Data Exploration

• Introduction To Type Of Data Variables
• Data Summarization Techniques
• Building A Data Dictionary
• Univariate Analysis
• Bivariate Analysis
• Outlier Treatment
• Missing Value Treatment.
• Case Study = Building A Data Dictionary And Exploratory Data Analysis Report For A Client After You Recieved Client Data.
  • Assignment = Generate A Exploratory Analysis Report
  • Tool = Excel

Descriptive statistics

• Measures of central tendency
• Measures of dispersion
• Range
• Skewness
• Interpretation of histograms
• Research methodologies
• Case Study = None
  • Assignment = Practice Exercises + Doubt Clearing + Answers
    Tool = Excel

Inferential statistics

• Basics of probability theory. Bayes Theorem
• Probability distribution functions – Uniform, Bernoulli, Binomial, Normal, Log Normal, T
• Continuous probability distributions
• Hypothesis testing – 1 Sided tests, 2-Sided tests. F test. T test. Chi Sq Test. ANOVA
• Case Study = Application of inferential tests
  • Assignment = Practice Excercises + Doubt Clearing + Answers
    Tool = Excel

Introduction to Python

• Introduction to Python
• Comparison of R
• SAS & Python
• Installing Python
• General programming rules
• Data structures
• Operators.
  
• Case Study = None
  
  • Assignment = Practice Excercises + Doubt Clearing + Answers
    Tool = Python

Python Programming – Fundamentals

• Control flow basics
• Custom functions
• All about libraries
• Numpy & Scipy libraries
  
• Case Study = Case Study on Data Analysis
  
  • Assignment = Practice Excercises + Doubt Clearing + Answers
    Tool = Python

Python Programming – Data Manipulation

• Data manipulation using Pandas
• Import and export
• Database access with SQL.
  
• Case Study = Case Study on Data Analysis
  
  • Assignment = Practice Excercises + Doubt Clearing + Answers
    Tool = Python

Python Programming – Statistics & Visualization

• Statistics and visualization using Python.
  
• Case Study = Case study on data exploration & insight generation
  
  • Assignment = Practice Excercises + Doubt Clearing + Answers
    Tool = Python

Python Programming – Application Development

• Application Development using Python.
  
• Case Study = Case study on building a standalone application using Python
  
  • Assignment = Practice Excercises + Doubt Clearing + Answers
    
  • Tool = Python

Linear Regression

• Introduction to linear regression technique & its uses
• Details of ordinary least squares estimation technique
• Modeling steps
• Variable handling
• Model statistics interpretation
• Validation of linear regression assumptions
• Metrics to measure model performance.
  
• Case Study = Case study on predicting house prices using real data.
  
  • Assignment = Case study on insurance claims using real data.
    
  • Tool = Python

Logistic Regression

• Introduction to logistic regression technique & its uses
• Maximum likelihood estimation technique
• Modeling steps
• Dependant variable definition
• Variable handling
• Weight of Evidence & Information Value
• Variable reduction
• Model statistics intepretation
• Metrics to measure model performance
  
• Case Study = Case study on predicting churn for a large telecom operator using real data.
  
  • Assignment = Case study on predicting customer cross sell for a large retailer using real data.
    
  • Tool = Python

Time series forecasting

• Learn basic concepts of time series modeling
• Basic techniques for forecasting
• Smoothing techniques
• Decomposition
• Understanding the fundamentals of ARIMA
• ARIMA modeling, model estimation & interpretation
• Forecasting with regression and time series data
• ARIMAX or dynamic regression models to build forecasting models with multiple regressors
  
• Case Study = Case study on predicting sales for a large european retailer using real data.
  
  • Assignment = Case study on predicting call volumes for a call centre.
    
  • Tool = Python

Clustering

• Introduction to clustering
• Types of clustering & their uses
• K-Means clustering
• Hierarchical clustering
  
• Case Study = Case study on retail customer segmentation using K Means clustering techniques on real data.
  
  • Assignment = Case study on product categorization using hierarchical clustering on real data.
    
  • Tool = Python

Introduction to machine learning

• How do machines learn?
• Choosing a machine learning algorithm
• Using Python for machine learning.
  
• Case Study = None
  
  • Assignment = None
    
  • Tool = Python

Classification using Nearest Neighbors

• Understanding classification using nearest neighbors
• The kNN algorithm – Calculating distance
• Choosing an appropriate k
• Preparing data for use with kNN.
• Case Study = Case study on diagnosing breast cancer using kNN algorithm.
  
  • Assignment = Case study on classifying the IRIS dataset using KNN
    
  • Tool = Python

Classification using Naive Bayes

• Understanding Naïve Bayes – basic concepts & algorithm.
  
• Case Study = Case study on filtering mobile phone spam with the naive Bayes algorithm.
  
  • Assignment = Case study on using Naïve Bayes Classifier to predict cancer
    
  • Tool = Python

Classification using decision trees and rules

• Understanding decision trees
• C5.0 decision tree algorithm
• Understanding classification rules.
  
• Case Study = Case study on identifying risky bank loand using C5.0 decision trees.
  
  • Assignment = Case study on identifying poisonous mushrooms with rule learners.
    
  • Tool = Python

Neural Networks

• Understanding neural networks. Activation functions. Network topology. Training neural networks with backpropagation.
  
• Case Study = Case study on modeling the strength of concrete with neural network.
  
  • Assignment = Case study on fitting a neural network model to predict the median value of owner-occupied homes (medv) using all the other continuous variables available.
    
  • Tool = Python

Support Vector Machines

• Understanding SVM. Classification with hyperplanes. Finding the maximum margin. Using kernels for nonlinear spaces.
  
• Case Study = Case study on optical character recognition using SVM.
  
  • Assignment = Case study on classifying telecom churn using SVM
    
  • Tool = Python

Market basket analysis

• Understanding association rules. Apriori algorithm.
  
• Case Study = Case study on frequently purchased items for a large retailer.
  
  • Assignment = Case study to use transactions from an open source dataset to find association rules using Apriori
    
  • Tool = Python

Text mining

• Main concepts and components of text mining
• Text mining tasks and approaches
• An understanding of the art of the possible in Text Analytics – the applicability
• components and benefits
  
• Case Study = Case study on analysis of book reviews on Amazon
  
  • Assignment = Practice case study on text mining on book reviews
    
  • Tool = Python

Social media Analysis

• Learn different text mining techniques to discover various textual patterns from the social sites.
• Learn how to
  • Access twitter data
  • Build frequent term network  Topic modelling
  • Analysis of followers & retweets
• Case Study = Case study to access a twitter account and create several visualizations to draw interesting insights
  
  • Assignment = Practice case study on text mining using another twitter account
    
  • Tool = Python

Sentiment Analysis

• Understand sentiment analysis and its key concepts
• Sentiment polarity
• Opinion summarization
• Feature extraction
• Classification based algorithms
• Application of SVM.
  
• Case Study = Case study to perform sentiment analysis on tweets
  
  • Assignment = Practice case study on sentiment analysis using another twitter account
    
  • Tool = Python