Most popular
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.
-57%
IBM Cognos Analytics – Empowering Decisions with IBM Cognos Analytics
What you'll learn
Week 1: Introduction to IBM Cognos Analytics (4 Hours)
Session 1 (2 Hours): Overview of IBM Cognos Analytics
Introduction to Business Intelligence and the Role of IBM Cognos Analytics
Navigating the IBM Cognos Analytics Interface
Overview of Key Features and Capabilities
Session 2 (2 Hours): Basic Reporting and Dashboarding
Connecting to Data Sources in Cognos
Creating Simple Reports and Dashboards
Basic Data Visualization Techniques
Week 2: Advanced Reporting and Data Exploration (6 Hours)
Session 3 (2 Hours): Advanced Reporting Techniques
Building Advanced Reports (List, Crosstab, and Chart Reports)
Applying Filters, Prompts, and Calculations
Report Formatting and Styling for Clarity and Impact
Session 4 (2 Hours): Interactive Dashboards and Visualization
Designing Interactive Dashboards
Advanced Visualization Techniques in Cognos
Using Drill-Throughs for Detailed Data Analysis
Session 5 (2 Hours): Data Exploration and Analysis
Exploratory Data Analysis in Cognos Analytics
Utilizing Cognos for Trend Analysis and Pattern Discovery
Integrating External Data and Leveraging Advanced Analytics
Week 3: Data Modeling and Framework Manager (6 Hours)
Session 6 (2 Hours): Introduction to Data Modeling
Basics of Data Modeling in Cognos
Creating and Managing Packages
Understanding the Framework Manager
Session 7 (2 Hours): Advanced Data Modeling Concepts
Building Dimensional Models
Working with Different Query Subjects
Implementing Security in Models
Session 8 (2 Hours): Practical Modeling Exercises
Hands-on Data Modeling Exercise
Best Practices in Data Modeling for Business Reporting
Reviewing and Optimizing Models
Week 4: Business Applications and Capstone Project (4 Hours)
Session 9 (2 Hours): Cognos Analytics in Business Contexts
Applying Cognos Analytics to Solve Business Problems
Case Studies: Cognos in Different Industry Settings
Discussing the Strategic Impact of Business Intelligence
Session 10 (2 Hours): Capstone Project and Course Wrap-Up
Developing a Comprehensive Business Intelligence Project Using Cognos Analytics
Presentation and Review of Capstone Projects
Course Summary and Pathways for Further Learning
This course should include a mix of lectures, hands-on practical exercises, and case studies. The capstone project in the final week would allow students to apply their learning to a comprehensive business intelligence task, ensuring they understand not only how to use IBM Cognos Analytics but also how to apply it strategically in a business context.
-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.
-57%
Looker Data Analytics – Unveiling Insights with Looker Data Analytics
What you'll learn
Week 1: Introduction to Looker and Basic Concepts (4 Hours)
Session 1 (2 Hours): Overview of Looker and Business Intelligence
Introduction to Business Intelligence and Looker's Role
Navigating the Looker Interface
Overview of Key Features and Functionalities
Session 2 (2 Hours): Connecting Data and Basic Reporting
Setting Up and Connecting Data Sources
Creating Basic Reports (Looks) and Exploring Data
Introduction to LookML (Looker Modeling Language)
Week 2: Advanced Reporting and Visualization (6 Hours)
Session 3 (2 Hours): Advanced Reporting Techniques
Building More Complex Reports and Dashboards
Exploring Advanced Visualization Options
Utilizing Filters, Parameters, and Derived Tables
Session 4 (2 Hours): Interactive Dashboards and Data Exploration
Designing Interactive and Dynamic Dashboards
Best Practices in Dashboard Layout and User Experience
Analyzing Data Trends and Patterns
Session 5 (2 Hours): Data Exploration and Analytics
Drill-Downs and Detailed Data Analysis
Utilizing Looker for Business Analytics
Sharing Insights and Collaborative Features
Week 3: LookML and Data Modeling (6 Hours)
Session 6 (2 Hours): Introduction to LookML
Basics of LookML and its Role in Looker
Creating and Managing LookML Models
Defining Dimensions, Measures, and Views
Session 7 (2 Hours): Advanced LookML Features
Advanced Model and View Development
Utilizing LookML for Complex Data Relationships
Implementing Data Governance in Looker
Session 8 (2 Hours): Hands-on LookML Workshop
Practical Exercise in Building and Optimizing a LookML Model
Troubleshooting Common Issues in LookML
Best Practices for Scalable and Maintainable Models
Week 4: Business Application and Capstone Project (4 Hours)
Session 9 (2 Hours): Looker in the Business Context
Case Studies: Real-world Applications of Looker in Various Industries
Strategic Decision-Making with Looker
Discussing Ethical Considerations in Data Analysis
Session 10 (2 Hours): Capstone Project and Course Wrap-Up
Developing a Comprehensive Business Analytics Project using Looker
Presentation and Critique of Capstone Projects
Course Summary and Future Learning Pathways
The course should be a mix of lectures, demonstrations, hands-on exercises, and case studies. The capstone project in the final week would allow students to apply their learning to develop a full-scale business analytics project, ensuring they understand not only how to use Looker but also how to apply it strategically in a business context.
-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%
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.
-59%
Beginner
Apache Spark for Big Data Analysis – Unleashing Insights: Spark in Big Data Analytics
₹1,449.00₹3,500.00
Apache Spark for Big Data Analysis – Unleashing Insights: Spark in Big Data Analytics
What you'll learn
Week 1: Introduction to Big Data and Apache Spark (4 Hours)
Session 1 (2 Hours): Fundamentals of Big Data
Introduction to Big Data: Concepts and Relevance in Business
Big Data Challenges and Technologies
Overview of the Big Data Ecosystem
Session 2 (2 Hours): Getting Started with Apache Spark
Introduction to Apache Spark and its Advantages
Understanding Spark’s Architecture and Components
Setting Up a Spark Environment (e.g., Databricks or Local Setup)
Week 2: Spark RDDs and DataFrames (6 Hours)
Session 3 (2 Hours): Working with RDDs (Resilient Distributed Datasets)
Creating and Manipulating RDDs
Performing Transformations and Actions on RDDs
Understanding Partitioning and Persistence in RDDs
Session 4 (2 Hours): Introduction to Spark DataFrames
Creating and Using DataFrames in Spark
DataFrame Operations and SQL Queries
Data Aggregation and Grouping Operations
Session 5 (2 Hours): Advanced DataFrame Operations
Advanced Data Processing Techniques
Working with Various Data Formats (JSON, CSV, Parquet)
Data Importing/Exporting Techniques in Spark
Week 3: Spark for Advanced Analytics (6 Hours)
Session 6 (2 Hours): Spark SQL for Big Data Analysis
Using Spark SQL for Complex Queries
Integrating SQL and DataFrame API
Exploring Spark SQL’s Optimization Techniques
Session 7 (2 Hours): Machine Learning with Spark MLlib
Introduction to Spark’s Machine Learning Library (MLlib)
Building Basic Machine Learning Models in Spark
Evaluating Model Performance
Session 8 (2 Hours): Streaming Data Analysis with Spark Streaming
Basics of Real-Time Data Processing
Building Streaming Applications in Spark
Integrating Streaming Data with Static Data Sources
Week 4: Business Applications and Capstone Project (4 Hours)
Session 9 (2 Hours): Applying Spark in Business Contexts
Case Studies: Real-World Applications of Spark in Business
Best Practices for Leveraging Spark for Business Insights
Discussing Ethical and Privacy Considerations in Big Data
Session 10 (2 Hours): Capstone Project and Course Wrap-Up
Developing a Comprehensive Big Data Project Using Apache Spark
Presentation of Capstone Projects
Course Summary and Pathways for Further Learning
The course should be a mix of theoretical explanations, demonstrations, and hands-on exercises, ideally using a cloud-based Spark environment like Databricks for practical sessions. The capstone project in the final week would allow students to apply their learning to a real-world business dataset, ensuring they understand how to use Apache Spark for big data analysis effectively in a business context.
-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.
-52%
Beginner
Customer Relationship Management (CRM) Systems – Optimizing Engagement: Mastering CRM Dynamics
₹1,449.00₹3,000.00
Customer Relationship Management (CRM) Systems – Optimizing Engagement: Mastering CRM Dynamics
What you'll learn
Week 1: Introduction to CRM and Its Strategic Importance (4 Hours)
Session 1 (2 Hours): Fundamentals of CRM
Introduction to Customer Relationship Management
Evolution of CRM and Its Role in Modern Businesses
Overview of CRM Systems and Technologies
Session 2 (2 Hours): Strategic Importance of CRM
Aligning CRM with Business Strategy
Understanding the Customer Journey
Role of CRM in Customer Retention and Acquisition
Week 2: CRM Systems and Their Functionalities (6 Hours)
Session 3 (2 Hours): Overview of Popular CRM Systems
Comparison of Different CRM Systems (e.g., Salesforce, Microsoft Dynamics, Zoho)
Cloud-based CRM vs. On-premise Solutions
Integration with Other Business Systems
Session 4 (2 Hours): Key Functionalities of CRM Systems
Sales Force Automation
Customer Service and Support
Marketing Automation within CRM
Session 5 (2 Hours): Data Management in CRM
Managing and Segmenting Customer Data
Data Analytics and Reporting Features
Ensuring Data Privacy and Compliance
Week 3: Implementing and Optimizing CRM Systems (6 Hours)
Session 6 (2 Hours): CRM Implementation Strategies
Best Practices for CRM Implementation
Overcoming Common Implementation Challenges
Case Studies of Successful CRM Implementations
Session 7 (2 Hours): User Adoption and Training
Strategies for Ensuring User Adoption and Engagement
Training Employees on CRM Usage
Measuring CRM Adoption Success
Session 8 (2 Hours): CRM System Optimization and Advanced Features
Ongoing Management and Maintenance of CRM Systems
Exploring Advanced Features (AI, IoT Integration, etc.)
Future Trends in CRM Technology
Week 4: Practical Applications and Capstone Project (4 Hours)
Session 9 (2 Hours): Integrating CRM into Business Processes
Developing Customer-Centric Strategies Using CRM
CRM in Customer Analytics and Insight Generation
Ethical Considerations in Customer Data Handling
Session 10 (2 Hours): Capstone Project and Course Wrap-Up
Group Project: Developing a CRM Strategy for a Real or Simulated Business Scenario
Presentation of Projects and Feedback
Course Summary and Pathways for Further Learning
The course should include a mix of lectures, case studies, and practical exercises, possibly using a CRM software simulation or sandbox environment. The capstone project in the final week would allow students to apply the concepts and techniques learned to a realistic business scenario, reinforcing their understanding and practical skills in CRM systems. This structure ensures that MBA students gain not only a technical understanding of CRM systems but also learn how to strategically apply these tools to enhance customer relationships and drive business success.
-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.
Trending
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.
-57%
IBM Cognos Analytics – Empowering Decisions with IBM Cognos Analytics
What you'll learn
Week 1: Introduction to IBM Cognos Analytics (4 Hours)
Session 1 (2 Hours): Overview of IBM Cognos Analytics
Introduction to Business Intelligence and the Role of IBM Cognos Analytics
Navigating the IBM Cognos Analytics Interface
Overview of Key Features and Capabilities
Session 2 (2 Hours): Basic Reporting and Dashboarding
Connecting to Data Sources in Cognos
Creating Simple Reports and Dashboards
Basic Data Visualization Techniques
Week 2: Advanced Reporting and Data Exploration (6 Hours)
Session 3 (2 Hours): Advanced Reporting Techniques
Building Advanced Reports (List, Crosstab, and Chart Reports)
Applying Filters, Prompts, and Calculations
Report Formatting and Styling for Clarity and Impact
Session 4 (2 Hours): Interactive Dashboards and Visualization
Designing Interactive Dashboards
Advanced Visualization Techniques in Cognos
Using Drill-Throughs for Detailed Data Analysis
Session 5 (2 Hours): Data Exploration and Analysis
Exploratory Data Analysis in Cognos Analytics
Utilizing Cognos for Trend Analysis and Pattern Discovery
Integrating External Data and Leveraging Advanced Analytics
Week 3: Data Modeling and Framework Manager (6 Hours)
Session 6 (2 Hours): Introduction to Data Modeling
Basics of Data Modeling in Cognos
Creating and Managing Packages
Understanding the Framework Manager
Session 7 (2 Hours): Advanced Data Modeling Concepts
Building Dimensional Models
Working with Different Query Subjects
Implementing Security in Models
Session 8 (2 Hours): Practical Modeling Exercises
Hands-on Data Modeling Exercise
Best Practices in Data Modeling for Business Reporting
Reviewing and Optimizing Models
Week 4: Business Applications and Capstone Project (4 Hours)
Session 9 (2 Hours): Cognos Analytics in Business Contexts
Applying Cognos Analytics to Solve Business Problems
Case Studies: Cognos in Different Industry Settings
Discussing the Strategic Impact of Business Intelligence
Session 10 (2 Hours): Capstone Project and Course Wrap-Up
Developing a Comprehensive Business Intelligence Project Using Cognos Analytics
Presentation and Review of Capstone Projects
Course Summary and Pathways for Further Learning
This course should include a mix of lectures, hands-on practical exercises, and case studies. The capstone project in the final week would allow students to apply their learning to a comprehensive business intelligence task, ensuring they understand not only how to use IBM Cognos Analytics but also how to apply it strategically in a business context.
-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.
-57%
Looker Data Analytics – Unveiling Insights with Looker Data Analytics
What you'll learn
Week 1: Introduction to Looker and Basic Concepts (4 Hours)
Session 1 (2 Hours): Overview of Looker and Business Intelligence
Introduction to Business Intelligence and Looker's Role
Navigating the Looker Interface
Overview of Key Features and Functionalities
Session 2 (2 Hours): Connecting Data and Basic Reporting
Setting Up and Connecting Data Sources
Creating Basic Reports (Looks) and Exploring Data
Introduction to LookML (Looker Modeling Language)
Week 2: Advanced Reporting and Visualization (6 Hours)
Session 3 (2 Hours): Advanced Reporting Techniques
Building More Complex Reports and Dashboards
Exploring Advanced Visualization Options
Utilizing Filters, Parameters, and Derived Tables
Session 4 (2 Hours): Interactive Dashboards and Data Exploration
Designing Interactive and Dynamic Dashboards
Best Practices in Dashboard Layout and User Experience
Analyzing Data Trends and Patterns
Session 5 (2 Hours): Data Exploration and Analytics
Drill-Downs and Detailed Data Analysis
Utilizing Looker for Business Analytics
Sharing Insights and Collaborative Features
Week 3: LookML and Data Modeling (6 Hours)
Session 6 (2 Hours): Introduction to LookML
Basics of LookML and its Role in Looker
Creating and Managing LookML Models
Defining Dimensions, Measures, and Views
Session 7 (2 Hours): Advanced LookML Features
Advanced Model and View Development
Utilizing LookML for Complex Data Relationships
Implementing Data Governance in Looker
Session 8 (2 Hours): Hands-on LookML Workshop
Practical Exercise in Building and Optimizing a LookML Model
Troubleshooting Common Issues in LookML
Best Practices for Scalable and Maintainable Models
Week 4: Business Application and Capstone Project (4 Hours)
Session 9 (2 Hours): Looker in the Business Context
Case Studies: Real-world Applications of Looker in Various Industries
Strategic Decision-Making with Looker
Discussing Ethical Considerations in Data Analysis
Session 10 (2 Hours): Capstone Project and Course Wrap-Up
Developing a Comprehensive Business Analytics Project using Looker
Presentation and Critique of Capstone Projects
Course Summary and Future Learning Pathways
The course should be a mix of lectures, demonstrations, hands-on exercises, and case studies. The capstone project in the final week would allow students to apply their learning to develop a full-scale business analytics project, ensuring they understand not only how to use Looker but also how to apply it strategically in a business context.
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₹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.
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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.
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Apache Spark for Big Data Analysis – Unleashing Insights: Spark in Big Data Analytics
₹1,449.00₹3,500.00
Apache Spark for Big Data Analysis – Unleashing Insights: Spark in Big Data Analytics
What you'll learn
Week 1: Introduction to Big Data and Apache Spark (4 Hours)
Session 1 (2 Hours): Fundamentals of Big Data
Introduction to Big Data: Concepts and Relevance in Business
Big Data Challenges and Technologies
Overview of the Big Data Ecosystem
Session 2 (2 Hours): Getting Started with Apache Spark
Introduction to Apache Spark and its Advantages
Understanding Spark’s Architecture and Components
Setting Up a Spark Environment (e.g., Databricks or Local Setup)
Week 2: Spark RDDs and DataFrames (6 Hours)
Session 3 (2 Hours): Working with RDDs (Resilient Distributed Datasets)
Creating and Manipulating RDDs
Performing Transformations and Actions on RDDs
Understanding Partitioning and Persistence in RDDs
Session 4 (2 Hours): Introduction to Spark DataFrames
Creating and Using DataFrames in Spark
DataFrame Operations and SQL Queries
Data Aggregation and Grouping Operations
Session 5 (2 Hours): Advanced DataFrame Operations
Advanced Data Processing Techniques
Working with Various Data Formats (JSON, CSV, Parquet)
Data Importing/Exporting Techniques in Spark
Week 3: Spark for Advanced Analytics (6 Hours)
Session 6 (2 Hours): Spark SQL for Big Data Analysis
Using Spark SQL for Complex Queries
Integrating SQL and DataFrame API
Exploring Spark SQL’s Optimization Techniques
Session 7 (2 Hours): Machine Learning with Spark MLlib
Introduction to Spark’s Machine Learning Library (MLlib)
Building Basic Machine Learning Models in Spark
Evaluating Model Performance
Session 8 (2 Hours): Streaming Data Analysis with Spark Streaming
Basics of Real-Time Data Processing
Building Streaming Applications in Spark
Integrating Streaming Data with Static Data Sources
Week 4: Business Applications and Capstone Project (4 Hours)
Session 9 (2 Hours): Applying Spark in Business Contexts
Case Studies: Real-World Applications of Spark in Business
Best Practices for Leveraging Spark for Business Insights
Discussing Ethical and Privacy Considerations in Big Data
Session 10 (2 Hours): Capstone Project and Course Wrap-Up
Developing a Comprehensive Big Data Project Using Apache Spark
Presentation of Capstone Projects
Course Summary and Pathways for Further Learning
The course should be a mix of theoretical explanations, demonstrations, and hands-on exercises, ideally using a cloud-based Spark environment like Databricks for practical sessions. The capstone project in the final week would allow students to apply their learning to a real-world business dataset, ensuring they understand how to use Apache Spark for big data analysis effectively in a business context.
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₹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.
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₹1,449.00₹3,000.00
Customer Relationship Management (CRM) Systems – Optimizing Engagement: Mastering CRM Dynamics
What you'll learn
Week 1: Introduction to CRM and Its Strategic Importance (4 Hours)
Session 1 (2 Hours): Fundamentals of CRM
Introduction to Customer Relationship Management
Evolution of CRM and Its Role in Modern Businesses
Overview of CRM Systems and Technologies
Session 2 (2 Hours): Strategic Importance of CRM
Aligning CRM with Business Strategy
Understanding the Customer Journey
Role of CRM in Customer Retention and Acquisition
Week 2: CRM Systems and Their Functionalities (6 Hours)
Session 3 (2 Hours): Overview of Popular CRM Systems
Comparison of Different CRM Systems (e.g., Salesforce, Microsoft Dynamics, Zoho)
Cloud-based CRM vs. On-premise Solutions
Integration with Other Business Systems
Session 4 (2 Hours): Key Functionalities of CRM Systems
Sales Force Automation
Customer Service and Support
Marketing Automation within CRM
Session 5 (2 Hours): Data Management in CRM
Managing and Segmenting Customer Data
Data Analytics and Reporting Features
Ensuring Data Privacy and Compliance
Week 3: Implementing and Optimizing CRM Systems (6 Hours)
Session 6 (2 Hours): CRM Implementation Strategies
Best Practices for CRM Implementation
Overcoming Common Implementation Challenges
Case Studies of Successful CRM Implementations
Session 7 (2 Hours): User Adoption and Training
Strategies for Ensuring User Adoption and Engagement
Training Employees on CRM Usage
Measuring CRM Adoption Success
Session 8 (2 Hours): CRM System Optimization and Advanced Features
Ongoing Management and Maintenance of CRM Systems
Exploring Advanced Features (AI, IoT Integration, etc.)
Future Trends in CRM Technology
Week 4: Practical Applications and Capstone Project (4 Hours)
Session 9 (2 Hours): Integrating CRM into Business Processes
Developing Customer-Centric Strategies Using CRM
CRM in Customer Analytics and Insight Generation
Ethical Considerations in Customer Data Handling
Session 10 (2 Hours): Capstone Project and Course Wrap-Up
Group Project: Developing a CRM Strategy for a Real or Simulated Business Scenario
Presentation of Projects and Feedback
Course Summary and Pathways for Further Learning
The course should include a mix of lectures, case studies, and practical exercises, possibly using a CRM software simulation or sandbox environment. The capstone project in the final week would allow students to apply the concepts and techniques learned to a realistic business scenario, reinforcing their understanding and practical skills in CRM systems. This structure ensures that MBA students gain not only a technical understanding of CRM systems but also learn how to strategically apply these tools to enhance customer relationships and drive business success.
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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.
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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.
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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.
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Understand the importance of data visualization in presenting statistical findings.
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Develop a solid foundation in key business-related hard skills.
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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.
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Week 1: Introduction to IBM Cognos Analytics (4 Hours)
Session 1 (2 Hours): Overview of IBM Cognos Analytics
Introduction to Business Intelligence and the Role of IBM Cognos Analytics
Navigating the IBM Cognos Analytics Interface
Overview of Key Features and Capabilities
Session 2 (2 Hours): Basic Reporting and Dashboarding
Connecting to Data Sources in Cognos
Creating Simple Reports and Dashboards
Basic Data Visualization Techniques
Week 2: Advanced Reporting and Data Exploration (6 Hours)
Session 3 (2 Hours): Advanced Reporting Techniques
Building Advanced Reports (List, Crosstab, and Chart Reports)
Applying Filters, Prompts, and Calculations
Report Formatting and Styling for Clarity and Impact
Session 4 (2 Hours): Interactive Dashboards and Visualization
Designing Interactive Dashboards
Advanced Visualization Techniques in Cognos
Using Drill-Throughs for Detailed Data Analysis
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Exploratory Data Analysis in Cognos Analytics
Utilizing Cognos for Trend Analysis and Pattern Discovery
Integrating External Data and Leveraging Advanced Analytics
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Session 6 (2 Hours): Introduction to Data Modeling
Basics of Data Modeling in Cognos
Creating and Managing Packages
Understanding the Framework Manager
Session 7 (2 Hours): Advanced Data Modeling Concepts
Building Dimensional Models
Working with Different Query Subjects
Implementing Security in Models
Session 8 (2 Hours): Practical Modeling Exercises
Hands-on Data Modeling Exercise
Best Practices in Data Modeling for Business Reporting
Reviewing and Optimizing Models
Week 4: Business Applications and Capstone Project (4 Hours)
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Applying Cognos Analytics to Solve Business Problems
Case Studies: Cognos in Different Industry Settings
Discussing the Strategic Impact of Business Intelligence
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Developing a Comprehensive Business Intelligence Project Using Cognos Analytics
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Course Summary and Pathways for Further Learning
This course should include a mix of lectures, hands-on practical exercises, and case studies. The capstone project in the final week would allow students to apply their learning to a comprehensive business intelligence task, ensuring they understand not only how to use IBM Cognos Analytics but also how to apply it strategically in a business context.
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Session 1 (2 Hours): Overview of Looker and Business Intelligence
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Navigating the Looker Interface
Overview of Key Features and Functionalities
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Utilizing Looker for Business Analytics
Sharing Insights and Collaborative Features
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Creating and Managing LookML Models
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The course should be a mix of lectures, demonstrations, hands-on exercises, and case studies. The capstone project in the final week would allow students to apply their learning to develop a full-scale business analytics project, ensuring they understand not only how to use Looker but also how to apply it strategically in a business context.
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Historical Context and Evolution of AI
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Introduction to Algorithms and Model Selection
Basic Tools and Software Overview (e.g., Python, TensorFlow)
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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.
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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.
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Session 1 (2 Hours): Introduction to Business Intelligence and Power BI
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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%
R for Data Science – Data Science Proficiency with R
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.
-64%
SAS Analytics Training – Mastering Analytics with SAS
SAS Analytics Training – Mastering Analytics with SAS” is a …
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
