How To Become Data Scientist in India? — Your Step-By-Step Guide

After the recent boom all around the world about Data Science, We have been getting plenty of calls and messages each day asking this one famous question,

“How do I start learning Data Science?”

To give an answer to your question, we decided to write this article.

There are plenty of websites, articles, or GitHub repositories that will give you a list of resources to start from. But this article will be a little different. We want to hold your hand and help you walk the baby steps to the Data Science world, and then leave you to fly on your own.

Let’s first start with Prerequisites.

If you come under one or more of the following categories, you can go ahead and start Data Science without any problem.

• I have worked with Python/R language.

• I have worked with Object-Oriented Languages such as Python, R, Javascript, SQL.

• I am a Python developer, have worked with datasets before.

• I have Basic knowledge of mathematics statistics.

• I am good at communication.

• I have Business Domain Understanding.

• I am a great problem solver.

Now, let’s move on to the steps to become a data scientist.

Step 1: Data Scientist Tool Set — The Starter Tools

Learn how to import data into Excel from an Access database and CSV file and work with Data using VLOOKUP and Pivot tables in Excel.

Write complex SQL queries to fetch data and solve business problems with the help of SQL.

Complete understanding of foundational python.

Step 2: Advanced Tools for Data Scientists

Understand the most important concepts relating to the R programming language.

Understand key concepts like Regression and Decision Trees in R programming.

Step 3: Basic Data Visualization

Plot categorical, quantitative, and mix of both types of data visualisations in Python.

Create data-driven impressive insights using various kinds of plots with Titles, Labels, Legends, etc, and carry out quick exploratory data analysis using visuals in python using subplots, pair plots, etc.

Step 4: Statistics & Mathematics for Data Science

Get a grasp on the most common statistical concepts applied in the field of data science and work through their application using Python coding and Google Colab notebooks.

Work with Confidence Intervals and Hypothesis Testing, Regression, Predictions, Classification Modelling as well as NLP.

Step 5: Machine Learning with Python

Understand the capabilities of machine learning (ML), and the knowledge to formulate your business problem to solve it effectively.

Build an army of powerful Machine Learning models and know-how to combine them to solve any problem.

Master your Machine Learning fundamentals with algorithms and clustering using K-means.

Step 6: Python Libraries For Data Science

Learn about the most useful Python Libraries For Data Science such as TensorFlow, NumPy, SciPy, Matplotlib, Pandas, Keras, SciKit-Learn, Statsmodels, Plotly, Seaborn.

Step 7: Business Intelligence

Ramp up your SQL skills by looking at advanced features of MySQL such as unions, views, triggers, stored procedures and more.

Step 8: Basic Data Analytics

Learn how to design databases to ensure data integrity. As a bonus, get a small introduction to NoSQL systems with MongoDB.

Work on Data Cleaning, Correlation Analysis, Time Series Analysis, Model Selection, Regression models, and Decomposition in Business Intelligence in Data Science.

Step 9: Data Engineering Basics

Learn about the most common topics within the field of big data, you’ll have the vocabulary, and skills needed to pursue applications in the field of Big Data.

Features and value of core Hadoop stack components including the MapReduce programming model. Learn concepts of Data Mining and get introduced to the core techniques using practical exercises in Python, R and, Rapid Miner. Understand Data reduction, Data clustering, Anomaly detection, Association analysis, Regression analysis, Sequence mining, and Text mining (Sentiment Analysis).

Step 10: Professional Data Engineering

Learn basic and advanced data visualization, dashboard and story development, and integration of Tableau with Python and R.

Achieve scalable, high-throughput, and fault-tolerant processing of data streams using PySpark.

Now, you know the steps to become a data scientist. You can start your journey to becoming a data scientist. Trycatch Classes is the best data science training institute in Mumbai that can help you kick start your career in this data science world.

Remember, your data science journey has only begun! There is so much to learn in the field of data science that it would take more than a lifetime to master. You don’t have to master it all to launch your data science career, you just have to get started & keep working on it.