Overview

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Begin your journey into practical statistics with this beginner-friendly course that makes complex concepts accessible. Learn to perform basic statistical analyses using R and Microsoft's tools, while using AI assistance to help understand and implement statistical concepts. Through hands-on practice with real datasets, you'll build confidence in conducting and interpreting statistical tests. By the end of this course, you will be able to: • Apply core statistical methods — including descriptive statistics, hypothesis testing, and regression analysis — to real-world datasets with confidence. • Use R and Microsoft tools to perform, visualize, and communicate statistical analyses effectively. • Interpret the results of statistical tests and translate findings into clear, actionable insights for non-technical audiences. • Design and execute a basic end-to-end statistical analysis workflow, from data preparation through to results and conclusions.

Syllabus

Introduction to Statistical Analysis in R

In this module, you’ll learn how to move beyond averages and test what matters. You’ll work with R to apply core statistical concepts, like significance testing, p-values, and confidence intervals, on real datasets. And when you’re ready, GitHub Copilot will help speed up your workflow without skipping the thinking. Whether you’re comparing customer ratings, assessing treatment outcomes, or validating business changes, this module gives you the tools to ask sharper questions and back them with evidence.

Simple and Multiple Regression Analysis

In this module, you’ll build regression models that explain relationships and forecast results, like how customer satisfaction might shift with service speed, or how multiple factors affect patient recovery. You’ll start simple, then move to more complex models, using R and GitHub Copilot to build, test, and troubleshoot your code efficiently. No fluff, just practical regression skills you’ll actually use.

Binary Logistic Regression

This module gives you the tools to model decisions, like whether a customer will convert, a treatment will succeed, or a transaction might fail. You’ll learn how logistic regression works, when to use it, and how to interpret the results. Through hands-on labs and AI-assisted coding, you’ll build models that do more than guess, they explain. By the end, you’ll be able to evaluate model performance and make confident, probability-based predictions.

Time Series Analysis

This module gives you the skills to break down time-based data and build forecasts you can trust. You’ll learn how to spot trends, understand seasonal shifts, and apply proven methods like moving averages and exponential smoothing. Whether you’re predicting sales, staffing needs, or web traffic, you’ll use R and GitHub Copilot to create models that support smarter, evidence-based decisions.

Integration with Microsoft Tools and Final Project

In this final module, you’ll apply your regression skills to a guided project that mirrors real analysis work. You’ll prepare data, build and validate a predictive model, and generate insights you can explain. You’ll also explore how R integrates with tools like Excel and Power BI, which are useful if you need to share results in business-friendly formats. This is your chance to practice end-to-end analysis and show what you can do with data.