Key takeaways
- The ebook has ten chapters built to be read in order.
- Each chapter has a reading time estimate and one exercise.
- A steady pace of two or three chapters a week finishes it in about four weeks.
- Exercises matter more than reading, so type every example.
- The final chapter combines everything into one capstone project.
What is data analytics ebook?
What is in the ebook?
The ebook follows the path of a real analysis. Early chapters build the base, middle chapters teach the tools and final chapters teach communication and a capstone. The reading times below are estimates for a beginner who types the examples and are not measured results.
What are the chapters?
| # | Chapter | Main ideas | Est. reading | Exercise |
|---|---|---|---|---|
| 1 | What data analytics is | Roles, workflow, types of analysis | 40 min | Describe one business question |
| 2 | Data and data types | Rows, columns, types, sources | 45 min | Classify ten fields |
| 3 | Excel for analysis | Tables, lookups, PivotTables | 90 min | Build a sales summary |
| 4 | SQL for analysis | SELECT, joins, grouping | 90 min | Answer five questions |
| 5 | Python for analysis | pandas, cleaning, plots | 90 min | Clean a messy CSV |
| 6 | Data cleaning | Missing values, duplicates, outliers | 60 min | Write a cleaning log |
| 7 | Statistics basics | Averages, spread, correlation, tests | 75 min | Interpret a small A/B test |
| 8 | Data visualisation | Chart choice, colour, labels | 60 min | Redraw one bad chart |
| 9 | Dashboards and storytelling | KPIs, layout, narrative | 75 min | Sketch a dashboard |
| 10 | Capstone project | End-to-end project | 120 min | Publish a project write-up |
How do I read it in four weeks?
Week 1: chapters 1 to 3. Week 2: chapters 4 and 5. Week 3: chapters 6 and 7. Week 4: chapters 8 to 10. Keep one hour a day for exercises. If a chapter feels hard, repeat its exercise instead of rushing on.
How should I use the exercises?
Each exercise is small on purpose. Do it on your own data or a public dataset, write two lines about what you found and keep the file in a folder named by chapter. By chapter ten you will have a small portfolio of work you can discuss in interviews.
What do I need before starting?
A computer, a spreadsheet tool, a free database tool such as SQLite, Python with pandas and a notebook editor. School-level maths is enough. No programming experience is assumed in the early chapters.
How do the chapters connect?
Chapter 2 gives the vocabulary used everywhere. Chapters 3 to 5 show the same task in three tools so that you see what changes and what stays the same. Chapter 6 shows why cleaning takes most of the time. Chapters 7 to 9 turn numbers into decisions. Chapter 10 asks you to combine everything.
Is this a substitute for a course?
It is self-study material. It does not include a certificate, live feedback or placement support. A trainer-led course adds practice with feedback, which many learners find useful.
Where can I read further?
The official documentation for each tool is the best reference once you finish a chapter; the sources list below links to the main ones.
What will I be able to do after each part?
After chapters 1 and 2 you can describe a data problem and identify the fields you need. After chapters 3 to 5 you can load, summarise and chart data in three tools. After chapters 6 and 7 you can clean data, explain basic statistics and avoid common traps such as treating a small difference as proof. After chapters 8 and 9 you can build a clear chart and a simple dashboard for a stated audience. After chapter 10 you have one finished project that you can show.
How do I stay consistent?
Fix a daily time, even forty minutes, and keep a simple log of the chapter and exercise finished. When you miss a day, restart the next day without trying to catch up all at once. Study with a friend if possible and explain a chapter to each other, which is a quick way to find gaps.
How should I take notes?
Write the main idea of each section in one sentence, add one example of your own and list one question. Review the notes at the end of each week. The notes become your revision sheet before interviews.
What if a chapter feels too hard?
Stop, reread the previous chapter's exercise and repeat it with a different dataset. Look at the official documentation for the term that confuses you. If you are stuck for more than an hour, write the exact question and ask a mentor or a community forum.
How do I know I have understood a chapter?
Close the book and explain the main idea aloud in two minutes, then redo the exercise with a new dataset. If you can do both without looking, move on. If not, reread only the parts you could not explain.
Download Data Analytics eBook PDF
Data Analytics eBook PDF
The PDF carries the same content as this page in a clean, print-ready layout, so you can read it offline and keep it for revision.
4 pages · 90 KB · PDF · Updated 5 Nov 2026