Module 1: Introduction to Data Science and R

• 1.1 What is Data Science?
  • Overview of Data Science and its applications
  • Data Science vs Machine Learning vs AI
  • The role of a Data Scientist
• 1.2 Introduction to R Programming
  • Why use R for Data Science?
  • Installing R and RStudio
  • Basic R Syntax: Variables, Data Types, Operators
  • R Data Structures: Vectors, Lists, Matrices, Data Frames, and Factors
• 1.3 Introduction to R Libraries for Data Science
  • Overview of essential R libraries: dplyr, ggplot2, tidyr, caret, lubridate

Module 2: Data Import, Cleaning, and Preprocessing

• 2.1 Data Import and Export
  • Importing data from CSV, Excel, SQL, and web scraping
  • Exporting data to different formats (CSV, Excel, etc.)
• 2.2 Data Cleaning and Transformation
  • Handling missing data (NA values)
  • Data Transformation with dplyr (select, filter, mutate, group_by, summarize)
  • Data wrangling with tidyr (gather, spread, separate, unite)
  • String manipulation with stringr
  • Working with dates using lubridate
• 2.3 Data Preprocessing for Machine Learning
  • Scaling and Normalization
  • Encoding Categorical Data (One-hot encoding)
  • Feature Engineering

Module 3: Data Visualization in R

• 3.1 Introduction to Data Visualization
  • Importance of Visualization in Data Science
  • Basic Visualization Principles
• 3.2 Basic Plotting with ggplot2
  • Grammar of Graphics (Understanding ggplot2 structure)
  • Scatter plots, bar plots, histograms, and box plots
• 3.3 Advanced Visualization with ggplot2
  • Customizing plots (labels, themes, and colors)
  • Faceting and multi-panel plots
  • Plotting time-series data
• 3.4 Interactive Visualizations
  • Interactive Plots with plotly and shiny

Module 4: Exploratory Data Analysis (EDA)

• 4.1 Introduction to EDA
  • Importance of EDA in the Data Science Workflow
  • Summary Statistics: Mean, Median, Mode, Standard Deviation
  • Distribution of Data (histograms, density plots)
• 4.2 Univariate and Bivariate Analysis
  • Visualizing distributions and relationships using ggplot2
  • Identifying correlations using correlation matrices
  • Box plots and violin plots for comparing distributions
• 4.3 Outlier Detection and Handling
  • Identifying outliers using box plots, scatter plots, and Z-scores
  • Handling outliers through removal or transformation
• 4.4 Data Profiling and Summary
  • Descriptive statistics using summary() and str()
  • Profiling data with the skimr package

Module 5: Statistical Analysis

• 5.1 Introduction to Statistics for Data Science
  • Descriptive Statistics: Mean, Median, Mode, Variance, Standard Deviation
  • Probability Distributions (Normal, Binomial, Poisson)
  • Central Limit Theorem
• 5.2 Hypothesis Testing
  • T-tests, Chi-Square Tests, ANOVA
  • p-values, Confidence Intervals, and Significance Levels
  • Assumptions in Statistical Tests
• 5.3 Correlation and Regression
  • Pearson Correlation Coefficient
  • Linear Regression (Simple and Multiple)
  • Interpreting Model Coefficients and Residuals
  • Logistic Regression for Binary Classification
  • Regularization: Lasso and Ridge

Module 6: Machine Learning with R

• 6.1 Introduction to Machine Learning in R
  • Overview of Supervised vs Unsupervised Learning
  • Preparing data for Machine Learning
  • Using the caret package for Model Training
• 6.2 Supervised Learning Algorithms
  • Linear Regression
  • Logistic Regression
  • Decision Trees and Random Forests
  • Support Vector Machines (SVM)
  • K-Nearest Neighbors (KNN)
• 6.3 Unsupervised Learning Algorithms
  • K-Means Clustering
  • Hierarchical Clustering
  • Principal Component Analysis (PCA)
  • DBSCAN and Other Clustering Methods
• 6.4 Model Evaluation and Tuning
  • Cross-validation
  • Hyperparameter Tuning using Grid Search
  • Evaluating Model Performance (Accuracy, Precision, Recall, F1-Score)
  • ROC Curve and AUC

Module 7: Advanced Topics in Machine Learning

• 7.1 Ensemble Methods
  • Bagging, Boosting, and Stacking
  • Random Forests and Gradient Boosting Machines (GBM)
  • XGBoost, LightGBM, and CatBoost
• 7.2 Model Interpretation and Explainability
  • Feature Importance using Random Forest and XGBoost
  • SHAP Values and LIME for Model Explainability
• 7.3 Time Series Analysis and Forecasting
  • Introduction to Time Series Data
  • Decomposition of Time Series (Trend, Seasonality, Residuals)
  • ARIMA Models and Forecasting
  • Exponential Smoothing (Holt-Winters)
• 7.4 Natural Language Processing (NLP)
  • Text Preprocessing: Tokenization, Lemmatization, Stopword Removal
  • Sentiment Analysis and Text Classification
  • Word Embeddings (Word2Vec, GloVe)
  • Topic Modeling with Latent Dirichlet Allocation (LDA)

Module 8: Data Science in Practice

• 8.1 Working with Big Data
  • Introduction to Big Data Concepts
  • Using data.table for large datasets
  • Parallel Processing in R
  • Introduction to Hadoop and Spark with R (via sparklyr)
• 8.2 Model Deployment
  • Deploying models using plumber for APIs
  • Packaging Models with docker
  • Deploying Shiny Apps for Interactive Dashboards
• 8.3 Building Data Pipelines
  • Extracting, Transforming, and Loading (ETL)
  • Automating Data Pipelines with drake and targets