Masterclass: Business Intelligence using Excel

• Understanding Excel’s role in Business Intelligence
• Data Entry, Formatting, and Organization
• Essential Formulas and Functions (SUM, AVERAGE, IF, COUNTIF, etc.)
• Sorting, Filtering, and Conditional Formatting
• Data Validation and Error Checking
• Introduction to Microsoft 365 Copilot for automating Excel tasks

Data Literacy and Statistical Concepts

• Understanding Data Types and Structures
• Descriptive Statistics: Mean, Median, Mode, Variance, Standard Deviation
• Data Relationships, Unique Keys, and Identifiers
• Statistical Inference and Sampling Techniques
• Regression Analysis Basics

Data Cleaning, Preparation, and Transformation

• Common Data Cleaning Techniques
• Excel Functions for Data Cleaning (LEFT, RIGHT, CONCATENATE, TRIM, etc.)
• Handling Missing or Duplicate Data
• Data Extraction from External Sources (CSV, Web, Databases)
• Using Power Query for Data Transformation

 
Data Modeling and Management

• Power Pivot for Handling Large Datasets
• Creating Data Models and Relationships
• Understanding Measures, Calculated Fields, and DAX Basics
• Merging and Appending Tables in Power Query
• Managing Data Refresh and Dependencies

Data Analysis with Advanced Excel

• IF, Nested IF, INDEX, MATCH, XLOOKUP
• Scenario and What-If Analysis
• Dynamic Arrays and Lookup Enhancements
• PivotTables for Summarization
• Interactive Pivot Charts and Slicers

Storytelling with Data

• Turning Data into Insights: Framing a Narrative
• Designing Charts that Tell a Story
• Choosing the Right Visualization for the Message
• Excel Dashboards for Decision-Making
• Using Copilot to Summarize and Explain Insights Automatically

Data Visualization and Communication 

• Principles of Effective Data Communication
• Chart Types and Their Best Use Cases (Bar, Line, Scatter, Pie, etc.)
• Data Visualization Add-ins and Plugins
• Integrating AI Tools (Excel Copilot) for Automated Insights and Reports
• Presenting Reports and Dashboards Effectively
  
  Masterclass: Microsoft 365 Copilot for Organizational Efficiency 

• Overview of Copilot in Microsoft 365 (Excel, PowerPoint, Word)
• Automating Data Summaries and Reports
• Generating Insights and Recommendations using Copilot
• Streamlining Collaboration and Communication in Teams
• Practical Case Study: Enhancing Productivity through AI

  
Hands-on Mini Project

• Build a Business Intelligence Dashboard in Excel using Power Query and Power Pivot
• Integrate Copilot to generate insights and summaries automatically
• Present a final BI report demonstrating data-driven decision-making

Introduction to PowerBI/ Connecting & Shaping Data

• Download and install Power BI Desktop, and adjust the settings.
• Understand the role that Power BI plays within the broader Microsoft ecosystem
• Explore core components of the Power BI Desktop interface

Review the business intelligence workflow.

• Explore Power BI’s query editor and understand the role that Power Query plays in the larger BI workflow
• Introduce different types of connectors and connectivity modes available for getting data into Power BI
• Review tools for checking data quality and key profiling metrics like column distribution, empty values, errors, and outliers
• Transform tables using text, numerical and date/time tools, pivot and group records, and create new conditional columns
• Practice combining, modifying, and refreshing queries

Data Modeling

• Understand the basic principles of data modeling, including normalization, fact & dimension tables, and common schemas
• Create table relationships using primary and foreign keys, and discuss different types of relationship cardinality
• Configure report filters and trace filter context as it flows between related tables in the model
•  Explore data modeling options like hierarchies, data categories, and hidden fields

DAX

• Introduce DAX fundamentals and learn when to use calculated columns and measures
• Understand the difference between row context and filter context, and how they impact DAX calculations
• Learn DAX formula syntax, basic operators and common function categories (math, logical, text, date/time, filter, etc.)
•  Explore nested functions, and more complex topics like iterators and time intelligence patterns

Data Visualization

• Review frameworks and best practices for visualizing data and designing effective reports and dashboards
• Explore tools and techniques for inserting, formatting and filtering visuals in the Power BI Report view
• Add interactivity using tools like bookmarks, slicer panels, parameters, tooltips, and report navigation
•  Learn how to configure row-level security with user roles
•  Optimize reports for mobile viewing using custom layouts

Installing SQL Workbench

Introduction to SQL

• Overview of Database Management Systems(DBMS)
• Basic SQL syntax and structure 
• Flow of SQL commands
• Data Types in SQL 
• Running SQL commands using SELECT statement
• Retrieving data from tables 

Filtering, Sorting, and Aggregating Data

• Using WHERE clause to filter data based on conditions
• Sorting query results using ORDER BY clause
• Limiting the number of results with LIMIT clause 
• Using aggregate functions for summary statistics 
• Grouping query results using GROUP BY clause
• Filtering grouped data with HAVING clause

Table Joins and Case Statements

• Case statements 
• Understanding relationships between tables
• Using joins to combine data from multiple tables

Advanced Query Techniques

• Further practice with different types of joins
• Working with subqueries 
• Working with derived tables
• Common table expressions (CTEs)

                                
Window Functions and Data Modification

• Working with window functions 
• Ranking data using RANK and DENSE_RANK
• Modifying data in tables using ALTER, RENAME, INSERT, UPDATE, DELETE
• Using UNION, INTERSECT, and EXCEPT to combine query results                     

Creating New Tables

• Further practice with window functions
• Creating new tables 
• Inserting values into new tables 
• Modifying and updating new tables

                              
Advanced Topics and Artificial Intelligence

• Working with Primary Keys
• Auto-Increments
• Updating Tables 
• Indexing 
• Using AI in SQL programming

                              
Date Variables and Artificial Intelligence 

• Dealing with date variables in SQL data
• Setting variables in SQL 
• Using AI in SQL programming
• Wrapping up 

 Introduction to AI for Analytics

• Role of AI in modern data teams
  

• When to use AI and when not to
  

• Human-in-the-loop decision making
  
  
   Prompt Engineering for Data Analysts

• Writing structured prompts for analytics
  

• Context-setting, constraints, examples, and task breakdown
  

• Creating reusable prompt templates (for SQL, EDA, reporting)

Tools Used:

ChatGPT, Gemini: Prompt experimentation, template creation
Julius AI: Prompts specifically designed for data tasks (SQL/EDA)

AI for Data Cleaning, Transformation & EDA

• Asking AI to propose cleaning steps
  
  

• Using AI to spot patterns, anomalies, correlations
  

• Drafting initial EDA summaries using AI

Tools Used: Microsoft Copilot for Excel → cleaning suggestions directly in Excel

3. AI for Dashboarding & Visualization

• Using AI to suggest KPIs, visuals, layouts
  

• AI for DAX explanations and Power BI insights
  

• Converting messy business questions into dashboard requirements

Microsoft Copilot in Power BI → AI-generated visual summaries & insights

AI for Insights & Business Storytelling

• Using AI to summarize datasets
  

• Drafting analytical emails, reports & executive summaries
  

• Creating data stories that non-technical stakeholders can understand

Microsoft Copilot for PowerPoint: Convert insights into slides

Responsible Use of AI in Analytics

• Privacy, security & data protection fundamentals
  

• Avoiding hallucinations
  

• Cross-checking insights with SQL, Excel & Power BI
  

• Ensuring transparency in AI-assisted workflows

Installation

• Introduction to the Python programming language and its applications
• Setting up the Python environment: installation of Python and necessary libraries
• Configuring the development environment: IDEs, text editors, and Jupyter Notebook
• Retrieving data from tables 

Python Basics

• Introduction to Python: history, features, and advantages
• Expressions and operators: arithmetic, assignment, comparison, and logical
• Understanding type() function and type inference
• Introduction to data structures: lists, tuples, and dictionaries

Python Basics

• Recap of Python basics
• Working with arithmetic operators: addition, subtraction, multiplication, division, modulus, and exponentiation
• Using comparison operators: equal to, not equal to, greater than, less than, etc.
• Logical operators: and, or, and not
• Exploring advanced data types: sets and strings manipulation.

Expressions, Conditional Statements & For Loop

• Evaluating expressions: operator precedence and associativity
• Introduction to conditional statements: if, elif, and else
• Executing code based on conditionals.
• Understanding the flow of control in conditional statements
• Iteration using the for loop: range(), iteration over lists, and strings.

                                
While loop, Break and Continue Statements, and Nested Loops

• Working with while loop: syntax, conditions, and examples
• Combining loops and conditionals
• Using the break statement to exit loops prematurely.
• Utilizing the continue statement to skip iterations.
• Implementing nested loops for complex iterations
                          

 Functions

• Introduction to functions: purpose, advantages, and best practices
• Defining and calling user-defined functions
• Parameters and arguments: positional, keyword, and default values
• Return statement and function output.
• Variable scope and lifetime
• Function documentation and code readability

                             
Exception Handling and File Handling

• Understanding exceptions: errors, exceptions, and exception hierarchy
• Handling exceptions using try-except blocks: handling specific exceptions, multiple exceptions, and else and finally clauses.
• Raising exceptions and creating custom exception classes
• File handling in Python: opening, reading, writing, and closing files.
• Working with different file modes and file objects

                             
Python Modules: NumPy and Matplotlib

• Introduction to the NumPy module: features and applications
• Working with multidimensional arrays: creation, indexing, slicing, and reshaping
• Performing element-wise operations: arithmetic, logical, and statistical
• Overview of the Matplotlib module: data visualization and plotting
• Customizing plots: line properties, markers, colors, labels, and legends.

Introduction and Missing Value Analysis

• Introduction to Exploratory Data Analysis (EDA)
• Importance of EDA in data analysis
• Steps involved in EDA
• Handling missing values: identification, analysis, and treatment strategies • Imputation techniques for missing values

Data Consistency, Binning, and Outlier Analysis

• Data consistency checks using fuzzy logic
• Binning and discretization techniques for continuous variables
• Outlier detection and analysis methods
• Handling outliers: techniques for treatment or removal

Feature Selection and Data Wrangling

• Importance of feature selection in EDA
• Feature selection techniques: filter methods, wrapper methods, and embedded methods
• Data wrangling: cleaning and transforming data for analysis
• Handling categorical variables: encoding techniques.

Inference, Hypothesis Testing, and Visualization

• Inference and hypothesis testing in EDA
• Common statistical tests: t-test, chi-square test, ANOVA, etc.
• Visualization techniques for EDA: histograms, box plots, scatter plots, etc. 
• Hands-on  practical session for complete EDA using a dataset

Overview of Machine Learning

• Supervised, unsupervised, and reinforcement learning.
• Machine learning workflow from data collection to model deployment.
• Introduction to Python libraries essential for ML (Scikit-learn, TensorFlow, PyTorch)

Supervised learning and Linear Regression

• Linear Regression: Concept, loss function, evaluation metrics (MSE, R²)
• Building a Linear Regression model in scikit-learn
• Visualizing predictions vs actuals

Logistic Regression and Tree-Based Models

• Logistic Regression: Theory, Interpretation, and Applications in Classification Problems
• Decision Trees: Construction, Pruning, and Model Interpretability
• Random Forests: Ensemble Learning, Feature Importance, and Performance Improvement
• Bagging and Boosting: Techniques for Reducing Overfitting and Enhancing Accuracy
• Hands-on Practical Session: Building and Evaluating a Classification Model

Clustering with K-Means

• Introduction to Unsupervised Learning and Clustering Concepts
• Understanding K-Means Clustering Algorithm
• Steps for Implementing K-Means in Scikit-learn
• Evaluating Cluster Quality using Silhouette Score and Davies–Bouldin Index
• Hands-on Activity: Performing Customer Segmentation using K-Means

Regression and Evaluation of Regression Methods

• Introduction to regression analysis
• Linear regression: assumptions, interpretation, and model evaluation • Evaluation metrics for regression: mean squared error, R-squared, etc.
• Other regression methods: polynomial regression, ridge regression, lasso regression

Introduction to No-Code Automation

• What is no-code automation?
  

• How analysts use Make & Power Automate
  

• Manual vs automated workflows
  

• Tools Used:

Make → workflow automation, multi-app integration
Power Automate → Microsoft ecosystem automation (Excel, SharePoint, Power BI, Teams)

Make for Data Automation

• • Creating automated workflows (“scenarios”)
    

• Triggers, actions, routers & scheduling
  

• • Connecting apps: Sheets, Excel Online, Databases, CRMs, APIs
    

• Automating repetitive analysis tasks
  
  Tools Used:
  

• • • Make → main no-code tool for multi-system automations
      

    • Webhooks & APIs → for pulling external data
      

• Google Sheets / Excel Online → store & update automated outputs
  
  

Power Automate for Business Workflows

• Using Power Automate inside the Microsoft ecosystem
  

• Connecting Power BI, Excel, Teams, SharePoint & Outlook
  

• Refreshing datasets automatically
  
  

• Automated approvals & stakeholder communication
  

  
  Tools Used:
  

  • Power Automate → core automation builder
    
    

  • SharePoint & OneDrive → automated storage
    
    

  • Teams / Outlook → auto-notifications, alerts & report sharing

 Foundations of Statistics

• Why statistics matter for data analytics
  
• Types of data (numerical, categorical, time-based)
  
• Descriptive vs inferential statistics
  
  
  Descriptive Statistics

• • Measures of central tendency: mean, median, mode
    
  • Measures of spread: range, variance, standard deviation, IQR
    

• Understanding distribution shape: skewness, outliers
  
  Probability & Distributions

• Basic probability concepts
  
• Real-world interpretation of probability (risk, likelihood)
  
• Common distributions used in analytics (normal, binomial, Poisson – concept level)
  
  Correlation & Relationships

• Correlation and covariance
  
• Correlation vs causation
  
  
• Reading and interpreting correlation patterns
  
  Hypothesis Testing

• • Null and alternative hypotheses
    
    
  • p-values and statistical significance
    
    
  • Common tests (at a practical level): t-test, chi-square
    
    

• How to decide “Is this difference real or just random?”
  
  Confidence Intervals & Uncertainty

• • What confidence intervals mean (in simple language)
    
    

• Margin of error and uncertainty in estimates
  
  Basics of Regression (Analytics Perspective)

• • Simple linear regression: line of best fit
    
  • Coefficients, intercept, R² – what they tell you
    

• Using regression to understand impact of one variable on another
  
  A/B Testing & Experiment Thinking

• • Control vs treatment groups
    
  • Designing simple experiments
    

• Evaluating uplift and significance in A/B results
  
  

Email Writing

• Basics of Professional Email Communication: Structure, tone, and etiquette.
• Writing Effective Subject Lines: Techniques to ensure your emails are opened.
• Emails for Networking: Approaching professionals and mentors in data science/AI.
• Follow-up Emails: Strategies for following up without being intrusive.

  Report Writing + Presentations

• Structure of a Data Science Report: Elements including abstract, methodology, results, and conclusion.
• Visualizing Data: Incorporating charts, graphs, and other visual tools to enhance comprehension.
• Creating Engaging Presentations: Tips for PowerPoint, storytelling, and engaging your audience.
• Presentation Skills: Delivering your message confidently, handling Q&A sessions.

  LinkedIn Optimization

• Building a Professional Profile: Key components of a LinkedIn profile for data science/AI professionals.
• Networking Strategies: Connecting with industry professionals and joining relevant groups.
• Content Sharing and Creation: Establishing thought leadership by sharing insights, articles, and engaging with community content.
  Resume/CV Writing

• Tailoring Your Resume for Data Science/AI: Highlighting relevant skills, projects, and experiences.
• Action Verbs and Quantifiable Achievements: Demonstrating impact in previous roles or projects.
• Design and Layout: Making your resume/CV visually appealing and easy to read.

  Cover Letter

• Structure of a Cover Letter: Introduction, body, and closing.
• Customizing Your Message: Researching the company and role to personalize content.
• Highlighting Fit and Value: Articulating how your skills and experiences align with the job requirements.

  Freelancing

• Getting Started with Freelancing: Platforms for data science/AI freelancers, setting up a profile.
• Finding Projects and Clients: Strategies to secure freelance work and build a portfolio.
• Pricing Your Services: Understanding market rates and value-based pricing.
• Client Management: Communicating effectively and managing expectations.

  Kaggle for Data Science

• Introduction to Kaggle: Overview of the platform, competitions, datasets, and notebooks.
• Participating in Competitions: Tips for success, collaboration, and learning from the community.
• Building a Portfolio: Using Kaggle to showcase your skills and projects to potential employers.

  GitHub

• Why GitHub for Data Scientists: Importance of version control and code sharing.
• Creating and Managing Repositories: Best practices for organizing and documenting projects.
• Collaborating on Projects: Contributing to open-source projects and collaborating with others.
• GitHub as a Portfolio: Presenting your work and contributions to potential employers.

  How to Crack Data Analysis Interviews

• Understanding the Interview Process: Types of interviews (technical, behavioral, case studies).
• Preparing for Technical Interviews: Common questions, coding challenges, and statistical questions.
• Behavioral Interview Preparation: Crafting your story, STAR method for responses.
• Mock Interviews: Practicing with peers or mentors to gain confidence.

  Global Market Understanding

• Data Science/AI Trends: Understanding global trends and emerging technologies.
• Cultural Competence: Working in multicultural teams and serving diverse user bases.
• Regulatory Environment: Overview of data privacy laws and ethical considerations in different regions.

  Storytelling Using Data

• Principles of Data Storytelling: Crafting narratives that resonate with your audience.
• Visual Narrative Techniques: Using data visualizations effectively in your story.
• Engaging Presentations: Combining data, visuals, and narrative for impactful presentations.

  Intro to Data Commons

• Understanding Data Commons: Concept, importance, and examples.
• Accessing and Contributing to Data Commons: Guidelines and best practices.