Master Data Science Training & Certification

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