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Fundamentals of Data Analytics (Python)


  • Data Visualization
  • Descriptive Statistics
  • Probability Distributions
  • Inferential Statistics
  • Linear Regression
  • Time Series Analysis
  • Monte Carlo Methods
  • Data Mining
  • Ethics

Complete Table of Contents


  • An exceptionally student-focused introduction to data analytics
  • Traditionally-hard topics are made learnable via hundreds of animations and learning questions
  • Included statistics/probability background enables all students to succeed
  • R coding practice are provided throughout to allow students to experiment
  • Commonly combined with “Statistics for Data Analytics“; numerous configurations possible

The zyBooks Approach

Data analytics is one of the fastest growing subjects today. Techniques in data analysis can help solve various problems such as identifying new opportunities to generate profit or improving health outcomes in hospitals. Since the subject relies heavily on statistics, the topic often pose difficulties for students. This zyBook represents entirely new material created specifically to help students master the subject. Written natively for the modern web, the zyBook uses less text, and teaches through hundreds of animations and learning questions.

The zyBook provides a solid background in probability and statistics needed to understand and apply techniques covered in later chapters such as time series analysis, Monte Carlo simulation, and data mining. A chapter on ethics provides real examples and encourages professionalism and safety.

In recent years, Python has gained ground a popular language among data analysts, researchers, and statisticians because of the language’s clean syntax and popularity among software developers. Links to a live coding environment are provided to allow students to practice writing python functions for data visualization, inferential statistics, linear regression, and other algorithms.