Master Data Science Course Program

Topic Covered:

Introduction to Business Analytics

• Introduction
• Objectives
• Need of Business Analytics
• Business Decisions
• Introduction to Business Analytics
• Features of Business Analytics
• Types of Business Analytics
• Descriptive Analytics
• Predictive Analytics
• Prescriptive Analytics
• Supply Chain Analytics
• Health Care Analytics
• Marketing Analytics
• Human Resource Analytics
• Web Analytics
• Application of Business Analytics
• Business Decisions
• Business Intelligence (BI)
• Data Science
• Importance of Data Science
• Data Science as a Strategic Asset
• Big Data
• Analytical Tools

Introduction to R

• Introduction
• Objectives
• An Introduction to R
• Comprehensive R Archive Network (CRAN)
• Cons of R
• Companies Using R
• Understanding R
• Installing R on Various Operating Systems
• Installing R on Windows from CRAN Website
• Install R
• IDEs for R
• Installing RStudio on Various Operating Systems
• Install R-Studio
• Steps in R Initiation
• Benefits of R Workspace
• Setting the Workplace
• Functions and Help in R
• Access the Help Document
• R Packages o Installing an R Package
• Install and Load a Package

R Data Structure

• Introduction
• Objectives
• Types of Data Structures in R
• Vectors
• Create a Vector
• Scalars
• Colon Operator
• Accessing Vector Elements
• Matrices
• Accessing Matrix Elements
• Create a Matrix
• Arrays
• Accessing Array Elements
• Create an Array
• Data Frames
• Elements of Data Frames
• Create a Data Frame
• Factors
• Create a Factor
• Lists
• Create a List
• Importing Files in R
• Importing an Excel File
• Importing a Minitab File
• Importing a Table File
• Importing a CSV File
• Read Data from a File
• Read Data from a File
• Exporting Files from R

Apply Functions

• Introduction
• Objectives
• Types of Apply Functions
• Apply() Function
• Lapply() Function
• Sapply() Function
• Tapply() Function
• Vapply() Function
• Mapply() Function
• Dplyr Package
• Installing the Dplyr Package
• Functions of the Dplyr Package
• Functions of the Dplyr Package – Select()
• Use the Select() Function
• Functions of Dplyr-Package – Filter()
• Use the Filter() Function
• Use Select Function
• Functions of Dplyr Package – Arrange()
• Use Arrange Function
• Functions of Dplyr Package – Mutate()
• Functions of Dply Package – Summarise()
• Use Summarise Function

Data Visualization

• Introduction
• Objectives
• Graphics in R
• Types of Graphics
• Bar Charts
• Creating Simple Bar Charts
• Editing a Simple Bar Chart
• Create a Stacked Bar Plot and Grouped Bar Plot
• Pie Charts
• Editing a Pie Chart
• Create a Pie Chart
• Histograms
• Creating a Histogram
• Kernel Density Plots
• Creating a Kernel Density Plot
• Create Histograms and a Density Plot
• Line Charts
• Creating a Line Chart
• Box Plots
• Creating a Box Plot
• Create Line Graphs and a Box Plot
• Heat Maps o Creating a Heat Map
• Create a Heatmap
• Word Clouds
• Creating a Word Cloud
• File Formats for Graphics Outputs
• Saving a Graphic Output as a File
• Save Graphics to a File
• Exporting Graphs in RStudio
• Exporting Graphs as PDFs in RStudio
• Save Graphics Using RStudio

Introduction to Statistics

• Introduction
• Objectives
• Basics of Statistics
• Types of Data
• Qualitative vs. Quantitative Analysis
• Types of Measurements in Order
• Nominal Measurement
• Ordinal Measurement
• Interval Measurement
• Ratio Measurement
• Statistical Investigation
• Normal Distribution
• Example of Normal Distribution
• Importance of Normal Distribution in Statistics
• Use of the Symmetry Property of Normal Distribution
• Standard Normal Distribution
• Use Probability Distribution Functions
• Distance Measures
• Distance Measures – A Comparison
• Euclidean Distance
• Example of Euclidean Distance
• Manhattan Distance
• Minkowski Distance
• Mahalanobis Distance
• Cosine Similarity
• Correlation
• Correlation Measures Explained
• Pearson Product Moment Correlation (PPMC)
• Pearson Correlation
• Dist() Function in R
• Perform the Distance Matrix Computations

Hypothesis Testing I

• Introduction
• Objectives
• Hypothesis
• Need of Hypothesis Testing in Businesses
• Null Hypothesis
• Alternate Hypothesis
• Null vs. Alternate Hypothesis
• Chances of Errors in Sampling
• Types of Errors
• Contingency Table
• Decision Making
• Critical Region
• Level of Significance
• Confidence Coefficient
• Bita Risk
• Power of Test
• Factors Affecting the Power of Test
• Types of Statistical Hypothesis Tests
• Upper Tail Test
• Test Statistic
• Factors Affecting Test Statistic
• Critical Value Using Normal Probability Table

Hypothesis Testing II

• Introduction
• Objectives
• Parametric Tests
• Z-Test
• Z-Test in R
• T-Test
• T-Test in R
• Use Normal and Student Probability Distribution Functions
• Testing Null Hypothesis
• Objectives of Null Hypothesis Test
• Three Types of Hypothesis Tests
• Hypothesis Tests About Population Means
• Decision Rules
• Hypothesis Tests About Population Means
• Hypothesis Tests About Population Proportions
• Chi-Square Test
• Steps of Chi-Square Test
• Degree of Freedom
• Chi-Square Test for Independence
• Chi-Square Test for Goodness of Fit
• Chi-Square Test for Independence
• Chi-Square Test in R
• Use Chi-Squared Test Statistics
• Introduction to ANOVA Test
• One-Way ANOVA Test
• The F-Distribution and F-Ratio
• F-Ratio Test
• F-Ratio Test in R
• One-Way ANOVA Test
• One-Way ANOVA Test in R
• Perform ANOVA

Regression Analysis

• Introduction
• Objectives
• Introduction to Regression Analysis
• Use of Regression Analysis
• Types Regression Analysis
• Simple Regression Analysis
• Multiple Regression Models
• Simple Linear Regression Model
• Perform Simple Linear Regression
• Correlation
• Correlation Between X and Y
• Find Correlation
• Method of Least Squares Regression Model
• Coefficient of Multiple Determination Regression Model
• Standard Error of the Estimate Regression Model
• Dummy Variable Regression Model
• Interaction Regression Model
• Non-Linear Regression
• Non-Linear Regression Models
• Perform Regression Analysis with Multiple Variables
• Non-Linear Models to Linear Models
• Algorithms for Complex Non-Linear Models

Classification

• Objectives
• Introduction to Classification
• Examples of Classification
• Classification vs. Prediction
• Classification System
• Classification Process
• Classification Process – Model Construction
• Classification Process – Model Usage in Prediction
• Issues Regarding Classification and Prediction
• Data Preparation Issues
• Evaluating Classification Methods Issues
• Decision Tree
• Decision Tree – Dataset
• Classification Rules of Trees
• Overfitting in Classification
• Tips to Find the Final Tree Size
• Basic Algorithm for a Decision Tree
• Statistical Measure – Information Gain
• Calculating Information Gain for Continuous-Value Attributes
• Enhancing a Basic Tree
• Decision Trees in Data Mining
• Model a Decision Tree
• Naive Bayes Classifier Model
• Features of Naive Bayes Classifier Model
• Bayesian Theorem
• Naive Bayes Classifier
• Applying Naive Bayes Classifier
• Naive Bayes Classifier – Advantages and Disadvantages
• Perform Classification Using the Naive Bayes Method
• Nearest Neighbor Classifiers
• Computing Distance and Determining Class
• Choosing the Value of K
• Scaling Issues in Nearest Neighbor Classification
• Support Vector Machines
• Advantages of Support Vector Machines
• Geometric Margin in SVMs
• Linear SVMs
• Non-Linear SVMs
• Support a Vector Machine

Clustering

• Introduction
• Objectives
• Introduction to Clustering
• Clustering vs. Classification
• Use Cases of Clustering
• Clustering Models
• K-means Clustering
• K-means Clustering Algorithm
• Pseudo Code of K-means
• K-means Clustering Using R
• K-means Clustering
• Perform Clustering Using K-means
• Hierarchical Clustering
• Hierarchical Clustering Algorithms
• Requirements of Hierarchical Clustering Algorithms
• Agglomerative Clustering Process
• Perform Hierarchical Clustering
• DBSCAN Clustering
• Concepts of DBSCAN
• DBSCAN Clustering Algorithm
• DBSCAN in R
• DBSCAN Clustering

Association

• Introduction
• Objectives
• Association Rule Mining
• Application Areas of Association Rule Mining
• Parameters of Interesting Relationships
• Association Rules
• Association Rule Strength Measures
• Limitations of Support and Confidence
• Apriori Algorithm
• Applying Apriori Algorithm
• Step 1 – Mine All Frequent Item Sets
• Algorithm to Find Frequent Item Set
• Ordering Items
• Candidate Generation
• Step 2 – Generate Rules from Frequent Item Sets
• Perform Association Using the Apriori Algorithm
• Perform Visualization on Associated Rules
• Problems with Association Mining

Topic Covered:

Introduction to Big data and Hadoop Ecosystem

• Introduction
• Overview to Big Data and Hadoop
• Hadoop Ecosystem

HDFS and YARN

• Introduction
• HDFS Architecture and Components
• Block Replication Architecture
• YARN Introduction

MapReduce and Sqoop

• Introduction
• Why Mapreduce
• Small Data and Big Data
• Data Types in Hadoop
• Joins in MapReduce
• What is Sqoop

Basics of Hive and Impala

• Introduction
• Interacting with Hive and Impala
• Working with Hive and Impala
• Data Types in Hive
• Validation of Data
• What is Catalog and Its Uses

Types of Data Formats

• Introduction
• Types of File Format
• Data Serialization
• Importing MySql and Creating hive to
• Parquet With Sqoop

Advanced Hive Concept and Data File Partitioning

• Introduction
• Overview of the Hive Query Language

Apache Flume and HBase

• Introduction
• Introduction to HBase

Pig

• Introduction
• Getting Datasets for Pig Development

Basics of Apache Spark

• Introduction
• Spark – Architecture, Execution, and Related Concepts
• RDD Operations
• Functional Programming in Spark

RDDs in Spark

• Introduction
• RDD Data Types and RDD Creation
• Operations in RDDs

Implementation of Spark Applications

• Introduction
• Running Spark on YARN
• Running a Spark Application
• Dynamic Resource Allocation
• Configuring Your Spark Application

Spark Parallel Processing

• Introduction
• Parallel Operations on Partitions

Spark RDD Optimization Techniques

• Introduction
• RDD Persistence

Spark RDD Optimization Techniques

• Spark Algorithm
• Introduction
• Spark: An Iterative Algorithm
• Introduction To Graph Parallel System
• Introduction To Machine Learning
• Introduction To Three C’s

Spark SQL

• Introduction
• Interoperating with RDDs

Apache Kafka

Core Java

Topic Covered:

Data Science

• Introduction to Data Science
• Different Sectors Using Data Science
• Purpose and Components of Python

Data Analytics

• Data Analytics Process
• Exploratory Data Analysis(EDA)
• EDA-Quantitative Technique
• EDA – Graphical Technique
• Data Analytics Conclusion or Predictions
• Data Analytics Communication
• Data Types for Plotting
• Data Types and Plotting

Statistical Analysis and Business Applications

• Introduction to Statistics
• Statistical and Non-statistical Analysis
• Major Categories of Statistics
• Statistical Analysis Considerations
• Population and Sample
• Statistical Analysis Process
• Data Distribution
• Dispersion o Histogram
• Testing

Python Environment Setup and Essentials

• Anaconda
• Installation of Anaconda Python Distribution (contd.)
• Data Types with Python
• Basic Operators and Functions

Mathematical Computing with Python (NumPy)

• Introduction to Numpy
• Activity-Sequence it Right
• Creating and Printing an array
• Class and Attributes of array
• Basic Operations
• Activity-Slice It
• Copy and Views
• Mathematical Functions of Numpy

Scientific computing with Python (Scipy)

• Introduction to SciPy
• SciPy Sub Package – Integration and Optimization
• SciPy sub package
• Calculate Eigenvalues and Eigenvector
• SciPy Sub Package – Statistics, Weave and IO

Data Manipulation with Pandas

• Introduction to Pandas
• Understanding DataFrame
• View and Select Data
• Missing Values
• Data Operations
• File Read and Write Support
• Pandas Sql Operation

Machine Learning with Scikit–Learn

• Machine Learning Approach
• How it Works
• Supervised Learning Model Considerations
• Scikit-Learn
• Supervised Learning Models – Linear Regression
• Supervised Learning Models – Logistic Regression
• Unsupervised Learning Models
• Pipeline
• Model Persistence and Evaluation

Natural Language Processing with Scikit Learn

• NLP Overview
• NLP Applications
• NLP Libraries-Scikit
• Extraction Considerations
• Scikit Learn-Model Training and Grid Search

Data Visualization in Python using matplotlib

• Introduction to Data Visualization
• Line Properties
• (x,y) Plot and Subplots
• Types of Plots

Web Scraping with BeautifulSoup

• Web Scraping and Parsing
• Understanding and Searching the Tree
• Navigating options
• Navigating a Tree
• Modifying the Tree
• Parsing and Printing the Document

Python integration with Hadoop MapReduce and Spark

• Why Big Data Solutions are Provided for Python
• Hadoop Core Components
• Python Integration with HDFS using Hadoop Streaming
• Using Hadoop Streaming for Calculating Word Count
• Python Integration with Spark using PySpark
• Using PySpark to Determine Word Count

Python Basics

Topic Covered:

Introduction to Business Analytics

• Introduction
• What Is in It for Me
• Types of Analytics
• Areas of Analytics

Formatting Conditional Formatting and Important Functions

• Introduction
• What Is in It for Me
• Custom Formatting Introduction
• Conditional Formatting Introduction
• Logical Functions
• Lookup and Reference Functions
• VLOOKUP Function
• HLOOKUP Function
• MATCH Function
• INDEX and OFFSET Function
• Statistical Function
• SUMIFS Function
• COUNTIFS Function
• PERCENTILE and QUARTILE
• STDEV, MEDIAN and RANK Function

Analyzing Data with Pivot Tables

• Introduction
• What Is in It for Me
• Pivot Table Introduction
• Concept Video of Creating a Pivot Table
• Grouping in Pivot Table Introduction
• Custom Calculation
• Calculated Field and Calculated Item
• Slicer Intro
• Creating a Slice
• Dashboarding
• Introduction
• What Is in It for Me
• What is a Dashboard
• Principles of Great Dashboard Design
• How to Create Chart in Excel
• Chart Formatting
• Thermometer Chart
• Pareto Chart
• Form Controls in Excel
• Interactive Dashboard with Form Controls
• Chart with Checkbox
• Interactive Chart

Business Analytics With Excel

• Introduction
• What Is in It for Me
• Concept Video Histogram
• Concept Video Solver Addin
• Concept Video Goal Seek
• Concept Video Scenario Manager
• Concept Video Data Table
• Concept Video Descriptive Statistics

Data Analysis Using Statistics

• Introduction
• Moving Average
• Hypothesis Testing
• ANOVA
• Covariance
• Correlation
• Regression
• Normal Distribution

Power BI

• Introduction
• Power Pivot
• Power View
• Power Query
• Power Map

Microsoft Power BI Desktop

Microsoft Power BI Recipes

Topic Covered:

Machine Learning Introduction

• Techniques of Machine Learning
• Supervised Learning
• Unsupervised Learning
• Semi-supervised Learning and Reinforcement Learning
• Some Important Considerations in Machine Learning

Data Preprocessing

• Data Preparation
• Feature engineering
• Feature scaling
• Datasets
• Dimensionality reduction

Math Refresher

• Eigenvalues, Eigenvectors, and Eigendecomposition
• Concepts of Linear Algebra
• Introduction to Calculus
• Probability and Statistics

Regression

• Regression and Its Types
• Linear Regression: Equations and Algorithms

Classification

• Logistic regression
• K-nearest neighbours
• Support Vector Machines
• Kernel SVM
• Naive Bayes
• Decision tree classifier
• Random forest classifier

Unsupervised learning – Clustering

• K-Means Clustering
• Clustering Algorithms

Introduction to Deep Learning

• Meaning and importance of deep learning
• Artificial Neural networks
• TensorFlow

Introduction to Artificial Intelligence and Machine Learning

• Artificial Intelligence
• Machine Learning o Machine Learning algorithms o Applications of Machine Learning

Python Programming for Beginners

Python Django From Scratch

Topic Covered:

Introduction to Deep Learning with TensorFlow

• Introduction to TensorFlow
• Intro to TensorFlow
• Computational Graph
• Key highlights
• Creating a Graph
• Regression example
• Gradient Descent
• TensorBoard
• Modularity
• Sharing Variables
• Keras

Perceptrons

• What is a Perceptron
• XOR Gate

Activation Functions

• Sigmoid
• ReLU
• Hyperbolic Fns
• Softmax

Artificial Neural Networks

• Introduction
• Perceptron Training Rule
• Gradient Descent Rule

Gradient Descent and Backpropagation

• Gradient Descent
• Stochastic Gradient Descent
• Backpropagation
• Some problems in ANN

Optimization and Regularization

• Overfitting and Capacity
• Cross-Validation
• Feature Selection
• Regularization
• Hyperparameters

Intro to Convolutional Neural Networks

• Intro to CNNs
• Kernel filter
• Principles behind CNNs
• Multiple Filters
• CNN applications

Intro to Recurrent Neural Networks

• Intro to RNNs
• Unfolded RNNs
• Seq2Seq RNNs
• LSTM
• RNN

Deep Learning applications

• Image Processing
• Natural Language Processing
• Speech Recognition
• Video Analytics