How Do I Begin A Career in Data Science

“Data is the new oil. We need to find it, extract it, refine it, distribute it and monetize it”
– David Buckingham

Table of Contents

  • Overview
  • Introduction to Data Science
  • Careers in Data Science
  • Skillsets and their roles
  • Where do I start?
  • How to land a job in Data Science?
  • Survey Reports and Stats

Overview

Is Data Science job really the sexiest job of the 21st century? Or is it a Hype? Is data the currency of future?

A few years ago when computer science and software engineering was so captivating, everyone wanted to land a job only as a programmer, web developer, software developer etc. It followed a basic law of economics – supply and demand.

Back then, the supply for computer science engineers was less and demand in the market was much higher. But the days are gone and now there’s been a huge increase in supply as the plethora of CS engineers is graduating every year. As the time passed, the salaries got lower but the software industry is still above the average pay.

Currently, the same is the scenario of the Data Science industry also where the supply is really low and the demand is high. As a consequence, the package offered is also quite tempting. The average package of a data scientist is about $130,000. Wow! That figure is really eye-catching, isn’t?

According to a survey done by Analytics India in 2018, the demand for data scientists and other Data Science careers is the highest in IT & Service industries followed by Banking and e-commerce.

By now, a lot of questions must be arising in your mind like how to make a career in Data Science? What are the skill sets required for it? Can I have a transition to Data Science from engineering or other professions? etc etc. So let’s discuss it one by one. Before we begin, let me give you a brief introduction on what is Data Science.

Introduction to Data Science

Technically, Data Science is the multidisciplinary blend of data interference, algorithm development, and technology for uncovering the finding from data and to solve complex problems analytically. To put it in simpler words, Data Science is discovering the data insights. For example :

  • NetFlix data mine studies the movie viewing pattern to understand their viewer’s interest.
  • Search engines like Google, Yahoo etc implements Data Science algorithms to produce the best search results in the blink of an eye.
  • Recommendation system like “people you may know” and suggestions from Facebook and LinkedIn also make use of Data Science algorithms.

There are a plethora of Data Science applications which you use in your day-to-day life.

Careers in Data Science

There are top 9 job roles in the world of Data Science. They are :

  1. Data Analyst
  2. Data Engineer
  3. Data Scientist
  4. Database Administrator
  5. Machine Learning Engineer
  6. Data Architect
  7. Business Analyst
  8. Statistician
  9. Data and Analytics Manager

Most of you would have come across these super cool job titles. What skills do you actually require to become one and what are their roles in the industry? Let’s have a look

Skillsets and their roles

Where do I start?

We often face such situations where we know our goals but have no clue where to begin. So here are some tips you could follow step by step to begin your journey towards the world of Data Science where you have a lot to explore and learn new things.

1. Choose your Data Science career wisely
Choosing a right career is the most crucial thing because it could turn your life 180o. I believe by now you have enough idea what a data scientist or a data architect or a Business analyst do. So choose a career depending on your interest and strengths. If you are still perplexed then get in touch with professionals who work in Data Science industry to have a clear idea and try to understand their role. I personally recommend you not to make any haste in choosing your career.

2. Introductory Online courses and MOOCs
Now that you have decided on a career, you need to develop its skills. The best way is to enroll for some online courses and MOOCs on Udemy, Coursera, Udacity etc. which has really good stuff to begin with. Here is a list of some popular courses.

3. Read Data Science blogs and books
You can find a gazillion of articles on Data Science, machine learning, statistics, actuarial science etc., at StepUp Analytics, coursera, analyticsvidhya etc. They are very easy to comprehend.

4. Start coding and get acquainted with data science tools
To grasp the concepts, it is important to practice what you have learned because you learn Data Science by doing and practicing, not just by reading or watching online tutorials. You can choose any programming language which you are comfortable with but Python and R are the most recommended programming languages. HackerRank is a good platform to practice coding.

5. Practice
Focusing on practical applications is more important than theory. Work on various projects and participate in online Data Science competition on Kaggle.

6. Build a network and stay updated
Having a Data Science network is also important for your profession and the best platform for this is LinkedIn. Keep yourself updated to meet the current needs in the market by subscribing to newsletters from Analytics India, StepUp Analytics, kdnuggets etc.

How to land a job in Data Science?

  • At 47.1% LinkedIn, the social networking website for professionals seems to be the first choice for looking for as well as posting a job in Data Science.
  • The next popular way to find a job is checking with friends and acquaintances to look for employment opportunities at 19%.
  • This is followed by popular job portal Naukri at 15.7%

Survey Reports and Stats

The above stats clearly give us a bigger picture of Data Science jobs in 2017 and the good news is that you need not have a formal degree in Data Science. Even if you are an engineer or from a non-Data Science background, you can still make a fantastic career in Data Science. All you need is some patience. Well, to bring to your notice, I’m also a B.Tech graduate and have been working in Data Science domain.

Aim, Execute, Conquer. Repeat!!

I hope you find this article helpful. Please share your thoughts/feedbacks in the comment section below.

Reference: kdnuggets and analyticsindiamag

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