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A man is looking at monitor after doing Data Science

The Data Industry in India is expected to become a $16 billion industry by 2025. According to the experts, NASSCOM is set out to set a target of becoming one among the top 3 on the global map


The opportunities in the industry have been mushrooming at an exponential pace and the youngsters and working professionals are finding it hard to face the conundrum between the best mode of learning to choose -

 

Self-learning or Formal Education : How to secure a flourishing career in Data Science Industry?

Both of them have their own pros and cons in terms of career trajectory, the multitude of available opportunities and compensation.

Self Study Approach

  1. Start from anywhere – JUST START

    To get going with the learning journey, you must not let the challenges underlying the mainstream training alternatives hold you back. You don’t need to know everything – as you will gradually learn everything by doing.

    So, it’s best to choose a project and get the ball rolling.
     
  2. Pick up a programming language

    You cannot learn data science without learning to code. As a data scientist, you will build algorithms and environments to run those algorithms. Here are a couple of popular options to start with –

    • Python – A very easy general-purpose language for beginners that offers libraries and community support. It helps you perform a wide range of data science tasks from statistical analysis to data visualisation and beyond.
    • R-programming – An interesting choice if you’re into hardcore research as it uses statistician syntax and translates massive data into robust and rich visualisation.
       
  3. Have a strong technical foundation

    It is really important to master the technical aspects of data science through the traditional style of learning. Here the conceptual clarity comes into play as it will determine how clearly you will be able to fetch meaningful insights by implementing quality raw data.
     

  4. Practice the tools

    There are many tools that data science professionals are expected to use to process and visualise the given data. A few of them include –

    1. Github
    2. Python
    3. Tableau
    4. SAS
    5. Stack Overflow
    6. MySQL
    7. Google BigQuery

Some of them can be overwhelming but remember what we learnt above – just start because you don’t need to know everything. Instead of worrying about the right approach, just hit the ground and get your hands dirty.

Online MBA in Data Science – Formal Education route

If truth be told, the advantage of undergoing formal education in any subject is a no-brainer.

It is a clear YES, if you were to compete with the cut-throat competition in today’s market and get that edge over your counterparts. Leading universities and MOOC platforms are making it pretty easy for you to satiate your hunger to learn and grow without losing your day-time job.

UPES CCE offers a one-of-its-kind Data Science online PGP course with an industry-recognised curriculum that enables young graduates to have a proper structure in their learning experience to become a ready-to-hire data scientist in 2022.