For becoming a Data Scientist, having knowledge of statistics and probability is as essential as having salt in food. Knowing them will help the data scientists interpret large data sets, get insights from them, and analyze them better.

• Krish Naik’s Statistics Playlist (Recommended)
• Coursera Statistics Course
• Khan Academy Statistics And Probability Course
• FreeCodeCamp Statistics Course (Recommended)

Structured Query Language (SQL) is used for extracting and communicating with large databases. One should focus on understanding the different types of normalization, writing nested queries, using co-related questions, group-by, performing join operations, etc., on the data and extract in raw format. This data will then further be cleaned either in Microsoft Excel or by using Python libraries.

• Freecodecamp SQL (Recommended)
• Intro To SQL By Kaggle (Recommended)
• Advanced SQL By kaggle
• Edureka’s SQL Playlist

When a Data Scientist is given a project, the majority of the time goes into cleaning the data set, removing unwanted values, handling missing values. It can be achieved by using some inbuilt python libraries like Pandas and Numpy.

One should also know how to manipulate data using Microsoft Excel.

• Blog — Cleaning Data Using Python (Recommended)
• Edureka’s Microsoft Excel Course
• Learning Pandas By Kaggle (Recommended)

Exploratory data analysis is the essential part when talking about data science. The data scientist has many tasks, including finding data patterns, analyzing data, finding the appropriate trends in the data and obtaining valuable insights, etc., from them with the help of various graphical and statistical methods, including:

A) Data Analysis using Pandas and Numpy

B) Data Manipulation

C) Data Visualization

• Intro To EDA By Code Heroku’s (Recommended)
• Blog — Performing EDA on Iris Data Set (Recommended)
• Coursera Course On EDA, Statistics, Probability

According to Google, “Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.”

It is the most crucial step in a life cycle of a data scientist where one has to build various models using machine learning algorithms and should be able to predict and come with the most optimum solution to solve any problem.

• Machine Learning By Andrew NG (Recommended)
• Deep Learning By Krish Naik

After acquiring the basics of Data Science, now it’s time to get hands-on experience in its part. There are many online platforms, like Kaggle and Analytics Vidhya, that can provide you with hands-on experience with both beginner and advanced level data sets. They can help you to understand various machine learning algorithms, different analyzing techniques, etc.