R and Python Course

Lesson 1 • Installing R and RStudio

Install R base and RStudio IDE on Windows, macOS, and Linux. Establishes the foundational R workspace used throughout the course.

Lesson 2 • Managing Packages and Libraries

Install and update packages using CRAN in R and pip/conda in Python. Ensures students can extend both environments with third-party tools.

Lesson 3 • Installing Python and Anaconda

Install Python via Anaconda distribution and configure Jupyter Notebook. Provides the Python workspace that mirrors the R setup for parallel learning.

Lesson 1 • Control Flow Structures

Write if/else statements, for loops, and while loops in both languages. Enables conditional logic and iteration essential for data processing scripts.

Lesson 2 • Error Handling and Debugging

Use tryCatch in R and try/except in Python to handle runtime errors gracefully. Builds defensive coding habits critical for production-quality scripts.

Lesson 3 • Writing and Calling Functions

Define reusable functions with parameters, default values, and return statements. Promotes modular code design applied in every subsequent chapter.

Lesson 4 • Variables and Data Types

Declare variables and work with numeric, character, logical, and complex types in both languages. Builds the type-awareness needed for all subsequent data manipulation.

Lesson 1 • Lists and Tuples

Store heterogeneous data in R lists and Python lists and tuples. Covers nested structures and element access patterns used in complex data pipelines.

Lesson 2 • Vectors and Arrays

Create and index one-dimensional vectors in R and NumPy arrays in Python. Establishes the atomic data container used in all numerical computation.

Lesson 3 • Dictionaries and Sets in Python

Use Python dictionaries for key-value storage and sets for unique-element collections. Provides the lookup and deduplication tools needed in data cleaning workflows.

Lesson 4 • Pandas DataFrames in Python

Build and manipulate Pandas DataFrames as Python's primary tabular structure. Mirrors the R data frame section to enable direct cross-language comparison.

Lesson 1 • Handling Missing Data

Detect, remove, and impute missing values using base R and Pandas methods. Ensures data quality before modeling or visualization steps.

Lesson 2 • Grouping and Aggregating Data

Summarize data by groups using group_by/summarize in R and groupby/agg in Python. Produces the summary statistics required for reporting and visualization.

Lesson 3 • Reshaping Data: Wide and Long Formats

Pivot data between wide and long formats using tidyr and Pandas melt/pivot. Prepares data in the shape required by visualization and modeling libraries.

Lesson 4 • Importing and Exporting Data

Read CSV, Excel, and JSON files into R and Python and write results back to disk. Establishes the data ingestion pipeline that feeds all transformation steps.

Lesson 1 • Statistical Visualization with Seaborn

Generate distribution, categorical, and relational plots using Seaborn's high-level API. Extends Matplotlib with built-in statistical summaries and attractive defaults.

Lesson 2 • Plotting with Matplotlib in Python

Create figures and axes using Matplotlib's object-oriented API. Provides the foundational Python plotting layer that Seaborn and Pandas build upon.

Lesson 3 • Customizing and Exporting Charts

Apply consistent styling, annotations, and export settings across R and Python plots. Ensures visuals meet professional reporting and publication standards.

Lesson 4 • Visualization Principles and Chart Selection

Apply visual encoding principles to choose the right chart for each data type. Grounds all subsequent plotting work in design best practices.

Lesson 1 • Hypothesis Testing

Run t-tests, chi-square tests, and ANOVA using R and scipy.stats in Python. Enables formal comparison of groups and relationships in data.

Lesson 2 • Probability Distributions

Sample from and evaluate normal, binomial, and Poisson distributions in R and Python. Builds the probabilistic foundation required for hypothesis testing and modeling.

Lesson 3 • Correlation and Simple Regression

Measure linear relationships with correlation coefficients and fit simple linear regression models. Prepares students for the full machine learning chapter that follows.

Lesson 4 • Confidence Intervals and Sampling

Construct confidence intervals and understand sampling variability in both languages. Connects probability theory to practical inference on real datasets.

Lesson 1 • Model Tuning and Validation

Optimize hyperparameters using grid search and cross-validation in both languages. Ensures models generalize beyond training data before deployment.

Lesson 2 • Regression Models

Train linear and polynomial regression models and assess fit using cross-validation. Extends the simple regression from Chapter 6 to regularized and nonlinear forms.

Lesson 3 • Classification Models

Train logistic regression, decision trees, and random forests for classification tasks. Covers the most widely used classifiers in professional ML workflows.

Lesson 4 • Machine Learning Workflow Overview

Frame the full ML pipeline from problem definition to model evaluation. Provides the conceptual map that organizes all subsequent modeling sections.

Lesson 1 • R Markdown for Dynamic Reports

Combine R code, narrative text, and output in a single R Markdown document. Enables analysts to deliver reproducible HTML, PDF, and Word reports.

Lesson 2 • Connecting R and Python in One Workflow

Call Python from R using the reticulate package and pass objects between sessions. Unlocks hybrid workflows that leverage the strengths of both languages.

Lesson 3 • Project Organization and Best Practices

Structure R and Python projects with consistent folder layouts, naming conventions, and README files. Ensures long-term maintainability and team collaboration.

Lesson 4 • Jupyter Notebooks for Python Reports

Structure analysis in Jupyter Notebooks with markdown cells, code cells, and rich output. Provides the Python equivalent of R Markdown for shareable analytical documents.