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Introduction to Python Data Analytics

Business Analytics

ISBN #978-1-952023-20-0

Introduction to Python Data Analytics

Mark Keith, PhD

This resource teaches students to turn raw data into decisions using Python. Students progress from programming fundamentals through cleaning, profiling, and visualizing data, into applied statistics like correlation, regression, and group comparisons, finishing with data storytelling and a capstone project that produces a full analysis supporting a real business decision. Coding exercises are checked with an automated Python grader for immediate feedback, and students learn to work alongside AI tools throughout. It's built for students with little or no background in programming or statistics who want to develop genuine technical skill in a field the U.S. Bureau of Labor Statistics projects will grow 34 percent through 2034.

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Course Objective

This resource offers an introduction to Python, statistics, data cleaning, and data analytics automation, equipping beginners with essential data science skills and concepts.

Table of Contents

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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