Business Analyst Course in Bahrain

Module 1.1: Introduction to Excel

• Excel Interface Overview (Ribbon, Toolbar, Sheets)
• Workbook vs. Worksheet
• Navigation Shortcuts (Ctrl + Arrow Keys, etc.)
• Basic Data Entry and Cell Formatting
• Saving, Opening, and Managing Excel Files
• Printing and Page Layout Settings

Module 1.2: Basic Excel Functions

• Basic Formulas (SUM, AVERAGE, MIN, MAX)
• Relative vs. Absolute Cell References ($A$1 vs. A1)
• Text Functions (CONCATENATE, LEFT, RIGHT, MID, LEN, TRIM)
• Logical Functions (IF, AND, OR)
• Basic Date Functions (TODAY, NOW, DATE, DATEDIF)

Module 1.3: Data Visualization & Charts

• Introduction to Charts (Bar, Line, Pie, Scatter, etc.)
• Creating and Formatting Charts
• Adding Data Labels, Legends, and Titles
• Sparklines and Conditional Formatting
• Best Practices for Data Visualization

Module 1.4: Intermediate Excel Functions

• Lookup Functions (VLOOKUP, HLOOKUP, XLOOKUP)
• INDEX and MATCH Functions
• Advanced Text Functions (TEXT, FIND, SEARCH, SUBSTITUTE)
• Data Cleaning Techniques (Remove Duplicates, Text to Columns)
• Working with Named Ranges

AI-Powered Learning Experience

• Intelligent Automation: Get formula suggestions, cleaning steps, and chart proposals
• Workflow Acceleration: Streamline repetitive tasks with consistent, AI-driven support
• Required Validation: Check AI outputs against business rules and standards
• Risk Control: Avoid context-free AI use in financial or statistical work
• Human Assurance: Flag results that ignore constraints or break analytical logic

Module 2.1: Analysis Techniques

• Sorting Data (Single & Multi-level)
• Filtering Data (Basic & Advanced Filters)
• Subtotals and Grouping Data
• Data Validation for Error Prevention
• Using Excel Tables for Data Management
• Array Formulas and Dynamic Arrays (FILTER, SORT, UNIQUE)
• Financial Functions (NPV, IRR, PMT) (Copilot can template calculations; contextual review required)
• Statistical Functions (MEDIAN, MODE, STDEV)
• Error Handling Functions (IFERROR, ISERROR)
• Advanced Date and Time Functions (WORKDAY, NETWORKDAYS, EOMONTH)

Module 2.2: Advanced Excel Functions

• Creating and Formatting PivotTables
• Grouping and Summarizing Data
• Calculated Fields and Items
• Creating PivotCharts
• Slicers and Timelines for Interactive Reports

Module 2.3: PivotTables & PivotCharts

• Introduction to Macros and VBA
• Recording and Running Macros
• Basic VBA Concepts (Variables, Loops, If Statements)

Module 2.4: Excel Automation with Macros

• Editing and Debugging Macro Code
• Best Practices for Macro Security

AI-Powered Learning Experience

• Smart Structuring: Get AI-driven pivots, macros, and anomaly highlights
• Faster Reporting: Accelerate recurring dashboards with consistent automated support
• Pattern Detection: Surface hidden outliers that manual review may miss
• KPI Ownership: Define performance measures using strategic business understanding
• Strategic Relevance: Challenge polished reports that ignore real stakeholder needs

Module 3.1: Introduction to SQL & Databases

• What is SQL? Overview and Importance
• Relational Database Concepts (Tables, Rows, Columns)
• Common Database Management Systems (MySQL, PostgreSQL, Oracle, SQL Server)
• SQL Syntax Rules
• Connecting to a Database (Using tools like MySQL Workbench or pgAdmin)

Module 3.2: Basic SQL Queries

• The SELECT Statement
• Selecting Specific Columns
• Using DISTINCT to Remove Duplicates
• Simple WHERE Clauses for Filtering
• Using ORDER BY for Sorting Data
• Basic LIMIT clause (for databases like MySQL/PostgreSQL) (AI can scaffold SELECTs; validate business logic)

Module 3.3: Filtering & Sorting Data

• Logical Operators (AND, OR, NOT)
• Filtering with BETWEEN, IN, and LIKE
• Handling NULL values (IS NULL, IS NOT NULL)
• Sorting Data with ORDER BY (Ascending & Descending)

Module 3.4: Aggregate Functions & Grouping

• Aggregate Functions (COUNT, SUM, AVG, MIN, MAX)
• Grouping Data with GROUP BY
• Filtering Groups with HAVING
• Using GROUP BY with Multiple Columns
• Understanding the Difference between WHERE and HAVING

Module 3.5: Joins & Data Relationships

• Introduction to Joins and Data Relationships
• INNER JOIN for Matching Data
• LEFT JOIN for Including Non-matching Records
• RIGHT JOIN and FULL OUTER JOIN (if supported by the database)
• Using Aliases for Table Names
• Joining Multiple Tables
• Best Practices for Writing Joins

AI-Powered Learning Experience

• Query Assistance: Get AI support for building and refining SQL
• Performance Tuning: Receive optimisation guidance for faster, efficient execution
• Error Prevention: Catch syntax issues and improve indexing recommendations
• Business Alignment: Define joins and filters using real operational logic
• Result Integrity: Challenge valid-looking queries that deliver misleading answers

Module 4.1: Introduction to Power BI

• Overview of Power BI components (Desktop, Service, Mobile).
• Copilot Introduction: How to use AI-powered features for data exploration.
• Installing Power BI Desktop and setting up the environment.
• Navigating the Power BI interface: ribbons, panes, and views.
• Hands-on Lab: Importing sample datasets and exploring Copilot.

Module 4.2: Data Preparation & Transformation

• Connecting to various data sources (Excel, SQL, Web APIs).
• Data profiling, data types, and handling missing values.
• Advanced transformations: merging, appending, pivoting, unpivoting data.
• Using Power Query Editor for complex data shaping.

Hands-on Lab: Transforming raw data into a clean dataset.

AI-Powered Learning Experience

• Automated Reporting: Generate visuals, summaries, and early insights within minutes
• Rapid Exploration: Use AI for fast analysis and initial storytelling
• Pattern Discovery: Surface trends and opportunities for deeper investigation
• Human Review: Align final reports with stakeholders and business strategy
• Strategic Relevance: Challenge beautiful dashboards that lack actionable answers

Module 5.1: Data Modeling & DAX Fundamentals

• Designing star and snowflake schemas.
• Creating and managing table relationships.
• DAX basics: calculated columns, measures, and quick measures.
• Using DAX for basic calculations like SUM, AVERAGE, COUNT.

Hands-on Lab: Building a data model for sales data analysis.

Module 5.2: Advanced DAX Functions & Optimization

• Advanced DAX functions: CALCULATE, FILTER, ALL, RELATED.
• Time intelligence functions for date-based calculations (e.g., YTD, QTD).
• Performance optimization techniques: DAX query tuning, minimizing model size.

Hands-on Lab: Creating advanced DAX measures for business insights.

AI-Powered Learning Experience

• Accelerated Development: Generate DAX formulas faster with AI assistance
• Performance Optimisation: Receive tuning guidance to improve query responsiveness
• Pattern Guidance: Identify efficient calculations and detect potential bottlenecks early
• Mandatory Validation: Check every AI formula against business logic
• Model Integrity: Prevent slow, bloated code that reduces dashboard performance

Module 6.1: Visualizations & Reporting Best Practices

• Building advanced visuals: combination charts, gauges, maps, and custom visuals.
• Creating dynamic reports with slicers, bookmarks, and drill-through.
• Designing for user experience: layout, color schemes, and storytelling.
• Best practices for dashboard design and performance.

Hands-on Lab: Creating an interactive sales performance dashboard.

Module 6.2: Power BI Service & Collaboration

• Publishing reports and dashboards to Power BI Service.
• Configuring dataset refresh schedules and managing gateways.
• Setting up Row-Level Security (RLS) for data access control.

Hands-on Lab: Publishing and sharing reports with user access control.

AI-Powered Learning Experience

• Intelligent Layouts: Get visual types, layouts, and refresh schedules suggested
• Rapid Prototyping: Use AI for first drafts and narrative starting points
• Design Ownership: Refine layouts using UX and decision-focused storytelling
• Critical Judgment: Challenge visuals that look good but lack meaning
• Business Impact: Deliver dashboards that drive clear, actionable decisions

Module 7.1 : Copilot in Power BI

• Using Copilot to generate natural language insights and queries.
• Automating data exploration and report generation.
• Implementing Copilot for predictive analytics and recommendations.

Hands-on Lab: Using Copilot for advanced data exploration.

Module 7.2 : Advanced Data Analysis Techniques

• Utilizing AI visuals like Key Influencers, Decomposition Tree, and Smart Narrative.
• Conducting clustering, anomaly detection, and forecasting.
• Interactive Q&A with natural language queries.

Hands-on Lab: Applying AI visuals to real-world business data.

Module 7.3 : Microsoft Fabric Deep Dive

• Overview of Microsoft Fabric: Synapse, Data Factory, Data Lake, and Power BI.
• Using OneLake for centralized data storage.
• Building Synapse Dataflows for real-time data ingestion.
• Creating data pipelines for ETL processes and integrating with Power BI.

Hands-on Lab: Setting up a Fabric workspace, creating dataflows, and integrating with Power BI.

AI-Powered Learning Experience

• Predictive Automation: Use Copilot to forecast trends and streamline ETL
• Scaled Intelligence: Detect anomalies and monitor pipelines across large datasets
• Quality Assurance: Enforce human checkpoints before downstream consumption
• Compliance Control: Keep oversight on regulation-sensitive, high-risk data flows
• Accountability Focus: Prevent hidden errors that escalate into business impact

Module 8.1 : Data Preparation using Tableau Prep

• Overview of Tableau Prep for data cleaning and transformation.
• Importing, filtering, and shaping data for analysis.
• Data profiling and preparing datasets for analysis.

Module 8.2 : Data Connection with Tableau Desktop

• Connecting to various data sources (spreadsheets, databases, cloud).
• Understanding live connections vs extracts.
• Managing data joins, unions, and blends.

Module 8.3 : Basic Visual Analytics

• Building foundational visualizations like bar charts, line charts, and scatter plots.
• Sorting, filtering, and grouping data.
• Working with visual marks (size, color, labels) for enhanced data presentation.

Module 8.4 : Calculations in Tableau

• Creating row-level and aggregate calculations.
• Using string functions, logical functions, and conditional calculations.
• Practical applications of calculations for deriving insights.

Module 9.1 : Advanced Visual Analytics

• Incorporating reference lines, trend lines, and forecasts.
• Using parameters to create dynamic visualizations.
• Highlight actions, sets, and advanced tooltips for interactivity.

Module 9.2 : Level of Detail (LOD) Expressions in Tableau

• Understanding Fixed, Include, and Exclude LOD calculations.
• Practical scenarios for using LOD expressions in reporting.
• Combining LOD expressions with other calculations.

Module 9.3 : Advanced Charts in Tableau

• Creating dual-axis charts, waterfall charts, and bullet graphs.
• Understanding when and how to use advanced charts for storytelling.
• Hands-on practice with custom chart creation.

Module 9.4 :Dashboards and Stories

• Building interactive dashboards from multiple sheets.
• Using dashboard actions to filter and highlight data dynamically.
• Creating stories to present data insights in a narrative format

AI-Powered Learning Experience

• Accelerated Analysis: Use Tableau AI for rapid exploration and forecasting
• Insight Generation: Identify key relationships and generate analytical hypotheses quickly
• Augmented Intelligence: Combine AI assistance with human critical thinking skills
• Context Validation: Verify AI findings against real business logic and relevance
• Analytical Integrity: Challenge implausible insights that conflict with domain expertise

Scenario: The organization plans to adopt AI Agents to manage parts of analytics workflows.

Phase 1 – Task & Workflow Design

• AI Agents handle: data cleaning, ETL automation, anomaly detection, refresh scheduling.
• Humans handle: KPI design, stakeholder alignment, interpreting insights, compliance oversight.

Phase 2 – Strategic Implementation Choices

• Where AI Agents Should Be Implemented: repetitive data cleaning, ETL pipelines, anomaly detection, refresh scheduling.
• Where AI Agents Should Not Be Implemented (Why): KPI design (business context), stakeholder alignment (trust & communication), interpreting insights (domain knowledge), compliance-sensitive decisions (ethical responsibility).

Phase 3 – Productivity vs. Risk Analysis

Activity (Assessment):

• Teams map workflows, dividing AI-suitable vs. human-critical tasks.
• Deliverable: workflow diagram or 2-page justification plan.

Outcome: Learners design balanced workflows leveraging AI efficiency while ensuring compliance, ethics, and strategic control.