Most popular
-50%
Beginner
Machine Learning and Artificial Intelligence in Business – Driving Business Innovation with AI and Machine Learning
₹1,499.00₹3,000.00
Machine Learning and Artificial Intelligence in Business – Driving Business Innovation with AI and Machine Learning
What you'll learn
Week 1: Introduction to AI and Machine Learning (4 Hours)
Session 1 (2 Hours): Introduction to AI and Machine Learning in Business
Overview of AI and Machine Learning
Historical Context and Evolution of AI
Key Concepts and Terminology
Session 2 (2 Hours): Understanding Machine Learning Models
Types of Machine Learning: Supervised, Unsupervised, Reinforcement
Introduction to Algorithms and Model Selection
Basic Tools and Software Overview (e.g., Python, TensorFlow)
Week 2: Data Management and Preprocessing (6 Hours)
Session 3 (2 Hours): Data Collection and Management
Understanding Data Types and Sources
Data Collection and Storage Strategies
Ethics and Privacy in Data Handling
Session 4 (2 Hours): Data Preprocessing and Feature Engineering
Data Cleaning and Transformation
Feature Selection and Engineering Techniques
Introduction to Data Visualization Tools
Session 5 (2 Hours): Exploratory Data Analysis (EDA)
Techniques for EDA
Identifying Patterns and Anomalies in Data
Using EDA Tools and Libraries
Week 3: Building and Evaluating Models (6 Hours)
Session 6 (2 Hours): Supervised Learning Techniques
Introduction to Regression and Classification Models
Building and Training Models
Case Studies in Business Applications
Session 7 (2 Hours): Unsupervised Learning and Clustering
Overview of Clustering Techniques
Dimensionality Reduction Methods
Practical Applications in Market Segmentation
Session 8 (2 Hours): Model Evaluation and Tuning
Techniques for Evaluating Model Performance
Overfitting, Underfitting, and Model Tuning
Cross-Validation and Hyperparameter Tuning
Week 4: Advanced Topics and Business Applications (4 Hours)
Session 9 (2 Hours): Advanced Topics in AI and ML
Introduction to Neural Networks and Deep Learning
AI in Natural Language Processing and Computer Vision
Emerging Trends and Future of AI in Business
Session 10 (2 Hours): Strategic Implementation and Capstone Project
Strategies for Implementing AI and ML in Business
Ethical Considerations and AI Governance
Capstone Project Presentation and Course Wrap-Up
The course would ideally blend lectures with hands-on exercises, case studies, and project work. The capstone project should involve applying AI and ML concepts to a real-world business problem, encouraging students to think critically about the strategic application of these technologies in a corporate setting. This structure ensures that MBA students gain not only the technical knowledge of AI and ML but also understand how to leverage these technologies for strategic advantage in various business sectors.
Featured
-54%
Mastering the Art of Effective Presentations
What you'll learn
Understand the key principles of effective presentation design and delivery.
Develop skills to structure and organize presentation content coherently.
Learn techniques for designing visually appealing and impactful slides.
Enhance verbal and non-verbal communication skills for effective delivery.
Manage public speaking anxiety and build confidence in presenting.
Master the art of engaging and interacting with diverse audiences.
Adapt presentation styles to suit different cultural contexts and settings.
Utilize various tools and technologies to enhance presentation effectiveness.
Receive and provide constructive feedback to refine presentation skills.
Apply learned presentation skills in real-life scenarios and case studies.
-52%
Beginner
Customer Relationship Management (CRM) Systems – Optimizing Engagement: Mastering CRM Dynamics
₹1,449.00₹3,000.00
Customer Relationship Management (CRM) Systems – Optimizing Engagement: Mastering CRM Dynamics
What you'll learn
Week 1: Introduction to CRM and Its Strategic Importance (4 Hours)
Session 1 (2 Hours): Fundamentals of CRM
Introduction to Customer Relationship Management
Evolution of CRM and Its Role in Modern Businesses
Overview of CRM Systems and Technologies
Session 2 (2 Hours): Strategic Importance of CRM
Aligning CRM with Business Strategy
Understanding the Customer Journey
Role of CRM in Customer Retention and Acquisition
Week 2: CRM Systems and Their Functionalities (6 Hours)
Session 3 (2 Hours): Overview of Popular CRM Systems
Comparison of Different CRM Systems (e.g., Salesforce, Microsoft Dynamics, Zoho)
Cloud-based CRM vs. On-premise Solutions
Integration with Other Business Systems
Session 4 (2 Hours): Key Functionalities of CRM Systems
Sales Force Automation
Customer Service and Support
Marketing Automation within CRM
Session 5 (2 Hours): Data Management in CRM
Managing and Segmenting Customer Data
Data Analytics and Reporting Features
Ensuring Data Privacy and Compliance
Week 3: Implementing and Optimizing CRM Systems (6 Hours)
Session 6 (2 Hours): CRM Implementation Strategies
Best Practices for CRM Implementation
Overcoming Common Implementation Challenges
Case Studies of Successful CRM Implementations
Session 7 (2 Hours): User Adoption and Training
Strategies for Ensuring User Adoption and Engagement
Training Employees on CRM Usage
Measuring CRM Adoption Success
Session 8 (2 Hours): CRM System Optimization and Advanced Features
Ongoing Management and Maintenance of CRM Systems
Exploring Advanced Features (AI, IoT Integration, etc.)
Future Trends in CRM Technology
Week 4: Practical Applications and Capstone Project (4 Hours)
Session 9 (2 Hours): Integrating CRM into Business Processes
Developing Customer-Centric Strategies Using CRM
CRM in Customer Analytics and Insight Generation
Ethical Considerations in Customer Data Handling
Session 10 (2 Hours): Capstone Project and Course Wrap-Up
Group Project: Developing a CRM Strategy for a Real or Simulated Business Scenario
Presentation of Projects and Feedback
Course Summary and Pathways for Further Learning
The course should include a mix of lectures, case studies, and practical exercises, possibly using a CRM software simulation or sandbox environment. The capstone project in the final week would allow students to apply the concepts and techniques learned to a realistic business scenario, reinforcing their understanding and practical skills in CRM systems. This structure ensures that MBA students gain not only a technical understanding of CRM systems but also learn how to strategically apply these tools to enhance customer relationships and drive business success.
-57%
Looker Data Analytics – Unveiling Insights with Looker Data Analytics
What you'll learn
Week 1: Introduction to Looker and Basic Concepts (4 Hours)
Session 1 (2 Hours): Overview of Looker and Business Intelligence
Introduction to Business Intelligence and Looker's Role
Navigating the Looker Interface
Overview of Key Features and Functionalities
Session 2 (2 Hours): Connecting Data and Basic Reporting
Setting Up and Connecting Data Sources
Creating Basic Reports (Looks) and Exploring Data
Introduction to LookML (Looker Modeling Language)
Week 2: Advanced Reporting and Visualization (6 Hours)
Session 3 (2 Hours): Advanced Reporting Techniques
Building More Complex Reports and Dashboards
Exploring Advanced Visualization Options
Utilizing Filters, Parameters, and Derived Tables
Session 4 (2 Hours): Interactive Dashboards and Data Exploration
Designing Interactive and Dynamic Dashboards
Best Practices in Dashboard Layout and User Experience
Analyzing Data Trends and Patterns
Session 5 (2 Hours): Data Exploration and Analytics
Drill-Downs and Detailed Data Analysis
Utilizing Looker for Business Analytics
Sharing Insights and Collaborative Features
Week 3: LookML and Data Modeling (6 Hours)
Session 6 (2 Hours): Introduction to LookML
Basics of LookML and its Role in Looker
Creating and Managing LookML Models
Defining Dimensions, Measures, and Views
Session 7 (2 Hours): Advanced LookML Features
Advanced Model and View Development
Utilizing LookML for Complex Data Relationships
Implementing Data Governance in Looker
Session 8 (2 Hours): Hands-on LookML Workshop
Practical Exercise in Building and Optimizing a LookML Model
Troubleshooting Common Issues in LookML
Best Practices for Scalable and Maintainable Models
Week 4: Business Application and Capstone Project (4 Hours)
Session 9 (2 Hours): Looker in the Business Context
Case Studies: Real-world Applications of Looker in Various Industries
Strategic Decision-Making with Looker
Discussing Ethical Considerations in Data Analysis
Session 10 (2 Hours): Capstone Project and Course Wrap-Up
Developing a Comprehensive Business Analytics Project using Looker
Presentation and Critique of Capstone Projects
Course Summary and Future Learning Pathways
The course should be a mix of lectures, demonstrations, hands-on exercises, and case studies. The capstone project in the final week would allow students to apply their learning to develop a full-scale business analytics project, ensuring they understand not only how to use Looker but also how to apply it strategically in a business context.
Featured
-56%
Cultivating Visionary Leadership: A Strategic Approach
What you'll learn
Understand the core principles and characteristics of visionary leadership.
Develop the ability to think strategically and envision future possibilities.
Enhance emotional intelligence to connect, inspire, and lead teams effectively.
Learn to communicate visions and strategies compellingly.
Foster innovation and creativity within teams and organizations.
Understand the ethical responsibilities and challenges associated with visionary leadership.
Build resilience and adaptability to lead through change and uncertainty.
Develop skills to align and motivate diverse teams towards common visions and goals.
Learn from real-world case studies of successful visionary leaders.
Apply visionary leadership principles in practical, real-life scenarios through exercises and discussions.
-59%
Beginner
Apache Spark for Big Data Analysis – Unleashing Insights: Spark in Big Data Analytics
₹1,449.00₹3,500.00
Apache Spark for Big Data Analysis – Unleashing Insights: Spark in Big Data Analytics
What you'll learn
Week 1: Introduction to Big Data and Apache Spark (4 Hours)
Session 1 (2 Hours): Fundamentals of Big Data
Introduction to Big Data: Concepts and Relevance in Business
Big Data Challenges and Technologies
Overview of the Big Data Ecosystem
Session 2 (2 Hours): Getting Started with Apache Spark
Introduction to Apache Spark and its Advantages
Understanding Spark’s Architecture and Components
Setting Up a Spark Environment (e.g., Databricks or Local Setup)
Week 2: Spark RDDs and DataFrames (6 Hours)
Session 3 (2 Hours): Working with RDDs (Resilient Distributed Datasets)
Creating and Manipulating RDDs
Performing Transformations and Actions on RDDs
Understanding Partitioning and Persistence in RDDs
Session 4 (2 Hours): Introduction to Spark DataFrames
Creating and Using DataFrames in Spark
DataFrame Operations and SQL Queries
Data Aggregation and Grouping Operations
Session 5 (2 Hours): Advanced DataFrame Operations
Advanced Data Processing Techniques
Working with Various Data Formats (JSON, CSV, Parquet)
Data Importing/Exporting Techniques in Spark
Week 3: Spark for Advanced Analytics (6 Hours)
Session 6 (2 Hours): Spark SQL for Big Data Analysis
Using Spark SQL for Complex Queries
Integrating SQL and DataFrame API
Exploring Spark SQL’s Optimization Techniques
Session 7 (2 Hours): Machine Learning with Spark MLlib
Introduction to Spark’s Machine Learning Library (MLlib)
Building Basic Machine Learning Models in Spark
Evaluating Model Performance
Session 8 (2 Hours): Streaming Data Analysis with Spark Streaming
Basics of Real-Time Data Processing
Building Streaming Applications in Spark
Integrating Streaming Data with Static Data Sources
Week 4: Business Applications and Capstone Project (4 Hours)
Session 9 (2 Hours): Applying Spark in Business Contexts
Case Studies: Real-World Applications of Spark in Business
Best Practices for Leveraging Spark for Business Insights
Discussing Ethical and Privacy Considerations in Big Data
Session 10 (2 Hours): Capstone Project and Course Wrap-Up
Developing a Comprehensive Big Data Project Using Apache Spark
Presentation of Capstone Projects
Course Summary and Pathways for Further Learning
The course should be a mix of theoretical explanations, demonstrations, and hands-on exercises, ideally using a cloud-based Spark environment like Databricks for practical sessions. The capstone project in the final week would allow students to apply their learning to a real-world business dataset, ensuring they understand how to use Apache Spark for big data analysis effectively in a business context.
-57%
IBM Cognos Analytics – Empowering Decisions with IBM Cognos Analytics
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.
Featured
-44%
Advanced Statistical Analysis for Business Success
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.
-59%
Beginner
Advanced Excel for Business -Excel Mastery: Business Insights & Efficiency
₹1,449.00₹3,500.00
Advanced Excel for Business -Excel Mastery: Business Insights & Efficiency
What you'll learn
Week 1: Excel Fundamentals and Data Management (4 Hours)
Session 1 (2 Hours): Review of Excel Basics and Introduction to Advanced Features
Quick Refresher on Basic Excel Functions and Formulas
Introduction to Advanced Excel Features (e.g., PivotTables, Advanced Charting)
Session 2 (2 Hours): Data Management and Analysis in Excel
Data Import and Cleaning Techniques
Data Sorting, Filtering, and Conditional Formatting
Introduction to Data Validation and Data Protection Methods
Week 2: Advanced Formulas and Functions (6 Hours)
Session 3 (2 Hours): Mastering Complex Formulas
Advanced Use of Logical Functions (IF, AND, OR)
Lookup Functions (VLOOKUP, HLOOKUP, INDEX, MATCH)
Date and Time Functions
Session 4 (2 Hours): Dynamic Formulas and Array Functions
Understanding and Implementing Array Formulas
Introduction to Dynamic Named Ranges
Using TEXT and Other String Functions
Session 5 (2 Hours): Practical Exercise on Business Data Analysis
Hands-on Case Study using Complex Formulas
Analyzing Business Data with Advanced Excel Functions
Week 3: PivotTables, Data Visualization, and Introduction to Dashboards (6 Hours)
Session 6 (2 Hours): Mastering PivotTables and PivotCharts
In-depth Exploration of PivotTables
Advanced PivotTable Techniques (Grouping, Slicers, Calculated Fields)
Creating and Customizing PivotCharts
Session 7 (2 Hours): Advanced Data Visualization Techniques
Advanced Charting and Graphing Techniques
Creating Interactive Excel Dashboards
Visualization Best Practices
Session 8 (2 Hours): Real-world Business Analysis
Applying PivotTables and Visualization Techniques to Business Case Studies
Interactive Dashboard Creation Based on Real Data
Week 4: Financial Modeling and Advanced Applications (4 Hours)
Session 9 (2 Hours): Introduction to Financial Modeling in Excel
Basic Principles of Financial Modeling
Building Financial Models (Cash Flow, Budgeting, Forecasting)
Sensitivity Analysis using Data Tables and Scenario Manager
Session 10 (2 Hours): Capstone Project and Course Wrap-up
Applying Advanced Excel Skills to a Comprehensive Business Case
Group Project Presentations and Feedback
Course Review and Discussion on Further Learning Paths
Each session should include a mix of lectures, demonstrations, and hands-on exercises. The emphasis should be on practical application, using real-world business scenarios and case studies to ensure students understand how to apply their Excel skills in a business context. The final capstone project should encourage students to use all the skills they've learned to solve a complex business problem. This structure will ensure that MBA students gain not only technical proficiency in advanced Excel features but also the ability to apply these skills strategically in their professional roles.
-50%
Leadership and Team Management
What you'll learn
Module 1: Introduction to Leadership (2 hours)
Session 1.1: Understanding Leadership (1 hour)
Definition of leadership
Leadership vs. Management
Leadership theories and styles
Session 1.2: Self-Assessment and Leadership (1 hour)
Leadership self-assessment tools
Identifying personal leadership style
Strengths and areas for improvement
Module 2: Core Leadership Skills (4 hours)
Session 2.1: Effective Communication and Active Listening (2 hours)
Communication in leadership
Active listening techniques
Giving and receiving feedback
Session 2.2: Emotional Intelligence in Leadership (2 hours)
Understanding emotional intelligence
EI for leadership effectiveness
Case studies and role plays
Module 3: Team Dynamics and Management (4 hours)
Session 3.1: Building and Managing Teams (2 hours)
Stages of team development
Team composition and diversity
Creating a positive team culture
Session 3.2: Conflict Resolution and Problem Solving (2 hours)
Conflict types and resolution strategies
Problem-solving in teams
Case simulations
Module 4: Strategic Leadership and Decision Making (4 hours)
Session 4.1: Vision, Strategy, and Execution (2 hours)
Developing a strategic vision
Aligning team with organizational goals
Execution of strategies
Session 4.2: Decision Making and Critical Thinking (2 hours)
Decision-making processes
Critical thinking skills
Decision-making exercises
Module 5: Motivation and Influencing Others (3 hours)
Session 5.1: Motivating Teams and Individuals (1.5 hours)
Motivation theories
Motivating diverse teams
Case studies
Session 5.2: Influence and Persuasion Skills (1.5 hours)
Influence without authority
Persuasion techniques
Influencing exercises
Module 6: Practical Leadership Challenges (3 hours)
Session 6.1: Leading Change and Innovation (1.5 hours)
Change management
Leading innovative teams
Group discussion on change scenarios
Session 6.2: Ethical Leadership and Social Responsibility (1.5 hours)
Ethics in leadership
Corporate social responsibility
Ethical dilemma case studies
Additional Elements:
Guest Speakers: Invite industry leaders to share real-world insights (integrated within relevant modules).
Interactive Activities: Group discussions, role-plays, and leadership simulations.
Assignments: Reflection papers, case study analysis, and leadership project proposals.
Feedback and Assessment: Peer reviews, instructor feedback, and a final leadership strategy project.
This structure aims to provide a comprehensive understanding of leadership and team management, combining theoretical knowledge with practical application, relevant for MBA students.
Trending
-59%
Beginner
Advanced Excel for Business -Excel Mastery: Business Insights & Efficiency
₹1,449.00₹3,500.00
Advanced Excel for Business -Excel Mastery: Business Insights & Efficiency
What you'll learn
Week 1: Excel Fundamentals and Data Management (4 Hours)
Session 1 (2 Hours): Review of Excel Basics and Introduction to Advanced Features
Quick Refresher on Basic Excel Functions and Formulas
Introduction to Advanced Excel Features (e.g., PivotTables, Advanced Charting)
Session 2 (2 Hours): Data Management and Analysis in Excel
Data Import and Cleaning Techniques
Data Sorting, Filtering, and Conditional Formatting
Introduction to Data Validation and Data Protection Methods
Week 2: Advanced Formulas and Functions (6 Hours)
Session 3 (2 Hours): Mastering Complex Formulas
Advanced Use of Logical Functions (IF, AND, OR)
Lookup Functions (VLOOKUP, HLOOKUP, INDEX, MATCH)
Date and Time Functions
Session 4 (2 Hours): Dynamic Formulas and Array Functions
Understanding and Implementing Array Formulas
Introduction to Dynamic Named Ranges
Using TEXT and Other String Functions
Session 5 (2 Hours): Practical Exercise on Business Data Analysis
Hands-on Case Study using Complex Formulas
Analyzing Business Data with Advanced Excel Functions
Week 3: PivotTables, Data Visualization, and Introduction to Dashboards (6 Hours)
Session 6 (2 Hours): Mastering PivotTables and PivotCharts
In-depth Exploration of PivotTables
Advanced PivotTable Techniques (Grouping, Slicers, Calculated Fields)
Creating and Customizing PivotCharts
Session 7 (2 Hours): Advanced Data Visualization Techniques
Advanced Charting and Graphing Techniques
Creating Interactive Excel Dashboards
Visualization Best Practices
Session 8 (2 Hours): Real-world Business Analysis
Applying PivotTables and Visualization Techniques to Business Case Studies
Interactive Dashboard Creation Based on Real Data
Week 4: Financial Modeling and Advanced Applications (4 Hours)
Session 9 (2 Hours): Introduction to Financial Modeling in Excel
Basic Principles of Financial Modeling
Building Financial Models (Cash Flow, Budgeting, Forecasting)
Sensitivity Analysis using Data Tables and Scenario Manager
Session 10 (2 Hours): Capstone Project and Course Wrap-up
Applying Advanced Excel Skills to a Comprehensive Business Case
Group Project Presentations and Feedback
Course Review and Discussion on Further Learning Paths
Each session should include a mix of lectures, demonstrations, and hands-on exercises. The emphasis should be on practical application, using real-world business scenarios and case studies to ensure students understand how to apply their Excel skills in a business context. The final capstone project should encourage students to use all the skills they've learned to solve a complex business problem. This structure will ensure that MBA students gain not only technical proficiency in advanced Excel features but also the ability to apply these skills strategically in their professional roles.
-50%
Leadership and Team Management
What you'll learn
Module 1: Introduction to Leadership (2 hours)
Session 1.1: Understanding Leadership (1 hour)
Definition of leadership
Leadership vs. Management
Leadership theories and styles
Session 1.2: Self-Assessment and Leadership (1 hour)
Leadership self-assessment tools
Identifying personal leadership style
Strengths and areas for improvement
Module 2: Core Leadership Skills (4 hours)
Session 2.1: Effective Communication and Active Listening (2 hours)
Communication in leadership
Active listening techniques
Giving and receiving feedback
Session 2.2: Emotional Intelligence in Leadership (2 hours)
Understanding emotional intelligence
EI for leadership effectiveness
Case studies and role plays
Module 3: Team Dynamics and Management (4 hours)
Session 3.1: Building and Managing Teams (2 hours)
Stages of team development
Team composition and diversity
Creating a positive team culture
Session 3.2: Conflict Resolution and Problem Solving (2 hours)
Conflict types and resolution strategies
Problem-solving in teams
Case simulations
Module 4: Strategic Leadership and Decision Making (4 hours)
Session 4.1: Vision, Strategy, and Execution (2 hours)
Developing a strategic vision
Aligning team with organizational goals
Execution of strategies
Session 4.2: Decision Making and Critical Thinking (2 hours)
Decision-making processes
Critical thinking skills
Decision-making exercises
Module 5: Motivation and Influencing Others (3 hours)
Session 5.1: Motivating Teams and Individuals (1.5 hours)
Motivation theories
Motivating diverse teams
Case studies
Session 5.2: Influence and Persuasion Skills (1.5 hours)
Influence without authority
Persuasion techniques
Influencing exercises
Module 6: Practical Leadership Challenges (3 hours)
Session 6.1: Leading Change and Innovation (1.5 hours)
Change management
Leading innovative teams
Group discussion on change scenarios
Session 6.2: Ethical Leadership and Social Responsibility (1.5 hours)
Ethics in leadership
Corporate social responsibility
Ethical dilemma case studies
Additional Elements:
Guest Speakers: Invite industry leaders to share real-world insights (integrated within relevant modules).
Interactive Activities: Group discussions, role-plays, and leadership simulations.
Assignments: Reflection papers, case study analysis, and leadership project proposals.
Feedback and Assessment: Peer reviews, instructor feedback, and a final leadership strategy project.
This structure aims to provide a comprehensive understanding of leadership and team management, combining theoretical knowledge with practical application, relevant for MBA students.
Featured
-45%
Mastery in Financial Modeling
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.
-63%
Tableau for Business Intelligence – Business Intelligence with Tableau
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.
-60%
Communication Skills
What you'll learn
Module 1: Fundamentals of Communication (3 hours)
Session 1.1: Introduction to Communication Skills (1.5 hours)
Overview of communication in business
Verbal, non-verbal, and written communication
Barriers to effective communication
Session 1.2: Non-Verbal Communication and Body Language (1.5 hours)
Understanding body language
Role of non-verbal cues in communication
Practical exercises
Module 2: Verbal Communication Skills (4 hours)
Session 2.1: Public Speaking and Presentation Skills (2 hours)
Techniques for effective public speaking
Structuring and delivering presentations
Interactive presentation practice
Session 2.2: Active Listening and Feedback (2 hours)
Principles of active listening
Giving and receiving constructive feedback
Listening exercises and role-play
Module 3: Written Communication Skills (4 hours)
Session 3.1: Business Writing Basics (2 hours)
Professional email and report writing
Clarity, tone, and conciseness in business writing
Writing exercises
Session 3.2: Advanced Business Documents (2 hours)
Crafting proposals and executive summaries
Persuasive writing techniques
Case studies and document analysis
Module 4: Interpersonal Communication (3 hours)
Session 4.1: Effective Interpersonal Skills (1.5 hours)
Building rapport and trust
Cultural sensitivity in communication
Role-playing scenarios
Session 4.2: Conflict Resolution and Difficult Conversations (1.5 hours)
Strategies for conflict resolution
Navigating difficult conversations
Group activities and discussions
Module 5: Communication in Teams and Leadership (3 hours)
Session 5.1: Team Communication Dynamics (1.5 hours)
Communication in teams and groups
Facilitating effective team meetings
Team communication exercises
Session 5.2: Communication for Leaders (1.5 hours)
Leadership communication styles
Influence and persuasion
Case studies and leadership scenarios
Module 6: Communication in a Digital World (3 hours)
Session 6.1: Digital Communication Tools and Etiquette (1.5 hours)
Email, social media, and virtual meeting etiquette
Leveraging digital tools for effective communication
Practical applications and simulations
Session 6.2: Managing Remote and Cross-Cultural Teams (1.5 hours)
Challenges of remote communication
Cross-cultural communication strategies
Interactive group exercises
Additional Elements:
Guest Speakers: Industry professionals discussing real-world communication challenges and solutions.
Interactive Activities: Role plays, group discussions, and communication games.
Assignments: Creating presentations, writing business documents, and conducting meeting simulations.
Feedback and Assessment: Peer evaluations, instructor feedback, and a final communication project or presentation.
This structure is designed to provide MBA students with a comprehensive understanding of communication skills, balancing theory with practical, real-world applications.
-50%
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
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.
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Supply Chain Analytics – Unlocking Efficiency with Supply Chain Analytics
₹1,449.00₹4,000.00
Supply Chain Analytics – Unlocking Efficiency with Supply Chain Analytics
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.
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Negotiation and Conflict Resolution
What you'll learn
Module 1: Fundamentals of Negotiation (4 hours)
Session 1.1: Introduction to Negotiation (2 hours)
Basics of negotiation
Different types of negotiation (distributive vs. integrative)
Key negotiation principles and theories
Session 1.2: Preparing for Negotiation (2 hours)
Setting objectives and limits
Understanding the other party’s interests
Role-play exercise: preparation for a negotiation scenario
Module 2: Advanced Negotiation Techniques (4 hours)
Session 2.1: Strategies and Tactics in Negotiation (2 hours)
Advanced negotiation strategies
Psychological aspects of negotiation
Interactive negotiation exercises
Session 2.2: Negotiation in Different Contexts (2 hours)
Negotiation in various business contexts
Cultural dimensions in negotiation
Case studies analysis
Module 3: Conflict Resolution Fundamentals (4 hours)
Session 3.1: Understanding Conflict (2 hours)
Types and sources of conflict
Conflict styles and assessment
Group activity: Identifying personal conflict style
Session 3.2: Communication Skills for Conflict Resolution (2 hours)
Effective communication in conflict situations
Active listening and empathy in conflict resolution
Role-play exercise: Conflict resolution scenarios
Module 4: Advanced Conflict Resolution Skills (4 hours)
Session 4.1: Mediation and Third-Party Interventions (2 hours)
The role of a mediator
Techniques for successful mediation
Simulation exercise: Mediating a business conflict
Session 4.2: Resolving Complex Conflicts (2 hours)
Dealing with complex and chronic conflicts
Negotiation and conflict resolution in teams
Case study: Complex conflict resolution
Module 5: Practical Applications and Simulations (4 hours)
Session 5.1: Integrating Negotiation and Conflict Resolution (2 hours)
Integrating skills in real-world scenarios
Strategy development for complex negotiations
Group simulation: Full negotiation and conflict resolution exercise
Session 5.2: Reflection and Personal Development (2 hours)
Reflecting on personal negotiation and conflict resolution style
Developing a personal action plan for improvement
Group discussion and feedback session
Additional Elements:
Guest Speakers: Inviting experienced negotiators and mediators to share insights and experiences.
Interactive Activities: Role-plays, simulations, and case study analyses for hands-on learning.
Assignments: Reflective essays, negotiation planning documents, and conflict resolution reports.
Feedback and Assessment: Peer reviews, instructor feedback, and a final negotiation or conflict resolution project.
This course structure aims to equip MBA students with both the theoretical knowledge and practical skills necessary for effective negotiation and conflict resolution in various business contexts.
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Google Data Studio – Insights at a Glance with Google Data Studio
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.
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SAS Analytics Training – Mastering Analytics with SAS
What you'll learn
Week 1: Introduction to SAS and Basic Data Handling (4 Hours)
Session 1 (2 Hours): Introduction to SAS
Overview of SAS Software: History and Industry Applications
Navigating the SAS Environment (SAS Studio, SAS Enterprise Guide)
Basic Syntax and Commands in SAS
Session 2 (2 Hours): Data Management Basics in SAS
Importing and Exporting Data in SAS
Data Manipulation Techniques (SORT, SET, MERGE)
Introduction to SAS Libraries and Data Sets
Week 2: Data Analysis and Reporting in SAS (6 Hours)
Session 3 (2 Hours): Descriptive Statistics and Data Summarization
Generating Descriptive Statistics in SAS (MEANS, FREQ, SUMMARY)
Data Summarization Techniques
Creating Basic Reports and Outputs
Session 4 (2 Hours): Data Visualization in SAS
Introduction to SAS Graphical Procedures (SGPLOT, SGPANEL)
Creating Charts and Graphs for Data Presentation
Customizing Visual Outputs in SAS
Session 5 (2 Hours): Advanced Data Analysis Techniques
Conducting Correlation and Regression Analysis
ANOVA and Other Statistical Tests
Introduction to Predictive Modeling in SAS
Week 3: Advanced SAS Programming and Analytics (6 Hours)
Session 6 (2 Hours): Advanced Data Manipulation
Advanced SAS Functions and Procedures
Data Cleaning and Preprocessing Techniques
Working with Dates and Times in SAS
Session 7 (2 Hours): SQL Programming in SAS
Introduction to PROC SQL for Data Querying
Combining SAS Datasets with SQL Joins
Advanced SQL Queries in SAS
Session 8 (2 Hours): Macro Programming in SAS
Basics of SAS Macro Language
Automating Tasks with Macros
Building and Using Macro Variables
Week 4: Business Applications and Capstone Project (4 Hours)
Session 9 (2 Hours): SAS in Business Contexts
Case Studies: SAS Applications in Finance, Marketing, and Operations
Discussing Ethical Implications of Data Analytics
Integrating SAS Analysis into Business Decision-Making
Session 10 (2 Hours): Capstone Project and Course Wrap-Up
Applying SAS Skills to a Real-World Business Problem
Presenting Capstone Project Findings
Course Summary and Recommendations for Continued Learning
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