TL;DR

• Data Acquisition
  • Excel, Statistics,Probability, SQL
• Data Preparation
  • Python, Pandas, Numpy, Matplotlib/Seaborn
• Exploratory data Analysis
  • Linear Algebra, Pandas
• Data Modeling
  • Calculus, Machine learning, TensorFlow
• Visualization
  • PowerBI, Tabelu
• Deployment
  • Heroku, AWS

1. Programming

Can be any programming language but python is recommended due to its popularity. Most of the companies ask this as a necessary requirement.

• Learn about Python
  • Basic, Intermediate
  • Advance, OOPs
• Data cleaning and formatting
  • Learn about Numpy
  • Learn about Pandas
• Learn about Visualization Library
  • Matplotlib, Seaborn
• Learn about Database
• Learn about Flask

2. Statistics and Probability

Understanding of Statistics is very significant as this is a part of Data analysis.

Probability is also significant to statistics, and it is considered a prerequisite for mastering machine learning.

• Descriptive stats
  • Measure of Central Tendency
    • Mean, Median, Mode
  • Measure of Variability
    • Range, Variance, Dispersion, Std
  • Charts - Data distribution
    • Histogram, Bar charts
• Inferential stats
  • Z-test
  • T-test
  • Hypothesis testing, P value

https://towardsdatascience.com/the-most-common-misinterpretations-hypothesis-testing-confidence-interval-p-value-4548a10a5b72

ML

https://www.simplilearn.com/tutorials/data-science-tutorial/what-is-data-science