Module 1: Data Science Project Lifecycle

• Recap of Demo
• Introduction to Types of Analytics
• Project life cycle
• An introduction to our E learning platform

Module 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 note book
• Set up Github
• Descriptive Stats in Python
• Pandas and Matplotlib / Seaborn

Module 3: Probability And Hypothesis Testing

• Random Variable
• Probability
• Probility Distribution
• Normal Distribution
• SND
• Expected Value
• Sampling Funnel
• Sampling Variation
• CLT
• Confidence interval
• Assignments Session-1 (1 hr)
• Introduction to Hypothesis Testing
• Hypothesis Testing with examples
• 2 proportion test
• 2 sample t test
• Anova and Chisquare case studies

Module 4: Exploratory Data Analysis -1

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

Module 5: Linear Regression

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

Module 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)

Module 7: Deployment

• R shiny
• Streamlit

Module 8: Data Mining 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, Jaquard’s Coefficient
• Mixed - Gower’s General Dissimilarity Coefficient
• Types of Linkages
• Single Linkage / Nearest Neighbour
• Complete Linkage / Farthest Neighbour
• Average Linkage
• Centroid Linkage
• Visualization of clustering algorithm using Dendrogram

Module 9: Dimension Reduction Techniques

• PCA and tSNE
• Why dimension reduction
• Advantages of PCA
• Calculation of PCA weights
• 2D Visualization using Principal components
• Basics of Matrix algebra

Module 10: Association Rules

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

Module 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

Module 12: Introduction To Supervised Machine Learning

• Workflow from data to deployment
• Data nuances
• Mindsets of modelling

Module 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

Module 14: Exploratory Data Analysis - 2

• Encoding Methods
• OHE
• Label Encoders
• Outlier detection-Isolation Fores
• Predictive power Score

Module 15: Feature Engineering

• Recurcive Feature Elimination
• PCA

Module 16: Model Validation Methods

• Splitting data into train and test
• Methods of cross validation
• Accuracy methods

Module 17: Ensembled Techniques

• Bagging
• Boosting
• Random Forest
• XGBM
• LGBM

Module 18: KNN And Support Vector Machines

• Deciding the K value
• Building a KNN model by splitting the data
• Understanding the various generalization and regulation techniques to avoid overfitting and underfitting
• Kernel tricks

Module 19: Regularization Techniques

• Lasso Regression
• Ridge Regression

Module 20: Neural Networks

• Artificial Neural Network
• Biological Neuron vs Artificial Neuron
• ANN structure
• Activation function
• Network Topology
• Classification Hyperplanes
• Best fit “boundary”
• Gradient Descent
• Stochastic Gradient Descent Intro
• Back Propogation
• Intoduction to concepts of CNN

Module 21: Text Mining

• Sources of data
• Bag of words
• Pre-processing, corpus Document-Term Matrix (DTM) and 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

Module 22: Natural Language Processing

• Sentiment Extraction
• Lexicons and Emotion Mining

Module 23: Naive Bayes

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

Module 24: Forecasting

• Introduction to time series data
• Steps of forecasting
• Components of time series data
• Scatter plot and Time Plot
• Lag Plot
• ACF - Auto-Correlation Function / 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
• Model-Based approaches
• AR (Auto-Regressive) 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
• Holts / Double Exponential Smoothing
• Winters / HoltWinters
• De-seasoning and de-trending
• Forecasting using Python and R

Module 25: Survival Analysis

• Concept with a business case

Module 26: End To End Project Description With Deployment

• End to End project Description with deployment using R and Python