Most of the Working Professionals or Freshers I meet everyday are asking me these common questions often.

“How Do I Become a Data Scientist”

“What is the Road-map to becoming a Data Scientist”

“Is it easy to Learn Data Science?”

“Are there Shortcuts to Master Data Science?”

“What is the Simplest Way to Master Machine Learning & Artificial Intelligence”

and many more! I hope you get the gist of what I am trying to say here. In short, people I often meet would like to know the right formula to master Data Science & quickly become a Data Scientist by profession.

I always give them the same constant answer.

There is No Ready-made Formula Or Standard Recipe to Master Data Science!

It takes years of practice and patience to learn the concepts, understand the strengths and weaknesses of each algorithm, mastering programming languages and knowing the nuances of Machine Learning concepts. And mainly, the learning path is NEVER the same for every individual.

research is important for data scientists

I have noticed every person in a class or a group always wants to become a Data Scientist or AI/ML Engineer. Seldom have I heard the words Business Intelligence Developer/Consultant, Data Analyst, Data Engineer, Statistician, Database Administrator, Big Data Developer and other similar roles. I am so unhappy with the current Media hype around Data Science & Data Scientists in specific. Although, this is a story for a different post. So, I wouldn’t want to venture here any further.

Given the complexity and practice needed for each individual, we could however still have a few checkpoints in general.

The core requirements for each individual to start learning the Art of Data Science would be as follows

  1. Know your School Level Statistics & Mathematics
  2. Know your Algorithms & Data structures
  3. Pick up a Programming Language of your choice
  4. Learn working with at least one database for extraction & usage of data
  5. Practice, practice some more practice!

Know your School Level Statistics & Mathematics

Might sound silly, but is the hard truth
reading mathematics is a must for data scientists

Many people who hated Mathematics & Statistics during school days are now seated in Machine Learning Courses, Advanced AI Courses & other Training programs. The only advice to all is go back to school. Pick up your Calculus, Linear Algebra, Statistics books once again for a good refresher. Most of us studied these subjects for the sake of passing our exams and never really were preparing for this reality which has hit us hard now.

It will never hurt us to revise these concepts, even now. This is more applicable for experienced professionals trying to learn Data Science from scratch.

Know your Algorithms & Data Structures

There are a ton of them, however a few are critical

It is imperative to learn a few core Algorithms and most importantly know-how of Data Structures is a must. Try to understand in detail the algorithms and techniques for

Descriptive Analysis
Inferential Analysis
Supervised Learning
Unsupervised Learning
Semi Supervised Learning
Reinforcement Learning

There are multiple algorithms that one can list under the above topic areas. However you must know at least a few core algorithms under each to start with. Later, through practice understand the more complex and advanced ones.

Pick up a Programming Language of your choice

This might be a free to choose for Freshers or adapt to Organization for Experienced Professionals
knowledge of python for data scientist is important

Try to experiment and play around with the basics of some popularly used Languages like PythonR Programming, Julia, Java, C++, Lisp. However, the current market trend seems to lean heavily towards Python for some reason, followed by R programming. However, feel free to pickup one that suits your style I would say. Do your homework.

Most of the times, you might have to adapt to the Organization’s needs and even learn something new altogether. As long as you are strong in your foundation of Algorithms & Data Structures, this part can be picked up easily. Sometimes, even with the help of Stackoverflow only!

Learn working with at least one database for extraction & usage of data

This is highly applicable for Freshers especially

Freshers seldom get to learning Databases properly. Pick up one like MySQL or SQL and start learning it thoroughly for good exposure to Databases & Structures. Most of the times, Data Scientists are expected to know how to handle Databases along with Algorithms and coding.

Practice, practice some more practice!

Might sound boring but DO NOT FORGET THIS!
natural language processing for data scientist

I have noticed even the most experienced Machine Learning engineers & Data Scientists forgetting core concepts or coding know how due to lack of practice. I have one ex-colleague of mine who used to work for a Startup in the NLP domain. He used to be an expert in all areas under NLP programming using python & Java.

Now, after shifting to a different company and role altogether, he is no longer in touch with the NLP techniques. Within a year’s time he has lost all confidence in working for an NLP project and has gone back to the basics once again to study a lot!

If this is the truth for experienced professionals like him, then imagine the plight of Freshers with zero work experience!

So, never forget to practice, practice and practice some more always!