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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

Data Analysis with pandas

Introduction Series DataFrame Combining Indexing File I/O Grouping Features Filtering Sorting Metrics Plotting To NumPy Quiz

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

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Introduction

Introduction

An overview of data processing and the NumPy library.

In this chapter, you'll learn how to perform data processing and manipulation using NumPy.

A. Data processing

When asked about Google's model for success, Peter Norvig, the director of research at Google, famously stated,

"We don't have better algorithms than anyone else; we just have more data."

Though probably an understatement (given the amount of talent employed at Google), the ...