Road Map to be the best Data Scientist and get paid the best in the market!
Tags: Python, Data Science, Kaggle, Machine Learning, Roadmaps

Do you want to be the best data scientist or do you want to earn the best in the market?

Both the questions have the same answer.

YOU HAVE TO BE THE BEST IN YOUR SKILL.

What is in it for me !!??

Data Scientists are paid best in the market because companies know the power of ML /AI and what these concepts can do to their business if applied correctly. Not everyone knows how to implement these concepts in the company and hence a data scientist is hired to apply these to improve the business and earn more profit for the company.

AI/ML has a vast majority of applications in the industry depending on the nature of the industry and the concepts that a data scientist should know also depends from industry to industry. BUT there are few concepts that are common to all and if mastered can make you the best data scientist in the industry.

Let’s move to details now!

Data Science is becoming a mature field data by day, more and more people now know the concepts and if you think only knowing Python and Linear Regression will give you a head start then I am sorry to burst your bubble!

You need more than Python and LR to get a job as Data Scientist and be the best.

W arning:

It is not going to be easy! Actually it was never easy to learn a new programming language and adjust your skill as per market. Still it is need of the hour! If you want to be paid better than others and want to stay in demand, then you have to adjust as per the market. And it has become important with passing generations as people are investing more and more every day in themselves.

If you are spending most of your time watching Netflix, then YOU ARE OUT OF THE GAME.

Road Map!

Now coming onto the actual Road Map, I am listing down all the points that I followed in my journey hoping that it will be helpful for you as well. You can improvise as you like!

1. The Language Decision (R/Python):

Choose the language you want to learn in your ML journey. Two main competitors in the ML world are R/Python. Mentioned below are pros and cons for both the languages to help you decide. Now do your own research before coming to a decision as this is one of the most essential steps. I chose Python!

Python:

Pro :

a) People with software engineering background may find Python comes more naturally to them than R.

b) Python is a general purpose programming language which can be used beyond data analysis applications.

Cons:

Python doesn’t have as many libraries for DS as R.

R:

Pro:

a) If you have no coding experience, then R may be easier to learn.

b) Statistical models can be written with only few lines of code.

Cons:

Not popular as Python for deep learning and NLP.

2) Learn R/Python Basics:

Whichever language you choose, start by learning the basics. Learn the syntax; explore how to implement various concepts in it. It can take about 1–1.5 months to learn the language of your choice depending on your background. If you know any programming language at this step then this can be easier for you but if you are new to programming world then this can be a bit difficult.

This is the most basic yet the most important step if you want to be the best data scientist.

DONOT SKIP THIS STEP BY JUMPING DIRECTLY TO ML CODING. This is required.

Resources: Courses on Udemy.com or YouTube for free stuff.

3) Learn few important data structures in R/Python :

Data Structures is what you will be using most in your journey. This step will help you in manipulating the data, investigating it and mould the data into the algorithm’s desired input. It will be very useful.

Some of the common data structures that I studied in Python are Data frames, lists, arrays, dictionary, sets and more. R will have different set of Data Structure.

Take your own time to learn them.

BTW, Data Science is 80% Data cleaning and manipulation and 20% predicting.

4) Learn basic Linear Algebra, Statistics and Probability:

This is also one of the important steps to be the best data scientist. This step will actually separate you from the average ones. You should know some basic concepts and that’s where you will be tested in the interview.

a) Statistics : “Statistics is the discipline that concerns the collection, organization, analysis, interpretation and presentation of data.”

Some topics to learn : Categorical/Continuous data (Types of data), numerical summaries, Correlation, ANOVA, Chi-square, Probability, distributions like normal distribution, confidence interval, test of significance.

b) Basic Algebra : “Linear Algebra is a branch of mathematics that describe coordinates and interactions of planes in higher dimensions and let you perform operations on them.”

Some topics to learn : Matrices and Vectors, Addition and Scalar Multiplication, Matrix Vector Multiplication, Matrix Matrix Multiplication, Matrix Multiplication Properties, Inverse and Transpose.

c) Probability : “Probability is the branch of mathematics concerning numerical descriptions of how likely an event is to occur.”

Models are designed to use probability. To actually understand a ML model, you should know this.

NOTE :

Step 2 — 4 can make or break the whole game! I hope you spend plenty of time and attention on this one. Focus on it for 1.5 month and you are done with most difficult part of the journey. Now the sail will be smoother from here.

5) Starting with Machine Learning Algorithm :

Once you are done with the previous steps, then comes the implementation of actual ML problem. This step can be approached in parts:

Part A:

Understand the problem. Why are you doing this? Why do you want to use ML for this? Can’t this be done with any other approach? Understanding Why of any problem will help you solve the problem efficiently.

Part B:

Start with data manipulation. I know, I know, you don’t know how to do it right!

You can use Kaggle or YouTube to get started with this part. Here all the concepts studied so far will be used. Algebra, Python/R, Stats and Probability along with Data Structure.

Start by going to Kaggle and pick a problem. You will get data along with it. Try to do the following:

a) Read the data in your desired structure.

b) Normalize the data.

c) Find relationship between variables.

d) Identify multi collinearity.

e) Remove no value add variables.

f) Visualize it! Most important step. See your data, your variables.

This is where you can actually play and understand your data.

Now you know why step 1,2,3 and 4 were important.

Part C :

Start Implementing your ML algorithms.

Now you know how to implement data structures in R/Python. Now you know how to clean your data. The last step is to actually use the ML Algorithm. Learn about some common ML Algorithms. Start with the easy ones and once you are pro in those, you can move to a tougher algorithm. You can also do the following :

a) Divide the data into test and train pieces.

b) Feed the train piece into ML to train your model.

c) Now predict and measure performance on your test piece.

d) Choose the best model.

Easy peasy! Wasn’t it :p

You are a data scientist now!

6) Practice :

Now to be the best data scientist, practice is the key. Most important step to be the best is that you have to practice. But you can reach this step only if you have completed all the step from 1 to 5.

Practice as much as you can. After all, someone wiser has quoted

“Practice makes a man perfect”.

You too can be perfect, just keep on practicing.

Refer Kaggle, kdnugget.com, AnalyticsVidhya.com or even Mahinehack.com for your practice.

This is how you can be the best data scientist and get your dream job!

Don’t stop and one day your dream job will be in your hands.

Bonus Tip :

Read a lot. If you come across any concept that you don’t know, read about it. Think about it and try to do a research on it. You never know when you might need it in your life.

Once you feel confident! Start applying for the jobs and with the knowledge you have acquired, you will definitely get the job.

Hope you liked this article. For the visual/auditory learners, I have also created a YouTube video on it. Check it out and Subscribe if you liked the video.