As data science continues to grow in popularity, many students are wondering if taking an internship in this field is worthwhile. With data analysis skills in high demand across many industries, a data science internship can provide valuable experience and opportunities. However, it does require a significant time commitment. By weighing the pros and cons, students can determine if a data science internship aligns with their academic and career goals.
The Pros of a Data Science Internship
There are many potential benefits to pursuing a data science internship while in school:
Gain Hands-On Experience
While coursework provides theoretical knowledge, an internship allows students to hone their skills in a real-world setting. Interns work on actual data analytics projects, learning how to clean, process, analyze, and visualize data. This practical experience with the full data science workflow is invaluable preparation for future jobs.
Improve Technical Skills
Under the guidance of experienced data scientists, interns pick up new programming languages, tools, and techniques. Employers look for familiarity with languages like Python and R, as well as visualization tools like Tableau. The technical training gained through an internship can make graduates much more employable.
Apply Classroom Concepts
An internship bridges the gap between academic coursework and real-world application. Students can take the statistical methods, modeling techniques, and coding skills learned in school and use them for business insights and data-driven decision making. This helps solidify classroom concepts through regular use.
Gain Domain Knowledge
Interns are immersed in the industry and business operations of their placement company. They learn the key objectives, challenges, and data needs of that field. This domain expertise makes graduates more knowledgeable candidates for full-time positions in that particular sector.
Build Your Professional Network
In the process of collaborating with data scientists, analysts, and other professionals at the company, interns expand their network. Developing strong connections early on can help with career advancement down the road. Maintaining contact after the internship ends provides a group of professionals to lean on for career advice.
Get a Head Start on Job Experience
Successfully completing an internship helps build an impressive resume. While most students graduate college with no experience, those who have done internships have a valuable extra credential that makes them more competitive applicants for data science roles. The work experience also qualifies them for more mid and senior-level positions right away, rather than just entry-level jobs.
Potential for a Full-Time Job Offer
Employers often use internships as an extended interview process to identify promising talent. Interns who perform well and demonstrate strong data science skills have a good chance of receiving a return offer to continue working at the company full-time after graduation. Conversion rates from intern to full-time employee are fairly high at many firms.
The Cons of a Data Science Internship
Despite the many positives, there are also some potential drawbacks of interning in data science:
It’s Unpaid
While data science internships at technology companies or other large corporations are often well-paid, positions at smaller businesses, startups, or nonprofits may be unpaid. Students have to carefully weigh whether they can financially afford an unpaid position, or balance it with a part-time job.
Fewer Credit Hours for School
Spending about 30 hours a week at an intensive internship leaves less time for a full course load. Interns may need to scale back their credit hours, which delays graduation. They miss out on some electives they may have wanted to take as well.
Potentially Draining Work
As with any job, internships come with frustrations like unclear objectives, tedious data cleaning, office politics, long hours, and demanding managers. Juggling this work while also attending school can be extremely tiring for some students.
Less Flexibility
Because they must adhere to a fixed work schedule and attend meetings, interns lose some flexibility during the semester. It may be harder to schedule classes, study for exams, or work on group projects. Traveling home on weekends gets more difficult too.
Possibility of a Bad Internship
Not all internships are created equal. Some provide excellent training and mentoring, while others leave interns doing basic data entry or other unchallenging work. At dysfunctional organizations, students may find a poor internship does not actually advance their data science skills as much as they had hoped.
Relocation May Be Necessary
The best data science internships may not be located right near a student’s college campus. Some positions require temporarily moving to a new city or state for the summer. Students have to factor in added costs like housing, transportation, and living expenses.
What Skills Do Data Science Interns Need?
To land a data science internship and thrive in the role, certain skills are required beyond just an academic background in the field. Here are some key technical and soft skills that make a successful intern:
Data Analysis and Modeling
Interns should possess strong analytical capabilities, including working knowledge of statistical concepts, machine learning models, and modeling techniques like regression and classification. Mathematical competence and comfort working with numbers is important.
Data Wrangling
Preparing raw data for analysis requires skills like data cleaning, transformation, merging datasets, handling missing values, and formatting across software platforms. An intern needs to be proficient at wrangling messy data into usable shape.
Coding Languages
Fluency in Python and/or R is an absolute necessity. SQL knowledge is also useful for manipulating databases. Some JavaScript, CSS, HTML help with full stack development and visualizations. Having a coding portfolio on GitHub is a plus.
Data Visualization
Presenting data insights through effective visualizations like charts, graphs, and dashboards is key. Familiarity with tools like Tableau, Power BI, or D3.js allows an intern to build reports and communicate findings.
Machine Learning
Experience with machine learning libraries like TensorFlow, PyTorch or Keras allows interns to train AI models on company data. Understanding different algorithms’ pros and cons enables selecting the right approach.
Communication Skills
Interns need to collaborate cross-functionally and explain data insights to less technical teams. Strong communication and presentation abilities are must-haves in data science roles.
Business Acumen
Aligning data projects to business goals and metrics is important. Domain knowledge of the company’s field provides useful context for an intern’s analytics work. Curiosity about the business demonstrates engagement.
Time Management and Organization
Juggling multiple projects and deadlines requires planning abilities. Being proactive, taking initiative, and getting work done without constant oversight is valued.
Analytical Thinking
Having intellectual curiosity and critical thinking skills leads to exploring data deeply. Logically breaking down problems and arriving at data-driven solutions is imperative.
Attention to Detail
Carefully validating assumptions, fixing errors, and reviewing work is elemental in data science. Spotting discrepancies and patterns in large datasets also takes precision.
Passion for Learning
As technology rapidly evolves, the drive to constantly upskill is important. Motivation to tackle new tools and techniques quickly is essential for any intern.
Where to Find Data Science Internships
For students interested in data science internships, there are many potential companies and organizations to explore across diverse industries:
Tech Companies
Major technology firms like Google, Microsoft, Facebook, and Amazon hire a high volume of data science interns every year. Their internship programs are highly competitive but offer valuable experience.
Management Consultancies
Consulting firms like Bain, McKinsey, Deloitte, and Accenture utilize data science across many client engagements and provide internship opportunities.
Finance and Banking
Banks, insurance companies, and financial service providers use data insights for risk analysis, fraud detection, investment decisions, and more. They recruit interns to help with ongoing data initiatives.
E-Commerce and Retail
Online retailers like Walmart and Amazon analyze customer data to improve shopping experiences. Brick-and-mortar retailers also leverage analytics and provide internships.
Government
Data science aids public policy decisions at federal, state, and local government agencies. Students can intern with groups like the U.S. Census Bureau.
Healthcare
Hospitals, pharmaceutical companies, and healthcare nonprofits use data science to improve patient outcomes, control costs, and advance medicine.
Media and Entertainment
From Netflix to Spotify to YouTube, media platforms perform extensive analytics. Gaming companies also hire data science interns.
Data Science Startups
Smaller tech startups building data products provide opportunities to get in on the ground floor of new tools and innovations.
Universities
Many university research labs utilize data science and may offer on-campus internships. These provide the convenience of staying local.
Application Tips for Data Science Internships
When applying for competitive data science internships, students can improve their chances with the following tips:
Highlight Technical Skills
Given data science’s technical nature, extensively detail programming language proficiency, tools and methodologies used, and previous hands-on projects on your resume. Demonstrate why you are qualified for the role’s skillset.
Showcase Analytical Thinking
Go beyond just listing coursework and describe specific analyses you have performed, data insights uncovered, and metrics improved through personal, academic, or previous internship work.
Link Projects to Business Impacts
Connect data science projects on your resume or portfolio to real business outcomes like increased revenue, lower costs, improved efficiency, better decision making, etc. Show how analytics adds value.
Outline Domain Knowledge
For positions in a specific industry, highlight any specialization through past internships, academic focus, or professional experiences that make you an expert in that domain.
Demonstrate Communication Abilities
In interviews and your resume, emphasize interpersonal and storytelling skills through leadership roles, presentations, explaining technical concepts to non-technical audiences, and collaborating across teams.
Show Initiative and Passion
Highlighting personal data science projects you did independently, leadership in campus data science clubs, and academic achievements exhibits genuine interest and drive beyond just required coursework.
Apply Early
Start searching for internships several months before the intended start date, as popular roles fill up quickly. Submitting applications early in the recruitment cycle increases chances of securing a spot.
Maximizing Your Data Science Internship Experience
Once you secure a data science internship, these strategies help you get the most out of it:
Ask Questions and Take Notes
Stay curious and engaged. Write down key points to retain more knowledge. Use any downtime to learn new skills through online courses.
Build Your Network
Get to know colleagues outside your immediate team. Connect on LinkedIn. Maintain contact after the internship by sharing relevant articles or congratulating their accomplishments.
Explore the Full Data Pipeline
Seek exposure to data collection, storage, processing, analysis, visualization and more. Understand the end-to-end workflow.
Work on Your Weak Areas
Volunteer for projects involving skills you want to develop, like a new coding language or data visualization tool. Be proactive about strengthening weaknesses.
Ask for More Responsibility
If you have capacity, request taking on more impactful projects. Offer to lead presentations. Seek opportunities to manage more of the data science process independently.
Show Initiative With Internal Data
Offer to tackle open questions by mining the company’s own data. Build models predicting employee attrition or identify trends in customer behavior. Proactively harness internal data.
Build Your Portfolio
Keep code repositories of analytics projects for future job searches. Create visualizations, dashboards, and reports to showcase in interviews and online portfolios.
Solicit Manager Feedback
Check in regularly with your manager. Ask how you can improve and what areas need work. Be receptive to feedback and incorporate it.
Explore Company Career Paths
Discuss if the company has rotational programs, part-time work during school, or future full-time opportunities. Understand paths to convert to a permanent position.
Deciding If a Data Science Internship Is Worth It
Considering these pros and cons and what you hope to gain from an internship can shape whether it makes sense for your goals:
Weigh Skill Development
Determine if you need the hands-on experience and technical growth at this stage or if you are better off focusing on coursework. Assess your current readiness for real-world application.
Evaluate Career Impact
Will the brand name recognition of the company or specific domain expertise significantly help your future data science job search? Or can school projects achieve similar resume benefits?
Consider Financial Factors
If unpaid, can you afford housing and living costs if relocating? Does losing some work study income by taking fewer classes matter for your budget?
Determine Your Readiness
Do you have the required data science skills and enough time and energy to balance the internship with academics? Or does school need to remain the priority?
Align with Interests
Does the internship provide valuable exposure to an industry that interests you for a full-time career? Or does the work seem misaligned with your passions?
Compare Job Prospects With and Without Experience
Research whether internship experience is required to get interviews for the data science roles and companies you want to target after graduation.
Weigh Competition’s Experience
If peers in your program are doing data science internships and gaining an edge, can you afford not to have that resume credential when competing for jobs?
Conclusion
A data science internship can serve as a springboard to a successful career by providing hands-on experience, networking opportunities, and resume-boosting credentials. However, it also represents a major time commitment and lifestyle change for students. One should carefully assess if the pros outweigh the cons based on personal career objectives, skills, interests, and constraints. With proper planning and preparation, a data science internship can be an invaluable experience on the path from college to data professional.
