Data Science Master Program - Full Course Reading Time: 5 minutes

Statistics Essentials for Analytics

All the topics in the following section will explain the basics of what it is, which scenario you want to use, What math behind it, How to implement with an analytic tool, what inferences you are getting from the final result.

Understanding the Data
Probability and its Uses
Statistical Inference
Data Clustering
Testing the Data
Regression Modelling

Data Science with Python

Module 1: Introduction to Data Science

- - What is Data Science?
  - What is Machine Learning?
  - What is Deep Learning?
  - What is AI?
  - Data Analytics & it’s types

Module 2: Introduction to Python

- - What is Python?
  - Why Python?
  - Installing Python
  - Python IDEs
  - Jupyter Notebook Overview

Module 3: Python Basics

- - Python Basic Data types
  - Lists
  - Slicing
  - IF statements
  - Loops
  - Dictionaries
  - Tuples
  - Functions
  - Array
  - Selection by position & Labels

Module 4: Python Packages

- - Pandas
  - Numpy
  - Sci-kit Learn
  - Mat-plot library

Module 5: Importing data

- - Reading CSV files
  - Saving in Python data
  - Loading Python data objects
  - Writing data to csv file

Module 6: Manipulating Data

- - Selecting rows/observations
  - Rounding Number
  - Selecting columns/fields
  - Merging data
  - Data aggregation
  - Data munging techniques

Module 7: Statistics Basics

- - Central TendencyMean
    Median
    
    Mode
    
    Skewness
    
    Normal Distribution
  - Probability BasicsWhat does mean by probability?
    Types of Probability
    
    ODDS Ratio?
  - Standard DeviationData deviation & distribution
    Variance
  - Bias variance Trade offUnderfitting
    Overfitting
  - Distance metricsEuclidean Distance
    Manhattan Distance
  - Outlier analysisWhat is an Outlier?
    Inter Quartile Range
    
    Box & whisker plot
    
    Upper Whisker
    
    Lower Whisker
    
    catter plot
    
    Cook’s Distance
  - Missing Value treatmentsWhat is a NA?
    Central Imputation
    
    KNN imputation
    
    Dummification
  - CorrelationPearson correlation
    Positive & Negative correlation
  - Error MetricsClassification
    Confusion Matrix
    
    Precision
    
    Recall
    
    Specificity
    
    F1 Score
  - RegressionMSE
    RMSE
    
    MAPE
    
    Module 8: Machine Learning
    
    Module 9: Supervised Learning
    - - Linear RegressionLinear Equation
        Slope
        
        Intercept
        
        R square value
      - Logistic regressionODDS ratio
        Probability of success
        
        Probability of failure
        
        ROC curve
        
        Bias Variance Tradeoff
    Module 10: Unsupervised Learning
    - K-Means
    - K-Means ++
    - Hierarchical Clustering
    Module 11: Other Machine Learning algorithms
    - K – Nearest Neighbour
    - Naïve Bayes Classifier
    - Decision Tree – CART
    - Decision Tree – C50
    - Random Forest

Data Science with R Language

Module 1: Introduction to Data Science Methodologies

Data Types
Introduction to Data Science Tools
Statistics
Approach to Business Problems
Numerical Categorical
R, Python, WEKA, RapidMiner

Module 2: Correlation / AssociationRegressionCategorical variables

Introduction to Correlation Spearman Rank Correlation
OLS Regression – Simple and Multiple Dummy variables
Multiple regression
Assumptions violation – MLE estimates
Using UCI ML repository dataset or Built-in R dataset

Module 3: Data Preparation

Data preparation & Variable identification
Advanced regression
Parameter Estimation / Interpretation
Robust Regression
Accuracy in Parameter Estimation
Using UCI ML repository dataset or Built-in R dataset

Module 4: Logistic Regression

Introduction to Logistic Regression
Logit Function
Training-Validation approach
Lift charts
Decile Analysis
Using UCI ML repository dataset or Built-in R dataset

Module 5: Cluster AnalysisClassification Models

Introduction to Cluster Techniques
Distance Methodologies
Hierarchical and Non-Hierarchical Procedure
K-Means clustering
Introduction to decision trees/segmentation with Case Study
Using UCI ML repository dataset or Built-in R dataset

Module 6: Introduction and to Forecasting Techniques

Introduction to Time Series
Data and Analysis
Decomposition of Time Series
Trend and Seasonality detection and forecasting
Exponential Smoothing
Building R Dataset
Sales forecasting Case Study

Module 7: Advanced Time Series Modeling

Box – Jenkins Methodology
Introduction to Auto Regression and Moving Averages, ACF, PACF
Detecting order of ARIMA processes
Seasonal ARIMA Models (P,D,Q)(p,d,q)
Introduction to Multivariate Time-series Analysis
Using built-in R datasets

Module 8: Stock market prediction

Live example/ live project
Using client given stock prices / taking stock price data

Module 9: Pharmaceuticals

Case Study with the Data
Based on open set data

Module 10: Market Research

Case Study with the Data
Based on open set data

Module 11: Machine Learning

Supervised Learning Techniques
Conceptual Overview
Unsupervised Learning Techniques
Association Rule Mining Segmentation

Module 12: Fraud Analytics

Fraud Identification Process in Parts procuring
Sample data from online
Text Analytics

Module 13: Text Analytics

Sample text from online

Module 14: Social Media Analytics

Social Media Analytics
Sample text from online

Tableau

Module 1: Tableau Course Material

- - Start Page
  - Show Me
  - Connecting to Excel Files
  - Connecting to Text Files
  - Connect to Microsoft SQL Server
  - Connecting to Microsoft Analysis Services
  - Creating and Removing Hierarchies
  - Bins
  - Joining Tables
  - Data Blending

Module 2: Learn Tableau Basic Reports

- - Parameters
  - Grouping Example 1
  - Grouping Example 2
  - Edit Groups
  - Set
  - Combined Sets
  - Creating a First Report
  - Data Labels
  - Create Folders
  - Sorting Data
  - Add Totals, Sub Totals and Grand Totals to Report

Module 3: Learn Tableau Charts

- - Area Chart
  - Bar Chart
  - Box Plot
  - Bubble Chart
  - Bump Chart
  - Bullet Graph
  - Circle Views
  - Dual Combination Chart
  - Dual Lines Chart
  - Funnel Chart
  - Traditional Funnel Charts
  - Gantt Chart
  - Grouped Bar or Side by Side Bars Chart
  - Heatmap
  - Highlight Table
  - Histogram
  - Cumulative Histogram
  - Line Chart
  - Lollipop Chart
  - Pareto Chart
  - Pie Chart
  - Scatter Plot
  - Stacked Bar Chart
  - Text Label
  - Tree Map
  - Word Cloud
  - Waterfall Chart

Module 4: Learn Tableau Advanced Reports

- - Dual Axis Reports
  - Blended Axis
  - Individual Axis
  - Add Reference Lines
  - Reference Bands
  - Reference Distributions
  - Basic Maps
  - Symbol Map
  - Use Google Maps
  - Mapbox Maps as a Background Map
  - WMS Server Map as a Background Map

Module 5: Learn Tableau Calculations & Filters

- - Calculated Fields
  - Basic Approach to Calculate Rank
  - Advanced Approach to Calculate Ra
  - Calculating Running Total
  - Filters Introduction
  - Quick Filters
  - Filters on Dimensions
  - Conditional Filters
  - Top and Bottom Filters
  - Filters on Measures
  - Context Filters
  - Slicing Fliters
  - Data Source Filters
  - Extract Filters

Module 6: Learn Tableau Dashboards

- - Create a Dashboard
  - Format Dashboard Layou
  - Create a Device Preview of a Dashboard
  - Create Filters on Dashboard
  - Dashboard Objects
  - Create a Story

Module 7: Server

- Tableau online.
- Overview of Tableau Server.
- Publishing Tableau objects and scheduling/subscription.