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Data Science Internship skills for your resume and career

Updated January 8, 2025

6 min read

Quoted Experts

Dr. Omayra Ortega Ph.D.,

Amy Ruffus-Doerr Ph.D.

One of the most important hard skills a research intern can possess is knowledge of programming languages such as Python. Programming skills are highly marketable, and will give research interns the edge up on other candidates. It's also important for research interns to have the hard skill of data analysis, and developing predictive models.

When it comes to soft skills, data science interns benefit from strong written and verbal communication skills, especially in data science internships involving writing. Data science interns need to be great at taking direction as well, so good listening skills are also crucial.

Below we've compiled a list of the most critical data science internship skills. We ranked the top skills for data science interns based on the percentage of resumes they appeared on. For example, 16.6% of data science internship resumes contained python as a skill. Continue reading to find out what skills a data science internship needs to be successful in the workplace.

15 data science internship skills for your resume and career

1. Python

Python is a widely-known programming language. It is an object-oriented and all-purpose, coding language that can be used for software development as well as web development.

Here's how data science interns use python:

Created multiple scripts in Python to perform data manipulation and analysis as well as calculations on energy production and data visualization.
Developed and deployed additional NLP applications (language identification and named entity recognition) with Python and Docker.

2. Visualization

Here's how data science interns use visualization:

Designed an interactive visualization interface to generate financial data summary and model output with R Shiny and Tableau.
Developed data visualization modules for the data collected from electrical, mechanical and chemical tests on batteries.

3. Data Analytics

Here's how data science interns use data analytics:

Analyzed and presented a case study on Energy Star product awareness in Kansas and Missouri, under the data analytics team.
Design and implement data mining algorithms, data cleaning procedures and data analytics, including machine learning and big data applications.

4. Java

Java is a widely-known programming language that was invented in 1995 and is owned by Oracle. It is a server-side language that was created to let app developers "write once, run anywhere". It is easy and simple to learn and use and is powerful, fast, and secure. This object-oriented programming language lets the code be reused that automatically lowers the development cost. Java is specially used for android apps, web and application servers, games, database connections, etc. This programming language is closely related to C++ making it easier for the users to switch between the two.

Here's how data science interns use java:

Developed Java Auto-correction algorithm using Artificial Intelligence and Probabilistic Machine Learning techniques for SAP Ana, a digital assistant for enterprise.
Established SVM classifier and discovered 8 different people moving patterns by defining and computing phone users' trajectory features in Java.

5. Data Visualization

Data visualization is the process of presenting data in a more beautiful, elegant, and descriptive way in front of others using visual elements such as charts, graphs, maps, or any other type of visual presentation. This makes the data more natural for the human mind to comprehend and thus makes it easier to spot trends, patterns, and outliers within large data sets.

Here's how data science interns use data visualization:

Designed interactive data visualizations using Tableau, which were published in UNDP s Africa Human Development Report 2016.
Create research segments to be aired on a financial news network featuring data visualizations and inferences.

6. R

R is a free software environment and a language used by programmers for statistical computing. The R programming language is famously used for data analysis by data scientists.

Here's how data science interns use r:

Analyzed internal WebBoard trends using R statistical software; identified significant trends, social network characteristics, and structure recommendations.
Created and documented survival analysis model using R and SmartBrief's databases to analyze subscriber engagement.

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7. Hadoop

Hadoop is an open-source software and procedures framework that is free for anyone to use on the internet. Hadoop aids in big data operations. It allows massive data storage, applications to be run on commodity hardware, and can easily manage to run various tasks occurring at the same time.

Here's how data science interns use hadoop:

Discovered interesting correlations between Wikipedia traffic volume spike and news events using R and Hadoop.
Played integral part in transitioning company from relational databases to Hadoop based data-store.

8. C++

C++ is a general-purpose programming language that is used to create high-performing applications. It was invented as an extension to the C language. C++ lets the programmer have a high level of domination over memory and system resources. C++ is an object-oriented language that helps you implement real-time issues based on different data functions

Here's how data science interns use c++:

Boggle -- Implement boggle board game with C++, ternary tries/ multi-tree data structure and using DFS algorithm.

9. Predictive Models

In an effort to understand what could happen in the future, predictive models provide the statistical analytics to estimate the possibilities. With predictive modelling, relevant data is collected for the subject that requires forecasting, analysed and modelled to create different outcomes. The importance of this is to provide a basis for decision making that will foster a desired outcome for a business or government.

Here's how data science interns use predictive models:

Analyzed customer transaction habits providing predictive models and summaries to identify new potential customers.
Participated in the development of predictive models with back-testing and cross-validation.

10. Analyze Data

Analyze data or data analysis refers to the practice of studying, organizing, and transforming data to make it more useful. It also includes the cleansing of non-useful information which helps in better decision making regarding any particular matter. Analyze data is a practice that is used widely in the field of business, social sciences, and science.

Here's how data science interns use analyze data:

Analyze databases to establish understanding of company infrastructure.
Analyze data to identify and interpret trends and patterns in complex data sets.

11. SAS

SAS stands for Statistical Analysis System which is a Statistical Software designed by SAS institute. This software enables users to perform advanced analytics and queries related to data analytics and predictive analysis. It can retrieve data from different sources and perform statistical analysis on it.

Here's how data science interns use sas:

Utilized statistical methods to forecast sales volume based on customer purchasing history data using SAS.
Adopted statistical modeling techniques in SAS to evaluate customer lifetime value with cohort analysis.

12. Power Bi

Here's how data science interns use power bi:

Created multiple data visualization solution for initial Power BI business preview.
Developed Reporting solutions using Power BI.

13. Regression

Here's how data science interns use regression:

Performed APP user data analysis in Excel by applying regression analysis and other statistical methods.
Developed single and multiple regression models to identify relationships between stock volatility and financial ratios

14. PowerPoint

Here's how data science interns use powerpoint:

Identified and communicated new trends to upper level management & presented insights using PowerPoint.
Presented data periodically to DoSomething.org chief officers (PowerPoint)

15. Machine Learning Techniques

Here's how data science interns use machine learning techniques:

Streamed log data from over 12000 servers at regular intervals and applied Machine Learning techniques on the go.
Implemented data mining and machine learning techniques to large vibration datasets of Solar's star engine: Titan 250.

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What skills help Data Science Interns find jobs?

Tell us what job you are looking for, we’ll show you what skills employers want.

What skills stand out on Data Science Internship resumes?

Dr. Omayra Ortega Ph.D.

Assistant Professor of Mathematics & Statistics, Sonoma State University

Everyone should have some familiarity with basic word processing software, spreadsheets, and some coding. It's impossible to know what coding languages will be in vogue when you are on the job market, but it is important to be familiar with at least one programming language. Most employers will be content that you have experience coding and are willing to learn a new language. The willingness to learn something new is essential. It's always best if you have the skills required for the job that you are applying to, but as the job market changes over time, it is difficult to predict what exactly employers will be looking for once you graduate. A good strategy for students still in school is to make sure that your soft skills are up to par. Leadership skills, the ability to work well in a team, public speaking skills, good writing proficiency, and generally communication skills are essential to every employer.

What Data Science Internship skills would you recommend for someone trying to advance their career?

Amy Ruffus-Doerr Ph.D.

Assistant Professor, Fontbonne University

If you can, make sure that your job during your gap year is at least tangentially related to what you want to do when you restart your education. Just being in the same building puts you in front of people in your field and gives you a chance to make connections and network. If you can get the most entry level position at a hospital or office building, take that over a bar tending gig. Look up who is working doing the job you hope to do one day and find a way to bump into them and introduce yourself. The world is a small place and you never know when a connection could open a door.

If that isn't possible, there are still virtual internships and volunteer options during the pandemic. Find one and add to your skill set. The last thing would be to start really working on your application materials. This is such a time-consuming process take the gap year to really make them stand out. Get feedback from professors, bosses, family members, everyone! One small spelling error could get your application tossed so it must be perfect. Working on your resume or CV will also help you determine your areas of weakness so you can add some of those skills during your gap year.

What type of skills will young Data Science Internships need?

Xingye Qiao Ph.D.

Associate Professor, Binghamton University

Computing skills are becoming increasingly important, as statistics embraces the data science revolution. Students need to be able to program (using R or Python or some other language), take the data from the web, reshape it, manipulate it to allow easier downstream analysis, and be able to communicate the finding professionally.

All these are, of course, on top of statistical thinking. Competitive student candidates should not only be an order-taker. They should ask hard questions and think about the data problem in the context of the environment that generates the said data. This is related to knowledge of the domains, human contexts, and all kinds of ethical considerations.

What technical skills for a Data Science Internship stand out to employers?

Lenny Fukshansky Ph.D.

Professor of Mathematics, Claremont McKenna College

I believe that the industry employers are constantly looking for people with a combination of strong analytic background (including mathematical modeling, statistics, and programming knowledge), good communication skills and leadership potential. We are obviously observing a rapid surge of data science, but it is important to keep in mind that just the familiarity with current data handling techniques is not sufficient for a successful career going forward. This knowledge has to rest on the understanding of fundamental mathematical and computer science apparatus from which data science has emerged. As such, I would recommend a major in mathematics, complemented by some data science courses or a minor.

What soft skills should all Data Science Internships possess?

David Brown

Professor of History, Elizabethtown College

It's critical to be able to work as a team. Empathy, understanding, a bit of diplomacy, and integrity - aside from the obvious need of technical competency - are highly valued. As always, motivation is terribly important and this can be conjoined with flexibility. If energy and attitude remain positive this will rub off on others and create an attractive dynamic that draws people in. Finally, the ability to make a decision and follow through with it is perhaps too little appreciated.

Do you want to know more?

List of data science internship skills to add to your resume

The most important skills for a data science internship resume and required skills for a data science internship to have include:

Python
Visualization
Data Analytics
Java
Data Visualization
R
Hadoop
C++
Predictive Models
Analyze Data
SAS
Power Bi
Regression
PowerPoint
Machine Learning Techniques
TensorFlow
Machine Learning Algorithms

Data Collection
Statistical Analysis
Math
MATLAB
Statistical Models
Patients
AWS
BI
B Testing
Machine Learning Models
Predictive Analytics
Pandas
JavaScript
Customer Service
Keras
Logistic Regression
NumPy

Text Mining
Exploratory Data Analysis
SQL Server
Linux
A/B
Facebook
Data Management
NLP
Data Processing
Data Quality
Extraction
PHP
Jupyter
Cloud Computing
ETL

Updated January 8, 2025

Zippia Team

Editorial Staff

The Zippia Research Team has spent countless hours reviewing resumes, job postings, and government data to determine what goes into getting a job in each phase of life. Professional writers and data scientists comprise the Zippia Research Team.

Updated January 8, 2025

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