become part of the Talent Pipeline for up to 6 months. After this period, you need to apply again to ensure that you’re an active part of the pipeline.

For further descriptions of the Talent Pipelines, scroll down to read more. From time to time, we also post specific positions that you may apply to directly. Internships in our affiliates outside Denmark are posted on our career site. 

See available positions

We are looking for students who are currently pursuing a Bachelor’s or Master’s degree within HR, Communication, Marketing, Psychology, Business Administration, or other HR, Marketing and Communication-related educations. This Talent Pipeline addresses internships within Talent Acquisition (Recruitment), Early Talent, Talent Management and Training & Development, Employer Branding, Communication, Marketing, Project Management, Training & Learning, and Administrative Support.

We are looking for students who are currently pursuing a Bachelor’s or Master’s degree in Engineering e.g. Mechanical, Chemical or Production Engineering, Innovation or another relevant field. This Talent Pipeline addresses Internships within data engineering and software engineering, production and test of production lines, automation disciplines, e.g. robotics, mechanical development and solutions, modelling (pilots, testing etc.), innovation- and process optimization and business support.

We are looking for students who are currently pursuing a Bachelor’s or Master’s degree in Finance, Strategy, Marketing, Business Analytics, Business Administration, Economics, Law or another relevant field. This Talent Pipeline addresses Internships within development and execution of business strategy, Supply Chain Management, Business Intelligence, data analytics, advising and supporting within global legal affairs, legal governance and compliance, Market Research and Customer Insights, Brand Strategy Development, and Project Management.

This Talent Pipeline addresses Internships within digital innovation and transformation, data science and data engineering, software development, IT security, IT automation, IT support and operations and supply chain analytics.

We are looking for students who are currently pursuing their Bachelor’s or Master’s degree within Life Sciences, e.g. Cell Biology, Human Biology, Biochemistry, Bioengineering, Medicine, Pharmacy or another relevant fields to join our Talent Pipeline within Life Sciences. This Talent Pipeline addresses Internships within research and development, medicine, clinical science, quality control, stem cell biology, and regulatory affairs.

Applicants must be studying for a Bachelor’s, Master’s or Doctoral* degree (*only for internships in the US) and with a minimum of 6 months of studies left.  Novo Nordisk’s corporate language is English therefore you must be able to write and speak English fluently.

From time to time, we also post specific positions that you may apply to directly. Other available positions are advertised on the  Jobs section .  You can also sign up to our job agent  to receive an email when a position matching your criteria becomes available. 

Yes, all internships are paid opportunities per default, unless you require an unpaid internship.

The duration of an internship varies between Novo Nordisk office locations. We typically offer 3-6-month internships, some with a possible extension up to 12 months in total.

Between 18-37 hours a week, typically starting out working as close to full time as possible during the initial period. Flexible hours may be available, and consideration will be given to essential study commitments.

Available Talent Pipelines are posted on the jobs section in February and September every year. Other available internship positions are posted time to time on the jobs section as well.

No, you will be responsible for your own living and accommodations costs as an intern.

No, candidates coming from abroad will be responsible for finding their own accommodation.

No, international candidates are responsible for applying for the correct full-time work permit prior to starting in an internship position.

Yes, we encourage students to apply to as many Talent Pipelines and other available positions that are appropriate for their educational background and interest. But please note that you can only ever be an intern within Novo Nordisk once.

You are not guaranteed a permanent position after doing an internship. However, you will get the opportunity to establish a network in Novo Nordisk which could be beneficial for future career opportunities.

You can either sign up for the Global Talent Hub  or come and meet us at one of career fair events. 

Interested candidates will have the opportunity to speak with Novo Nordisk representatives to discuss career aspirations, gain insight into the organization and ask questions. Come find us at one of the many events we attend throughout the year!

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Applied Data Science

Research projects/internships.

Specific for Applied Data Science students: It is not possible to go abroad for internship. All internships have to be done in the Netherlands.

Research projects

Within all Master's programmes one or more research projects are mandatory (see ‘Study programme’ for general information).

• within the university, step 1 only
• in The Netherlands, step 1 and step 2
• abroad (see also: ‘stay abroad’), step 1, step 2 and step 3

It may also be possible to do an extracurricular internship; less research-oriented but more geared towards applying skills learned in your Master's programme and getting a closer experience of a real company environment. Please ask your programme coordinator for opportunities within your programme.

Step 1: GSNS Research Project Application

You are required to apply for approval of your research project by submitting a request via OSIRIS Case . Important: in order to apply completely and correctly, you must have discussed the project setup with your intended project supervisor beforehand! 

• Please select ‘My Cases’, ‘Start Case’ and then ‘Research Project GSNS’.
• We advise you to study the research project application form in OSIRIS Case previous to discussing it with your supervisor, or fill it out together, to make sure you obtain all of the information required (also because there is no option to save the responses in the form and edit this later).
• After submitting your request, it will be forwarded to your master’s programme coordinator, the board of examiners and student affairs for checks and approvals.
• After approval of your project it will be automatically registered in Osiris. If something needs to be amended, you will be notified by email.

Step 2: Outside UU: GSNS work placement agreement

• Your research project is outside the UU-> use the GSNS work placement agreement  and get the signature of the host organisation.
• If the host organisation requires changes in the GSNS work placement agreement -> contact the internship contract coordinator  [email protected] .
• If the host organisation only uses its own agreement -> contact the internship contract coordinator  [email protected] .
• In all cases the signature of the internship contract coordinator is needed. If the UU is no party in the host organisation agreement the approval of the internship contract coordinator remains necessary. In some cases an appendix to this kind of agreement has to be drafted and signed by all parties.
• Upload the fully signed work placement agreement with your application form to Osiris Case to start the approval process by the Board of Examiners. Be aware, for a research project outside Utrecht University a signed placement agreement is necessary to get approval.

Step 3: Outside NL: Stay Abroad request

• If your research project/thesis is outside of the Netherlands you have to do a Stay Abroad request in Osiris Student after receiving the approval by the Board of Examiners. Information can be found on the webpage  Stay Abroad . Please read the whole page carefully. If you have questions please contact the international office. Be aware that arranging a research project abroad takes time.
• You might find this checklist  useful during the organisation of your traineeship abroad.
• It is important to check the travel advice for your destination given by the Ministry of Foreign Affairs. Please type in your destination on the website (in Dutch). If your country has orange or red areas your Stay Abroad request cannot be validated.

Step 4: Assessment Forms (incl. Assessment Rubrics) and procedures

The GSNS research project is split into two parts, your project supervisor will fill out the assessment form part 1 (please see an  example assessment form , this may slightly differ from the one in OSIRIS) at the end of the first part of your project. At the end of your project your supervisor will fill out the assessment form part 2 (please see an  example assessment form , this may slightly differ from the one in OSIRIS). This is done within Osiris and your grade will automatically be registered in Osiris.

Please note that the Protocol delay in graduation applies when a project is delayed ( see Education and Examination Regulations (EER), appendix ). Application for an extension of the research project deadline for part 1 or part 2 has to be submitted to the Board of Examiners via OSIRIS Student > ‘My Cases’> ‘Start Case’  > ‘Request to the Board of Examiners’ > ‘request type 7 Delay of research or thesis project.

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Utrecht University Heidelberglaan 8 3584 CS Utrecht The Netherlands Tel. +31 (0)30 253 35 50

• Your Future /

In November each year, Amsterdam Data Science organises a thesis fair where companies are able to meet and match with students enrolled in the Data Science Master Track . This match-making event is an excellent opportunity for students to find the right project for them and companies to find the students they need.

Additionally, our University partners offer a number of outlets, for example:

For Internships:

• Student Projects Page, Information Sciences,  UvA:  Helps students and organisations to find each other, so that together they will make great graduation projects happen. As organisations offer their proposals for projects, students will be able to browse, discuss, and select projects of their choice. We host annual Thesis Fair events, where organisations can be present to meet student. Speeddating sessions are organised to match students with their favorite organisations and projects.
• VU Internship Office:  The Internship Office for Mathematics and Computer Science at the VU maintains contacts with organisations in various sectors and industries. Some organisations notify of vacancies and projects, but many select their internship students on the basis of open applications.
• Master of Computational Science Projects, UvA-VU: Website showing the available student projects options and contact details for Computational Bio-Medicine, Computational Biology, Computational Finance, Complex Systems Theory, and Urban Complex System students at the UvA and VU.

For Vacancies:

• AUAS (HvA):  Check out the list of internship bureaus of the Amsterdam University of Applied Sciences (Hogeschool van Amsterdam).
• Job Board, UvA:   A platform where you can post vacancies for jobs and internships free of charge. The Job board is exclusively available to UvA students and recent graduates in all fields.
• VIA, UvA:   Check out the VIA pages for available internships, part time jobs and jobs for young professionals or if you would like to post a vacancy. VIA is a study association for Computer Science, Artificial Intelligence and Information Science students at the UvA.
• STORM, VU:  Check out the STORM pages for internships, part-time jobs and jobs for young professionals. STORM is the study association of the departments Mathematics and Informatics from the Faculty of the Exact Sciences at the Free University in Amsterdam.
• StudiStartupJob:  Is an initiative of StudiJob,  StartupAmsterdam  and  IXA . A recruitment agency that specialises in helping Amsterdam’s university ( UvA ,  HvA  and  VU ) students find part-time jobs. StudiStartupJob enables students to do relevant, paid, knowledge-intensive work in addition to their studies.
• Amsterdam Science Park Vacancies and Internships:   A growing number of life science and high tech companies are choosing Amsterdam Science Park as their home base. To allow them to make optimal use of the available talent pool of Faculty of Science and AUC students, an online matching portal went live. The website also allows students to search for internships and vacancies at companies and research institutes located at Amsterdam Science Park.

Master's Degree in Data Science

The master focuses on the acquisition of capabilities that are relevant to the real world and can be effectively applied in the real world both in academic research and in private companies and public bodies. Hence the master foresees a traineeship/internship period followed by a final exam consisting in a discussion of an original work.

Several options are possible. The internship can be in fact spent in a company, public body or research institute, or group. The internship (or "Tirocinio curriculare" in Italian) grants 8 CFUs, while the thesis grants 16 CFUs. The student has two options: 1. Internship report.She/he can decide whether to discuss a thesis based on the work done during the internship. In this case, the internship must be prolonged to cover 22 CFUs (the remaining 2 CFUs being for the preparation of the final Thesis). Since 1 CFU corresponds to 25 hours of work, in this case, the internship corresponds to ca. 550 hours of work in the hosting body, and the final exam will consist in the discussion of an original work written by the student on the results obtained during the internship. 2. Academic thesis. The student may decide to keep the internship and the thesis separate. In this case, the internship corresponds to only 200 hours of work in the hosting body. In this case, the internship can also be spent in a Research Institute (both public and private). The final exam will then consist of a discussion of an original work done by the student on a research project. Also, in this case, the internship and the academic thesis can be -if necessary- paired.

Internship report The student, during the internship, will be under the joint supervision of a person delegated to this task by the host, and of a UNINA professor designated by the coordinator upon request of the student. At the end of the internship, the student will present a written report and a work diary which will be used by the academic supervisor to evaluate the results of the internship. Reports must be in English. In the case of an extended internship (22 CFUs), the final exam will be based on the discussion of an extended written report which must match the standards of an academic thesis (see below). The contents of this report will be regulated by a specific agreement between the host institution and the LMDS, to protect intellectual property and/or copyrights).

Academic thesis It can be done within UNINA or at other Universities in Italy or abroad or at research Institutes both national (such as INFN, INAF, INGV, CNR) and international. Research theses are always conducted under the supervision of a faculty member. In the case of a thesis conducted in collaboration with UNINA but outside the SPSB or at other institutions (see above) a second supervisor (domain expert) will be required. The thesis must lead to an original result, must be written in English, and must be exhaustive, i.e. it must be understandable also by a competent person who is not an expert of the specific field. Data science is also about communicating the results of your work to domain experts and, therefore this is a very important requisite. The thesis or final report must be written in Latex using the template downloadable here.

Procedures for the Internship UNINA has a specific office taking care of internships . Please check the site to make sure that you have the most recent documentation and to monitor special calls (in Italian "Bandi") for internship applications. As a first step, you must read the student's vademecum. Unfortunately, it is in Italian. If you need an English translation, contact the coordinator. The procedures, however, are summarised in what follows: First of all, be aware that the procedures for traineeships/internships are rather different for activities "intramoenia" (i.e. in other Departments or University structures outside of the SPSB) or "extramoenia" (i.e. outside UNINA.)

Internships should be performed in the second semester of the second year but exceptions are possible if agreed upon - well in advance - with the coordinator of the Master. Internships require the existence of an agreement between the University Federico II (or a Department) and the host institution. Here you can find a list of existing agreements . If the company you are interested in is not in this list, DO NOT PANIC!. It is possible to stipulate specific agreements with new legal entities and to do so, you just need to follow the procedure outlined below.

1. Inform the coordinator of the master. 2. Download the right form: ( Italy only , European countries , and non-European countries ). This agreement will define the collaboration between the two parts. Get it signed by the host institution (two copies). 3. Hand the signed copies to the coordinator who will transmit them to the delegate for the traineeships. 4. Wait until the bureaucracy has been completed and the host appears in the list of the agreements (see above). Usually it takes between 20 and 30 days. 5. Download the " Student's guide ". 6. Fill this online form . 7. Go on "modulistica" and click on "Compila progetto Fomrativo extramoenia" and fill the online form (there is also the English version).. During the internship, the student is required to fill the activity booklet downloadable here . IMPORTANT NOTICE: remember that in signing the various documents you also agree to respect the intellectual rights and any "privacy agreement" foreseen in the contract. This clause holds during and after the traineeship period. At the end of the internship, the host institution and the academic tutors will fill out a report summarizing your activities and your performances. This report together with your relationship must be handed (or e-mailed) to the coordinator of the master who will transmit the results to the secretary.

Procedures for an Academic Thesis The thesis work must take place in the second semester of the Second Year. In any case, it cannot start before the student has completed all but three exams. Students must: Read the regulation (Ita, EN) and chack that you have fulfilled all requirements. Pay special attention in respecting the deadlines and the procedures. Fill out this online form

Once it is completed the student needs to: i) Reserve a slot in the planned session (see calendar). To do so, he needs to fill out this form and to pay the "laurea Tax" using this bulletin. ii) 15 days before the session he must hand to the secretary (either via mail or in person using a CD or a USB pen) a pdf file containing the manuscript. This manuscript must be written in Latex or Tex using the template which can be downloaded here. iii) the final exam will consist of a 30/40 minutes presentation plus Q&A time.

The internal regulation for the evaluation of the final score (which will take into account the academic record and the final exam) can be found here.

• The Master in a nutshell
• C1 Data Science for Public Administration, Economics and Management
• C2 Data Science for Information Technologies
• C3 Data Science for Fundamental Science
• C4 Data Science for Intelligent Systems
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Data and Analytics

We use data and analytics to help executives, employees, customers and suppliers make the right decisions.

Data Analytics

In Data Analytics, we help our customers collect, analyze and use data to understand potential futures, improve business performance and transform the way they work. As a data analyst you help our customers make decisions based on (big) data and you are the analysis expert who thinks through, researches and uses your own experiences to make our services even better. There are various teams within PwC that you can call on. For example, with our Data Analytics Assurance team. Or if you're more interested in people and the HR function, then perhaps People Analytics is for you. Maybe you fit in better with the exciting world of Forensic Technology? Or Deal Analytics where it's all about data, but also about your business insight and knowledge of the sector.  

We want all these teams to grow with specialists from different backgrounds and qualities. Your passion to drive our customers forward is what's most important to us. We will teach you the right skills.

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Data Analytics - Today and Tomorrow

A short film showing how the world of data is changing and how organisations can seize the opportunities that data and analytics offer.

Our Data & Analytics team

Data & technology assurance, forensic technology solutions, people analytics, data analytics consulting, smart automation & quantative solutions, deal analytics.

As part of the Risk Assurance Data & Technology team, you work with the most innovative team of data specialists within PwC. You help clients with their data questions, for example, getting a better grip on data quality, and visualizing the data analyses in an informative and clear manner. 

You help clients in the area of providing certainty from and about their data. You consult with customers, retrieve the data from their systems, clean and prepare, run analyses, and create impactful visualizations to turn data into useful information. 

You will support us in maximizing the added value of our digital assets , but also in solving relevant complex customer problems, aimed at adding trust or improving transparency and risk management.

Within the Risk Assurance Data & Technology team we make use of various techniques, such as Robotic Process Automation (RPA), Artificial Intelligence (AI), and Machine Learning (ML).  We often work with SQL, Python, R, PowerBI and Alteryx. 

Would you like to help customers turn data into their new superpower? Do you want to keep developing yourself and use your knowledge to contribute to further digitisation? Then come and work at PwC!

Fraud and noncompliance with rules and regulations (such as KYC regulations) is happening all around us. You and your colleagues are the team that helps our clients detect, investigate and prevent it in the future. Within FTS you have Data analytics (DA) and Digital E-discovery (DeFD) teams. These are an integral part of the forensics investigation team and support the core forensics colleagues in appropriately collecting, processing and extracting meaningful insights from various forms of (structured and unstructured) datasets. Their work is revolving around 3 pillars: Providing insights using Data analytics and Machine Learning, Supporting investigations and Automating compliance and governance. You will work with a broad set of technologies/languages as such: Python, SQL,  diverse machine learning models, application of Responsible AI principles, NLP, network analyses and many more. Together with this unique team of experts you will deliver outstanding results while creating great experiences for our clients and as well as ourselves.

The analysis of HR (and overall people) data is becoming increasingly important in providing organizations with key insights in enriching their end-to-end employee journey: From recruiting to performance management, ensuring the wellbeing of your workforce, and retaining and rewarding key talent, people Analytics allows organizations to use bottom-up insights for well-informed top-down decision making. Depending where our clients are in their journey of forming / developing / upscaling their people analytics function, you will help in all aspects: from strategy to execution. What use cases are most impactful for your organization? What data is readily available to quickly drive your decision making? How to set up your people analytics function in terms of skills / scope and scalability? Always with strict consideration of privacy and ethics, the team of People and Organization leverages data from HRIS, pension, reward, employee engagement, and exit interview systems to help answer all the above and many other people related questions. You will work across all sectors, with opportunities in a global network using state-of-the art tooling in data gathering, analysis and automation.

Within this multidisciplinary team, you will use your analytics skills to come up with practical solutions for complex problems within the public, commercial and financial industry.

You will advise our clients on how to make use of the enormous amount of data and help them convert this data to discover opportunities for growth, innovation and competitive advantage. Together you will develop for our clients models, apps, and data driven solutions based on advanced technologies including machine learning, artificial intelligence and cloud solutions. As a data expert, you'll often work with colleagues from other PwC teams such as Finance, Risk, Technology and Strategy Consulting.

Within the Data Analytics Consulting team you continuously work on your knowledge and analytical skills. In addition, you will be trained to keep abreast of digital trends related to our field. You will help to develop innovative solutions for the client. That makes it fun to work with us.

The experts of the Smart Automation & Quantitative Solutions teams deliver and consult on scalable digital assets and services. We focus on extracting, modelling, analyzing and visualization of data to enable our clients in having improved insights, optimized compliance & reporting processes and increased control over its tax and finance function, all at lower cost.

You will be part of a young and dynamic team from day one and will get the opportunity to specialize in different technologies like RPA, Machine Learning and Artificial Intelligence. Together with the team you will work on cases to help clients translate their strategy into applied technology solutions. You will use a broad range of analytic tools and support clients in their digital transformation, ready to deal with changing requirements and unlocking possibilities that Data and Technology bring!

Our Deal Analytics team addresses complex questions in the fast-paced Mergers & Acquisitions (‘Deals’) environment. Projects involve supporting clients buying and selling companies in which data analytics plays an increasingly important role. You work with data from the sales, marketing, operational- and financial systems of the client to identify value drivers and advice on possibilities to improve. In terms of analysis, you focus not on one single technique, but make sure you have a wide range of techniques and tools at your disposal to optimally help your clients.

In your daily job, you will work on thinks like: identifying and addressing business hypotheses using data analytics techniques, performing the analyses in a condensed timeline and with high quality standards and working with the deal team to incorporate the analyses in the wider PwC view on the target;

You will get the chance to be part of a growing team in one of the most successful Deal practices in the market. You will work in teams consisting of colleagues with different backgrounds and expertise, in which you are responsible for the data analytics driven insights. You will work on 8 – 10 projects annually for both corporate & Private Equity clients, giving you a unique insight into the Dutch & international business world.

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Data & Analytics

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Data Science and Machine Learning Internship Program

Data Science and Machine Learning Professionals Work at

Upskill with Data Science & Machine Learning Internship Program

Attend a Free Data Science and Machine Learning Internship Demo Session

• Learn about Data Science and Machine Learning in Detail
• Attend the Demo & Earn a Certificate of Participation

Exciting Industry Trends

As per Economic Times 11.5  million data science  jobs will be added in India by 2026.

Average salary of a Entry Level Data Scientist in India is  ₹8 LPA & goes up to  ₹30 LPA.

Data Science jobs have seen 36% growth year-on-year since 2020.

Check what our learner's have to say

Got my desired job in a top it company thanks to edureka, very in-depth course for a fresh start to my career, highly recommended, best decision of my life to kickstart my career, very helpful guidance, an amazing course for beginners, beginner to fullstack developer, thanks to this course and the instructors, build real life projects using data science and machine learning.

CAPSTONE PROJECT

Movie Recommender System like Netflix

Skillfully Build an End-to-End Machine Learning Model from Scratch

Build a Machine Learning Model to identify and recommend the most likely movies/shows the user would watch in a video streaming platform like Netflix to help the customers enjoy their experience of the platform along with increasing customer engagement and time spent on it.

Travel Aggregator Analysis

Learn Python from scratch and develop your own travel aggregator to compare prices for upcoming journeys.

Heart Disease Prediction

Create a machine learning model to determine probability for a patient having heart disease or attack.

Human Resource Management System

Design and implement a HR Management System as per the design requirements of the project.

• Core Infrastructure Company Data Visualization

Build this project using Tableau, a data visualisation tool used to create visually compelling data dashboards.

Why learn Data Science and Machine Learning?

• Companies like Deloitte, PWC, EY, Amazon and AWS are hiring for Data Science Professionals
• Get Eligible for Multiple Job Opportunities and work in diverse roles and technologies
• Data Science is an evergreen field as it adds great value to the businesses and plays a major role in decision making

Data Science and Machine Learning Technologies you will learn

Internship program certificates

Edureka Super Intern Certificate

To be awarded to learners who will build their own product and perform the best. Learners also get a chance to be a teaching assistant

Course Completion Certificate

Edureka Internship Certificate

To be awarded to everyone irrespective of performance

Get 3 More certification

Data Science and Machine Learning Internship Program Curriculum

Python for data science.

• Python Essentials
• NumPy and Pandas
• Data Visualization
• Web Scraping
• Probability and Statistics
• Exploratory Data Analysis
• Interview Preparation

Hands on Experience

• Building a Travel Aggregator Model

Skills you will Learn

• Python from Level 0
• Array Manipulation Using NumPy
• Data Pre-processing and Manipulation Using Pandas

Database Management with Microsoft SQL Server

• Database Essentials
• Querying Data using Built-in Functions and T-SQL
• Stored Procedures
• Concurrency Control
• Designing a HR Management System
• Database Architecture
• DML and DDL Commands
• Advanced SQL Queries

Machine Learning

• Supervised Machine Learning
• Classification
• Unsupervised Machine Learning
• Dimensionality Reduction
• Recommendation Engine
• Time Series Analysis
• Model Evaluation and Optimization
• Performance Metrics and Hyperparameter Tuning
• Creating a ML Model for Heart Disease Prediction
• Machine Learning from Level 0

Deep Learning and Data Visualization Using Tableau

• Deep Learning
• CNN, RNN, and LSTM
• Natural Language Processing
• Text Pre-processing and Feature Extraction
• Sentiment Analysis
• Data Connections and Visualizations
• Advanced Visualization
• Interactive Dashboards
• Deep Learning from Level 0
• Neural Network with Tensorflow 2.x
• Deep Learning using CNN and RNN
• Data Connection and Visualization
• Sharing Insights through Dashboards

Interested?

Register for the upcoming demo session now:

Attend and get a Certificate of Participation!

Data Science and Machine Learning Internship Admission Process

Frequently asked questions, who is this data science machine learning internship course for, what are the prerequisites to enrol in this data science and machine learning internship course, what is the mode of data science and machine learning intern training, who will be the trainer for the data science machine learning internship program, can i master python and become a data scientist in such a short duration, how much time do i need to spend per week to do well in this data science and machine learning internship course, will i get an internship certificate irrespective of my performance, how can i become an edureka super intern, how is this data science and machine learning internship program different from other programs, will i get a paid internship offer or full-time offer from edureka or any other organization after this course, are there emi options available, how can i contact you, trending certification courses.

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Master Data Science

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The two year Master program in Data Science is built on the Data Science B.Sc. program and can be started in either the spring or fall semester. It is a  joint project of the Faculties of Statistics, Computer Science and Mathematics . As the tasks are always and rapidly changing in this research field, the study program provides a sound statistical and mathematical theory and teaches contemporary methods and knowledge. Hence, the students are capable to do scientific research, have a scientific awareness and can trade it responsibly. The interdisciplinary ability to communicate about methods and their application, especially at the interface between statistics, computer science, mathematics and fields of application, is a key aspect of the study program.

Students who have already completed a B.Sc. program in Statistics, Mathematics, Computer Science, or related fields can be admitted for this Master program (possibly with certain  restrictions ).

All mandatory courses are  taught in English , elective courses in German may be offered.

Structure of study program

This course offers different core and elective modules. The selection of modules can be chosen from a wide range of different courses according to the individual interests of the student. Through internships and project work courses as well as the Master thesis, students have the opportunity to apply their gained knowledge. Based upon previous studies, the advanced courses cover statistical theory, statistical learning, big data, and the application of methods in chosen areas of study (case study, seminars and electives). Emphasis is placed on the development and application of efficient procedures for analyzing in particular very large amounts of data. There are eleven modules (including a six month Master thesis) during the two years of study.

Perspectives

This Master program gives students the opportunity to deepen and broaden their research skills and  methodological competence, subsequent to their B.Sc. in Data Science or related programs. They are able to develop new methods and to manage and direct large data projects. The students become experts in this field and are highly sought after by employers in many areas, particularly for the independent handling of projects with large amounts of data.

For qualified students, the program opens up the possibility for doctoral studies afterwards.

The English-language program prepares students for an international professional orientation and, on the other hand, gives foreign students the opportunity to gain a foothold in Germany.

Important files and more information

Admission to the Master program is only possible if certain requirements have been met. These and further information on enrollment can be found  here .

• Terms of Study
• Amendments of Terms of Study, from winter semester 2020/21
• Module Handbook M.Sc. Data Science
• Recommended Course of Study M.Sc. Data Science

Note: In each case, the terms of study are relevant; the recommended course of study represents only a possibility.

Search term

Internships.

Maastricht University (UM) encourages bachelor’s and master’s students to do internships within the Netherlands or around the globe. At UM we see an internship as an experience-based opportunity, where students receive credits for their supervised work experience.

Four types of internships

Internship options per faculty, what can you expect from um students.

• What do we expect from you?

Depending on the learning objectives, we facilitate five types of internships for our students in both our bachelor’s and master’s programmes:

• Practical internship – students gain relevant and necessary work experience A practical internship provides students with work experience that is tightly connected to their studies. It brings students into contact with potential employers and supports them in developing the necessary and required competencies as defined in national/occupational guidelines.  
• Research internships – students conduct academic/scientific research Research interns are actively involved in (part of) the empirical cycle of the research.  
• Job taster – students briefly gain work experience at an organisation to discover job opportunities and to explore whether such a type of organisation fits their ambitions Job tasters are short internships (up to a maximum of 8 weeks). Job tasters allow students to expand their knowledge and understanding in a practical context for a brief period of time. Students need to apply for a job taster at their faculty’s Internship Office. A job taster results in an internship report in which students reflect on their learning objectives and the way the job taster has contributed to realising these objectives.  
• ...@work In their second year of study, talented and motivated bachelor’s students start working at (international) organisations in the Meuse-Rhine Euregion. During this two-year work/study programme, students tackle challenging assignments associated with the subject of their programme. Students are guided in these tasks by a supervisor from the company as well as one of our supervisors. In addition, the students follow all their regular courses and take exams. Currently …@work is only implemented at the bachelor’s programme Data Science and Knowledge Engineering.

Along with our highly skilled students you also benefit from:

• the expertise of UM’s supervisors for advice and quality control
• adequate support from our internship offices, internship coordinators and academic supervisors
• Department of Data Science and Knowledge Engineering (DKE)

Skills, knowledge and abilities

Students of the Department of Data Science and Knowledge Engineering (DKE) are equipped with a unique combination of knowledge and skills in applied mathematics, computer science and artificial intelligence. The computer science courses focus on software, programming, algorithms and logic. In courses in applied mathematics students get acquainted with the most important concepts, methods and techniques from a practical perspective. While courses in artificial intelligence teach them to reason using available knowledge and introduces them to machine learning and intelligent search.

Availability of students

To start working with students within the KE@Work program in September, you will need submit your project ideas to the faculty staff before the end of February of that year (i.e. 7 months in advance). Students will spend two-and-a-half days at your organization and two-and-a-half days following their regular University courses.

• Faculty of Arts and Social Sciences

Students of the Faculty of Arts and Social Sciences (FASoS) are trained in the understanding of the interrelationships of Europeanisation, globalization, scientific and technological development, political change and cultural innovation. They’re curious about how today’s societies cope with these challenges through practices of remembrance, governance techniques, strategies for managing knowledge, technologies and risks and ways of dealing with diversity and inequality.

• Faculty of Health, Medicine and Life Sciences

Students of the Faculty of Health, Medicine & Life Sciences are well on their way to become independently thinking and adaptive professionals who are lifelong learners and have a broad, state-of-the-art view on health and health care. They are prepared for a future in which they can successfully function in the health-related work field and academic arena, in an international society that is continually subject to demographic, technological, social and scientific changes. Availability of students

Please bear in mind that students of the Faculty of Health, Medicine & Life Sciences (FHML) will start looking for internship opportunities in September. If you would like to work with students of FHML please get in touch with Mariette Cruijssen .

• Faculty of Psychology and Neuroscience

Students of the Faculty of Psychology and Neuroscience (FPN) are curious about the human brain and behavior. This curiosity drives them to explore the broad field of psychology from a biological and cognitive perspective. They study mental processes, such as language, memory, thinking and perception, learn about the sources of our behavior, and how you can measure brain activity with an MRI-scanner. Students are educated across four main research lines: Cognitive Neuroscience, Clinical Psychological Science, Neuropsychology and Psychopharmacology, and Work and Social Psychology.

Procedure: If you would like to work with students of the Faculty of Psychology and Neuroscience (FPN) please get in touch with the faculty staff . They will guide you through the process and help you navigate through their faculty-specific Askpsy-system . Availability of students

• Faculty of Law

Skills, knowledge and abilities Students of the Faculty of Law are well on their way to become excellent jurists. They bring with them a thorough knowledge of national jurisdictions and of European and comparative law as well as a deep understanding of the function of law in society and of other methods of regulation, conflict resolution and conflict avoidance.

If you would like to work with students of the Faculty of Law, please send an email to the internship office . Within four weeks of submitting the internship proposal the student will know whether or not he or she can do an internship in your organisation.

• School of Business and Economics

Students of the School of Business and Economics bring with them an international perspective on, and profound knowledge in, the fields of econometrics, business Intelligence and data science, business economics, labour economics, organizational studies, learning and development, consultancy, management, fiscal economics, international business and emerging markets.

Availability of students  

If you would like to work with students of the School of Business and Economics (SBE) please get in touch with the Internship Office . It can take up to ten working days for the internship office to approve an internship.

• University College Maastricht

Skills, knowledge and abilities With the support of committed staff, UCM students can choose from more than 150 different courses in the Humanities, Social Sciences and Sciences to build an academic profile that helps them reach their future goals. Students can focus on a particular discipline (e.g. psychology, economics, history, biology) or instead opt for courses around a combination of themes or disciplines. For instance, a curriculum could include economics, law and international relations, or sciences and psychology, or cultural studies and history.

Availability of bachelor's students

Applied Research Internship (ARI) Organisations can offer cases or problems that students will tackle in ARI. Students work individually or in small teams, conducting research and preparing recommendations for their client. During the two 7-week periods, students combine their ARI project with their coursework at the college. Their availability during these periods is 8 hours per week. During the closing one-month period, they are available full time to work from their client’s office. When : February-June or September-January

ThinkTank project Within 4 weeks, a group of 8 to 10 students analyse, research and formulate a set of recommendations in response to a complex and multidimensional problem or issue presented by you. When:  June or January

If your organisation is interested in offering a project to (a) UCM student(s), please contact the ARI/ThinkTank coordination office via t. +31 (0)43 388 54 72 or email: [email protected] . 

• they are equipped with a solid mix of academic knowledge and have a hands-on mentality, enabling them to make a smooth transition from their studies to your organisation
• they are trained to provide solutions for real-life issues and are use to working in teams. Both elements are key tenets of our innovative education model Problembased Learning which teaches our students the practical application of theory and provides them with excellent analytical, project management and communication skills
• they are accustomed to working in our Interational Classroom, the  tutorial groups in which they work and study with people from different cultural backgrounds and learn to approach issues from a variety of perspectives. This makes them well-prepared for the rapid changes in today’s globalising labour market
• our students and graduates are up to speed with the latest techniques and developments in your field

What do we expect from you?

• you need to arrange a workplace for our students according to the standards and norms of your organisation
• you are required to support the student when needed; the required support is explained in the regulations per programme
• our students have to be present for at least 75% of the time of the internship; as internships are first and foremost a learning experience for our students,  being present at your organisation is an integral part of this experience
• we don’t require an internship fee for our students; this you can decide upon this with the student yourself and is thereforeleft open as part of the internship contract
• we use a standardised internship contract; this contract addresses the concept of ‘duty of care’ for our students and comprises all the required legal regulations.
• in accordance with Dutch law you are liable for damage to, or damaged caused by, our students during their internship. Students are not allowed to o internships at organisations that do not provide the necessary insurances.

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Computer Science Thesis-Internship Notice Summer 23-24

BSCS Internship Course Registration Notice Summer 2023-2024

BSCS Thesis-SP, MSCS Thesis / Cont Final Registration Notice Summer 2023-2024