- 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
Online
₹ 28,500
Quick facts
particular | details | |||
---|---|---|---|---|
Medium of instructions
English
|
Mode of learning
Self study, Virtual Classroom
|
Mode of Delivery
Video and Text Based
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Frequency of Classes
Weekdays
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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
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