Why you will fail to be a “GREAT” Data Scientist?

  1. The correct way of learning data science is to perfect the mathematics behind it. Only when you can understand and do lots of linear algebra and calculus can you call yourself an artificial intelligence enthusiast.
  2. Who cares about mathematics? It’s just Python anyway, with libraries and Github repos available for most of the tasks. It’s more of a “tweaking-the-code” job instead of being a mathematical one.
  1. Don’t ignore competitive programming at all. Unless you can change your ideas to code, you can’t be termed as someone who knows computer science, let alone a data scientist. The knowledge of data structures, algorithms, and time complexities are equally relevant to a data science role as a developer’s role.
  2. There are specific roles for each part of an artificial intelligence pipeline. Some common names include data analysts, data engineers, and data scientists. If you are thinking of going into any of the first two, knowledge of databases and SQL is a must. Even with data scientists, it’s always good to have some SQL skills so that you don’t have to depend on someone else for your data needs.
  3. Computation power can be a big headache while doing data science projects. To solve this, either use free resources like Kaggle and Colab notebooks or intern somewhere that can provide cloud processing power to you. Internships are very important to learn the gap between WHAT YOU SUPPLY AND WHAT CONSUMERS WANT TO CONSUME.
  4. Do some courses on linear algebra and calculus. Also, implement whatever you are learning while doing lectures on computer vision, natural language processing, etc. Good implementation courses are often available on Udacity and other independent creators. Mathematical courses are available on Youtube (Stanford, UC Berkeley, MIT anything you like) and Coursera. SOLVING PROBLEM SETS AND HOMEWORK IS A BIG MUST.
  5. It’s equally important to read research papers as well as blogs of AI teams of Facebook, Google, Uber, etc. They give you approaches to solve real-world problems and expand your mind. If you don’t do any of the two, start doing them and STRIKE A BALANCE.
  6. Diversify your projects in terms of the areas they cover. It gives you a more comprehensive understanding of things, and the CV also fares well for most companies.

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Data Science Engineer at ShareChat, IIITM Gwalior, India. Curious about economics, politics, history, mythology and information; rishabhrjjain1997@gmail.com

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Rishabh Jain

Rishabh Jain

Data Science Engineer at ShareChat, IIITM Gwalior, India. Curious about economics, politics, history, mythology and information; rishabhrjjain1997@gmail.com

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