# 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
• 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
• 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
• 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
• 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