Business Analytics with R Course Syllabus

1. Data Science Project Lifecycle

• Recap of Demo
• Introduction to Types of Analytics
• Project Lifecycle
• An Introduction to Our E-Learning Platform

2. Introduction to Basic Statistics Using R and Python

• Data Types
• Measure of Central Tendency
• Measures of Dispersion
• Graphical Techniques
• Skewness & Kurtosis
• Box Plot
• R
• R Studio
• Descriptive Stats in R
• Python (Installation and Basic Commands) and Libraries
• Jupyter Notebook
• Set up GitHub
• Descriptive Stats in Python
• Pandas and Matplotlib / Seaborn

3. Probability and Hypothesis Testing

• Random Variable
• Probability
• Probability Distribution
• Normal Distribution
• Standard Normal Distribution (SND)
• Expected Value
• Sampling Funnel
• Sampling Variation
• Central Limit Theorem (CLT)
• Confidence Interval
• Assignments Session-1 (1 hr)
• Introduction to Hypothesis Testing
• Hypothesis Testing with Examples
• Two-Proportion Test
• Two-Sample T-Test
• ANOVA and Chi-square Case Studies

4. Exploratory Data Analysis - 1

• Visualization
• Data Cleaning
• Imputation Techniques
• Scatter Plot
• Correlation Analysis
• Transformations
• Normalization and Standardization

5. Linear Regression

• Principles of Regression
• Introduction to Simple Linear Regression
• Multiple Linear Regression

6. Logistic Regression

• Multiple Logistic Regression
• Confusion Matrix
• False Positive, False Negative
• True Positive, True Negative
• Sensitivity, Recall, Specificity, F1 Score
• Receiver Operating Characteristics Curve (ROC Curve)

7. Deployment

• R Shiny
• Streamlit

8. Data Mining and Unsupervised Clustering

• Supervised vs Unsupervised Learning
• Data Mining Process
• Hierarchical Clustering / Agglomerative Clustering
• Measure of Distance
• Numeric - Euclidean, Manhattan, Mahalanobis
• Categorical - Binary Euclidean, Simple Matching Coefficient, Jaccard’s Coefficient
• Mixed - Gower’s General Dissimilarity Coefficient
• Types of Linkages: Single Linkage, Complete Linkage, Average Linkage, Centroid Linkage
• Visualization of Clustering Algorithm Using Dendrogram

9. Dimension Reduction Techniques

• Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (tSNE)
• Why Dimension Reduction?
• Advantages of PCA
• Calculation of PCA Weights
• 2D Visualization Using Principal Components
• Basics of Matrix Algebra

10. Association Rules

• What is Market Basket / Affinity Analysis?
• Measure of Association
• Support
• Confidence
• Lift Ratio
• Apriori Algorithm

11. Recommender System

• User-Based Collaborative Filtering
• Measure of Distance / Similarity Between Users
• Driver for Recommendation
• Computation Reduction Techniques
• Search-Based Methods / Item to Item Collaborative Filtering
• Vulnerability of Recommender Systems

12. Introduction to Supervised Machine Learning

• Workflow from Data to Deployment
• Data Nuances
• Mindsets of Modelling

13. Decision Tree

• Elements of Classification Tree - Root Node, Child Node, Leaf Node, etc.
• Greedy Algorithm
• Measure of Entropy
• Attribute Selection Using Information Gain
• Implementation of Decision Tree Using C5.0 and Sklearn Libraries

14. Exploratory Data Analysis - 2

• Encoding Methods
• One-Hot Encoding (OHE)
• Label Encoders
• Outlier Detection - Isolation Forest
• Predictive Power Score

15. Feature Engineering

• Recursive Feature Elimination
• PCA

16. Model Validation Methods

• Splitting Data into Train and Test
• Methods of Cross Validation
• Accuracy Methods

17. Ensemble Techniques

• Bagging
• Boosting
• Random Forest
• XGBoost (XGBM)
• LightGBM (LGBM)

18. K-Nearest Neighbors (KNN) and Support Vector Machines (SVM)

• Deciding the K Value
• Building a KNN Model by Splitting the Data
• Understanding the Various Generalization and Regularization Techniques to Avoid Overfitting and Underfitting
• Kernel Tricks

19. Regularization Techniques

• Lasso Regression
• Ridge Regression

20. Neural Networks

• Artificial Neural Network (ANN)
• Biological Neuron vs. Artificial Neuron
• ANN Structure
• Activation Function
• Network Topology
• Classification Hyperplanes
• Best Fit “Boundary”
• Gradient Descent
• Stochastic Gradient Descent Intro
• Back Propagation
• Introduction to Concepts of Convolutional Neural Networks (CNN)

21. Text Mining

• Sources of Data
• Bag of Words
• Pre-Processing, Corpus Document-Term Matrix (DTM) and Term-Document Matrix (TDM)
• Word Clouds
• Corpus Level Word Clouds
• Sentiment Analysis
• Positive Word Clouds
• Negative Word Clouds
• Unigram, Bigram, Trigram
• Vector Space Modelling
• Word Embedding
• Document Similarity Using Cosine Similarity

22. Natural Language Processing (NLP)

• Sentiment Extraction
• Lexicons and Emotion Mining

23. Naive Bayes

• Probability – Recap
• Bayes Rule
• Naive Bayes Classifier
• Text Classification Using Naive Bayes

24. Forecasting

• Introduction to Time Series Data
• Steps of Forecasting
• Components of Time Series Data
• Scatter Plot and Time Plot
• Lag Plot
• Auto-Correlation Function (ACF) / Correlogram
• Visualization Principles
• Naive Forecast Methods
• Errors in Forecast and its Metrics
• Model-Based Approaches
• Linear Model
• Exponential Model
• Quadratic Model
• Additive Seasonality
• Multiplicative Seasonality
• Auto-Regressive (AR) Model for Errors
• Random Walk
• ARMA (Auto-Regressive Moving Average), Order p and q
• ARIMA (Auto-Regressive Integrated Moving Average), Order p, d and q
• Data-Driven Approach to Forecasting
• Smoothing Techniques
• Moving Average
• Simple Exponential Smoothing
• Holt's / Double Exponential Smoothing
• Winters / Holt-Winters
• De-Seasoning and De-Trending
• Forecasting Using Python and R

25. Survival Analysis

• Concept with a Business Case

26. End-to-End Project Description with Deployment

• End-to-End Project Description with Deployment Using R and Python