What will you learn in Statistics Essentials for Analytics Course
Grasp core statistical concepts: descriptive statistics, probability distributions, sampling, and data visualization
Apply inferential techniques: confidence intervals, hypothesis testing (t-tests, chi-square, ANOVA) for data-driven decisions
Build and interpret simple predictive models: linear and logistic regression fundamentals
Understand non-parametric tests (Mann–Whitney, Kruskal–Wallis) for data that violate parametric assumptions
Explore time-series basics: trend decomposition, autocorrelation, and forecasting fundamentals
Program Overview
Module 1: Foundations of Statistical Thinking
⏳ 1 week
Topics: Populations vs. samples, scales of measurement, exploratory data analysis principles
Hands-on: Summarize a dataset with measures of central tendency and dispersion
Module 2: Probability & Distributions
⏳ 1 week
Topics: Basic probability rules, discrete (Binomial, Poisson) and continuous (Normal, Exponential) distributions
Hands-on: Compute and visualize distribution PDFs and CDFs; simulate random sampling
Module 3: Sampling & Estimation
⏳ 1 week
Topics: Sampling methods, Central Limit Theorem, point vs. interval estimation
Hands-on: Derive and interpret confidence intervals for means and proportions
Module 4: Hypothesis Testing
⏳ 1 week
Topics: Null/alternative setup, Type I/II errors, p-values, one- and two-sample t-tests, chi-square tests
Hands-on: Conduct and report results of a t-test and chi-square goodness-of-fit test
Module 5: Comparing Multiple Groups
⏳ 1 week
Topics: One-way and two-way ANOVA, assumptions checking, post-hoc analysis
Hands-on: Analyze variance across groups and apply Tukey’s HSD for pairwise comparisons
Module 6: Non-Parametric Methods
⏳ 1 week
Topics: Mann–Whitney U, Wilcoxon signed-rank, Kruskal–Wallis tests
Hands-on: Use non-parametric tests on skewed or ordinal data
Module 7: Regression Analysis Essentials
⏳ 1 week
Topics: Simple linear regression, least squares estimation, logistic regression basics
Hands-on: Fit and interpret a linear model; assess goodness-of-fit and residuals
Module 8: Introduction to Time Series
⏳ 1 week
Topics: Trend, seasonality, autocorrelation, moving averages, ARIMA overview
Hands-on: Decompose a time series and generate a basic forecast
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Job Outlook
Data Analyst: $65,000–$90,000/year — use statistics to derive business insights and inform strategy
Business Intelligence Specialist: $70,000–$100,000/year — design dashboards, perform ad hoc analyses, and report results
Quality Analyst / Statistician: $60,000–$85,000/year — apply statistical methods to ensure process and product quality
Foundational statistics skills are essential across finance, healthcare, marketing analytics, and engineering domains.
Specification: Statistics Essentials for Analytics
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