Path to Data Science (The Innovative way)

innovative-coder (32)in #technology • 7 years ago

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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7 years ago in #technology by innovative-coder (32)

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