Data Analyst Course in Oman

Module 1.1: Introduction to Python

• Overview of Python: history, features, and use cases.
• Installing Python and setting up the development environment.
• Basic syntax: variables, data types, and simple operations.
• Hands-on: Writing a simple Python program to understand basic concepts.

Module 1.2: Sequences and File Operations

• Lists, tuples, sets, dictionaries

• File handling (read/write)

• Probability basics & random numbers

• Hands-on: Coin toss/dice roll simulator

Module 1.3 Functions and OOP

• Functions, scope, arguments

• Classes, objects, inheritance, polymorphism

• Hands-on: Class for random variables

Module 1.4 Modules and Exceptions

• Importing modules

• Exception handling

• Hands-on: Using Python’s statistics module

AI-Powered Excel Mastery

• Formula Acceleration: Generate formulas and cleaning steps automatically with Copilot
• Visual Guidance: Receive chart type suggestions aligned to data patterns
• Workflow Efficiency: Streamline formatting and data preparation with AI support
• Required Validation: Check every AI output against real business rules
• Human Judgment: Lead financial and statistical decisions where context matters

Module 2.1 : Array Manipulation with NumPy

• Creating & manipulating arrays
• Descriptive statistics (mean, median, variance, std)
• Probability distributions with NumPy random

Hands-on: Simulating data (AI can generate examples but analyst validates)

Module 2.2: Introduction to Pandas

• Import/export, Series, DataFrames
• Data cleaning & transformation
• Data distribution exploration

Hands-on: Compute statistics from real datasets

Module 2.3: Advanced Data Manipulation with Pandas

• Merge, join, reshape
• GroupBy, pivot tables, time-series
• Correlation & covariance

Hands-on: Apply grouped stats

Module 2.4: Data Visualization with Matplotlib & Seaborn

• Line, bar, scatter plots
• Histograms, box plots

Hands-on: Visualizing distributions

Module 2.5: Advanced Visualization

• Subplots, multiple axes, customization
• Heatmaps, pair plots
• Regression & statistical relationships

Hands-on: Density plots

Module 2.6: Data Gathering and Web Scraping using Python

• BeautifulSoup for static pages
• Selenium for dynamic pages

Hands-on: Scraping & analyzing data

AI-Enhanced Business Analysis

• Smart Structuring: Get AI-driven pivots, macros, and anomaly detection
• Reporting Velocity: Speed dashboard creation and recurring reporting cycles
• KPI Ownership: Define performance measures using business strategy, not AI
• Stakeholder Relevance: Ensure outputs answer real decision-making needs
• Business Value: Deliver insight that drives action, not just presentation

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-Accelerated SQL Development

• Intelligent Assistance: Generate queries, fix syntax, and refine joins
• Performance Optimisation: Use AI to improve speed and efficiency
• Business Alignment: Ensure results reflect real operational logic and intent
• Critical Judgment: Challenge perfect code that answers the wrong question
• Strategic Ownership: Deliver outputs that support accurate decision-making

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-Driven Visual Intelligence

• Visual Acceleration: Generate reports, layouts, and insights within minutes
• Storytelling Launchpad: Use AI for first drafts and narrative direction
• Contextual Refinement: Apply business understanding to shape final messaging
• Decision Focus: Build dashboards that answer real strategic questions
• Impact Over Aesthetics: Prioritise clarity and action, not surface-level polish

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 Data Modeling Excellence

• Formula Acceleration: Generate DAX and receive optimisation guidance instantly
• Validation Discipline: Check every calculation against real business logic
• Smart Usage: Apply AI for standard calculations and tuning insights
• Performance Protection: Prevent bloated code that slows model responsiveness
• Scalable Accuracy: Deliver results that are fast, precise, and reliable

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 (AI can watch refresh failures & alert)
• Setting up Row-Level Security (RLS) for data access control (AI proposes rules; compliance sign-off required)

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

AI as Your Design Partner

• Design Acceleration: Get layout ideas, visuals, and refresh guidance fast
• Creative Launchpad: Use AI to explore storytelling directions confidently
• Audience Focus: Shape dashboards around real stakeholder needs and expectations
• Dual Standard: Deliver visuals that are clear and meaningful
• Executive Value: Combine aesthetic quality with practical business insight

Module 7.1: Copilot in Power BI

• Using Copilot to generate natural language insights and queries
• Automating data exploration and report generation (AI drafts first-pass analyses; PM/BA refines)
• 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 (AI accelerates discovery; analyst validates causality)
• 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.

Enterprise AI at Scale

• Predictive Intelligence: Use Copilot and Fabric for trends and anomalies
• Automated Pipelines: Orchestrate ETL processes with AI-driven efficiency
• Quality Assurance: Enforce human checkpoints for compliance-sensitive workflows
• Early Detection: Catch data issues before they escalate into risk
• Decision Integrity: Safeguard reliable insights for confident business action

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 (AI “Show Me” assists; analyst crafts narrative)

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.

Scenario: Organization adopts AI Agents for analytics workflows.

Phase 1 – Task & Workflow Design

• AI handles: data cleaning, ETL, anomaly detection, refresh scheduling
• Humans handle: KPI design, stakeholder alignment, insights, compliance

Phase 2 – Strategic Implementation

• AI best for: cleaning, pipelines, anomaly detection, scheduling
• AI not for: KPIs, alignment, insight interpretation, compliance decisions

Phase 3 – Activity & Outcome

• Task: Teams map AI vs. human roles in analytics workflows
• Deliverable: Workflow diagram or 2-page justification

Outcome: Balance AI efficiency with human oversight, ensuring compliance & accountability