Fundamentals of Data Analytics
zyBooks 2017

Table of Contents

1. Data Visualization
1.1 Introduction to data analytics
1.2 Introduction to data visualization
1.3 Types of data
1.4 Bar charts
1.5 More bar charts
1.6 Pie charts
1.7 Scatter plots
1.8 Line charts
1.9 Tables
1.10 Data and chart guidelines
1.11 Spreadsheets
1.12 Spreadsheet plotting
1.13 Dot plots
1.14 Animations
1.15 Data visualization: Case study
1.16 Dashboards

2. Descriptive Statistics
2.1 Measures of center
2.2 Measures of spread
2.3 Empirical rule and z-scores
2.4 Box plots
2.5 Histograms
2.6 Populations and samples

3. Probability
3.1 Experiments and events
3.2 Discrete random variables and their distributions
3.3 Properties of discrete probability distributions
3.4 Continuous probability distributions and properties
3.5 Specific distributions
3.6 Combinations and permutations

4. Inferential Statistics
4.1 Hypotheses
4.2 Hypothesis testing
4.3 Comparing two population means
4.4 Confidence intervals

5. Linear Regression
5.1 Simple linear regression
5.2 Model parameter estimation: Least squares
5.3 Model interpretation
5.4 Model assessment 1
5.5 Model assessment 2
5.6 Multiple regression
5.7 Categorical predictors and non-linear relationships
5.8 Linear regression example

6. Time Series Analysis
6.1 Time series patterns and forecasting accuracy
6.2 Moving average and exponential smoothing forecasting
6.3 Forecasting using regression

7. Monte Carlo
7.1 What-if analysis
7.2 Basic simulation
7.3 Advanced simulations
7.4 Applications

8. Data Mining
8.1 Data mining overview
8.2 Preparing data for mining
8.3 Preparing data for mining (continued)
8.4 Assessing data mining model performance
8.5 Assessment (continued)
8.6 Supervised learning
8.7 Unsupervised learning

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