Are you looking for Data Science Internship?

Data science is one of the most popular and fastest-growing fields across the tech industry, but also one of the most competitive professions. If you are interested in pursuing a career in data science, getting an internship is a great way to gain some valuable hands-on experience, as well as networking opportunities with potential employers and mentors. However, getting an internship in data science is not easy.

You need to have a good understanding of data science life cycles, various tools, and processes with strong resume and portfolio projects. In this article, I will share some tips and guides on how to prepare and apply for internships to help you to start your career in Data Science. But before that, let’s first understand what the key role of a data scientist and their required skills are.

Responsibilities

• Collecting data using various methods such as manual entry, web scraping, etc.

• Preprocessing and analyzing that raw data for further use.

• Building a model to find trends and patterns within the data using various Machine Learning algorithms to address business problems.

• Presenting those insights in report format or other data visualization using programming language or dedicated visualization tools.

Skills

As a data science intern, you need to have some of the skills to fulfill these requirements. Data Science is a vast field and with its fast growth, it’s really hard to keep updating yourself and to know everything just by reading. It is not necessary to know everything but having a good knowledge of the most common skills and hands-on experience using those skills by doing some projects will help you get that data science internship a little faster.

Technical Skills

To perform all these critical responsibilities, data scientists must have a strong understanding of the following technical skills.

• A firm knowledge of Programming languages such as SQL, Python, or R. It is mainly used to write programs or to automate the process of data collection and processing.

• Strong knowledge of data management tools such as Hadoop, and Spark to perform ETLs (Extraction, Transformation, and Loading).

• A good knowledge of statistics and mathematics for data analysis.

• A sound understanding of artificial intelligence (AI) including common machine learning algorithms, NLPs, and deep learning.

• The ability to perform visualizations using data visualization tools such as Tableau or Power BI.

Soft Skills

Communication skills are equally important as technical skills for data science roles as data scientists need to collaborate with other team members, such as data analysts, business analysts, data engineers, and other software developers. Along with soft skills; It is important to have good business domain knowledge and other people skills such as teamwork and storytelling to tell and illustrate stories that convey the results to business partners and stakeholders to explain how insights can be used to solve business problems.

These are the most in-demand skills for data scientists' roles. Various online platforms provide these skills for free or paid certification for you to try, such as edX, Coursera, etc.

You must have strong fundamentals of data science to excel in your career as a data scientist. Here are some of the tips that can help you to build a strong data science portfolio and eventually help you land your first internship or job.

Tip #1: Build a strong foundation in Statistics and Mathematics

Data science is based on mathematics and statistics, so you need to have a solid understanding of these subjects. You should be familiar with at least some basic concepts such as linear algebra, calculus, probability, hypothesis testing, regression, classification, clustering, and more.

There are various analytical tools and techniques available to use for statistical analysis while working on real-world problems; so, you should have some knowledge about them.

Tip #2: Learn programming languages and tools

Programming is an essential skill for data science, you will need to write code to manipulate, analyze and visualize data. You should have a good understanding of at least one programming language that is widely used in data science such as Python or R.

You should also learn how to use libraries and frameworks that are relevant to data science, such as NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch, Matplotlib, Django, Flask, etc.

You should have a good understanding of the working of basic supervised and unsupervised machine learning algorithms such as Linear Regression, Logistic Regression, K-Means Clustering, etc. You should also be able to work with tools such as Jupyter Notebook, Google Colab, GitHub, SQL, and more.

Tip #3: Build your portfolio.

After you have a good understanding of programming languages and tools for data science; it’s time to work on your portfolio. One of the best ways to demonstrate your skills and passion for data science is to work on personal projects that involve real-world data and problems.

Sharing your work is a great way to showcase your data science skills. Your portfolio is where you can share your data science projects and demonstrate your skills and creativity. Having a portfolio can help you stand out from the other candidates and impress potential employers with your work. Here are some suggestions on how to build your portfolio project.

• First, choose projects that are relevant to the domain you are interested in. For example, if you want to work in healthcare, then you can choose projects that involve the prediction of disease using pattern recognition, Image segmentation, etc.

• Use publicly available real-world datasets, or you can collect your data as well by using web scraping or an API. There are various data sources available online where you can find datasets such as Kaggle, UCI Machine Learning Repository, Google Dataset search, etc.

• Follow the data science process of defining the problem, collecting and cleaning data, exploring and analyzing data, building and evaluating models, and communicating the results.

• Use appropriate tools and techniques for each stage of the process. For example, you can use Python or R with Pandas or tidy verse for data wrangling and manipulation and analysis, matplotlib or ggplot2 for data visualization, Scikit-Learn or TensorFlow for Machine Learning, etc.

• Convert your project into a prediction service by building a web application using Python Frameworks such as Django or Flask or Streamlit and deploy your model on a cloud to use as a service.

• Document your code and results and explain the logic and reasoning for each step. Use comments, docstrings, and markdown cells to explain your code wherever necessary to make it more readable and understandable.

• Present your results clearly and concisely. Use charts, graphs, tables, etc. to visualize your findings and insights. Use storytelling techniques to narrate your process and conclusions. Highlight the key takeaways and implications of your analysis.

• Last but not least is to publish your projects online using platforms such as GitHub, and Hashnode by writing a blog that explains your project. Include a link to your portfolio on your resume and social media profiles.

Tip #4: Build your resume

After you have done some data science projects it's time to build a resume. Your resume is the first thing that hiring managers will look at when they screen your application. Therefore, you need to make sure that your resume showcases your relevant skills, education, and achievements in data science.

• Use a clear and concise format that highlights your key information and makes it easy to read.

• List your education, and emphasize your skills and tools that are relevant to data science such as programming languages, frameworks, libraries, databases, etc.

• Include your projects with brief explanations; Explains the impact and results. Include necessary links to your projects such as blogs that explain the project or to an application that is deployed on the cloud.

• Include any publications, or certifications that showcase your achievements or recognition in data science.

Tip #5: Build your network

Networking is another important aspect of getting an internship in data science. Having a strong network can help you learn from others and get feedback and advice from experienced professionals in the field. Here are some suggestions on how to build your network:

• Connect with people who work in data science or related fields on LinkedIn or other social media platforms. Send personalized messages that introduce yourself and express your interest in learning from them or working with them.

• Join online communities and forums that are related to data science. For example, you can join online communities such as Reddit, Stack Overflow, Discord or Slack, etc.

• Participate in online events and webinars that are related to data science. For example, you can attend online workshops, hackathons, and meetups where you could meet people and share ideas.

• Reach out to alumni from your school or program who work in data science or related fields. Ask them about their career path, their current role.

• Ask for referrals from the people who work in companies or organizations that you are interested in. Referrals can increase your chances of getting an interview and an offer.

Tip #6: Apply for Internships

Finally, apply for multiple internships that match your skills and interest.

• There are various sources from where you can apply for data science internships such as LinkedIn, indeed, Glassdoor, and Internshala. You can apply for an internship directly from the company’s website as well.

• Alter your resume according to the job requirements by using keywords and phrases from job descriptions to optimize your resume for applicant tracking systems.

• Prepare for the interviews by reviewing your fundamentals and practicing common data science interview questions and topics. Practice your coding, problem-solving, communication, and presentation skills by doing mock interviews with mentors, or friends. You could also try online platforms such as interviewing.io.

• You should also be ready to explain your projects in detail. You should follow up with recruiters after each stage of the interview; send a thank you note. Ask for feedback or update on the status of your application.

Getting an internship in data science is challenging but also rewarding. It will help you to gain valuable work experience or might give you a full-time job opportunity in the same company. I hope this article will help you to improve your chances of getting an internship in data science.

Good Luck!