How to land a data science internship: Guide + List 2023
An entry point into a professional career in data science could be an internship. Internships can offer networking possibilities, the chance to deal with complex data sets, the opportunity to engage in a team atmosphere, and the potential to directly see the effects of your subject of study while providing hands-on experience in the working world.
What, though, can you anticipate from a data science internship, and where can you look for one?
This article will teach you everything you need to know about data science internships, including their advantages and prerequisites, where to look for them, and what to think about before applying.
You'll also look for low-cost online courses that can assist you to develop the essential abilities you'll need to succeed in the sector.
Data science internship: Benefits
- Hands-on experience with real projects and data.
- Skill development in various data science tools and techniques.
- Exposure to various industries and their data needs.
- Networking opportunities with industry professionals.
- Potential for full-time job offers after completion of the internship.
- Opportunity to learn and work in a team setting.
Data science internship: Duties and Responsibilities
- Cleaning and preprocessing data.
- Analyzing data and generating insights.
- Creating visualizations and reports.
- Building and predictive testing models.
- Collaborating with team members on project design and implementation.
- Presenting findings and insights to stakeholders.
- Staying up-to-date with new tools and techniques in data science.
Data science internship: Skills Required
- Strong analytical and problem-solving skills.
- Familiarity with data science tools such as Python or R.
- Ability to work with large datasets.
- Knowledge of statistical techniques and machine learning algorithms.
- Familiarity with databases and SQL.
- Effective communication and presentation skills.
- Ability to work in a team and ability to adapt to new technologies.
Data science internship: Technical Skills
- Programming languages such as Python or R.
- Data visualisation tools such as Matplotlib, Seaborn, or Tableau.
- Machine learning libraries such as sci-kit-learn, TensorFlow, or Keras.
- SQL and database management.
- Data cleaning and preprocessing techniques.
- Statistical analysis and hypothesis testing.
- Data manipulation and wrangling using Pandas or Numpy.
How to find Data Science Internships
- University/College career services: Many universities have career services that offer internships to students.
- Online job boards: Websites such as Indeed, Glassdoor, and LinkedIn have many internship listings.
- Company websites: Check the career section of companies in the data science field to see if they offer internships.
- Professional networks: Connect with professionals in the data science field through LinkedIn or attend industry events to learn about internships.
- Internship programs: Organizations such as Data Science Internships (dsi.io) offer data science-specific internship programs.
- Reach out to data science organisations: Non-profits and research organisations that focus on data science may offer internships.
- Apply for data science competitions: Companies may offer internships to the winners of data science competitions.
Data science internship requirements
- Education: Pursuing a degree in computer science, mathematics, statistics, or a related field.
- Technical skills: Proficiency in programming languages such as Python or R and data analysis tools.
- Problem-solving skills: Ability to analyse and solve complex problems.
- Communication skills: Effective communication and presentation skills.
- Passion for data science: Interest in learning about and working with data.
- Relevant coursework or projects: Coursework or projects related to data science or experience working with data.
- Internship duration: Internships can be full-time or part-time and last from a few weeks to several months.
- Availability: Interns are expected to be available during the agreed-upon internship duration.
Tips for applying to Data Science Internships
- Tailor your resume: Highlight relevant coursework, projects, and technical skills demonstrating your passion for and knowledge of data science.
- Prepare for technical interviews: Brush up on data science concepts and be ready to walk through your projects and problem-solving approaches.
- Network: Connect with professionals in the data science field and attend events to learn about internships and make connections.
- Show enthusiasm: Express your passion for data science and demonstrate why you would be an excellent fit for the internship.
- Show your portfolio: Create a portfolio of data science projects to showcase your skills and abilities.
- Research the company: Research the company and their projects to understand better how your skills and interests align with its goals.
- Be proactive: Reach out to companies you are interested in to inquire about available internships.
- Follow up: After applying, follow up with the company to show your interest and inquire about the status of your application.
Popular data science internships
- Google: Google offers data science internships in various locations and departments, including machine learning and data analytics.
- Amazon: Amazon's data science internships include positions in areas such as machine learning, big data, and artificial intelligence.
- Microsoft: Microsoft's data science internships are available in machine learning, data analysis, and artificial intelligence.
- Facebook offers data science internships in data analytics, machine learning, and artificial intelligence.
- IBM: IBM offers data science internships in data analytics, machine learning, and artificial intelligence.
- Airbnb: Airbnb offers data science internships in areas such as data analysis, machine learning, and artificial intelligence.
- Uber: Uber offers data science internships in data analysis, machine learning, and artificial intelligence.
- NVIDIA: NVIDIA offers data science internships in machine learning, data analytics, and artificial intelligence.