Most popular
-59%
Time Management and Productivity
What you'll learn
Module 1: Fundamentals of Time Management (4 hours)
Session 1.1: Understanding Time Management (2 hours)
Introduction to time management and its importance
Self-assessment: Personal time management evaluation
Principles of effective time management
Session 1.2: Setting Goals and Priorities (2 hours)
Techniques for setting SMART goals
Prioritizing tasks using methods like Eisenhower Matrix
Exercise: Setting personal and professional goals
Module 2: Planning and Organizing (4 hours)
Session 2.1: Strategic Planning and Scheduling (2 hours)
Creating effective plans and schedules
Tools for planning (calendars, planners, apps)
Activity: Developing a weekly plan
Session 2.2: Organizational Skills for Productivity (2 hours)
Techniques for organizing work and personal spaces
Systems for managing paperwork and digital files
Workshop: Organizational system setup
Module 3: Overcoming Time Management Challenges (4 hours)
Session 3.1: Managing Distractions and Interruptions (2 hours)
Identifying and dealing with common distractions
Strategies for managing interruptions
Group discussion: Sharing experiences and strategies
Session 3.2: Dealing with Procrastination (2 hours)
Understanding the psychology of procrastination
Strategies to overcome procrastination
Exercise: Implementing anti-procrastination techniques
Module 4: Advanced Time Management Techniques (4 hours)
Session 4.1: Leveraging Technology for Time Management (2 hours)
Exploring time management and productivity tools
Integrating technology effectively into time management strategies
Hands-on session: Using productivity apps
Session 4.2: Time Management for Teams (2 hours)
Collaborative time management techniques
Managing team deadlines and priorities
Role-play: Leading a time-sensitive project
Module 5: Building and Sustaining Productivity Habits (4 hours)
Session 5.1: Developing Personal Productivity Habits (2 hours)
Building and maintaining productive habits
Techniques for continuous self-improvement
Workshop: Habit formation and tracking
Session 5.2: Integrating Time Management into Daily Life (2 hours)
Applying time management skills in real-life scenarios
Work-life balance and maintaining productivity
Group activity: Developing a personal time management plan
Additional Elements:
Guest Speakers: Time management experts or professionals sharing personal experiences and tips.
Interactive Activities: Workshops, group discussions, and role-plays.
Assignments: Time audits, goal setting exercises, and productivity plan development.
Feedback and Assessment: Peer reviews, instructor feedback, and a final presentation or report on personal time management strategies.
This course aims to provide MBA students with the skills and techniques necessary to effectively manage their time, increase productivity, and maintain a healthy work-life balance.
-63%
Beginner
Enterprise Resource Planning (ERP) Systems – Integrating Efficiency with ERP System Expertise
₹1,499.00₹4,000.00
Enterprise Resource Planning (ERP) Systems – Integrating Efficiency with ERP System Expertise
What you'll learn
Week 1: Introduction to ERP Systems (4 Hours)
Session 1 (2 Hours): Fundamentals of ERP Systems
Overview of ERP Systems and Their Evolution
Key Components and Architecture of ERP Systems
Role and Benefits of ERP in Business Integration
Session 2 (2 Hours): Types of ERP Systems and Vendors
Overview of Major ERP Systems (e.g., SAP, Oracle, Microsoft Dynamics)
Cloud-based vs. On-premises ERP Solutions
Selecting an ERP System: Factors to Consider
Week 2: ERP Implementation and Modules (6 Hours)
Session 3 (2 Hours): ERP Implementation Process
Steps in ERP Implementation
Project Planning and Management for ERP Implementation
Risk Management in ERP Projects
Session 4 (2 Hours): Functional Modules of ERP Systems
In-depth Look at Key ERP Modules (Finance, HR, Supply Chain, etc.)
Integration of ERP Modules in Business Processes
Hands-on Exercise with a Simulated ERP Environment
Session 5 (2 Hours): ERP System Customization and Configuration
Customization vs. Configuration in ERP Systems
Best Practices in ERP System Customization
Case Studies on Successful ERP Customizations
Week 3: ERP and Business Process Integration (6 Hours)
Session 6 (2 Hours): Business Process Reengineering and ERP
Understanding Business Process Reengineering (BPR)
Aligning ERP Systems with Business Processes
Impact of ERP on Organizational Structure and Culture
Session 7 (2 Hours): ERP System Upgrades and Maintenance
Managing ERP System Upgrades
Routine Maintenance and Support for ERP Systems
Training and User Support Strategies
Session 8 (2 Hours): Data Management and Analytics in ERP
Role of ERP in Data Management and Business Intelligence
Leveraging ERP Data for Analytics and Decision Making
Case Study on ERP-Enabled Business Analytics
Week 4: Advanced Topics and Emerging Trends (4 Hours)
Session 9 (2 Hours): Emerging Trends in ERP
ERP in the Era of Big Data and IoT
Mobile ERP and Cloud Computing Trends
Future Outlook of ERP Systems
Session 10 (2 Hours): Capstone Project and Course Wrap-Up
Group Project on ERP Implementation Strategy
Presentations and Discussions on ERP Case Studies
Course Summary and Discussion on Further Learning Opportunities
The course would ideally blend theoretical insights with practical, hands-on exercises, possibly using an ERP simulation tool or case studies. The final capstone project should involve applying ERP concepts to a real-world business scenario, encouraging students to think strategically about the use of ERP systems in an organizational context. This structure ensures that MBA students gain a comprehensive understanding of ERP systems, their implementation challenges, and their strategic value in business operations.
-50%
Beginner
Power BI tailored for MBA students – Power BI for Future Business Leaders
₹1,499.00₹3,000.00
Power BI tailored for MBA students – Power BI for Future Business Leaders
What you'll learn
Week 1: Introduction and Basics of Power BI (4 Hours)
Session 1 (2 Hours): Introduction to Business Intelligence and Power BI
Overview of Business Intelligence (BI)
Introduction to Power BI and its position in the market
Installing Power BI Desktop
Basic Navigation and Interface Overview
Session 2 (2 Hours): Getting Started with Power BI
Connecting to Data Sources
Basic Data Import and Transformation using Power Query
Introduction to Data Modeling
Week 2: Data Analysis and Visualization (6 Hours)
Session 3 (2 Hours): Deep Dive into Data Analysis
Advanced-Data Transformation Techniques
Understanding Data Types and Categories
Introduction to DAX (Data Analysis Expressions) Basics
Session 4 (2 Hours): Creating Basic Visuals
Exploring Various Visualization Types
Creating and Customizing Charts and Graphs
Using Slicers and Filters for Interactive Reports
Session 5 (2 Hours): Advanced Visualization Techniques
Advanced DAX for Complex Calculations
Creating Interactive Dashboards
Best Practices for Data Visualization
Week 3: Reporting and Dashboarding (6 Hours)
Session 6 (2 Hours): Effective Reporting
Designing Compelling Reports
Storytelling with Data
Incorporating MBA Concepts into Reports (e.g., Financial Ratios, Market Analysis)
Session 7 (2 Hours): Advanced Dashboard Features
Drill-through and Drill-down Capabilities
Conditional Formatting and Data Alerts
Q&A and Natural Language Queries
Session 8 (2 Hours): Publishing and Sharing
Publishing Reports to Power BI Service
Sharing Dashboards and Reports
Collaboration Features in Power BI
Week 4: Integration and Real-World Application (4 Hours)
Session 9 (2 Hours): Integrating Power BI with Other Tools
Integration with Excel and Other Microsoft Services
Connecting Power BI to External Databases
Overview of APIs and Embedded Analytics
Session 10 (2 Hours): Capstone Project and Course Wrap-up
Applying Skills to a Real-World Business Case
Creating a Full Report and Dashboard from Scratch
Course Recap and Further Learning Resources
Each session can include a mix of lectures, hands-on exercises, and real-world case studies to ensure that students not only learn the technical aspects of Power BI but also understand how to apply these skills in a business context. The capstone project in the final session should ideally be a comprehensive task that requires students to apply all the skills they've learned throughout the course.
Featured
-39%
Mastering Hard Skills in the Modern Business World
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.
-63%
Intermediate
Digital Marketing Analytics – Metrics to Strategy: Mastering Online Impact
₹1,500.00₹4,000.00
Digital Marketing Analytics – Metrics to Strategy: Mastering Online Impact
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.
-63%
Beginner
Microsoft Excel Power Query, Power Pivot & DAX – Excel Advanced Tools: Power Query, Pivot & DAX Mastery
₹1,500.00₹4,000.00
Microsoft Excel Power Query, Power Pivot & DAX – Excel Advanced Tools: Power Query, Pivot & DAX Mastery
What you'll learn
Week 1: Introduction to Power Query and Data Manipulation (4 Hours)
Session 1 (2 Hours): Fundamentals of Power Query
Introduction to Power Query and its Business Applications
Navigating the Power Query Interface
Basic Data Importing and Transformation Techniques
Session 2 (2 Hours): Advanced Data Handling in Power Query
Merging and Appending Queries
Working with Different Data Sources (Web, SQL, etc.)
Advanced Data Transformations and Cleaning Techniques
Week 2: Power Pivot and Data Modeling (6 Hours)
Session 3 (2 Hours): Introduction to Power Pivot
Overview of Power Pivot and its Role in Data Analysis
Creating Data Models in Power Pivot
Understanding Relationships and Data Model Optimization
Session 4 (2 Hours): Advanced Data Modeling in Power Pivot
Working with Large Datasets
Creating Complex Relationships and Hierarchies
Introduction to Key Performance Indicators (KPIs) in Power Pivot
Session 5 (2 Hours): PivotTables and PivotCharts with Power Pivot
Building Advanced PivotTables and PivotCharts
Using Slicers and Timelines for Interactive Reports
Combining Power Query and Power Pivot for Analysis
Week 3: Introduction to DAX (Data Analysis Expressions) (6 Hours)
Session 6 (2 Hours): Basics of DAX
Understanding DAX and its Syntax
Basic DAX Functions and Formulas
Creating Calculated Columns and Measures
Session 7 (2 Hours): Advanced DAX Functions
Time Intelligence Functions
Advanced DAX Formulas for Complex Calculations
Understanding Context in DAX (Row vs. Filter Context)
Session 8 (2 Hours): Practical DAX Applications
Applying DAX in Real-World Business Scenarios
Using DAX for Data Analysis and Reporting
Hands-On Exercises and Case Studies
Week 4: Integrating Skills and Capstone Project (4 Hours)
Session 9 (2 Hours): Integrating Power Query, Power Pivot, and DAX
Combining Skills for Comprehensive Data Analysis
Best Practices for Building Scalable and Efficient Excel Models
Addressing Common Business Data Analysis Challenges
Session 10 (2 Hours): Capstone Project and Course Wrap-Up
Applying Learned Skills to a Real-World Business Problem
Group Project Presentation and Review
Course Summary and Pathways for Further Learning
The course should include a mix of theoretical instruction, practical demonstrations, and hands-on exercises. The capstone project in the final week should involve a comprehensive business analysis task, enabling students to apply their newly acquired skills in Power Query, Power Pivot, and DAX to a real-world business scenario. This structure ensures that MBA students not only learn advanced Excel features but also understand how to apply these tools strategically for business analysis and decision-making.
Featured
-40%
Mastering Soft Skills for Business Success
What you'll learn
Understand the importance and impact of soft skills in the business world.
Develop effective communication and interpersonal skills.
Enhance emotional intelligence for better relationship management.
Cultivate a growth mindset for continuous learning and development.
Master time management and organizational skills.
Build resilience and stress management capabilities.
Enhance critical thinking and problem-solving skills.
Foster a culture of empathy and understanding in a leadership role.
Improve negotiation and conflict resolution skills.
Prepare for a successful career by integrating soft skills into your professional life.
-59%
Database Management and SQL – Structured Data Mastery: SQL & Beyond
What you'll learn
Week 1: Introduction to Databases and SQL (4 Hours)
Session 1 (2 Hours): Fundamentals of Database Systems
Overview of Database Management Systems (DBMS)
Types of Databases: Relational vs. Non-Relational
Basic Database Terminology and Concepts
Session 2 (2 Hours): Introduction to SQL
Understanding SQL and its Role in Databases
Basic SQL Syntax and Commands
Setting Up a SQL Environment (e.g., MySQL, PostgreSQL)
Week 2: SQL for Data Retrieval (6 Hours)
Session 3 (2 Hours): Basic SQL Queries
SELECT Statements, WHERE Clauses
Working with Columns and Rows
Sorting and Filtering Data
Session 4 (2 Hours): Advanced Data Retrieval
JOIN Operations: Combining Data from Multiple Tables
Aggregating Data with GROUP BY and HAVING Clauses
Subqueries and Nested Queries
Session 5 (2 Hours): Practical SQL Exercises
Hands-on SQL Query Exercises
Case Studies on Data Retrieval in Business Scenarios
Week 3: Database Design and Data Manipulation (6 Hours)
Session 6 (2 Hours): Database Design Principles
Understanding Data Modeling and Entity-Relationship Diagrams
Normalization and Database Schemas
Creating Tables and Setting Primary/Foreign Keys
Session 7 (2 Hours): SQL for Data Manipulation and Management
INSERT, UPDATE, and DELETE Operations
Data Integrity and Transactions
Basics of Stored Procedures and Functions
Session 8 (2 Hours): Working with Real-World Data
Importing and Exporting Data
SQL for Business Analytics and Reporting
Integrating SQL Queries with Business Intelligence Tools
Week 4: Advanced Topics and Practical Applications (4 Hours)
Session 9 (2 Hours): Advanced SQL and Performance Optimization
Advanced SQL Features (e.g., Window Functions, Indexing)
Query Optimization Techniques
Understanding SQL Execution Plans
Session 10 (2 Hours): Capstone Project and Course Wrap-Up
Applying SQL Skills to a Real Business Case
Group Project Presentations
Course Summary and Further Learning Resources
This course would ideally mix theoretical lectures with practical exercises and case studies. Each session should include hands-on SQL practice, progressively building in complexity. The final capstone project should involve a comprehensive business-related database task, requiring students to apply all the skills learned throughout the course. This structure ensures that MBA students not only understand the technical aspects of SQL and database management but also how to apply these skills in a business context.
-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.
Trending
-60%
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.
-64%
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
-60%
Emotional Intelligence and Interpersonal Skills
What you'll learn
Module 1: Introduction to Emotional Intelligence (EI) (3 hours)
Session 1.1: Understanding EI and Its Importance (1.5 hours)
Introduction to EI concepts
The significance of EI in professional and personal settings
Self-assessment: Identifying personal EI levels
Session 1.2: Components of EI (1.5 hours)
Exploring the five components of EI: self-awareness, self-regulation, motivation, empathy, social skills
Interactive activity: Recognizing EI traits in various scenarios
Module 2: Self-Awareness and Self-Regulation (4 hours)
Session 2.1: Developing Self-Awareness (2 hours)
Techniques for enhancing self-awareness
Understanding and managing emotions
Reflection exercise: Personal emotional analysis
Session 2.2: Mastering Self-Regulation (2 hours)
Strategies for managing impulses and emotional reactions
Stress management techniques
Group exercise: Role-playing challenging situations
Module 3: Empathy and Social Skills (4 hours)
Session 3.1: Cultivating Empathy (2 hours)
Understanding and developing empathy
The role of empathy in effective communication and relationship building
Empathy exercises: Perspective-taking activities
Session 3.2: Enhancing Social Skills (2 hours)
Effective communication and interpersonal interaction
Building rapport and networking skills
Interactive session: Practicing networking in a simulated setting
Module 4: EI in Leadership and Teamwork (4 hours)
Session 4.1: EI in Leadership (2 hours)
The impact of EI on leadership effectiveness
Leadership styles and emotional intelligence
Case study analysis: Leaders with high EI
Session 4.2: EI in Team Dynamics (2 hours)
Promoting a positive team environment using EI
Conflict resolution and collaboration
Team activity: EI in team problem-solving
Module 5: Practical Application and Development (5 hours)
Session 5.1: Personal EI Development Plan (2.5 hours)
Creating a personal plan for developing EI
Setting goals and identifying resources for EI improvement
Workshop: Developing and sharing personal EI plans
Session 5.2: Integrating EI in Professional Scenarios (2.5 hours)
Applying EI skills in business contexts (negotiations, customer relations, etc.)
Role-play scenarios: Implementing EI in complex situations
Group discussion and feedback
Additional Elements:
Guest Speakers: Industry professionals sharing insights on the importance of EI in business.
Interactive Activities: Role-plays, group discussions, and self-reflection exercises.
Assignments: Journaling emotional responses, EI development plans, and case study reports.
Feedback and Assessment: Continuous feedback from peers and instructors, and a final presentation on the personal EI development plan.
This course aims to enhance MBA students' understanding and application of emotional intelligence and interpersonal skills, vital for personal growth and professional success.
-68%
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
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.
-64%
QlikView/Qlik Sense Training – Interactive Data Discovery with Qlik
What you'll learn
Week 1: Introduction to QlikView and Qlik Sense (4 Hours)
Session 1 (2 Hours): Fundamentals of QlikView and Qlik Sense
Overview of QlikView and Qlik Sense: Features and Differences
The Role of Qlik in Business Intelligence and Data Visualization
Installing and Setting Up QlikView and Qlik Sense
Session 2 (2 Hours): Basics of Data Loading and Modeling
Connecting to Data Sources
Basic Data Modeling Techniques in Qlik
Understanding Qlik’s Associative Model
Week 2: Data Visualization and Dashboard Creation (6 Hours)
Session 3 (2 Hours): Creating Basic Visualizations
Exploring Chart Types and Visualization Options
Designing Interactive Dashboards in Qlik Sense
Best Practices for Effective Data Presentation
Session 4 (2 Hours): Advanced Visualization Techniques
Utilizing Advanced Chart Types and Custom Visualizations
Implementing Set Analysis for Comparative Analysis
Hands-on Practice with Complex Dashboard Creation
Session 5 (2 Hours): Storytelling and Reporting in Qlik
Using Qlik Sense for Storytelling and Report Generation
Integrating Narrative into Dashboards
Exporting Reports and Visualizations
Week 3: Advanced Topics in QlikView and Qlik Sense (6 Hours)
Session 6 (2 Hours): Advanced Data Modeling
Advanced Data Transformation Techniques
Scripting in QlikView for Custom Data Models
Complex Data Structures and Optimization Techniques
Session 7 (2 Hours): Set Analysis and Data Exploration
Deep Dive into Set Analysis for Complex Data Queries
Dynamic Data Exploration Techniques
Hands-on Exercises on Data Exploration
Session 8 (2 Hours): Integrating Qlik with External Data Sources
Connecting to Various Data Sources (SQL, Web, APIs)
Implementing Data Security and Governance
Best Practices for Data Integration
Week 4: Practical Applications and Capstone Project (4 Hours)
Session 9 (2 Hours): Applying Qlik in Business Scenarios
Case Studies: How Businesses Leverage Qlik for Analytics
Discussing Real-World Business Problems and Solutions
Ethical Considerations in Data Visualization
Session 10 (2 Hours): Capstone Project and Course Wrap-Up
Developing a Comprehensive Qlik Project based on Real Business Data
Presentation and Review of Capstone Projects
Course Summary and Future Learning Opportunities
The course should be a mix of lectures, demonstrations, and practical exercises, with a focus on applying QlikView/Qlik Sense skills to business analytics scenarios. The capstone project in the final week would allow students to apply their learning to develop a full-scale business intelligence solution, ensuring they not only know how to use Qlik tools but also understand their strategic application in business.
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Critical Thinking and Problem-Solving
What you'll learn
Module 1: Foundations of Critical Thinking (4 hours)
Session 1.1: Introduction to Critical Thinking (2 hours)
Understanding critical thinking and its importance in business
Elements and standards of critical thought
Interactive activity: Identifying examples of critical thinking in business scenarios
Session 1.2: Barriers to Critical Thinking (2 hours)
Common cognitive biases and logical fallacies
Strategies to overcome these barriers
Group discussion: Analyzing case studies with cognitive biases
Module 2: Advanced Critical Thinking Skills (4 hours)
Session 2.1: Analytical Thinking and Decision Making (2 hours)
Techniques for systematic analysis and evaluation
Decision-making models and tools
Exercise: Applying analytical tools in case studies
Session 2.2: Creative Thinking and Innovation (2 hours)
Techniques to foster creativity and innovation
Balancing analytical and creative thinking
Creative brainstorming session: Developing innovative solutions to business problems
Module 3: Problem-Solving Strategies (4 hours)
Session 3.1: Systematic Problem-Solving Approaches (2 hours)
Problem-solving methodologies (e.g., root cause analysis, PDCA cycle)
Structuring and prioritizing problems
Workshop: Solving structured business problems
Session 3.2: Complex and Unstructured Problem Solving (2 hours)
Approaches for complex and unstructured problems
Case studies: Solving real-world business problems
Group exercise: Collaborative problem-solving
Module 4: Application in Business Contexts (4 hours)
Session 4.1: Critical Thinking in Strategic Planning (2 hours)
Applying critical thinking in strategy formulation and execution
Scenario planning and risk assessment
Group activity: Developing a strategic plan for a case study company
Session 4.2: Problem-Solving in Leadership and Management (2 hours)
The role of critical thinking and problem-solving in leadership
Managing team problem-solving dynamics
Role-play: Leading a problem-solving meeting
Module 5: Integrating Critical Thinking and Problem-Solving Skills (4 hours)
Session 5.1: Integrative Case Studies (2 hours)
Comprehensive case studies integrating critical thinking and problem-solving
Group analysis and presentation of solutions
Session 5.2: Developing a Personal Action Plan (2 hours)
Reflecting on personal critical thinking and problem-solving skills
Developing a plan for ongoing skill development
Peer review and feedback on action plans
Additional Elements:
Guest Speakers: Experts and practitioners sharing their experiences in applying critical thinking and problem-solving in business.
Interactive Activities: Group discussions, case analyses, and role-playing exercises.
Assignments: Reflective essays, case study analyses, and action plan development.
Feedback and Assessment: Continuous feedback during activities and a final assessment based on a case study presentation or action plan review.
This course structure is designed to equip MBA students with robust critical thinking and problem-solving skills, preparing them for complex decision-making and strategic planning in their professional careers.
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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
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.
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R for Data Science – Data Science Proficiency with R
What you'll learn
Week 1: Introduction to R and Basic Concepts (4 Hours)
Session 1 (2 Hours): Getting Started with R
Introduction to R and its Importance in Data Science
Setting Up the R Environment (R and RStudio Installation)
Basic Syntax, Variables, and Data Types in R
Session 2 (2 Hours): Data Manipulation Basics
Reading and Writing Data in R
Introduction to Data Manipulation with dplyr
Basic Data Cleaning Techniques
Week 2: Data Analysis and Visualization in R (6 Hours)
Session 3 (2 Hours): Exploratory Data Analysis (EDA)
Conducting EDA with R
Descriptive Statistics and Summarization
Handling Missing Values and Outliers
Session 4 (2 Hours): Data Visualization with ggplot2
Basics of ggplot2 for Data Visualization
Creating Various Types of Plots (Bar, Line, Scatter, Histogram)
Customizing Plots for Clarity and Aesthetics
Session 5 (2 Hours): Advanced Data Visualization
Advanced ggplot2 Features
Interactive Visualization with Plotly
Creating Dashboards and Reports
Week 3: Statistical Modeling and Machine Learning in R (6 Hours)
Session 6 (2 Hours): Introduction to Statistical Modeling
Linear Regression Analysis
Logistic Regression for Categorical Data
Model Diagnostics and Interpretation
Session 7 (2 Hours): Machine Learning Basics in R
Introduction to Machine Learning with R
Building Classification and Regression Models
Evaluating Model Performance
Session 8 (2 Hours): Advanced Topics in Machine Learning
Decision Trees and Random Forests
Clustering Techniques (k-means, Hierarchical)
Introduction to Text Mining and Sentiment Analysis
Week 4: Business Applications and Capstone Project (4 Hours)
Session 9 (2 Hours): R in Business Contexts
Case Studies: Real-World Applications of R in Business
Data-Driven Decision-Making in Business
Ethical Considerations in Data Science
Session 10 (2 Hours): Capstone Project and Course Wrap-Up
Applying R Skills to a Business-Related Data Science Project
Presentation and Discussion of Capstone Projects
Course Summary and Recommendations for Further Learning
The course should include a mix of theoretical instruction, practical demonstrations, and hands-on exercises using R. The capstone project in the final week should involve applying R skills to a real-world business problem, enabling students to demonstrate their ability to use R for data-driven decision-making in a business context.
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Time Management and Productivity
What you'll learn
Module 1: Fundamentals of Time Management (4 hours)
Session 1.1: Understanding Time Management (2 hours)
Introduction to time management and its importance
Self-assessment: Personal time management evaluation
Principles of effective time management
Session 1.2: Setting Goals and Priorities (2 hours)
Techniques for setting SMART goals
Prioritizing tasks using methods like Eisenhower Matrix
Exercise: Setting personal and professional goals
Module 2: Planning and Organizing (4 hours)
Session 2.1: Strategic Planning and Scheduling (2 hours)
Creating effective plans and schedules
Tools for planning (calendars, planners, apps)
Activity: Developing a weekly plan
Session 2.2: Organizational Skills for Productivity (2 hours)
Techniques for organizing work and personal spaces
Systems for managing paperwork and digital files
Workshop: Organizational system setup
Module 3: Overcoming Time Management Challenges (4 hours)
Session 3.1: Managing Distractions and Interruptions (2 hours)
Identifying and dealing with common distractions
Strategies for managing interruptions
Group discussion: Sharing experiences and strategies
Session 3.2: Dealing with Procrastination (2 hours)
Understanding the psychology of procrastination
Strategies to overcome procrastination
Exercise: Implementing anti-procrastination techniques
Module 4: Advanced Time Management Techniques (4 hours)
Session 4.1: Leveraging Technology for Time Management (2 hours)
Exploring time management and productivity tools
Integrating technology effectively into time management strategies
Hands-on session: Using productivity apps
Session 4.2: Time Management for Teams (2 hours)
Collaborative time management techniques
Managing team deadlines and priorities
Role-play: Leading a time-sensitive project
Module 5: Building and Sustaining Productivity Habits (4 hours)
Session 5.1: Developing Personal Productivity Habits (2 hours)
Building and maintaining productive habits
Techniques for continuous self-improvement
Workshop: Habit formation and tracking
Session 5.2: Integrating Time Management into Daily Life (2 hours)
Applying time management skills in real-life scenarios
Work-life balance and maintaining productivity
Group activity: Developing a personal time management plan
Additional Elements:
Guest Speakers: Time management experts or professionals sharing personal experiences and tips.
Interactive Activities: Workshops, group discussions, and role-plays.
Assignments: Time audits, goal setting exercises, and productivity plan development.
Feedback and Assessment: Peer reviews, instructor feedback, and a final presentation or report on personal time management strategies.
This course aims to provide MBA students with the skills and techniques necessary to effectively manage their time, increase productivity, and maintain a healthy work-life balance.
-63%
Beginner
Enterprise Resource Planning (ERP) Systems – Integrating Efficiency with ERP System Expertise
₹1,499.00₹4,000.00
Enterprise Resource Planning (ERP) Systems – Integrating Efficiency with ERP System Expertise
What you'll learn
Week 1: Introduction to ERP Systems (4 Hours)
Session 1 (2 Hours): Fundamentals of ERP Systems
Overview of ERP Systems and Their Evolution
Key Components and Architecture of ERP Systems
Role and Benefits of ERP in Business Integration
Session 2 (2 Hours): Types of ERP Systems and Vendors
Overview of Major ERP Systems (e.g., SAP, Oracle, Microsoft Dynamics)
Cloud-based vs. On-premises ERP Solutions
Selecting an ERP System: Factors to Consider
Week 2: ERP Implementation and Modules (6 Hours)
Session 3 (2 Hours): ERP Implementation Process
Steps in ERP Implementation
Project Planning and Management for ERP Implementation
Risk Management in ERP Projects
Session 4 (2 Hours): Functional Modules of ERP Systems
In-depth Look at Key ERP Modules (Finance, HR, Supply Chain, etc.)
Integration of ERP Modules in Business Processes
Hands-on Exercise with a Simulated ERP Environment
Session 5 (2 Hours): ERP System Customization and Configuration
Customization vs. Configuration in ERP Systems
Best Practices in ERP System Customization
Case Studies on Successful ERP Customizations
Week 3: ERP and Business Process Integration (6 Hours)
Session 6 (2 Hours): Business Process Reengineering and ERP
Understanding Business Process Reengineering (BPR)
Aligning ERP Systems with Business Processes
Impact of ERP on Organizational Structure and Culture
Session 7 (2 Hours): ERP System Upgrades and Maintenance
Managing ERP System Upgrades
Routine Maintenance and Support for ERP Systems
Training and User Support Strategies
Session 8 (2 Hours): Data Management and Analytics in ERP
Role of ERP in Data Management and Business Intelligence
Leveraging ERP Data for Analytics and Decision Making
Case Study on ERP-Enabled Business Analytics
Week 4: Advanced Topics and Emerging Trends (4 Hours)
Session 9 (2 Hours): Emerging Trends in ERP
ERP in the Era of Big Data and IoT
Mobile ERP and Cloud Computing Trends
Future Outlook of ERP Systems
Session 10 (2 Hours): Capstone Project and Course Wrap-Up
Group Project on ERP Implementation Strategy
Presentations and Discussions on ERP Case Studies
Course Summary and Discussion on Further Learning Opportunities
The course would ideally blend theoretical insights with practical, hands-on exercises, possibly using an ERP simulation tool or case studies. The final capstone project should involve applying ERP concepts to a real-world business scenario, encouraging students to think strategically about the use of ERP systems in an organizational context. This structure ensures that MBA students gain a comprehensive understanding of ERP systems, their implementation challenges, and their strategic value in business operations.
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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.
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Understand the role of financial modeling in investment analysis and business valuation.
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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.
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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.
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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.
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Understand the importance and impact of soft skills in the business world.
Develop effective communication and interpersonal skills.
Enhance emotional intelligence for better relationship management.
Cultivate a growth mindset for continuous learning and development.
Master time management and organizational skills.
Build resilience and stress management capabilities.
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Improve negotiation and conflict resolution skills.
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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.
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Apply hard skills to real-world business scenarios through case studies and hands-on exercises.
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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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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
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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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Data Analytics and Statistical Analysis for MBA – From Data to Decisions: Advanced Statistical Techniques for MBAs
“Data Analytics and Statistical Analysis for MBA – From Data …
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
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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.
