Entering directly into a data science field can be tough, but an internship is one of the best ways for you to get some practical experience. You need a mix of technical skills, project experience, networking, and strategic applications to secure a data science internship.
In this blog, you will get a complete guide on how you can navigate the process, which includes building your skills to landing your first internship.
Table of Contents
Essential Skills Needed for a Data Science Internship
If you want to qualify for an internship, you need a good foundation in technical and analytical skills.
1. Programming Language
- Python: It is the most widely used language in data science with vast library support such as NumPy, Pandas, Scikit-learn, etc.
- R: R language is mostly used for working with statistics and academic research.
- SQL: SQL(Structured Query Language) is important for querying databases.
- You need to learn the techniques for cleaning and processing your data. You can use Pandas and NumPy for this.
- You need to work with real-world datasets such as CSV, JSON, and SQL databases.
3. Basics of Machine Learning
- You need a clear and deep understanding of supervised and unsupervised learning.
- You should be familiar with frameworks such as Scikit-learn, TensorFlow, or PyTorch.
4. Data Visualization Techniques
- You also need to have an idea of various data visualization techniques, such as Matplotlib & Seaborn for Python.
- Get some exposure to building business dashboards by using Tableau or Power BI.
5. SQL & Databases
- Learn to write complex queries for extracting meaningful data from large datasets.
- Familiarity with relational databases like PostgreSQL and MySQL is required.
6. Understanding the Business
- Ability to translate data insights into business decisions.
- Understanding domain-specific problems in finance, healthcare, and marketing.
Creating a Strong Portfolio
A strong portfolio represents your skills and experience.
1. Make Personal Projects
- Predictive modeling (for example, predicting plot prices using regression).
- Sentiment analysis (for example, analyzing Instagram data).
- Recommendation systems (e.g., movie recommendation using collaborative filtering).
2. Contribute to Kaggle and GitHub
- Participate in various Kaggle competitions to gain real-world experience.
- Upload projects with good documentation onto GitHub.
3. Online Courses & Certifications
- Data Science Course by Intellipaat
- Google Data Analytics Certificate
- IBM Data Science Professional Certificate
Making your Resume And Cover Letter
1. Key Sections in a Data Science Resume:
- Header: Name, contact information, LinkedIn, GitHub, personal website, if available.
- Summary: A 2-3 line objective, which focuses on skills and interests.
- Technical Skills: Python, SQL, ML libraries, data visualization tools.
- Projects: Specify 2-3 data science projects with a brief explanation.
- Education: degree, relevant coursework, and certifications.
- If any prior experience: freelance, research, open-source contributions.
- Certifications: Intellipaat Data Science certifications
2. Things to remember for writing a strong Cover Letter:
- Make sure to write a personalized letter for each company.
- Write how passionate you are about data science.
- Mention your relevant skills and projects that will help you.
- Mention why you want to intern at this company.
Where Can You Find Data Science Internship Opportunities
1. Search in Online Job Portals
- LinkedIn Jobs
- Indeed
- Glassdoor
- Internshala (for Indian students)
2. Look into the Company Career Pages
Many tech companies have data science internships available. Below is a list of a few top choices:
- Google STEP Internship
- Microsoft Data Science Internship
- Amazon Machine Learning Internship
- Facebook Data Science Internship
3. Connect through LinkedIn & Networking events
- Connect with the recruiter & professionals for Data Science.
- Join LinkedIn groups associated with data science.
- Attend hackathons & meetups.
Alternative Methods to Acquire Experience
1. Contribute to Open-Source
- Start contributing to data science projects present on GitHub.
- Work on Apache Spark and TensorFlow repositories.
2. Freelancing And Internships at Startups
- Check on websites like Upwork, Fiverr, and Toptal as they offer small data science projects that will help you gain experience with real-world problems.
- There are many small startups that often require data analysis help but may not post internships publicly. Try reaching out to them following the ways explained above.
3. Volunteering for NGOs
- You can help several NGOs analyze their data on fundraising, social impact, or health trends.
- Platforms like DataKind offer several volunteering opportunities.
Conclusion
If you want to get into a data science internship, you need to have a combination of technical skills, a strong portfolio, social networking, and a well-prepared resume. You can start this by learning Python, SQL, and ML along with working with some real-world projects, and then apply strategically to the opportunities. Even when you don’t get an internship, you can keep improving your skills through open-source contributions, freelancing, and networking. If you want to excel in your skills and pursue a career in Data Science, you can refer to the Intellipaat Data Science Course.