What are the steps to start a career in Data Science from Scratch?

Career Coaching
#1

Data is indeed in demand for today’s generation for all the right reasons. But whats the correct step to move ahead and start a journey remains a question of many people.

Starting from scratch in any particular field demands having an academic exposure for developing the right kind of basic skills needed to be a data scientist.

There are many courses available online as well as offline to move ahead with data science. However, doing a proper degree course is very beneficial as it has a value recognition which helps you get internships and job placements later.

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#2

  1. Do 1 bootcamp data science course without delving into too much depth.

  2. If you are already working somewhere, try and apply basic data analytics and data science in extra time / over the weekends and try to do a meaningful small project. If the company permits, try to move internally into the data science department (of course assuming if there’s such department). If your company doesn’t have any data driven projects or there’s no any data based department, try working on a project from online free datasets in a domain allied to your working job.

  3. If you are not working, pick any data science project from kaggle. And try to hunt for an internship where you will learn

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#3

If you’re new to data science, there are a number of online beginning courses you may take to get a better understanding of the basics.

The Topics are:

Programming: Your first duty will be to decide whether you’ll use Python or R and then dive into coding.

Linear Algebra: You’ll need to know how to express data sets as matrices and comprehend concepts like vectorization and orthogonality because you’ll be working with data.

Calculus: To compute and discover a solution to your problem more quickly, many of the models you’ll build and utilize will use tools like derivatives, integrals, and optimization.

Probability: When using data science, you’ll frequently be attempting to forecast the future. Therefore, you’ll want to know how likely something is to occur or why two events are connected.

Statistics: You’ll need things like the mean and percentiles to explain the data you’ll be examining, as well as tests to verify your hypothesis.

Machine Learning: Perhaps the most fundamental aspect of data science, you’ll want to anticipate something at some point during your project, and this is where machine learning comes into play.

After this, learn data science libraries and become proficient in using them.

You can try this learning path on data science for guidance