DATASCIENCE ONLINE TRAINING

Basic Conceptsof Statistics:

1.     Descriptive Statistics and Probability Distributions:

·         Introduction about Statistics

·         Different Types of Variables

·         Measures of Central Tendency with examples

§  Mean

§  Mode

§  Median

·         Measures of Dispersion

§  Range

§  Variance

§  Standard Deviation

                                                

·         Probability & Distributions

·         Probability Basics

·         Binomial Distribution and its properties

·         Poisson distribution and its properties

·         Normal distribution and its properties

2.     Inferential Statistics and Testing of Hypothesis

·         Sampling methods

§  Sampling and types of sampling

§  Definitions of Sample and Population

§  Importance of sampling in real time

§  Different methods of sampling

§  Simple Random Sampling with replacement and without replacement

§  Stratified Random Sampling

·         Different methods of estimation

·         Testing of Hypothesis & Tests

§  Null Hypothesis and Alternate Hypothesis

§  Level of Significance and P value

§  t-test and its properties

§  Chi-square test and it’s properties

§  Z test

·         Analysis of Variance

§  F-test

§  One and Two way ANOVA

3.     Covariance & Correlation

§  Importance and Properties of Correlation

§  Types of Correlation with examples

Predictive Modeling Steps and Methodology with Live example:

·       Data Preparation

§  Variable Selection

§  Transformation of the variables

§  Normalization of the variables

·       Exploratory Data analysis

§  Summary Statistics

§  Understanding the patterns of the data at single and multiple dimensions

§  Missing data treatment using different methods

§  Outlier’s identification and treating outliers

§  Visualization of the data using the One Dimensional, Two Dimensional and Multi Dimensional Graphs.

Bar chart, Histogram, Box plot, Scatter plot, Bubble chart, Word cloud etc…

·       Model Development

§  Selection of the sample data

§  Selecting the appropriate model based on the requirement and data availability

·       Model Validation

§  Model Implementation

§  Key Statistical parameters checking

§  Validating the model results with the actual result

·       Model Implementation

§  Implementing the model for future prediction

·       Real time telecom business use case with detail explanation

·       Introducing couple of real time use cases and solutions of Banking and Retail domains using the different statistical methods.

Supervised Techniques:

·         Multiple linear Regression

§  Linear Regression - Introduction - Applications

§  Assumptions of Linear Regression

§  Building Linear Regression Model

§  Understanding standard metrics (Variable significance, R-square/Adjusted R-Square, Global hypothesis etc)

§  Validation of Linear Regression Models (Re running Vs. Scoring)

§  Standard Business Outputs (Decile Analysis, Error distribution (histogram), Model equation, drivers etc)

§  Interpretation of Results - Business Validation - Implementation on new data

§  Real time case study of Manufacturing and Telecom Industry to estimate the future revenue using the models

·          Logistic Regression

§  Logistic Regression - Introduction - Applications

§  Linear Regression Vs. Logistic Regression Vs. Generalized Linear Models

§  Building Logistic Regression Model

§  Understanding standard model metrics (Concordance, Variable significance, Hosmer Lemeshov Test, Gini, KS, Misclassification etc)

§  Validation of Logistic Regression Models (Re running Vs. Scoring)

§  Standard Business Outputs (Decile Analysis, ROC Curve)

§  Probability Cut-offs, Lift charts, Model equation, drivers etc)

§  Interpretation of Results - Business Validation - Implementation on new data

§  Real time case study to Predict the Churn customers in the Banking and Retail industry

·         Partial Least Square Regression

§  Partial Least square Regression - Introduction - Applications

§  Difference between Linear Regression and Partial Least Square Regression

§  Building PLS Model

§  Understanding standard metrics (Variable significance, R-square/Adjusted R-Square, Global hypothesis etc)

§  Interpretation of Results - Business Validation - Implementation on new data

§  Sharing the real time example to identify the key factors which are driving the Revenue

Variable Reduction Techniques

·         Factor Analysis

·         Principle component analysis

§  Assumptions of PCA

§  Working Mechanism of PCA

§  Types of Rotations

§  Standardization

§  Positives and Negatives of PCA

Supervised Techniques Classification:

·         CHAID

·         CART

·         Difference between CHAID and CART

·         Random Forest

§  Decision tree vs. Random Forest

§  Data Preparation

§  Missing data imputation

§  Outlier detection

§  Handling imbalance data

§  Random Record selection

§  Random Forest R parameters

§  Random Variable selection

§  Optimal number of variables selection

§  Calculating Out Of Bag (OOB) error rate

§  Calculating Out of Bag Predictions

·         Couple of Real time use cases which are related to Telecom and Retail Industry. Identification of the Churn.

Unsupervised Techniques:

·         Segmentation for Marketing Analysis

§  Need for segmentation

§  Criterion of segmentation

§  Types of distances

§  Clustering algorithms

§  Hierarchical clustering

§  K-means clustering

§  Deciding number of clusters

§  Case study

·         Business Rules Criteria

·         Real time use case to identify the Most Valuable revenue generating Customers.

Time series Analysis:

·         Forecasting - Introduction - Applications

·         Time Series Components( Trend, Seasonality, Cyclicity and Level) and Decomposition

·         Basic Techniques –

§  Averages,

§  Smoothening etc

·         Advanced Techniques

§   AR Models,

§  ARIMA

§  UCM

§  Hybrid Model

·         Understanding Forecasting Accuracy - MAPE, MAD, MSE etc

·         Couple of use cases, To forecast the future sales of products

Text Analytics:

·         Gathering text data from web and other sources

·         Processing raw web data

·         Collecting twitter data with Twitter API

·         Naive Bayes Algorithm

§  Assumptions and of Naïve Bayes

§  Processing of Text data

§  Handling Standard and Text data

§  Building Naïve Bayes Model

§  Understanding standard model metrics

§  Validation of the Models (Re running Vs. Scoring)

·         Sentiment analysis

§  Goal Setting

§  Text Preprocessing

§  Parsing the content

§  Text refinement

§  Analysis and Scoring

·         Use case of Health care industry, To identify the sentiment of the patients on Specified hospital by extracting the data from the TWITTER.

Visualization Using Tableau:

·         Live connectivity from R to Tableau

·         Generating the Reports and Charts

R PROGRAMMING

SESSION 1: Getting Started with R

·       What is statistical programming?

·       The R package

·       Installation of R

·       The R command line

·       Function calls, symbols, and assignment

·       Packages

·       Getting help on R

·       Basic features of R

·       Calculating with R

SESSION 2: Matrices, Array, Lists, and Data Frames

·        Character vectors

·        Operations on the logical vectors

·        Creating the matrices and operations on it

·        Creating the array and operations on it

·        Creating the lists and operations on it

·        Making data frames

·        Working with data frames

SESSION3: Getting Data in and out of R

·       Importing Data into R

·       Exporting Data in R

·       Copy Data from Excel to R

·       Loading and Saving Data with R

·       Importing different types of file formats

SESSION4: Data Manipulation and Exploration:

·       Variable transformations

·       Creating Dummy variables

·       Data set options (Rename, Label)

·       Keep / Drop Columns

·       Identification and Dealing with the Missing data

·       Sorting the data

·       Handling the Duplicates

·       Joining and Merging (Inner,Left,Right and Cross Join)

·       Calculating Descriptive Statistics

·       Summarize numeric variables

·       Summarize factor variables

·       Transpose Data

·       Aggregated functions using Group by

·       dplyr anddatatable packages for the data manipulation

·       Data preparation using the sqldf package

SESSION5: Conditional Statements and Loops:

·       If Else

·       Nested If Else

·       For Loop

·       While Loop

SESSION6: Functions:

·       Character Functions

·       Numeric Functions

·       Apply Function on Rows

·       Converting a factor to integer

·       Indexing Operators in List

SESSION7: Graphical procedures

·       Pie chart

·       Bar Chart

·       Box plot

·       Scatter plot

·       Multi Scatter plot

·         Word cloud etc.…

SESSION8: Advanced R and Real time analytics examples:

·       Data extraction from the Twitter

·       Text Data handling

·       Positive and Negative word cloud

·       Required packages for the analytics

·       Sentiment analysis using the real time example

·       R code automation

·       Time series analysis with the real time Telecom data

·       Couple of examples with the time series data

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