Most popular
Beginner
Excel Data Analysis and Visualization – Transforming Data into Visual Stories with Excel
₹999.00₹3,000.00
Excel Data Analysis and Visualization – Transforming Data into Visual Stories with Excel
20 hours
Beginner
What you'll learn
Week 1: Advanced Excel for Data Analysis (4 Hours)
Session 1 (2 Hours): Excel Refresher and Introduction to Advanced Features
Quick Review of Basic Excel Functions
Introduction to Advanced Data Functions and Formulas
Managing and Organizing Data in Excel
Session 2 (2 Hours): Data Analysis Techniques in Excel
Data Validation and Conditional Formatting
Advanced Data Analysis with PivotTables and PivotCharts
Introduction to Excel's Data Analysis ToolPak
Week 2: Data Visualization in Excel (6 Hours)
Session 3 (2 Hours): Creating Basic Charts and Graphs
Types of Charts in Excel and When to Use Them
Customizing Charts and Graphs
Best Practices in Data Visualization
Session 4 (2 Hours): Advanced Charting Techniques
Advanced Chart Types (e.g., Waterfall, Sunburst, Histogram)
Dynamic and Interactive Charts
Using Sparklines and Conditional Formatting for Visualization
Session 5 (2 Hours): Dashboard Creation in Excel
Principles of Dashboard Design
Integrating Multiple Data Sources into a Dashboard
Interactive Elements in Dashboards (e.g., Slicers, Form Controls)
Week 3: Advanced Data Manipulation and Analysis (6 Hours)
Session 6 (2 Hours): Utilizing Advanced Excel Functions
Exploring Advanced Functions (e.g., INDEX, MATCH, OFFSET)
Introduction to Array Formulas
Text and Date Functions for Data Preparation
Session 7 (2 Hours): Introduction to Macros and VBA
Basics of Excel Macros for Automation
Introduction to VBA (Visual Basic for Applications)
Simple VBA Scripts for Data Analysis
Session 8 (2 Hours): Solving Real-World Business Problems
Applying Excel Skills to Business Case Studies
Data Analysis Techniques in Real-World Scenarios
Group Exercise on Business Data Analysis
Week 4: Integrating Excel with Other Tools and Capstone Project (4 Hours)
Session 9 (2 Hours): Excel Integration with External Data Sources
Importing and Exporting Data from/to Different Formats
Connecting Excel with Databases and Web Data
Excel and Power BI Integration Basics
Session 10 (2 Hours): Capstone Project and Course Wrap-Up
Final Project: Creating a Comprehensive Business Analysis Report using Excel
Presentation of Capstone Projects
Course Summary and Pathways for Further Learning
Each session should include a mix of theoretical instruction and hands-on exercises, progressively building in complexity. The capstone project should involve a comprehensive task that requires students to apply all the skills they've learned, ideally focusing on a real-world business scenario. This structure ensures that MBA students not only gain technical proficiency in Excel for data analysis and visualization but also understand how to apply these skills in a business context.
Beginner
Supply Chain Analytics – Unlocking Efficiency with Supply Chain Analytics
₹1,449.00₹4,000.00
Supply Chain Analytics – Unlocking Efficiency with Supply Chain Analytics
20 hours
Beginner
What you'll learn
Week 1: Introduction to Supply Chain Management and Analytics (4 Hours)
Session 1 (2 Hours): Fundamentals of Supply Chain Management
Overview of Supply Chain Concepts and Components
Role of Analytics in Supply Chain Management
Introduction to Supply Chain Models
Session 2 (2 Hours): Introduction to Supply Chain Analytics Tools and Techniques
Overview of Analytical Tools Used in Supply Chain (e.g., Excel, R, Python)
Basic Data Analysis Techniques for Supply Chain Data
Case Studies Highlighting the Importance of Analytics in Supply Chain
Week 2: Data Management and Descriptive Analytics (6 Hours)
Session 3 (2 Hours): Data Management in Supply Chains
Data Collection and Management Strategies in Supply Chains
Data Quality and Governance in Supply Chain Analytics
Introduction to Data Warehousing and Data Lakes
Session 4 (2 Hours): Descriptive Analytics in Supply Chain
Using Descriptive Analytics to Understand Historical Performance
Key Performance Indicators (KPIs) in Supply Chain Management
Visualization Techniques for Supply Chain Data
Session 5 (2 Hours): Practical Exercise on Descriptive Analytics
Hands-on Exercise with a Supply Chain Dataset
Creating Dashboards and Reports for Supply Chain Performance
Week 3: Predictive Analytics and Optimization Techniques (6 Hours)
Session 6 (2 Hours): Introduction to Predictive Analytics in Supply Chain
Overview of Predictive Modeling Techniques
Forecasting Demand and Inventory Requirements
Predictive Maintenance in Supply Chain Operations
Session 7 (2 Hours): Supply Chain Optimization Techniques
Linear Programming and Network Optimization Models
Optimization of Logistics and Distribution Networks
Case Study on Supply Chain Optimization
Session 8 (2 Hours): Advanced Topics in Predictive Analytics
Machine Learning Applications in Supply Chain
Scenario Planning and Risk Analysis in Supply Chain
Real-time Analytics and IoT in Supply Chain Management
Week 4: Strategic Applications and Capstone Project (4 Hours)
Session 9 (2 Hours): Integrating Analytics into Supply Chain Strategy
Building Data-Driven Supply Chain Strategies
Sustainability and Ethics in Supply Chain Analytics
Future Trends in Supply Chain Analytics
Session 10 (2 Hours): Capstone Project and Course Wrap-Up
Group Project: Developing an Analytical Solution for a Supply Chain Problem
Presentations of Capstone Projects
Course Summary and Pathways for Further Learning
The course should be a mix of lectures, case studies, and practical exercises, ideally using real-world supply chain data. The capstone project in the final week would allow students to apply the concepts and techniques learned to a real or simulated supply chain problem, reinforcing their understanding and practical skills in supply chain analytics. This structure ensures that MBA students are not only knowledgeable about analytical techniques but also understand how to apply these skills effectively in the context of supply chain management.
Mastering Hard Skills in the Modern Business World
18 hours
Beginner
What you'll learn
Develop a solid foundation in key business-related hard skills.
Gain proficiency in data analysis and interpretation.
Enhance financial literacy and understanding of financial statements.
Master project management tools and techniques.
Develop expertise in essential software and technological tools used in businesses.
Enhance problem-solving and critical thinking skills.
Apply hard skills to real-world business scenarios through case studies and hands-on exercises.
Understand the integration of various hard skills for holistic professional development.
Stay updated with the latest trends and advancements in business-related hard skills.
Prepare for successful careers in diverse fields with enhanced hard skill sets.
Beginner
Financial Modeling and Valuation – Crafting Financial Blueprints and Value Assessments
₹1,299.00₹4,000.00
Financial Modeling and Valuation – Crafting Financial Blueprints and Value Assessments
20 hours
Beginner
What you'll learn
Week 1: Introduction to Financial Modeling (4 Hours)
Session 1 (2 Hours): Basics of Financial Modeling
Introduction to Financial Modeling: Concepts and Applications
Overview of Financial Statements (Income Statement, Balance Sheet, Cash Flow Statement)
Building a Basic Financial Model in Excel
Session 2 (2 Hours): Best Practices in Financial Modeling
Spreadsheet Design and Best Practices
Ensuring Accuracy and Integrity in Financial Models
Basic Excel Functions and Tools for Financial Modeling
Week 2: Advanced Financial Modeling Techniques (6 Hours)
Session 3 (2 Hours): Cash Flow Projections and Forecasting
Techniques for Projecting Income Statement and Balance Sheet
Building a Cash Flow Statement from Projections
Sensitivity Analysis and Scenario Building
Session 4 (2 Hours): Advanced Excel Techniques for Financial Modeling
Advanced Functions and Formulas in Excel
Data Validation, What-If Analysis, and Goal Seek
Introduction to Macros and VBA for Automation
Session 5 (2 Hours): Financial Modeling for Decision Making
Case Studies: Financial Modeling in Mergers and Acquisitions, Project Finance, and Valuation
Analyzing Financial Models for Strategic Decisions
Week 3: Valuation Techniques and Applications (6 Hours)
Session 6 (2 Hours): Introduction to Valuation Methods
Overview of Valuation Techniques: DCF, Comparables, Precedent Transactions
Discounted Cash Flow (DCF) Valuation Model
Cost of Capital: WACC and CAPM
Session 7 (2 Hours): Comparable Company Analysis (CCA) and Precedent Transactions
Performing a Comparable Company Analysis
Precedent Transaction Analysis: Methodology and Application
Case Study: Valuation using CCA and Precedent Transactions
Session 8 (2 Hours): Real-world Valuation Challenges
Dealing with Uncertainty and Risk in Valuation
Valuation of Startups and Non-traditional Companies
Impact of Economic and Market Conditions on Valuation
Week 4: Practical Application and Capstone Project (4 Hours)
Session 9 (2 Hours): Integrating Financial Modeling and Valuation into Business Strategy
Strategic Implications of Financial Models and Valuations
Communicating Results: Making Effective Financial Presentations
Ethical Considerations in Financial Modeling and Valuation
Session 10 (2 Hours): Capstone Project and Course Wrap-Up
Group Project: Developing a Comprehensive Financial Model and Valuation for a Real or Simulated Company
Presentation of Capstone Projects
Course Summary and Pathways for Further Learning
Each session should include a mix of theoretical instruction, case studies, and hands-on exercises, primarily in Excel. The capstone project in the final week would involve applying all the learned concepts to a comprehensive financial modeling and valuation exercise, providing practical experience in the application of these skills. This structure ensures that MBA students not only acquire technical financial modeling and valuation skills but also understand how to apply these skills in real-world business scenarios.
Tableau for Business Intelligence – Business Intelligence with Tableau
20 hours
Beginner
What you'll learn
Week 1: Introduction to Tableau and Data Visualization (4 Hours)
Session 1 (2 Hours): Fundamentals of Tableau
Introduction to Business Intelligence and Data Visualization
Overview of Tableau: Features and Capabilities
Installing Tableau and Navigating the Interface
Session 2 (2 Hours): Basic Data Visualization Concepts
Principles of Effective Data Visualization
Connecting to Data Sources in Tableau
Creating Basic Charts and Graphs
Week 2: Intermediate Tableau Skills and Visualization Techniques (6 Hours)
Session 3 (2 Hours): Exploring Data with Tableau
Deep Dive into Tableau Data Handling
Data Preparation and Cleaning in Tableau
Advanced Chart Types and Visualization Techniques
Session 4 (2 Hours): Building Interactive Dashboards
Designing Dashboards for Business Intelligence
Adding Filters, Actions, and Tooltips for Interactivity
Best Practices for Dashboard Layout and Design
Session 5 (2 Hours): Data Storytelling with Tableau
Crafting a Narrative with Data
Techniques for Effective Data Presentation and Storytelling
Utilizing Tableau’s Story Points Feature
Week 3: Advanced Tableau Features and Analytics (6 Hours)
Session 6 (2 Hours): Advanced Data Analysis in Tableau
Advanced Calculations with Tableau (Calculated Fields, Table Calculations)
Using Parameters for Dynamic Visualizations
Introduction to Geographic Mapping in Tableau
Session 7 (2 Hours): Tableau for Business Analytics
Analyzing Business Data in Tableau (Sales, Finance, Marketing)
Time-Series Analysis and Forecasting
Exploring Tableau Statistical Functions
Session 8 (2 Hours): Integrating Tableau with Other Tools
Integrating Tableau with SQL and Other Databases
Utilizing Tableau with Big Data
Exporting and Sharing Tableau Insights
Week 4: Practical Application and Capstone Project (4 Hours)
Session 9 (2 Hours): Implementing Tableau in Business Strategy
Case Studies of Tableau in Business Decision-Making
Ethical Considerations in Data Visualization
Strategies for Effective Use of BI Tools in Organizations
Session 10 (2 Hours): Capstone Project and Course Wrap-Up
Developing a Comprehensive Tableau Project on a Business Case
Presentation of Capstone Projects
Course Summary and Pathways for Further Learning
The course should include a mix of lectures, demonstrations, and practical exercises, with a focus on applying Tableau skills to real-world business scenarios. The capstone project in the final week would allow students to apply their learning to develop a full-fledged Tableau dashboard based on a realistic business dataset, ensuring they understand not only how to use Tableau but also how to apply it strategically in a business context.
Advanced Statistical Analysis for Business Success
15.5 hours
Beginner
What you'll learn
Gain a comprehensive understanding of foundational and advanced statistical concepts.
Develop proficiency in using statistical software for data analysis.
Learn to apply descriptive and inferential statistical methods in business contexts.
Enhance skills in interpreting statistical results and making data-informed decisions.
Build competency in conducting hypothesis testing and regression analysis.
Understand the importance of data visualization in presenting statistical findings.
Develop a critical mindset to assess the reliability and validity of statistical analyses.
Learn strategies to handle missing data and outliers in datasets.
Acquire skills to perform multivariate statistical analysis.
Apply statistical knowledge in real-world business scenarios through case studies and projects.
Google Data Studio – Insights at a Glance with Google Data Studio
20 hours
All Levels
What you'll learn
Week 1: Introduction to Google Data Studio and Data Visualization (4 Hours)
Session 1 (2 Hours): Fundamentals of Google Data Studio
Introduction to Business Intelligence and Data Visualization
Overview of Google Data Studio: Features and Capabilities
Setting Up and Navigating Google Data Studio
Session 2 (2 Hours): Connecting to Data Sources
Integrating Various Data Sources (Google Sheets, Analytics, SQL databases)
Understanding Data Source Fields and Data Types
Basic Data Transformations and Preparations
Week 2: Building Reports and Visualizations (6 Hours)
Session 3 (2 Hours): Creating Basic Reports and Charts
Designing Reports with Tables, Scorecards, and Basic Charts
Customizing Data Visualization Elements
Best Practices in Report Layout and Design
Session 4 (2 Hours): Advanced Visualization Techniques
Exploring Advanced Chart Types (e.g., Geo Maps, Scatter Charts)
Using Filters and Date Range Controls
Interactive Features in Reports (e.g., Drill Downs)
Session 5 (2 Hours): Enhancing Reports with Styling and Branding
Advanced Styling Options for Professional-Looking Reports
Adding Branding Elements and Custom Themes
Conditional Formatting for Enhanced Data Presentation
Week 3: Advanced Features and Business Applications (6 Hours)
Session 6 (2 Hours): Advanced Data Studio Features
Using Calculated Fields and Custom Formulas
Introduction to Blended Data and Data Joining
Implementing Row-Level Security and Access Controls
Session 7 (2 Hours): Data Exploration and Insights
Exploring Data with Explorer Mode
Using Data Studio for Business Analytics and Insights
Case Studies: Real-World Business Applications
Session 8 (2 Hours): Collaborating and Sharing in Data Studio
Collaborative Features in Google Data Studio
Sharing and Embedding Reports
Best Practices for Managing and Organizing Reports
Week 4: Practical Application and Capstone Project (4 Hours)
Session 9 (2 Hours): Integrating Google Data Studio in Business Strategy
Developing Data-Driven Business Strategies Using Reports
Discussing Ethical Considerations in Data Reporting
Strategies for Effective Data Storytelling in Business
Session 10 (2 Hours): Capstone Project and Course Wrap-Up
Developing a Comprehensive Business Intelligence Dashboard for a Real or Simulated Business Scenario
Presentation of Capstone Projects
Course Summary and Pathways for Further Learning
The course should include a mix of lectures, hands-on exercises, and case studies, with a strong emphasis on practical application using real-world business scenarios. The capstone project in the final week should involve creating a comprehensive business intelligence dashboard, allowing students to apply their learning in a realistic business context. This structure ensures MBA students not only acquire technical skills in Google Data Studio but also understand how to strategically apply these skills for business analysis and decision-making.
Mastery in Financial Modeling
17.5 hours
Beginner
What you'll learn
Acquire a comprehensive understanding of financial modeling principles and techniques.
Develop proficiency in Excel for financial analysis and modeling.
Learn to construct and interpret financial statements within a modeling context.
Master the art of building various types of financial models including DCF, M&A, and LBO models.
Enhance skills in conducting sensitivity and scenario analysis.
Gain insights into best practices for model design, structure, and validation.
Understand the role of financial modeling in investment analysis and business valuation.
Develop the ability to communicate and present modeling results effectively to stakeholders.
Apply financial modeling techniques to real-world business scenarios and case studies.
Cultivate critical thinking and problem-solving skills in financial decision-making.
IBM Cognos Analytics – Empowering Decisions with IBM Cognos Analytics
20 hours
Beginner
What you'll learn
Week 1: Introduction to IBM Cognos Analytics (4 Hours)
Session 1 (2 Hours): Overview of IBM Cognos Analytics
Introduction to Business Intelligence and the Role of IBM Cognos Analytics
Navigating the IBM Cognos Analytics Interface
Overview of Key Features and Capabilities
Session 2 (2 Hours): Basic Reporting and Dashboarding
Connecting to Data Sources in Cognos
Creating Simple Reports and Dashboards
Basic Data Visualization Techniques
Week 2: Advanced Reporting and Data Exploration (6 Hours)
Session 3 (2 Hours): Advanced Reporting Techniques
Building Advanced Reports (List, Crosstab, and Chart Reports)
Applying Filters, Prompts, and Calculations
Report Formatting and Styling for Clarity and Impact
Session 4 (2 Hours): Interactive Dashboards and Visualization
Designing Interactive Dashboards
Advanced Visualization Techniques in Cognos
Using Drill-Throughs for Detailed Data Analysis
Session 5 (2 Hours): Data Exploration and Analysis
Exploratory Data Analysis in Cognos Analytics
Utilizing Cognos for Trend Analysis and Pattern Discovery
Integrating External Data and Leveraging Advanced Analytics
Week 3: Data Modeling and Framework Manager (6 Hours)
Session 6 (2 Hours): Introduction to Data Modeling
Basics of Data Modeling in Cognos
Creating and Managing Packages
Understanding the Framework Manager
Session 7 (2 Hours): Advanced Data Modeling Concepts
Building Dimensional Models
Working with Different Query Subjects
Implementing Security in Models
Session 8 (2 Hours): Practical Modeling Exercises
Hands-on Data Modeling Exercise
Best Practices in Data Modeling for Business Reporting
Reviewing and Optimizing Models
Week 4: Business Applications and Capstone Project (4 Hours)
Session 9 (2 Hours): Cognos Analytics in Business Contexts
Applying Cognos Analytics to Solve Business Problems
Case Studies: Cognos in Different Industry Settings
Discussing the Strategic Impact of Business Intelligence
Session 10 (2 Hours): Capstone Project and Course Wrap-Up
Developing a Comprehensive Business Intelligence Project Using Cognos Analytics
Presentation and Review of Capstone Projects
Course Summary and Pathways for Further Learning
This course should include a mix of lectures, hands-on practical exercises, and case studies. The capstone project in the final week would allow students to apply their learning to a comprehensive business intelligence task, ensuring they understand not only how to use IBM Cognos Analytics but also how to apply it strategically in a business context.
Beginner
Data Analytics and Statistical Analysis for MBA – From Data to Decisions: Advanced Statistical Techniques for MBAs
₹1,499.00₹3,000.00
Data Analytics and Statistical Analysis for MBA – From Data to Decisions: Advanced Statistical Techniques for MBAs
20 hours
Beginner
What you'll learn
Week 1: Introduction to Data Analytics and Basic Statistics (4 Hours)
Session 1 (2 Hours): Introduction to Data Analytics
Overview of Data Analytics in Business
Role of Data Analytics in Decision-Making
Introduction to Statistical Concepts
Session 2 (2 Hours): Basics of Descriptive Statistics
Measures of Central Tendency (Mean, Median, Mode)
Measures of Variability (Range, Variance, Standard Deviation)
Data Visualization Basics (Histograms, Box Plots)
Week 2: Exploratory Data Analysis and Inferential Statistics (6 Hours)
Session 3 (2 Hours): Exploratory Data Analysis (EDA)
EDA Techniques
Understanding Data Distributions
Introduction to Statistical Software (e.g., R, Python)
Session 4 (2 Hours): Probability and Probability Distributions
Basic Probability Concepts
Discrete and Continuous Distributions (e.g., Binomial, Normal)
Session 5 (2 Hours): Basics of Inferential Statistics
Sampling and Estimation
Hypothesis Testing Concepts
Introduction to Regression Analysis
Week 3: Advanced Statistical Techniques (6 Hours)
Session 6 (2 Hours): Linear Regression Analysis
Simple and Multiple Linear Regression
Interpreting Regression Output
Assumptions and Diagnostics in Regression
Session 7 (2 Hours): Time Series Analysis and Forecasting
Components of Time Series Data
Moving Averages, Smoothing Techniques
Introduction to ARIMA Models
Session 8 (2 Hours): Decision Trees and Clustering
Basics of Classification and Clustering
Introduction to Decision Trees
Basics of K-Means Clustering
Week 4: Application of Data Analytics in Business (4 Hours)
Session 9 (2 Hours): Data Analytics in Finance and Marketing
Case Studies in Financial Analytics
Market Analysis and Consumer Behavior Studies
Session 10 (2 Hours): Capstone Project and Course Wrap-up
Application of Learned Techniques to a Business Case
Group Project Presentation
Course Summary and Path Forward for Further Learning
Each session would ideally include a mix of lecture, discussion, and hands-on exercises using statistical software. The capstone project in the final session should be a comprehensive task that requires students to apply all the skills they've learned, ideally focusing on a real-world business scenario. This structure ensures that MBA students not only understand the theoretical underpinnings of statistical analysis but also how to apply these techniques in a business context.
Trending
Beginner
Excel Data Analysis and Visualization – Transforming Data into Visual Stories with Excel
₹999.00₹3,000.00
Excel Data Analysis and Visualization – Transforming Data into Visual Stories with Excel
20 hours
Beginner
What you'll learn
Week 1: Advanced Excel for Data Analysis (4 Hours)
Session 1 (2 Hours): Excel Refresher and Introduction to Advanced Features
Quick Review of Basic Excel Functions
Introduction to Advanced Data Functions and Formulas
Managing and Organizing Data in Excel
Session 2 (2 Hours): Data Analysis Techniques in Excel
Data Validation and Conditional Formatting
Advanced Data Analysis with PivotTables and PivotCharts
Introduction to Excel's Data Analysis ToolPak
Week 2: Data Visualization in Excel (6 Hours)
Session 3 (2 Hours): Creating Basic Charts and Graphs
Types of Charts in Excel and When to Use Them
Customizing Charts and Graphs
Best Practices in Data Visualization
Session 4 (2 Hours): Advanced Charting Techniques
Advanced Chart Types (e.g., Waterfall, Sunburst, Histogram)
Dynamic and Interactive Charts
Using Sparklines and Conditional Formatting for Visualization
Session 5 (2 Hours): Dashboard Creation in Excel
Principles of Dashboard Design
Integrating Multiple Data Sources into a Dashboard
Interactive Elements in Dashboards (e.g., Slicers, Form Controls)
Week 3: Advanced Data Manipulation and Analysis (6 Hours)
Session 6 (2 Hours): Utilizing Advanced Excel Functions
Exploring Advanced Functions (e.g., INDEX, MATCH, OFFSET)
Introduction to Array Formulas
Text and Date Functions for Data Preparation
Session 7 (2 Hours): Introduction to Macros and VBA
Basics of Excel Macros for Automation
Introduction to VBA (Visual Basic for Applications)
Simple VBA Scripts for Data Analysis
Session 8 (2 Hours): Solving Real-World Business Problems
Applying Excel Skills to Business Case Studies
Data Analysis Techniques in Real-World Scenarios
Group Exercise on Business Data Analysis
Week 4: Integrating Excel with Other Tools and Capstone Project (4 Hours)
Session 9 (2 Hours): Excel Integration with External Data Sources
Importing and Exporting Data from/to Different Formats
Connecting Excel with Databases and Web Data
Excel and Power BI Integration Basics
Session 10 (2 Hours): Capstone Project and Course Wrap-Up
Final Project: Creating a Comprehensive Business Analysis Report using Excel
Presentation of Capstone Projects
Course Summary and Pathways for Further Learning
Each session should include a mix of theoretical instruction and hands-on exercises, progressively building in complexity. The capstone project should involve a comprehensive task that requires students to apply all the skills they've learned, ideally focusing on a real-world business scenario. This structure ensures that MBA students not only gain technical proficiency in Excel for data analysis and visualization but also understand how to apply these skills in a business context.
Beginner
Supply Chain Analytics – Unlocking Efficiency with Supply Chain Analytics
₹1,449.00₹4,000.00
Supply Chain Analytics – Unlocking Efficiency with Supply Chain Analytics
20 hours
Beginner
What you'll learn
Week 1: Introduction to Supply Chain Management and Analytics (4 Hours)
Session 1 (2 Hours): Fundamentals of Supply Chain Management
Overview of Supply Chain Concepts and Components
Role of Analytics in Supply Chain Management
Introduction to Supply Chain Models
Session 2 (2 Hours): Introduction to Supply Chain Analytics Tools and Techniques
Overview of Analytical Tools Used in Supply Chain (e.g., Excel, R, Python)
Basic Data Analysis Techniques for Supply Chain Data
Case Studies Highlighting the Importance of Analytics in Supply Chain
Week 2: Data Management and Descriptive Analytics (6 Hours)
Session 3 (2 Hours): Data Management in Supply Chains
Data Collection and Management Strategies in Supply Chains
Data Quality and Governance in Supply Chain Analytics
Introduction to Data Warehousing and Data Lakes
Session 4 (2 Hours): Descriptive Analytics in Supply Chain
Using Descriptive Analytics to Understand Historical Performance
Key Performance Indicators (KPIs) in Supply Chain Management
Visualization Techniques for Supply Chain Data
Session 5 (2 Hours): Practical Exercise on Descriptive Analytics
Hands-on Exercise with a Supply Chain Dataset
Creating Dashboards and Reports for Supply Chain Performance
Week 3: Predictive Analytics and Optimization Techniques (6 Hours)
Session 6 (2 Hours): Introduction to Predictive Analytics in Supply Chain
Overview of Predictive Modeling Techniques
Forecasting Demand and Inventory Requirements
Predictive Maintenance in Supply Chain Operations
Session 7 (2 Hours): Supply Chain Optimization Techniques
Linear Programming and Network Optimization Models
Optimization of Logistics and Distribution Networks
Case Study on Supply Chain Optimization
Session 8 (2 Hours): Advanced Topics in Predictive Analytics
Machine Learning Applications in Supply Chain
Scenario Planning and Risk Analysis in Supply Chain
Real-time Analytics and IoT in Supply Chain Management
Week 4: Strategic Applications and Capstone Project (4 Hours)
Session 9 (2 Hours): Integrating Analytics into Supply Chain Strategy
Building Data-Driven Supply Chain Strategies
Sustainability and Ethics in Supply Chain Analytics
Future Trends in Supply Chain Analytics
Session 10 (2 Hours): Capstone Project and Course Wrap-Up
Group Project: Developing an Analytical Solution for a Supply Chain Problem
Presentations of Capstone Projects
Course Summary and Pathways for Further Learning
The course should be a mix of lectures, case studies, and practical exercises, ideally using real-world supply chain data. The capstone project in the final week would allow students to apply the concepts and techniques learned to a real or simulated supply chain problem, reinforcing their understanding and practical skills in supply chain analytics. This structure ensures that MBA students are not only knowledgeable about analytical techniques but also understand how to apply these skills effectively in the context of supply chain management.
Mastering Hard Skills in the Modern Business World
18 hours
Beginner
What you'll learn
Develop a solid foundation in key business-related hard skills.
Gain proficiency in data analysis and interpretation.
Enhance financial literacy and understanding of financial statements.
Master project management tools and techniques.
Develop expertise in essential software and technological tools used in businesses.
Enhance problem-solving and critical thinking skills.
Apply hard skills to real-world business scenarios through case studies and hands-on exercises.
Understand the integration of various hard skills for holistic professional development.
Stay updated with the latest trends and advancements in business-related hard skills.
Prepare for successful careers in diverse fields with enhanced hard skill sets.
Beginner
Financial Modeling and Valuation – Crafting Financial Blueprints and Value Assessments
₹1,299.00₹4,000.00
Financial Modeling and Valuation – Crafting Financial Blueprints and Value Assessments
20 hours
Beginner
What you'll learn
Week 1: Introduction to Financial Modeling (4 Hours)
Session 1 (2 Hours): Basics of Financial Modeling
Introduction to Financial Modeling: Concepts and Applications
Overview of Financial Statements (Income Statement, Balance Sheet, Cash Flow Statement)
Building a Basic Financial Model in Excel
Session 2 (2 Hours): Best Practices in Financial Modeling
Spreadsheet Design and Best Practices
Ensuring Accuracy and Integrity in Financial Models
Basic Excel Functions and Tools for Financial Modeling
Week 2: Advanced Financial Modeling Techniques (6 Hours)
Session 3 (2 Hours): Cash Flow Projections and Forecasting
Techniques for Projecting Income Statement and Balance Sheet
Building a Cash Flow Statement from Projections
Sensitivity Analysis and Scenario Building
Session 4 (2 Hours): Advanced Excel Techniques for Financial Modeling
Advanced Functions and Formulas in Excel
Data Validation, What-If Analysis, and Goal Seek
Introduction to Macros and VBA for Automation
Session 5 (2 Hours): Financial Modeling for Decision Making
Case Studies: Financial Modeling in Mergers and Acquisitions, Project Finance, and Valuation
Analyzing Financial Models for Strategic Decisions
Week 3: Valuation Techniques and Applications (6 Hours)
Session 6 (2 Hours): Introduction to Valuation Methods
Overview of Valuation Techniques: DCF, Comparables, Precedent Transactions
Discounted Cash Flow (DCF) Valuation Model
Cost of Capital: WACC and CAPM
Session 7 (2 Hours): Comparable Company Analysis (CCA) and Precedent Transactions
Performing a Comparable Company Analysis
Precedent Transaction Analysis: Methodology and Application
Case Study: Valuation using CCA and Precedent Transactions
Session 8 (2 Hours): Real-world Valuation Challenges
Dealing with Uncertainty and Risk in Valuation
Valuation of Startups and Non-traditional Companies
Impact of Economic and Market Conditions on Valuation
Week 4: Practical Application and Capstone Project (4 Hours)
Session 9 (2 Hours): Integrating Financial Modeling and Valuation into Business Strategy
Strategic Implications of Financial Models and Valuations
Communicating Results: Making Effective Financial Presentations
Ethical Considerations in Financial Modeling and Valuation
Session 10 (2 Hours): Capstone Project and Course Wrap-Up
Group Project: Developing a Comprehensive Financial Model and Valuation for a Real or Simulated Company
Presentation of Capstone Projects
Course Summary and Pathways for Further Learning
Each session should include a mix of theoretical instruction, case studies, and hands-on exercises, primarily in Excel. The capstone project in the final week would involve applying all the learned concepts to a comprehensive financial modeling and valuation exercise, providing practical experience in the application of these skills. This structure ensures that MBA students not only acquire technical financial modeling and valuation skills but also understand how to apply these skills in real-world business scenarios.
Tableau for Business Intelligence – Business Intelligence with Tableau
20 hours
Beginner
What you'll learn
Week 1: Introduction to Tableau and Data Visualization (4 Hours)
Session 1 (2 Hours): Fundamentals of Tableau
Introduction to Business Intelligence and Data Visualization
Overview of Tableau: Features and Capabilities
Installing Tableau and Navigating the Interface
Session 2 (2 Hours): Basic Data Visualization Concepts
Principles of Effective Data Visualization
Connecting to Data Sources in Tableau
Creating Basic Charts and Graphs
Week 2: Intermediate Tableau Skills and Visualization Techniques (6 Hours)
Session 3 (2 Hours): Exploring Data with Tableau
Deep Dive into Tableau Data Handling
Data Preparation and Cleaning in Tableau
Advanced Chart Types and Visualization Techniques
Session 4 (2 Hours): Building Interactive Dashboards
Designing Dashboards for Business Intelligence
Adding Filters, Actions, and Tooltips for Interactivity
Best Practices for Dashboard Layout and Design
Session 5 (2 Hours): Data Storytelling with Tableau
Crafting a Narrative with Data
Techniques for Effective Data Presentation and Storytelling
Utilizing Tableau’s Story Points Feature
Week 3: Advanced Tableau Features and Analytics (6 Hours)
Session 6 (2 Hours): Advanced Data Analysis in Tableau
Advanced Calculations with Tableau (Calculated Fields, Table Calculations)
Using Parameters for Dynamic Visualizations
Introduction to Geographic Mapping in Tableau
Session 7 (2 Hours): Tableau for Business Analytics
Analyzing Business Data in Tableau (Sales, Finance, Marketing)
Time-Series Analysis and Forecasting
Exploring Tableau Statistical Functions
Session 8 (2 Hours): Integrating Tableau with Other Tools
Integrating Tableau with SQL and Other Databases
Utilizing Tableau with Big Data
Exporting and Sharing Tableau Insights
Week 4: Practical Application and Capstone Project (4 Hours)
Session 9 (2 Hours): Implementing Tableau in Business Strategy
Case Studies of Tableau in Business Decision-Making
Ethical Considerations in Data Visualization
Strategies for Effective Use of BI Tools in Organizations
Session 10 (2 Hours): Capstone Project and Course Wrap-Up
Developing a Comprehensive Tableau Project on a Business Case
Presentation of Capstone Projects
Course Summary and Pathways for Further Learning
The course should include a mix of lectures, demonstrations, and practical exercises, with a focus on applying Tableau skills to real-world business scenarios. The capstone project in the final week would allow students to apply their learning to develop a full-fledged Tableau dashboard based on a realistic business dataset, ensuring they understand not only how to use Tableau but also how to apply it strategically in a business context.
Advanced Statistical Analysis for Business Success
15.5 hours
Beginner
What you'll learn
Gain a comprehensive understanding of foundational and advanced statistical concepts.
Develop proficiency in using statistical software for data analysis.
Learn to apply descriptive and inferential statistical methods in business contexts.
Enhance skills in interpreting statistical results and making data-informed decisions.
Build competency in conducting hypothesis testing and regression analysis.
Understand the importance of data visualization in presenting statistical findings.
Develop a critical mindset to assess the reliability and validity of statistical analyses.
Learn strategies to handle missing data and outliers in datasets.
Acquire skills to perform multivariate statistical analysis.
Apply statistical knowledge in real-world business scenarios through case studies and projects.
Google Data Studio – Insights at a Glance with Google Data Studio
20 hours
All Levels
What you'll learn
Week 1: Introduction to Google Data Studio and Data Visualization (4 Hours)
Session 1 (2 Hours): Fundamentals of Google Data Studio
Introduction to Business Intelligence and Data Visualization
Overview of Google Data Studio: Features and Capabilities
Setting Up and Navigating Google Data Studio
Session 2 (2 Hours): Connecting to Data Sources
Integrating Various Data Sources (Google Sheets, Analytics, SQL databases)
Understanding Data Source Fields and Data Types
Basic Data Transformations and Preparations
Week 2: Building Reports and Visualizations (6 Hours)
Session 3 (2 Hours): Creating Basic Reports and Charts
Designing Reports with Tables, Scorecards, and Basic Charts
Customizing Data Visualization Elements
Best Practices in Report Layout and Design
Session 4 (2 Hours): Advanced Visualization Techniques
Exploring Advanced Chart Types (e.g., Geo Maps, Scatter Charts)
Using Filters and Date Range Controls
Interactive Features in Reports (e.g., Drill Downs)
Session 5 (2 Hours): Enhancing Reports with Styling and Branding
Advanced Styling Options for Professional-Looking Reports
Adding Branding Elements and Custom Themes
Conditional Formatting for Enhanced Data Presentation
Week 3: Advanced Features and Business Applications (6 Hours)
Session 6 (2 Hours): Advanced Data Studio Features
Using Calculated Fields and Custom Formulas
Introduction to Blended Data and Data Joining
Implementing Row-Level Security and Access Controls
Session 7 (2 Hours): Data Exploration and Insights
Exploring Data with Explorer Mode
Using Data Studio for Business Analytics and Insights
Case Studies: Real-World Business Applications
Session 8 (2 Hours): Collaborating and Sharing in Data Studio
Collaborative Features in Google Data Studio
Sharing and Embedding Reports
Best Practices for Managing and Organizing Reports
Week 4: Practical Application and Capstone Project (4 Hours)
Session 9 (2 Hours): Integrating Google Data Studio in Business Strategy
Developing Data-Driven Business Strategies Using Reports
Discussing Ethical Considerations in Data Reporting
Strategies for Effective Data Storytelling in Business
Session 10 (2 Hours): Capstone Project and Course Wrap-Up
Developing a Comprehensive Business Intelligence Dashboard for a Real or Simulated Business Scenario
Presentation of Capstone Projects
Course Summary and Pathways for Further Learning
The course should include a mix of lectures, hands-on exercises, and case studies, with a strong emphasis on practical application using real-world business scenarios. The capstone project in the final week should involve creating a comprehensive business intelligence dashboard, allowing students to apply their learning in a realistic business context. This structure ensures MBA students not only acquire technical skills in Google Data Studio but also understand how to strategically apply these skills for business analysis and decision-making.
Mastery in Financial Modeling
17.5 hours
Beginner
What you'll learn
Acquire a comprehensive understanding of financial modeling principles and techniques.
Develop proficiency in Excel for financial analysis and modeling.
Learn to construct and interpret financial statements within a modeling context.
Master the art of building various types of financial models including DCF, M&A, and LBO models.
Enhance skills in conducting sensitivity and scenario analysis.
Gain insights into best practices for model design, structure, and validation.
Understand the role of financial modeling in investment analysis and business valuation.
Develop the ability to communicate and present modeling results effectively to stakeholders.
Apply financial modeling techniques to real-world business scenarios and case studies.
Cultivate critical thinking and problem-solving skills in financial decision-making.
IBM Cognos Analytics – Empowering Decisions with IBM Cognos Analytics
20 hours
Beginner
What you'll learn
Week 1: Introduction to IBM Cognos Analytics (4 Hours)
Session 1 (2 Hours): Overview of IBM Cognos Analytics
Introduction to Business Intelligence and the Role of IBM Cognos Analytics
Navigating the IBM Cognos Analytics Interface
Overview of Key Features and Capabilities
Session 2 (2 Hours): Basic Reporting and Dashboarding
Connecting to Data Sources in Cognos
Creating Simple Reports and Dashboards
Basic Data Visualization Techniques
Week 2: Advanced Reporting and Data Exploration (6 Hours)
Session 3 (2 Hours): Advanced Reporting Techniques
Building Advanced Reports (List, Crosstab, and Chart Reports)
Applying Filters, Prompts, and Calculations
Report Formatting and Styling for Clarity and Impact
Session 4 (2 Hours): Interactive Dashboards and Visualization
Designing Interactive Dashboards
Advanced Visualization Techniques in Cognos
Using Drill-Throughs for Detailed Data Analysis
Session 5 (2 Hours): Data Exploration and Analysis
Exploratory Data Analysis in Cognos Analytics
Utilizing Cognos for Trend Analysis and Pattern Discovery
Integrating External Data and Leveraging Advanced Analytics
Week 3: Data Modeling and Framework Manager (6 Hours)
Session 6 (2 Hours): Introduction to Data Modeling
Basics of Data Modeling in Cognos
Creating and Managing Packages
Understanding the Framework Manager
Session 7 (2 Hours): Advanced Data Modeling Concepts
Building Dimensional Models
Working with Different Query Subjects
Implementing Security in Models
Session 8 (2 Hours): Practical Modeling Exercises
Hands-on Data Modeling Exercise
Best Practices in Data Modeling for Business Reporting
Reviewing and Optimizing Models
Week 4: Business Applications and Capstone Project (4 Hours)
Session 9 (2 Hours): Cognos Analytics in Business Contexts
Applying Cognos Analytics to Solve Business Problems
Case Studies: Cognos in Different Industry Settings
Discussing the Strategic Impact of Business Intelligence
Session 10 (2 Hours): Capstone Project and Course Wrap-Up
Developing a Comprehensive Business Intelligence Project Using Cognos Analytics
Presentation and Review of Capstone Projects
Course Summary and Pathways for Further Learning
This course should include a mix of lectures, hands-on practical exercises, and case studies. The capstone project in the final week would allow students to apply their learning to a comprehensive business intelligence task, ensuring they understand not only how to use IBM Cognos Analytics but also how to apply it strategically in a business context.
Beginner
Data Analytics and Statistical Analysis for MBA – From Data to Decisions: Advanced Statistical Techniques for MBAs
₹1,499.00₹3,000.00
Data Analytics and Statistical Analysis for MBA – From Data to Decisions: Advanced Statistical Techniques for MBAs
20 hours
Beginner
What you'll learn
Week 1: Introduction to Data Analytics and Basic Statistics (4 Hours)
Session 1 (2 Hours): Introduction to Data Analytics
Overview of Data Analytics in Business
Role of Data Analytics in Decision-Making
Introduction to Statistical Concepts
Session 2 (2 Hours): Basics of Descriptive Statistics
Measures of Central Tendency (Mean, Median, Mode)
Measures of Variability (Range, Variance, Standard Deviation)
Data Visualization Basics (Histograms, Box Plots)
Week 2: Exploratory Data Analysis and Inferential Statistics (6 Hours)
Session 3 (2 Hours): Exploratory Data Analysis (EDA)
EDA Techniques
Understanding Data Distributions
Introduction to Statistical Software (e.g., R, Python)
Session 4 (2 Hours): Probability and Probability Distributions
Basic Probability Concepts
Discrete and Continuous Distributions (e.g., Binomial, Normal)
Session 5 (2 Hours): Basics of Inferential Statistics
Sampling and Estimation
Hypothesis Testing Concepts
Introduction to Regression Analysis
Week 3: Advanced Statistical Techniques (6 Hours)
Session 6 (2 Hours): Linear Regression Analysis
Simple and Multiple Linear Regression
Interpreting Regression Output
Assumptions and Diagnostics in Regression
Session 7 (2 Hours): Time Series Analysis and Forecasting
Components of Time Series Data
Moving Averages, Smoothing Techniques
Introduction to ARIMA Models
Session 8 (2 Hours): Decision Trees and Clustering
Basics of Classification and Clustering
Introduction to Decision Trees
Basics of K-Means Clustering
Week 4: Application of Data Analytics in Business (4 Hours)
Session 9 (2 Hours): Data Analytics in Finance and Marketing
Case Studies in Financial Analytics
Market Analysis and Consumer Behavior Studies
Session 10 (2 Hours): Capstone Project and Course Wrap-up
Application of Learned Techniques to a Business Case
Group Project Presentation
Course Summary and Path Forward for Further Learning
Each session would ideally include a mix of lecture, discussion, and hands-on exercises using statistical software. The capstone project in the final session should be a comprehensive task that requires students to apply all the skills they've learned, ideally focusing on a real-world business scenario. This structure ensures that MBA students not only understand the theoretical underpinnings of statistical analysis but also how to apply these techniques in a business context.
Featured Courses
Mastery in Financial Modeling
17.5 hours
Beginner
Welcome to “Mastery in Financial Modeling,” a specialized course designed …
₹440.00₹800.00
Mastery in Financial Modeling
17.5 hours
Beginner
What you'll learn
Acquire a comprehensive understanding of financial modeling principles and techniques.
Develop proficiency in Excel for financial analysis and modeling.
Learn to construct and interpret financial statements within a modeling context.
Master the art of building various types of financial models including DCF, M&A, and LBO models.
Enhance skills in conducting sensitivity and scenario analysis.
Gain insights into best practices for model design, structure, and validation.
Understand the role of financial modeling in investment analysis and business valuation.
Develop the ability to communicate and present modeling results effectively to stakeholders.
Apply financial modeling techniques to real-world business scenarios and case studies.
Cultivate critical thinking and problem-solving skills in financial decision-making.
Advanced Statistical Analysis for Business Success
15.5 hours
Beginner
Welcome to “Advanced Statistical Analysis for Business Success,” a comprehensive …
₹390.00₹700.00
Advanced Statistical Analysis for Business Success
15.5 hours
Beginner
What you'll learn
Gain a comprehensive understanding of foundational and advanced statistical concepts.
Develop proficiency in using statistical software for data analysis.
Learn to apply descriptive and inferential statistical methods in business contexts.
Enhance skills in interpreting statistical results and making data-informed decisions.
Build competency in conducting hypothesis testing and regression analysis.
Understand the importance of data visualization in presenting statistical findings.
Develop a critical mindset to assess the reliability and validity of statistical analyses.
Learn strategies to handle missing data and outliers in datasets.
Acquire skills to perform multivariate statistical analysis.
Apply statistical knowledge in real-world business scenarios through case studies and projects.
Mastering Hard Skills in the Modern Business World
18 hours
Beginner
Introduction: The global business environment is becoming increasingly competitive, and …
₹490.00₹800.00
Mastering Hard Skills in the Modern Business World
18 hours
Beginner
What you'll learn
Develop a solid foundation in key business-related hard skills.
Gain proficiency in data analysis and interpretation.
Enhance financial literacy and understanding of financial statements.
Master project management tools and techniques.
Develop expertise in essential software and technological tools used in businesses.
Enhance problem-solving and critical thinking skills.
Apply hard skills to real-world business scenarios through case studies and hands-on exercises.
Understand the integration of various hard skills for holistic professional development.
Stay updated with the latest trends and advancements in business-related hard skills.
Prepare for successful careers in diverse fields with enhanced hard skill sets.
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Digital Marketing Analytics – Metrics to Strategy: Mastering Online Impact
20 hours
Intermediate
“Digital Marketing Analytics – Metrics to Strategy: Mastering Online Impact” …
₹1,500.00₹4,000.00
What you'll learn
Week 1: Introduction to Digital Marketing and Analytics (4 Hours)
Session 1 (2 Hours): Fundamentals of Digital Marketing
Overview of Digital Marketing Landscape
Key Digital Marketing Channels (SEO, PPC, Social Media, Email Marketing)
Introduction to Digital Marketing Strategy
Session 2 (2 Hours): Basics of Digital Marketing Analytics
Role of Analytics in Digital Marketing
Understanding Key Metrics and KPIs
Introduction to Analytics Tools (e.g., Google Analytics)
Week 2: Analytics Tools and Techniques (6 Hours)
Session 3 (2 Hours): Web Analytics
In-depth Exploration of Google Analytics
Tracking Website Traffic and User Behavior
Analyzing and Interpreting Web Data
Session 4 (2 Hours): Social Media Analytics
Overview of Social Media Analytics Tools
Measuring Engagement and Performance on Different Platforms
Case Studies in Social Media Analytics
Session 5 (2 Hours): SEO and Content Analytics
SEO Tools and Techniques for Analysis
Content Performance Metrics and Analysis
Integrating SEO and Content Strategy with Overall Digital Marketing
Week 3: Advanced Analytics and Data-Driven Strategies (6 Hours)
Session 6 (2 Hours): Email Marketing and CRM Analytics
Analyzing Email Marketing Campaigns
Integrating CRM Data for Enhanced Insights
Personalization and Segmentation Strategies
Session 7 (2 Hours): PPC and Paid Media Analytics
Analyzing and Optimizing PPC Campaigns
Understanding Attribution Models in Paid Media
Budget Allocation and ROI Calculation
Session 8 (2 Hours): Data Visualization and Reporting
Tools for Creating Marketing Dashboards (e.g., Tableau, Power BI)
Visualizing Marketing Data for Insights
Preparing and Presenting Marketing Reports
Week 4: Practical Application and Capstone Project (4 Hours)
Session 9 (2 Hours): Integrating Digital Marketing Analytics into Business Strategy
Developing Data-Driven Marketing Strategies
Case Studies of Successful Digital Marketing Campaigns
Discussion on Ethical Considerations in Digital Marketing
Session 10 (2 Hours): Capstone Project and Course Wrap-Up
Group Project: Creating a Digital Marketing Analytics Strategy for a Real-World Business Scenario
Project Presentations and Feedback
Course Summary and Future Learning Pathways
This course would ideally blend theoretical instruction with practical, hands-on exercises using real-life case studies and analytics tools. The capstone project should be a comprehensive exercise where students apply analytics concepts to develop a digital marketing strategy for a business case, providing practical experience in utilizing analytics for strategic decision-making. This structure ensures that MBA students gain not only technical skills in digital marketing analytics but also understand how to apply these insights in a broader business context.