Introduction to Python Data Analytics

Business analytics

ISBN:

978-1-952023-20-0

Introduction to Python Data Analytics

Mark Keith, PhD

Every organization collects data, but the people who can turn it into decisions are in demand, with the U.S. Bureau of Labor Statistics projecting 34 percent growth in data science jobs through 2034. This resource teaches students with little or no programming or statistics background to be those people, using Python. Students progress from programming fundamentals through cleaning, profiling, and visualizing real datasets, into applied statistics like correlation, regression, and group comparisons. They finish with data storytelling and a capstone project that produces a full analysis supporting a real business decision. An automated Python grader gives immediate feedback on coding exercises, and students learn to work alongside AI tools throughout.

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Foreword: Why Data Science? Chapter 1: Introduction Chapter 2: Python: Variables Chapter 3: Python: Flow Control Chapter 4: Python: I-P-O Chapter 5: Python: Collections Chapter 6: Python: Iterations Chapter 7: Python: Packages Chapter 8: Python: DataFrames Chapter 9: Python: Reading/Writing Chapter 10: Python: Advanced DataFrames Chapter 11: Python: Debugging Chapter 12: Python: Functions Chapter 13: Univariate: Statistics Chapter 14: Univariate: Visualizations Chapter 15: Bivariate: Num/Num: Stats Chapter 16: Bivariate: Num/Num: Viz Chapter 17: Bivariate: Cat/Num: Stats Chapter 18: Bivariate: Cat/Num: Viz Chapter 19: Bivariate: Cat/Cat: Stats and Viz Chapter 20: Multivariate: Numeric Chapter 21: Multivariate: Categorical Chapter 22: Prep: Data Wrangling Chapter 23: Prep: Skewness Chapter 24: Prep: Outliers Chapter 25: Prep: Missing Data Supplement: Datasets Supplement: Automation: Univariate Analyses Supplement: Automation: Bivariate Analyses (in progress)

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