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    Post Graduate Certification Program in Business Intelligence and Analytics

    BY
    EduBridge

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    Mode

    Online

    Fees

    ₹ 28500

    Quick Facts

    particular details
    Medium of instructions English
    Mode of learning Self study, Virtual Classroom
    Mode of Delivery Video and Text Based
    Frequency of Classes Weekdays
    Learning efforts 12-15 Hours Per Week

    Course and certificate fees

    Fees information
    ₹ 28,500
    certificate availability

    Yes

    certificate providing authority

    EduBridge

    The syllabus

    Term-1: Foundation

    Foundation course in BI & Analytics
    • Introduction to Business Analytics
    • R for Data Science
    • Introduction to R and R Studio
    • Dealing with Data Using R
    • Visualization Using R
    • R-Markdown
    • Missing Value Treatment
    • Exploratory Data Analysis Using R
    Business Finance
    • Fundamentals of Finance
    • Finance as a Function
    • Financial Management Decisions
    • Financial Statement Analysis Using Financial Ratios
    • Working Capital Management
    • Understanding the Concepts of Working Capital
    • Operating and Cash Operating Cycle of a Firm
    • Investment and Financing of Working Capital
    • Capital Budgeting
    • Discounted Cash Flow
    • Time Value of Money
    • Net Present Value
    • Making Capital Investment Decision
    • Capital Structure
    Marketing and CRM
    • Core Concepts of Marketing
    • Segmentation, Targeting & Positioning
    • Marketing Mix
    • Customer Life Time Value
    • Customer Relationship
    • Management Framework
    • Consumer Behaviour
    • Regency, Frequency & Monetary Analysis
    • Computation of CLTV
    Statistics for decision making
    • Analysis of Variance
    • Regression Analysis
    • Simple Linear Regression
    • Ordinary Least Sum of Squares
    • Simple Linear Regression: Assumptions & Evaluation
    • Basics of ANOVA
    • One Way ANOVA
    • Applications of ANOVA
    • ANOVA with Interaction Effects
    • Two Way ANOVA
    • Dimension Reduction Techniques
    • Requirement for Dimension Reduction
    • Principal Component Analysis
    • Factor Analysis
    • Case Study: Hands-On Using Python/R

    Term-2: Predictive Analytics

    Introduction to predictive analytics
    • Case Study
      • - Data Pre-processing in R
    • Case Study 2
      • - K-means cluster analysis for Indian liver
    • Case Study 3
      • - Sales prediction
    • Case Study 4 - Parkinsons Data
      • -Random Forest Approach
    • Case Study 5 - Classification model for thoracic surgery data
    Simple Linear Regression (SLR)
    • Case-let Overview
    • Introduction to Regression
    • Model Development
    • Model Validation
    • Demo using Excel & SPSS
    Multiple Linear Regression (MLR)
    • Multiple Linear Regression
    • Estimation of Regression Parameters
    • Model Diagnostics
    • Dummy, Derived & Interaction Variables
    • Multi-collinearity
    • Model Deployment
    • Demo using SPSS
    Logistic Regression
    • Discrete choice models
    • Logistic Regression
    • MLE Estimation of Parameters
    • Logistic Model Interpretation
    • Logistic Model Diagnostics
    • Logistic Model Deployment
    • Demo using SPSS
    Decision Trees & Unstructured DA
    • Introduction to Decision Trees
    • Chi-Square Automatic Interaction
    • Detectors (CHAID)
    • Classification and Regression Tree (CART)
    • Analysis of Unstructured data
    • Naive Bayes algorithm
    • Demo using SPSS
    Forecasting and Time series Analysis
    • Forecasting
    • Time Series Analysis
    • Additive & Multiplicative models
    • Exponential smoothing techniques
    • Forecasting Accuracy
    • Auto-regressive & Moving average models
    • Demo using SPSS
    Machine Learning Overview
    • Types of Machine Learning Algorithms
    • Supervised Learning Algorithms
    • Unsupervised Learning Algorithms
    • Implement and demonstrate the
    • FIND-S algorithm
    • Candidate-Elimination algorithm
    • ID3 algorithm
    • Backpropagation algorithm
    • naïve Bayesian classifier
    • Bayesian Network
    • k means algorithm
    • k-Nearest Neighbour algorithm
    • Locally Weighted Regression algorithm

    Term-3: Business Intelligence tools

    Introduction to data visualization
    • Introduction to data visualization
    • Bar chart
    • Pie chart
    • Stacked area chart
    • Line chart
    • Histogram
    • Scatter Plot
    • Combo Plots-Part 1-Scatter and trendline (Regression Plot)
    • Combo Plots-Part 2-Bar and Line
    Data warehouse-introduction
    • Business Intelligence
    • General concept of Data Warehouse
    • Dimensional modelling
    • ETL and Metadata
    • Online Analytical Processing (OLAP)
    • Data Mining
    • Data Modelling for Business Intelligence
    • Introduction to Data Modelling
    • Understanding Business Requirements
    • The Data Life cycle
    • Conceptual Model
    • Logical Model
    Informatica
    • Working with Flat Files
    • Overview Flat File Properties
    • Mapplet
    • Workflow Schedule
    • Update Strategy Transformation
    • Sequence Generator Transformation
    • Additional Transformations
    • Reusable Transformations
    • SQL transformation
    Datastage
    • Introduction to IBM IIS and Datastage
    • Introduction to IBM IIS
    • Datastage runtime Architecture and various stages
    • Platform Architecture
    • Standard data transformation Techniques
    • Transforming Data
    • Metadata in the Parallel Framework
    • Explain and Create schemes
    Microsoft Power BI
    • Introduction to Power BI
    • Power BI Desktop & Data Transformation
    • Data Analysis Expressions (DAX)
    • Data Visualization
    • Power BI Service,Q&A, and Quick Insights
    • Connectivity Modes
    • Power BI Report Servers
    • Using R & Python in Power BI
    • Advanced Analytics In Power BI
    Data visualization with R
    • Create basic bar charts, histograms,pie charts, scatter plots, line plots, box plots, and maps using R and related packages.
    • Customize charts and plots using themes and faceting.
    • Create maps using the Leaflet package for R.
    • Create interactive dashboards using the Shiny package for R.
    Introduction to SAS
    • Module Introduction
    • SAS- Overview
    • SAS Studio
    • SAS- Program Structure
    • SAS: Data Sets
    • SAS: Variables
    • Lab Activity Time- SAS Variables
    • SAS: Arrays
    • Lab Activity - SAS Strings & Arrays
    • SAS: Numeric Formats
    • SAS: Operators
    • Lab Activity Time- SAS Operators
    • SAS: Loops
    • SAS: Decision Making
    • SAS: Input Methods
    • SAS Macros
    • Lab Activity Time- SAS Macros
    • SAS: Date & Time
    • SAS: Reading Raw Data
    • SAS Reading & Writing Data Sets
    • Concatenate Data sets
    • SAS: Merge Data sets
    • SAS: Subsetting Data sets
    • SAS: Sorting Data sets
    • SAS: Format Data sets
    • Lab Activity Time-SAS Merge, Concatenate, Sub-setting, Sorting,
    • Format Data Sets
    • SAS: SQL
    • SAS: Output Delivery System
    • SAS: Simulations
    • SAS: Data Visualization
    • Lab Activity Time-SAS Data Visualization
    • SAS: Enterprise Guide
    • Visual Analytics
    • JMP
    Tableau
    • Introduction to Visualization and Tableau
    • Tableau essentials
    • Creating visualizations in Tableau
    • Filter groups and sets
    • Formulas in Tableau
    • Level of detailed expressions
    • Optimizing tableau performance
    • Advanced visualization in Tableau
    • Connecting to different data sources
    • Hands-on with Tableau

    Term-4: Applications of Business Analytics

    Introduction to Big Data
    • Introduction to Big Data
    • Characteristics of Big Data
    • Hadoop Architecture
    • Hadoop Components
    Big Data Analytics
    • Introduction to MapReduce
    • MapReduce with Java
    Marketing and retail analytics
    • Marketing and retail terminologies
    • Customer Analytics
    • KNIME
    • Retail Dashboard
    • Customer Churn
    • Association rules mining
    Web and social media analytics
    • Web analytics understanding the matrix
    • Basic and advanced web matrix
    • Google analytics
    • Campaign Analytics
    • Text Mining
    Supply Chain & Logistics Analytics
    • Introduction to supply chain
    • RNN and its mechanism
    • Designing optimal strategies
    • Inventory control and management
    • Inventory classification
    • Inventory modeling

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