Most popular
-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.
-50%
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.
-67%
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.
-50%
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.
-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.
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.
-64%
Beginner
Supply Chain Analytics – Unlocking Efficiency with Supply Chain Analytics
₹1,449.00₹4,000.00
Supply Chain Analytics – Unlocking Efficiency with Supply Chain Analytics
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.
-60%
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.
-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
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Acquire a comprehensive understanding of financial modeling principles and techniques.
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Learn to communicate visions and strategies compellingly.
Foster innovation and creativity within teams and organizations.
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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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Develop a solid foundation in key business-related hard skills.
Gain proficiency in data analysis and interpretation.
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Master project management tools and techniques.
Develop expertise in essential software and technological tools used in businesses.
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Understand the ethical responsibilities and challenges associated with visionary leadership.
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