This course teaches basic statistical concepts and practices used in data science. It explores how data can be summarized, compared, and analyzed for patterns. The course also covers the misuse of statistics and how it can lead to misunderstandings or distortions. Overall, it aims to help you understand and apply statistics in your daily life.
Overview
Syllabus
- Introduction to Statistics
- Introduction to Statistics
- Numbers in Everyday Life
- Use and Abuse of Numbers
- Bias
- Different Kinds of Analytic Approaches
- Downloads
- Assignment Week 1
- Descriptive Statistics
- Data Types
- Tabular & Graphical Methods to Describe Data
- Sampling and Populations
- Measures of Central Tendency
- Measures of Dispersion
- Outliers
- Distorting the Truth Using Descriptive Statistics
- Downloads
- Assignment Week 2
- Correlation and Linear Regression
- Correlation as a Statistical Measure
- Correlation Versus Causation
- Scatter Plots and Line of Best Fit
- Linear Regression
- Interpreting Results
- Downloads
- Assignment Week 3
- Introduction to Probability
- Introduction to Probability
- Conditional Probability
- Understanding Bayes' Theorem
- Examples of Bayes' Theorem in Practice
- Downloads
- Assignment Week 4
- Probability Distributions
- Properties of Distributions
- The Normal Distribution
- Kurtosis and Skewness
- Using the Normal Distribution to Calculate Probability
- Hypothesis Testing
- Downloads
- Assignment Week 5
- Connecting to Your SAP Solutions
- How Does This Help in the Real World?
- Critically Evaluating Reports
- Lying and Misleading with Statistics
- Tools and References
- Downloads
- Assignment Week 6
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