Getting an internship as a data science student can be a challenging but rewarding experience. Internships provide invaluable hands-on experience and are often a stepping stone to a full-time job. With competition increasing for data science roles, having relevant experience from an internship can give you a competitive edge.
Why Complete an Internship?
Here are some of the key benefits of doing an internship as a data science student:
- Gain practical experience – Apply what you have learned in school to real-world projects and problems. This experience is highly valued by employers.
- Explore different roles – Try out different types of data science work like data engineering, machine learning, business analytics etc. This helps you figure out what you want to specialize in.
- Develop technical skills – Get exposure to industry tools and technologies not covered in school programs like Hadoop, Spark, cloud platforms etc.
- Networking – Meet professionals in the industry and expand your professional network.
- Potential job offer – Many companies use internships as a recruiting tool and make full-time job offers to interns who perform well.
Completing one or more data science internships can help you gain the required knowledge and experience to be a competitive candidate when applying for full-time positions.
When to Do an Internship?
For data science students, the ideal time to complete an internship is:
- After 2nd year – Once you’ve taken some foundational computer science and statistics courses, you’ll have enough knowledge to contribute value as an intern.
- Before final year – Doing an internship before your final year leaves time to convert to a full-time job offer from the company.
- Summer – Most data science internships align with the summer break at universities.
However, there are no hard rules on when you have to do an internship. Opportunities may arise at any time during your studies. Take advantage of them when you can.
Where to Find Data Science Internships?
Here are some top places to search for data science internships:
- Company career sites – Many major technology, finance, healthcare and retail companies actively recruit data science interns.
- Job boards – Look on sites like LinkedIn, Indeed, Glassdoor, AngelList, BuiltIn for internship postings.
- University career center – Register with your campus career center which partners with companies looking to hire student interns.
- Conferences and events – Data science conferences like ODSC are great places to network and find internships.
- Professional networks – Talk to your professors, alumni networks, mentors about open internship opportunities.
Cast a wide net using these options when searching for your ideal data science internship.
How to Get a Data Science Internship?
Follow these key steps to land a data science internship:
- Target relevant companies – Look for companies in tech, finance, healthcare etc. with active data teams and internship programs.
- Polish your resume – Tailor your resume to highlight technical skills, school projects and any prior internships or research.
- Highlight coursework – List relevant coursework in math, statistics, computer science to showcase your knowledge.
- Learn in-demand skills – Pursue electives, online courses and certifications in areas like Python, R, SQL, Hadoop, Spark etc.
- Ace the interviews – Prepare for technical interviews focused on statistics, coding and SQL. Review common data science interview questions.
- Apply early – Start applying 4-6 months in advance of when you want to start the internship.
- Follow up – Send follow-up emails to check on status of application. Follow up shows initiative.
With proper preparation and persistence, you can land exciting data science internship opportunities.
What Do Data Science Interns Do?
Data science internship roles and responsibilities may include:
- Collecting, cleaning and preprocessing data
- Performing exploratory data analysis
- Applying machine learning algorithms
- Developing predictive models andprototypes
- Visualizing and presenting data insights
- Documenting work and results
- Learning and using new data tools and technologies
You will work on real data challenges and projects under the guidance of experienced data scientists as an intern.
Company | Industry | Sample Projects |
---|---|---|
Walmart | Retail | Demand forecasting, inventory optimization |
Tech | Member profile analysis, talent intelligence | |
Tech | Ad optimization, user growth analytics | |
JPMorgan Chase | Finance | Fraud detection, trading signals modeling |
Pfizer | Healthcare | Clinical trial data analysis, epidemiology modeling |
The types of projects and analyses you work on will depend on the specific company and industry.
Top Skills to Develop
Here are some of the key technical and professional skills to develop to be successful as a data science intern:
Technical Skills
- Python – Core programming language for data tasks
- R – Statistical programming language for exploratory analysis
- SQL – Querying language for accessing and manipulating databases
- Data Visualization – Creating charts, graphs and dashboards
- Machine Learning – Supervised and unsupervised learning algorithms
- Big Data – Tools like Hadoop, Spark for large-scale data
- Cloud Platforms – AWS, GCP, Azure services and architecture
Professional Skills
- Communication – Summarize analyses and convey insights effectively
- Business Acumen – Understand how data connects to business metrics and goals
- Creativity – Develop innovative approaches to problem solving
- Collaboration – Teamwork, coordinating with stakeholders
- Ethics – Follow best practices for data privacy, transparency and bias
- Curiosity – Innate desire to analyze and solve problems through data
- Time Management – Prioritize tasks efficiently even when given multiple projects
A combination of hard technical expertise and soft skills are required to thrive as a data science intern.
How to Make the Most of Your Internship?
Follow these tips to maximize your learning from your data science internship experience:
- Ask questions – Don’t be afraid to ask your manager and team members questions to deepen your understanding.
- Find a mentor – Seek out a mentor who can offer advice and support during your internship.
- Attend events – Go to meetings, conferences and networking events to meet more people.
- Take on challenges – Look for challenges to build your skills even if it takes you out of your comfort zone.
- Track your work – Maintain a daily journal, samples and recordings to showcase your projects.
- Learn business context – Understand how your analyses provide business insights and impact.
- Solicit Feedback – Ask managers for continuous, constructive feedback on your performance.
- Keep in touch – Maintain relationships with your manager and colleagues after internship ends.
By fully engaging with the internship experience, you can build connections and demonstrate your capabilities.
Common Interview Questions
Here are some common interview questions to expect for a data science internship:
- What coursework have you taken that prepares you for this role?
- What statistical and machine learning techniques are you familiar with?
- How proficient are you with programming languages like Python and R?
- What experience do you have working with large, complex data sets?
- How would you handle missing or corrupt data in a dataset?
- What data visualization tools and techniques do you know?
- How do you determine which machine learning algorithm to apply to a problem?
- How would you communicate data analysis results to a non-technical audience?
- What ethics rules should be followed when working with user data?
- What interests you about this internship role and our company?
Prepare examples that highlight your data skills, knowledge and enthusiasm for learning.
Tips for Creating a Strong Resume
To create a resume that will stand out for data science internships, follow these tips:
- Showcase technical skills at the top – List programming languages, tools and certifications.
- Highlight relevant coursework – Include courses in statistics, algorithms, multivariate calculus etc.
- Add academic projects – Describe 1-2 robust school projects showcasing your work.
- Include publications (if any) – Research papers, journal publications demonstrate your capabilities.
- Incorporate visualizations – Charts, plots and info-graphics reflect your data storytelling skills.
- Tailor to each role – Customize your resume with keywords from each internship posting.
- Follow best practices – Stick to one page, clean formatting, clear structure, concise bullet points.
An impressive, tailored resume will help your data science internship application stand out.
Conclusion
Completing a data science internship as a student is the best way get real experience doing hands-on work with data. Look for internships at reputable companies that align with your interests and career goals. Thoroughly prepare for the technical interview questions likely to be asked. Showcase your specialized coursework, programming skills and passion for the field throughout the application process. If you perform well during your internship, it can potentially convert into a full-time job offer once you graduate. Taking the time to find and excel at a data science internship early in your career can pay huge dividends by landing you your dream job after college.