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Getting Started with Data Science

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What is Data Science?

Data Science vs. Data Analysis vs. Data Engineering Descriptive and Predictive Analytics Data Science Life Cycle Structured vs. Semi-Structured vs. Unstructured Data

Applications of Data Science

Applications in Healthcare and Recommender Systems Image Analysis

Overview of Libraries

Beautiful Soup (Scraping Data from Simple HTML)Beautiful Soup (Scraping Data from Html Table)Scrapy Numpy Basics Numpy Array Creation Numpy Array Manipulation Sorting Numpy Arrays Basic Statistics on Numpy Arrays Broadcasting in Numpy Arrays Pandas Spacy Part 1 Spacy Part 2 Seaborn

Data Manipulation with NumPy

Introduction NumPy Arrays NumPy Basics Math Random Indexing Filtering Statistics Aggregation Saving Data Quiz

Data Analysis with pandas

Probability and Statistics

Probability Statistics Joint Probability Conditional Probability and Bayes Theorem Measures of Locations Measures of Variability Probability Distributions (Binomial and Bernoulli Distributions)Gaussian Distribution Poisson Distribution Skewness and Kurtosis Sampling Methods Key Concepts in Statistics Statistical Hypothesis Testing

Conclusion

Introduction

An overview of data analysis with pandas.

We'll cover the following

A. Data analysis
B. pandas
C. Matplotlib and pyplot

In this chapter, you'll use pandas to analyze Major League Baseball (MLB) data. The data comes courtesy of Sean Lahman and contains statistics for every player, manager, and team in MLB history. The full database can be found and downloaded here.