Data Analytics (Python, SQL, Excel & Power BI)

Data Analytics with Excel, Python, and Power BI is a comprehensive 4-month course designed to equip students with the essential skills for data analysis and visualization. The course covers:

• Excel Proficiency: Mastering data cleaning, advanced formulas, pivot tables, and macros.
• Python Expertise: Learning data manipulation with Pandas, visualizations using Matplotlib and Seaborn, and automating workflows.
• Power BI Skills: Building interactive dashboards, creating advanced visualizations, and leveraging DAX for analytics.

Career readiness is integrated with regular CV workshops to help students effectively showcase their skills. The program concludes with a hands-on project and guidance for industry preparation, ensuring students are job-ready.

Who Should Attend?

• Senior students preparing for a Data Analyst career.
• Graduates seeking to enter the field of Data Analytics and Data Visualization.
• Working professionals looking to enhance their analytical skills and advance their careers.
• Entrepreneurs interested in leveraging data analytics to make informed business decisions.

Why CDIP?

• Trainer: Experienced and Expert Industry Professionals.
• Hands-on Training: Hands-on training with Industry-Oriented projects.
• Certification: Certification from UIU upon completion.
• Grooming: CV Development & Interview Simulation.
• Career Support: Career counseling and job placement assistance.
• Flexible Schedule: Weekly one class (Friday).

Md. Istiak Sarwar
Lead Analytics Architect, Grameenphone Ltd
Experience: 14+ years
Linkedin: https://www.linkedin.com/in/md-istiak-sarwar/

Tentative Course Outline

Week 1: Analytics Mindset, Industry Orientation & Data Foundations
Topics
• Understanding the Role of a Data Analyst
• Industry Expectations and Career Opportunities
• Current Industry Demand and Job Market Trends
• Analytics Technology Landscape
• Why SQL?
• Why Excel?
• Why Python?
• Why Power BI?
• Analytical Tools Overview
• Database Concepts
• Data Warehousing Fundamentals
• Data Pipelines
• ETL vs ELT

Goal: Develop a strong understanding of the data analytics profession, modern data ecosystems, and the
technologies that enable data-driven decision making in organizations.

Week 2: SQL Fundamentals for Data Analysis
Topics
• Introduction to SQL
• Database Objects & Relationships
• SQL Syntax & Query Structure
• SELECT Statements
• WHERE Clause & Filtering
• ORDER BY & Sorting
• Data Types and Data Conversion
• Built-in Functions
• Logical Operators

Goal: Build foundational SQL skills to retrieve, filter, and organize data efficiently from relational databases.

Week 3: SQL Analytics & Business Reporting
Topics
• SQL Join Concepts
• INNER, LEFT, RIGHT and FULL Joins
• Aggregate and Windows Functions (Count, AVG, SUM, Min, Max, etc.)
• GROUP BY & HAVING
• Business KPI Calculations
• Analytical Query Techniques
• Multi-Table Analysis
• Reporting-Oriented Query Development

Goal: Develop the ability to combine data from multiple sources and generate meaningful business insights
using SQL.

Week 4: Advanced SQL & Data Manipulation
Topics
• Data Manipulation Language (DML)
• Data Definition Language (DDL)
• INSERT, UPDATE, DELETE Operations
• Subqueries
• Common Table Expressions (CTE)
• Set Operations (UNION, INTERSECT)
• Query Optimization Fundamentals
• DISTINCT vs GROUP BY
• SQL Best Practices

Goal: Master advanced SQL techniques for transforming, managing, and optimizing analytical datasets.

Week 5: Excel for Data Analytics Fundamentals
Topics
• Analytics Workflow in Excel
• Excel Interface & Productivity Features
• Data Entry & Management
• Essential Analytical Functions (Sum, Avg, Count, etc.)
• Sorting & Filtering
• Cell Referencing Techniques
• Data Exploration Fundamentals

Goal: Develop proficiency in using Excel as an analytical tool for data exploration and business reporting.

Week 6: Data Cleaning & Preparation in Excel
Topics
• Data Quality Assessment
• Handling Missing Values
• Duplicate Detection & Removal
• Text-to-Columns
• Data Standardization
• Data Validation
• Conditional Formatting
• Basic Data Visualization

Goal: Learn how to prepare raw datasets for analysis by applying industry-standard data cleaning and
quality assurance techniques.

Week 7: Advanced Excel Analytics
Topics
• Pivot Tables & Pivot Charts
• Advanced Lookup Techniques
• VLOOKUP, HLOOKUP, XLOOKUP
• INDEX-MATCH
• Text Functions
• Logical Functions
• Dynamic Reporting Techniques
• Business Analytics Use Cases

Goal: Use advanced Excel functions and analytical techniques to perform business analysis and automate
reporting processes.

Week 8: Python Fundamentals for Data Analytics
Topics
• Python Environment Setup
• Introduction to VS Code
• Variables & Data Types
• Operators
• Conditional Statements
• Loops & Iteration
• Functions
• String Manipulation
• Python Programming Best Practices

Goal: Build a strong programming foundation to automate analytical tasks and support data-driven problem
solving.

Week 9: Data Analysis with Pandas
Topics
• Introduction to Pandas
• DataFrames & Series
• Importing Data Sources (Reading CSV, Excel files)
• Data Exploration Techniques
• Data Filtering & Selection
• Data Cleaning with Pandas
• Handling Missing Values
• Data Transformation
• Aggregation & Grouping
• Exploratory Data Analysis (EDA)

Goal: Perform end-to-end data preparation and exploration analysis using Python and Pandas.

Week 10: Mid-Term Assessment – SQL, Excel & Python
Assessment Components
• SQL Assessment
o Data Retrieval
o Filtering & Sorting
o Joins
o Aggregations
o GROUP BY & HAVING
o Business Query Development
• Excel Assessment
o Data Cleaning
o Lookup Functions
o Pivot Tables
o Conditional Formatting
o Business Reporting
• Python Assessment
o Variables & Functions
o Pandas Operations
o Data Cleaning
o Data Aggregation
o Basic EDA

Goal: Evaluate learners’ proficiency in SQL, Excel, and Python and identify areas requiring improvement
before progressing to Business Intelligence and Dashboard Development.

Week 11: Power BI Fundamentals
Topics
• Introduction to Business Intelligence
• Power BI Ecosystem
• Data Connectivity
• Power Query Fundamentals
• Data Transformation
• Creating Reports & Visualizations
• Fields, Measures & Dimensions
• Dashboard Fundamentals

Goal: Build foundational business intelligence skills by creating interactive reports and visualizations in
Power BI.

Week 12: Advanced Power BI & Data Modeling
Topics
• Data Modeling Concepts
• Relationship Management
• Star & Snowflake Schemas
• Calculated Columns & Measures
• Introduction to DAX
• Advanced DAX Functions
• Advanced Visualizations
• SQL Integration with Power BI
• DirectQuery vs Import Mode
• Enterprise Reporting Best Practices

Goal: Design scalable analytical data models and develop advanced dashboards capable of supporting
business decision-making.

Week 13: Capstone Project Initiation
Topics
• Business Problem Identification
• Requirement Gathering
• Dataset Selection
• Project Scope Definition
• Data Exploration
• Data Cleaning Strategy
• Project Planning & Milestones
• Stakeholder Perspective

Goal: Translate a real-world business problem into a structured analytics project using industry-standard
methodologies.

Week 14: Dashboard Design & Data Storytelling
Topics
• Dashboard Design Principles
• KPI Framework Development
• Data Storytelling Techniques
• User-Centric Visualization Design
• Interactive Dashboard Development
• Report Publishing & Sharing
• Insight Communication

Goal: Transform analytical findings into compelling visual stories that drive business action and executive
decision-making.

Week 15: Capstone Project Presentation
Topics
• Presentation Techniques
• Insight Communication
• Business Recommendation Development
• Stakeholder Management
• Peer Review & Feedback
• Project Evaluation Framework

Goal: Demonstrate the ability to communicate analytical insights effectively to both technical and business
stakeholders.

Week 16: Career Readiness & Professional Development
Topics
• Data Analyst Resume Development
• LinkedIn Profile Optimization
• Portfolio Building
• Interview Preparation
• Case Study Discussions
• Career Planning & Growth Strategy

Goal: Prepare students to successfully enter the analytics job market with a professional portfolio, interview
readiness, and a clear career development plan.

Program Learning Outcomes:
Upon successful completion of this program, participants will be able to:
• Understand modern data ecosystems and enterprise data architecture.
• Acquire, clean, transform, and analyze structured datasets.
• Write complex SQL queries for business reporting and analytical problem-solving.
• Perform data extraction, transformation, aggregation, and optimization.
• Build automated analytical models and dynamic reports using advanced Excel functions and Pivot
Tables.
• Utilize Python and Pandas for data cleaning, transformation, exploratory analysis, and workflow
automation.
• Design data models and develop interactive dashboards using Power BI and DAX.
• Apply data storytelling techniques to communicate insights and recommendations effectively.
• Complete a real-world analytics project demonstrating end-to-end analytical competency.
• Develop a professional portfolio, optimized LinkedIn profile, and interview readiness for Data
Analyst roles.