Path to Data Science (The Innovative way)

Mathematics and Statistics along with Programming form the fundamentals of data science. I firmly believe in the saying “ Deeper the roots, taller the trees grow “. Hence it is of the utmost importance to have abundance of knowledge of these subjects. I will also list down a couple books with authors to refer to enrich your knowledge of statistics but I will also list down topics that are used in data science so that you know what to learn and how much to learn.

For learning these concepts you can refer to - https://github.com/InnovativeCoder/Essential-Books

Mathematics & Statistics

• Linear Algebra
• Random Variables
• Statistical Distributions
• Probability theory ( Calculating MGF, CGF, Mean, Median, Mode, Variance Maximum likelihood Expectation, Central limit theorems, ANOVA )
• Calculus
• Fitting of a distribution
• Sampling
• Testing of a hypothesis
• Bayesian Modeling
• Regression and Time Series
• Naive Bayes Theorem
• Optimization
• Regression and Time Series

Programming

Now here, there is a lot of debate on Python vs R. Both languages have their own pros and cons. Personally, I would recommend Python as it is a general multi-purpose language.

• Intermediate Python for Data Science
• Importing Data in Python
• Pandas Foundation
• Python
• Databases in Python
• Manipulating Data Frames with pandas
• Data Visualization with Python
• Interactive Data Visualization
• Merging DataFrames with pandas

(You can refer to MIT lectures for this which are freely available on Youtube)

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