Statistics Essentials for Analytics Course Syllabus

Overview: This self-paced course spans approximately 8 weeks with a weekly commitment of 5–7 hours, designed for beginners seeking a solid foundation in statistics for data analytics. Through a blend of theory and hands-on labs using real datasets, you'll progress from basic statistical concepts to regression and time-series fundamentals, gaining practical skills for data-driven decision making. Each module includes guided exercises to reinforce learning and build confidence in applying statistical techniques.

Module 1: Foundations of Statistical Thinking

Estimated time: 6 hours

• Populations vs. samples
• Scales of measurement
• Exploratory data analysis principles
• Measures of central tendency and dispersion

Module 2: Probability & Distributions

Estimated time: 6 hours

• Basic probability rules
• Discrete distributions: Binomial and Poisson
• Continuous distributions: Normal and Exponential
• PDFs and CDFs visualization
• Random sampling simulation

Module 3: Sampling & Estimation

Estimated time: 6 hours

• Sampling methods
• Central Limit Theorem
• Point estimation vs. interval estimation
• Confidence intervals for means and proportions

Module 4: Hypothesis Testing

Estimated time: 6 hours

• Null and alternative hypotheses
• Type I and Type II errors
• p-values interpretation
• One- and two-sample t-tests
• Chi-square goodness-of-fit test

Module 5: Comparing Multiple Groups

Estimated time: 6 hours

• One-way and two-way ANOVA
• Checking ANOVA assumptions
• Post-hoc analysis with Tukey’s HSD

Module 6: Non-Parametric Methods

Estimated time: 6 hours

• Mann–Whitney U test
• Wilcoxon signed-rank test
• Kruskal–Wallis test
• Applications on skewed or ordinal data

Module 7: Regression Analysis Essentials

Estimated time: 6 hours

• Simple linear regression
• Least squares estimation
• Model interpretation and residuals
• Logistic regression basics

Module 8: Introduction to Time Series

Estimated time: 6 hours

• Trend and seasonality decomposition
• Autocorrelation analysis
• Moving averages
• ARIMA model overview
• Basic forecasting techniques

Prerequisites

• Basic understanding of high school-level mathematics
• Familiarity with using spreadsheets or basic software tools
• No prior programming or advanced math required

What You'll Be Able to Do After

• Summarize and visualize data using descriptive statistics
• Apply probability distributions to model real-world phenomena
• Conduct hypothesis tests and interpret p-values correctly
• Perform ANOVA and non-parametric tests to compare groups
• Build and evaluate simple linear and logistic regression models