easy tips to crack your first data science Internship | EduGrad

Getting a data science job or any job for that matter is not hard. It is all about knowing where to find the right resources which you can use to learn topics that can help you ace a data science interview.

At Edugrad, we mentor thousands of learners on how to pursue a career in data science every single month and have a comprehensive checklist that learners can use to find their first data science jobs after completing their graduation. Here are the steps that you need to complete to crack your first data science internship.

Master your basics first:

The first thing to do before trying to secure a data science internship is to get familiar with the content, topics, and terminologies of the subject by getting a good data science course syllabus. There are several good sites and books that you can look at to polish your basics if you are starting out on your data science career.

Many online Platforms offer the courses that a beginner can start their programming skills with. After gaining the basic level python programming experience, you can start with an advancement to do in a particular domain. EduGrad has evolved the learning path to master the data science technologies by designing edge to edge curriculum required in Industry.

If you are someone who likes reading books, then Data Science from Scratch published by O’Reilly is a good book to master your concepts in the subject.

Some free links for you to learn data science from:

What do you want to become?

Data science is a vast field that consists of many fields within it. It is important that you identify the profession you would like to pursue in data science.

A statistician might work with tools such as R, Excel or MATLAB whilst a data scientist would work with languages such as Python, Spark, SQL, etc.. That is why you should do thorough research in data science and determine the profession you would like to pursue before beginning the journey. We are not here to make the decision for you but can guide you on the right path.

Here are some good resources to start within helping you choose the right path:

Master linear algebra, statistics, and probability

Linear algebra, statistics, and probability are some of the basic concepts in mathematics that you need to master before learning data science. Linear algebra is used extensively in machine learning to understand how machine learning algorithms work.

Here is a good link to polish your algebra basics.

In addition to algebra, you will also need to master probability and statistics to ace interviews at startups and big firms alike. If you are eager to know how to become a data scientist, here are some good sources to start with statistics and probability

  1. Khan Academy statistics and probability series (a good place to learn for beginners)
  2. A visual introduction to probability and statistics from Brown University
  3. Intro to Descriptive Statistics from Udacity
  4. Intro to Inferential Statistics from Udacity

Master coding skills (either Python or R) 

Pick a coding language, be it Python or R programming for data science. There are several pros and cons that are associated with each programming language. If you are puzzled about what language to choose, check our blog here where we talk about data analytics trends and help you make the right choice based on your interests and goals.

Don’t try to study multiple programming languages when you are a beginner as it will not only confuse you when it comes to your goals and makes it tougher to ace an interview. Try to get some data science Interview questions before going for an interview.

Build your online portfolio

Building online portfolio for Data science | EduGrad

Now, that you have mastered your concepts, it is time to start building your online data science resume to attract recruiters from small fast-growing startups and big companies alike. LinkedIn is a good place to start building your resume. As the world’s leading site for professionals to network, you can update your profile and have meaningful conversations with titans of the industry.

Writing blogs on the topics you have learned is also a good way to create a portfolio that attracts the right kind of audience.

Interact with the community/network: 

At Edugrad, we have a vibrant community where students undergoing training in data analytics can interact with companies that are looking to hire talented people, to create great products. 

Online and offline learning centers have a community of people who will be more than glad to help you in finding the right job with the help of their connections. That is why it is essential that you collaborate with people who study with you. You never know how important a role they might play in helping you find a dream job.

Undertake practical projects

Last but not least, undertaking practical projects bridge the gap between theoretical and practical learning. That is why we ensure that our pupils undertake countless practical projects that are relevant to the industry.

If you are looking to get into a data science internship in India, honing your practical skills is a sure-shot way to get your recruiter’s attention.

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