Fundamentals of Data Analytics
zyBooks 2018

Table of Contents

1. Data Visualization
1.1 What is data?
1.2 What is data visualization?
1.3 Python for data visualization
1.4 Data frames
1.5 Bar charts
1.6 Pie charts
1.7 Scatter plots
1.8 Line charts

2. Descriptive Statistics
2.1 Survey sampling
2.2 Measures of center
2.3 Measures of spread
2.4 Box plots
2.5 Histograms
2.6 Violin plots

3. Probability Distributions
3.1 Experiments and events
3.2 Random variables and their distributions
3.3 Discrete random variables and their distributions
3.4 Properties of discrete probability distributions
3.5 Properties of continuous probability distributions
3.6 The normal distribution
3.7 The student’s t-disbtribution
3.8 The f-distribution

4. Inferential Statistics
4.1 Confidence intervals
4.2 Confidence intervals for population means
4.3 Confidence intervals for population proportions
4.4 Hypothesis tests
4.5 One-sample hypothesis tests for population means
4.6 One-sample z-test for population proportions
4.7 Two-sample z-test for population means
4.8 Two sample z-test for population proportions
4.9 Analysis of variance (ANOVA)

5. Linear Regression
5.1 Simple linear regression
5.2 Least squares method
5.3 Simple linear regression assumptions
5.4 Interpreting linear models
5.5 Correlation
5.6 Model assessment
5.7 Multiple regression
5.8 Categorical predicators and non-linear relationships

6. Time Series Analysis
6.1 What is time series?
6.2 Time series patterns and stationarity
6.3 Moving average and exponential smoothing forecasting
6.4 Forecasting using regression

7. Monte Carlo Methods
7.1 What is a Monte Carlo simulation?
7.2 Building simulations
7.3 Optimizing and forecasting

8. Data Mining
8.1 What is data mining?
8.2 Data preparation
8.3 Model evaluation
8.4 Supervised learning
8.5 Unsupervised learning

9. Ethics
9.1 Misleading statistics
9.2 Abuse of the p-value
9.3 Data privacy
9.4 Ethical guidelines