This hands‑on course is designed for learners with little or no prior background in Data Analysis. It focuses on building data analysis fundamentals, confidence with tools, and the analyst mindset—how to ask clear questions, organize data, design simple metrics, and communicate insights for decision‑making. The course is fully practical, lab‑based, and project‑oriented, using available local and global realistic business and NGO datasets relevant to the region.
Course Materials
- Raw Data
- Cheatsheets
Target Audience
- Fresh graduates from any discipline
- Working professionals transitioning into data roles
- NGO staff involved in reporting, Monitoring & Evaluation (M&E)
- Absolute beginners with basic computer use skills
Learning Objectives
- Use computers, files, and spreadsheets confidently for data work
- Analyze data using Excel, introductory Python (pandas), and Power BI
- Clean and organize real‑world, messy datasets
- Design basic KPIs and performance indicators
- Build clear, beginner‑level dashboards for decision makers
- Present findings in simple, non‑technical language
- Prepare a small professional portfolio for entry‑level Data Analyst roles
Course Content
- Digital literacy for data work
- Excel fundamentals to intermediate analysis
- Introductory probability and descriptive statistics
- Gentle introduction to Python for data analysis
- Power BI for beginner-level dashboards
- Business and NGO data use cases
- Data storytelling, reporting, and employability skills
Course Assessment
- The passing grade is 70%.
- Project 1: Excel Basics (15%)
- Project 2: Survey / NGO Analysis (15%)Project 3: Python Data Analysis (20%)Project 4: Power BI Dashboard (20%)
- Project 5: Capstone + Presentation (30%)
Course Schedule
| Week | Topics & Subtopics | Activities (Detailed) | Practical Activity | Learning Outcomes |
| 1 | Digital Literacy & Data Basics: computer basics, internet navigation, file management, data types, CSV vs Excel, data ethics | Instructor-led walkthroughs; internet services, guided computer setup; file/folder organization drills; identifying data types from real examples; short group discussions | Organize personal course workspace; classify sample datasets | Navigate digital tools confidently and understand basic data concepts |
| 2 | Excel Fundamentals: interface, basic formulas (SUM, AVERAGE, COUNT), sorting, filtering | Step-by-step Excel labs; formula practice sheets; error correction exercises; peer review of spreadsheets | Analyze a simple sales spreadsheet using formulas and filters | Perform basic calculations and organize data accurately |
| 3 | Excel Analysis & KPIs: pivot tables, charts, basic KPIs | Pivot table demonstrations; chart comparison exercises; KPI brainstorming sessions; instructor feedback rounds | Project 1:Build Sales & Inventory Tracker (Excel) | Summarize data and design basic performance metrics |
| 4 | Introductory Statistics: averages, percentages, trends, basic probability | Guided calculations in Excel; trend interpretation workshops; real-life probability examples; group problem-solving | Interpret trends and percentages in real datasets | Understand what numbers mean and avoid common misinterpretations |
| 5 | NGO & Survey Data: survey data, indicators, data consistency | Cleaning survey responses; indicator definition exercises; consistency checks; applied NGO case discussions | Project 2:Survey / NGO Data Analysis | Analyze and summarize non-business datasets confidently |
| 6 | Python Basics: variables, data types, lists, pandas intro | Guided Python installation; code-along sessions; simple coding challenges; instructor troubleshooting | Load and explore a CSV file using Python | Run simple Python scripts for data exploration |
| 7 | Python for Data Analysis: cleaning, filtering, grouping, charts | Hands-on pandas labs; cleaning messy datasets; groupby practice; visualization walkthroughs | Project 3:Beneficiary / Customer Analysis | Analyze datasets too large or complex for Excel |
| 8 | Power BI Foundations: data import, Power Query, visuals | Power BI setup support; ETL demonstrations; visual-building labs; instructor-led corrections | Build first Power BI report from raw data | Prepare and model data for dashboards visually |
| 9 | Dashboard Design: charts, KPIs, layout, interactivity | Dashboard critique sessions; layout redesign exercises; slicer/filter practice; usability testing | Project 4:Business Performance Dashboard | Build clear, interactive beginner-level dashboards |
| 10 | Capstone Project: tool selection, messy data, reporting | Supervised project work; one-on-one coaching; data cleaning clinics; reporting drafts | Project 5:End-to-End Analysis (Capstone) | Complete a full analysis workflow independently |
| 11 | Career Readiness: data storytelling, CV, interviews | Storytelling workshops; mock interviews; CV/LinkedIn clinics; peer presentations | Capstone presentation to mock panel | Communicate insights clearly and prepare for employment |