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

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).

Farahnaz Reza
Senior Contextual Interaction Developer, Grameenphone Ltd
Experience: 5+ years
Linkedin: https://www.linkedin.com/in/farahnaz-reza/

5+ years of expertise in the fields of Customer Lifecycle management, Contextual AI, Data Analytics, Digital Analytics.

Tentative Course Outline

Week 1: Analytics Mindset & Job-Role Orientation

Goal: Help students understand why analytics exists and how it is used in jobs.
Class 1: Skills & Concepts

• What data analytics means in real business 
• Types of decisions data supports (growth, engagement, pricing) 
• Understanding context vs raw numbers 
• Overview of job roles: 
  • Campaign Manager 
  • Digital Analyst 
  • App Engagement Analyst 
  • Retail Analyst 
  • Pricing Analyst 

Business Case Discussion

• Data usage drops during Ramadan
  → Why immediate aggressive offers may be the wrong decision 

Tools

• No tools yet (thinking before tools) 

Week 2: Excel Basics for Campaign & Digital Analysis

Goal: Learn Excel fundamentals through real campaign use cases.

Excel Skills Taught

• SUM, AVERAGE, COUNT 
• Cell referencing (absolute vs relative – basic) 
• Sorting & filtering data 
• Simple column & bar charts 

Use Case

• Campaign performance summary 
• Channel penetration comparison (App vs USSD vs Digital) 

Decision Logic

• One-month drop vs six-month trend 
• When data should not trigger action 

Week 3: Excel for Customer Behavior & Engagement

Goal: Understand user behavior using Excel logic.

Excel Skills Taught

• IF function 
• COUNTIF, SUMIF 
• Text functions: LEFT, RIGHT, LEN 
• Removing duplicates 
• Handling missing values 

Use Case

• Identifying active vs inactive users 
• Engagement drop vs seasonal behavior 

Decision Logic

• Engagement decline ≠ churn 
• Behavior needs trend analysis 

Week 4: Excel for Retail & Pricing Decisions

Goal: Use Excel to simulate real retail and pricing logic.

Excel Skills Taught

• VLOOKUP / INDEX-MATCH (basic) 
• IF with nested conditions 
• Percentage change calculation 
• Simple scenario tables 

Use Case

• Retail commission calculation 
• Product price comparison 
• Revenue impact estimation 

Decision Logic

• Higher commission ≠ higher sales 
• Price increase vs volume trade-off 

Week 5: SQL Basics – Asking Questions from Data

Goal: Teach SQL as a structured way to ask questions.

SQL Skills Taught

• SELECT 
• WHERE 
• ORDER BY 
• Basic filtering (AND, OR) 

Use Case

• Product performance comparison 
• Channel-wise usage analysis 

Interview Skill

• Translating business questions into queries 

Week 6: SQL for Comparison & Strategy

Goal: Compare groups and identify patterns.

SQL Skills Taught

• GROUP BY 
• COUNT, SUM, AVG 
• Basic JOIN (visual explanation) 
• HAVING (simple logic) 

Use Case

• Regional performance 
• Retailer comparison 
• Pricing impact analysis 

Decision Logic

• Best-performing segment ≠ most profitable 

Week 7: Python Basics for Analytical Thinking

Goal: Introduce Python gently as an automation helper.

Python Skills Taught

• Variables 
• Lists & dictionaries 
• If-else conditions 
• For loops (basic) 
• Simple calculations 

Use Case

• Automating repetitive Excel-style logic 
• Data validation checks 

Decision Logic

• When automation helps vs manual analysis 

Week 8: Pandas for Working with Real Data

Goal: Work confidently with real datasets.

Python / Pandas Skills

• Reading CSV & Excel files 
• Filtering rows 
• Selecting columns 
• Handling missing values 
• Basic aggregation 

Use Case

• App engagement analysis 
• Customer usage patterns 

Decision Logic

• Why averages can hide real problems 

Week 9: Data Visualization for Business Communication

Goal: Learn how visuals support decisions.

Skills Taught

• Bar charts 
• Line charts 
• Histograms 
• Choosing the right chart for the question 

Use Case

• Engagement trend visualization 
• Campaign performance comparison 

Interview Skill

• Explaining insights to non-technical managers 

Week 10: Power BI Fundamentals

Goal: Build reporting mindset.

Power BI Skills

• Importing Excel & SQL data 
• Data cleaning using Power Query 
• Understanding fields & measures 
• Basic visuals 

Use Case

• Campaign dashboard 
• Digital penetration dashboard 

Decision Logic

• What managers actually care about in reports 

Week 11: Role-Based Dashboards in Power BI

Goal: Align dashboards with job roles.

Skills Taught

• Filters & slicers 
• KPI cards 
• Cross-filtering visuals 

Role-Based Dashboards

• Campaign Manager dashboard 
• App Engagement dashboard 
• Retail & Pricing overview 

Decision Logic

• When dashboards say “wait”, not “act” 

Week 12: Project Kickoff – Choose Your Role

Goal: Apply skills to real problems.

Skills Taught

• Problem framing 
• Defining KPIs 
• Dataset understanding 
• Analysis planning 

Output

• Role-specific project selection 
• Clear problem statement 

Week 13: Statistics for Decision Confidence

Goal: Teach only what analysts actually use.

Statistics Skills

• Mean, median, mode 
• Variance & distribution 
• Trend vs anomaly 
• Confidence in insights 

Use Case

• Why one-month data is misleading 
• Supporting decisions with evidence 

Week 14: Business Case Framing, Storytelling & LinkedIn Optimization

Goal: Make students interview-ready.

Skills Taught

• Structuring business cases 
• Explaining: 
  • Problem → Analysis → Decision → Impact 
• Role-based interview answers 
• LinkedIn headline & project framing 
• Avoiding buzzwords 

Practice

• Mock interview explanations 
• Peer feedback 

Week 15: Final Project Presentations

Goal: Simulate real industry scenarios.

Skills Demonstrated

• Analytical thinking 
• Decision justification 
• Dashboard storytelling 
• Business communication 

Week 16: Career Strategy & Wrap-Up

Goal: Prepare students for the job market.

Skills Covered

• Role-based CV writing 
• Interview Q&A (Excel, SQL, Python, Power BI) 
• Career roadmap discussion 
• Next learning steps

Key Takeaways for Students:

•  Practical Excel, SQL, Python, Power BI skills
•  Role-based analytical thinking
•  Strong decision-making ability
•  Interview & LinkedIn readiness
•  Confidence to explain why, not just how

Note: The course outline is subject to modification based on students’ comprehension levels and industry needs to ensure relevance and effectiveness.