Data Science

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DATA SCIENCE Course Syllabus:

Basic Concepts of Statistics:

Descriptive Statistics and Probability Distributions:

Introduction to 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
  1. Inferential Statistics and Testing of Hypothesis
  • Sample 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 its properties
  • Z test
  • Analysis of Variance
  • F-test
  • One and Two way ANOVA
  1. Covariance & Correlation
  • Importance and Properties of Correlation
  • Types of Correlation with examples

Predictive Modeling Steps and Method with the 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 many dimensions
  • Missing data treatment using different methods
  • Outlier’s identification and treating outliers
  • Visualization of the data use Dimensional Types
  • 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 rule 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 a couple of real time use cases.

   Supervised Techniques:

  • Many linear Regressions
  • 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 Manufacturing and Telecom Industry revenue using the models
  • Logistic Regression
  • Logistic Regression – Introduction – Applications
  • Linear Regression vs. Logistic Regression vs. Generalized Linear Models
  • Building Logistic Regression Model
  • Standard model metrics (Concordance, Variable significance, Hosmer Lemeshow 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
  • A couple of Real time uses cases which related to Telecom and Retail Industry. Identify 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
  • 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 the 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, identify the 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 and data table 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

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