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Data Analytics

Beginner 8 weeks $185.50 DigitalTechnology

Practice useful digital habits, clear communication, and confident online work.

Data Analytics

Beginner · 8 weeks

Paid course
Mastering Data Analytics: From First Steps to Expert
Smart information skills take the guesswork out of school, work, and business growth. This program teaches you how to collect messy details, uncover hidden patterns, design interactive reports, and use metrics to drive successful outcomes.

It is perfect for ambitious minds ready to start without any prior experience, gain advanced technical capabilities, and command total control over numbers.
185 lessons8 weeks$185.50

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Your learning path

Lessons and quizzes unlock in sequence.

185 steps
Show all 185 steps
  1. 1Week 1 · Welcome to Data Analytics — What Analysts Actually DoLesson + quiz
  2. 2The four jobs — and the 80/20 that surprises peopleLesson + quiz
  3. 3You do not need to become eight experts. You need to know which tool fitsLesson + quiz
  4. 4EXAMPLELesson + quiz
  5. 5TERMS YOU NOW OWNLesson + quiz
  6. 6Week 2 · Data Everywhere — Where It Comes From & Why It Is MessyLesson + quiz
  7. 7One habit to install today: when data arrives, always ask three questions —Lesson + quiz
  8. 8Build it — play data detectiveLesson + quiz
  9. 9TERMS YOU NOW OWNLesson + quiz
  10. 10Week 3 · Your Analyst Workbench — Installing the 2026 ToolkitLesson + quiz
  11. 11Build it — the guided installLesson + quiz
  12. 12TERMINALLesson + quiz
  13. 13Run it. You should see the little table from Week 1 — now built by Python, withLesson + quiz
  14. 14TIP — POWERSELLER SHORTCUT: THE NUCLEAR VERSIONLesson + quiz
  15. 15Week 4 · Numbers vs Words — Data Types & Your First Excel AnalysisLesson + quiz
  16. 16How to spot text-disguised-as-number, in one second: in a column ofLesson + quiz
  17. 17EXAMPLELesson + quiz
  18. 18Page 18Lesson + quiz
  19. 19WRAP-UPLesson + quiz
  20. 20Week 5 · Your First Real Analysis — Sales Data at a Bigger ScaleLesson + quiz
  21. 21PYTHONLesson + quiz
  22. 22Write the answers in plain sentences — “Revenue grew from £X in January to £YLesson + quiz
  23. 23WRAP-UPLesson + quiz
  24. 24Week 6 · Mini-Project — End-to-End Analysis of a 200-Row DatasetLesson + quiz
  25. 25PYTHONLesson + quiz
  26. 26Always clean duplicates before summing — a duplicate row is double-countedLesson + quiz
  27. 27COMMON MISTAKE — SUMMING BEFORE DEDUPLICATINGLesson + quiz
  28. 28Week 7 · The Formulas That Matter — XLOOKUP, SUMIFS & FriendsLesson + quiz
  29. 29EXAMPLELesson + quiz
  30. 30EXAMPLELesson + quiz
  31. 31of SUMIFS — four regions × four quarters = 16 formulas, or one pivot (nextLesson + quiz
  32. 32WRAP-UPLesson + quiz
  33. 33Week 8 · Cleaning Data Properly — Power Query in 60 MinutesLesson + quiz
  34. 34Step 1 — Connect. Select any cell in the raw data on a sheet (or paste the 205Lesson + quiz
  35. 35Where Power Query wins (and where it stops)Lesson + quiz
  36. 36• Data model — the name for related tables living together in Excel’s PowerLesson + quiz
  37. 37Week 9 · Pivot Tables Deep — Slicers, Grouping & Calculated FieldsLesson + quiz
  38. 38Calculated fields: doing math the pivot can’tLesson + quiz
  39. 39Dynamic columns bonus: with months in Columns, Analyze › Field Buttons letsLesson + quiz
  40. 40WRAP-UPLesson + quiz
  41. 41Week 10 · Charts That Tell the Truth — And the Ones That LieLesson + quiz
  42. 42The five classic deception tricks (memorise and hunt them)Lesson + quiz
  43. 43Excel’s charting defaults are 80% of the way; the 20% is editing the title into aLesson + quiz
  44. 44Week 11 · Power Pivot & DAX — Data Models, Relationships & First MeasuresLesson + quiz
  45. 45PYTHONLesson + quiz
  46. 46(Inside DAX, [column] names a column; row-level multiplication is legal hereLesson + quiz
  47. 47TERMS YOU NOW OWNLesson + quiz
  48. 48Week 12 · Excel Project — Build the Monthly Sales DashboardLesson + quiz
  49. 49EXAMPLELesson + quiz
  50. 50Step 4 — The Notes sheet (method, 8 lines)Lesson + quiz
  51. 51• Report connections — slicers driving multiple pivots at once.Lesson + quiz
  52. 52Week 13 · Python From Zero — The Language in Analyst’s ClothingLesson + quiz
  53. 53PYTHONLesson + quiz
  54. 54PYTHONLesson + quiz
  55. 55PYTHONLesson + quiz
  56. 56COMMON MISTAKE — INDENTATION IS NOT COSMETICSLesson + quiz
  57. 57Week 14 · Jupyter & NumPy — The Analyst’s WorkbenchLesson + quiz
  58. 58PYTHONLesson + quiz
  59. 59PYTHONLesson + quiz
  60. 60COMMON MISTAKE — THE NOTEBOOK THAT ONLY WORKS IN YOUR MEMORYLesson + quiz
  61. 61Week 15 · pandas I — DataFrames, Selection & GroupingLesson + quiz
  62. 62The boolean mask is the workhorse: any condition on the DataFrame returns aLesson + quiz
  63. 63PYTHONLesson + quiz
  64. 64top five rows are the fastest way to see what is actually in the data beforeLesson + quiz
  65. 65Week 16 · pandas II — Joins, Reshaping, Time Series & Cleaning at ScaleLesson + quiz
  66. 66price_y . If that surprises you mid-analysis, your keys or column names areLesson + quiz
  67. 67Cleaning at scale: the reusable functionLesson + quiz
  68. 68print the report. Then generate it again with seed 17 and rerun. Same function,Lesson + quiz
  69. 69WRAP-UPLesson + quiz
  70. 70Week 17 · Visualization in Python — Matplotlib, Seaborn & PlotlyLesson + quiz
  71. 71Seaborn: statistical shapes in one lineLesson + quiz
  72. 72The one rule that governs all threeLesson + quiz
  73. 73Week 18 · Getting Data Into Python — Files, APIs & Scraping, Done EthicallyLesson + quiz
  74. 74Parquet is the format to know going forward: it stores types and compression,Lesson + quiz
  75. 75PYTHONLesson + quiz
  76. 76PYTHONLesson + quiz
  77. 77• robots.txt / ToS — the site’s rules about automated access; read themLesson + quiz
  78. 78Week 19 · Automation — Scripts, Scheduling & Reusable PipelinesLesson + quiz
  79. 79PYTHONLesson + quiz
  80. 80Run it: python run_monthly.py . Read summary.txt . Read the last lines ofLesson + quiz
  81. 81Scheduling: let the operating system be the triggerLesson + quiz
  82. 82• Scheduler (Task Scheduler / cron) — the OS feature that runs a programLesson + quiz
  83. 83Week 20 · Python Project — End-to-End E-Commerce AnalysisLesson + quiz
  84. 84PYTHONLesson + quiz
  85. 85PYTHONLesson + quiz
  86. 86EXAMPLELesson + quiz
  87. 87WRAP-UPLesson + quiz
  88. 88Week 21 · SQL From Zero — SELECT, WHERE & Thinking in TablesLesson + quiz
  89. 89The sentence, in orderLesson + quiz
  90. 90PYTHONLesson + quiz
  91. 91strftime formats a date into text; GROUP BY 1 means “group by the firstLesson + quiz
  92. 92• Filter (WHERE) — the keep-only-rows clause.Lesson + quiz
  93. 93Week 22 · Joins & Aggregations — GROUP BY, HAVING & JOINs in DepthLesson + quiz
  94. 94Joins: combining tables by a keyLesson + quiz
  95. 95returns 87, 13 orders had no match (inner) — now you know, instead of finding itLesson + quiz
  96. 96COMMON MISTAKE — HAVING WHERE YOU NEED WHERE (AND VICE VERSA)Lesson + quiz
  97. 97Week 23 · Window Functions — Ranks, Running Totals & CohortsLesson + quiz
  98. 98• DENSE_RANK — ties share, no skip (1, 2, 2, 3).Lesson + quiz
  99. 99A first cohort: repeat purchaseLesson + quiz
  100. 100A cohort is a group defined by when they did something (their first order’sLesson + quiz
  101. 101WRAP-UPLesson + quiz
  102. 102Week 24 · Data Modeling — Star Schemas, Warehouses & Why Structure MattersLesson + quiz
  103. 103Why analysts love the star (the three reasons)Lesson + quiz
  104. 104PYTHONLesson + quiz
  105. 105PYTHONLesson + quiz
  106. 106Compare these to the same questions against an unmodelled flat CSV: the joinsLesson + quiz
  107. 107• Foreign key — the column in the fact that points at a dimension row.Lesson + quiz
  108. 108Week 25 · R in 60 Minutes — The Tidyverse for AnalystsLesson + quiz
  109. 109RLesson + quiz
  110. 110PYTHONLesson + quiz
  111. 111TIP — WHEN A TEAM SAYS “WE USE R”Lesson + quiz
  112. 112Week 26 · Connecting It All — Python, SQL, DuckDB & the Modern Data StackLesson + quiz
  113. 113PYTHONLesson + quiz
  114. 114No load step, no staging, no ETL — SQL runs on the file. This is why DuckDB hasLesson + quiz
  115. 115You do not need to have used Fivetran or dbt to place them. When someoneLesson + quiz
  116. 116• dbt — SQL-based transformation: models, tests, documentation,Lesson + quiz
  117. 117Week 27 · Statistics Without Tears — Distributions, Spreads & UncertaintyLesson + quiz
  118. 118The chart is the lesson: most orders are small, a thin tail stretches right (theLesson + quiz
  119. 119PYTHONLesson + quiz
  120. 120• Skew — asymmetry; the tail drags the mean.Lesson + quiz
  121. 121huge n is more informative than a p of 0.049 with a tiny one. Report the effectLesson + quiz
  122. 122Sample size: power, before the testLesson + quiz
  123. 123The peeking trap, named: checking the test daily and stopping the moment pLesson + quiz
  124. 124WRAP-UPLesson + quiz
  125. 125Week 29 · Forecasting — From Moving Averages to ARIMA & ProphetLesson + quiz
  126. 126The three defensible forecastsLesson + quiz
  127. 127PYTHONLesson + quiz
  128. 128COMMON MISTAKE — FITTING ON THE FUTURE (DATA LEAKAGE)Lesson + quiz
  129. 129WRAP-UPLesson + quiz
  130. 130Week 30 · Machine Learning for Analysts — scikit-learn, Validation & Honest ModelsLesson + quiz
  131. 131PYTHONLesson + quiz
  132. 132(Three models, one line each — that is the whole API. The discipline lives in theLesson + quiz
  133. 133relaxed. Both models were trained on pre-cut data only; we will re-evaluateLesson + quiz
  134. 134WRAP-UPLesson + quiz
  135. 135Week 31 · Dashboards & Storytelling — Power BI, Tableau & Executive CommunicationLesson + quiz
  136. 136Build it — Power BI on the star schemaLesson + quiz
  137. 137EXAMPLELesson + quiz
  138. 138• Tableau — the named BI alternative; same anatomy, different grammar.Lesson + quiz
  139. 139Week 32 · The Modern Data Stack in Production — dbt, Airflow & Data QualityLesson + quiz
  140. 140SQLLesson + quiz
  141. 141known-stable total (e.g. “total orders this month” within ±10% of last month,Lesson + quiz
  142. 142WRAP-UPLesson + quiz
  143. 143AI Session 1 · Meet Your AI Co-Analyst — Prompting for Data WorkLesson + quiz
  144. 144The context part is where amateurs fail. “Here is my data: orders, products,Lesson + quiz
  145. 145EXAMPLELesson + quiz
  146. 146TIP — BUILD THE PROMPT LIBRARY IN A NOTEBOOK CELLLesson + quiz
  147. 147AI Session 2 · AI for Data Tasks — Summaries, Code & the HallucinationsLesson + quiz
  148. 148EXAMPLELesson + quiz
  149. 149Task 3 — draft the one-page narrativeLesson + quiz
  150. 150COMMON MISTAKE — THE FULL-PIPELINE DELEGATIONLesson + quiz
  151. 151AI Session 3 · Responsible AI — Bias, Privacy & Knowing When Not to Use AILesson + quiz
  152. 152Privacy: what never goes into a cloud modelLesson + quiz
  153. 153The do-not-use listLesson + quiz
  154. 154COMMON MISTAKE — “IT’S JUST A HELPING HAND, NO DISCLOSURE NEEDED”Lesson + quiz
  155. 155WRAP-UPLesson + quiz
  156. 156Pro Session 1 · Capstone I — Customer Churn, End to EndLesson + quiz
  157. 157PYTHONLesson + quiz
  158. 158And the churn definition (the honest one): a customer churns if their lastLesson + quiz
  159. 159Phase 1 — The model in the database (SQL does the heavyLesson + quiz
  160. 160SQLLesson + quiz
  161. 161Phase 2 — The model, with the honest splitLesson + quiz
  162. 162Phase 3 — The dashboard (plotly, the portable Power BI)Lesson + quiz
  163. 163Phase 4 — The one-page narrativeLesson + quiz
  164. 164STRETCH — THE MONTHLY RE-SCORELesson + quiz
  165. 165Pro Session 2 · Capstone II — Campaign ROI, Forecasting & the Live DashboardLesson + quiz
  166. 166PYTHONLesson + quiz
  167. 167PYTHONLesson + quiz
  168. 168Phase 2 — The next-quarter forecast (two models, oneLesson + quiz
  169. 169£B) under the planned spend schedule; the moving-average baseline gives £Y,Lesson + quiz
  170. 170Phase 4 — The narrative, with the decisionLesson + quiz
  171. 171STRETCH — THE WHAT-IF, DONE PROPERLYLesson + quiz
  172. 172Pro Session 3 · The Data Portfolio & the 2026 Job Market — Case Interviews & BeyondLesson + quiz
  173. 1732. Capstone II — campaign ROI + forecast (the honesty ladder; the what-ifLesson + quiz
  174. 174EXAMPLELesson + quiz
  175. 175the decision you’d recommend for each branch. The trap: jumping to “marketingLesson + quiz
  176. 176The two numbers that matter: 20 applications in 14 days (the market is a volumeLesson + quiz
  177. 177professional move is the curation, and the curation rule is the five-artefactLesson + quiz
  178. 178WRAP-UPLesson + quiz
  179. 179Appendix C · Staying Current: Fact Sheet, Ritual & Career MapLesson + quiz
  180. 180The stack (the four verbs, and who holds them)Lesson + quiz
  181. 181Power Query cleaning pandas — read_csv , drop_duplicates , Weeks 16–18Lesson + quiz
  182. 182• Local models (via Ollama and the like) are the standard answer for “sensitiveLesson + quiz
  183. 183exactly what changed — a month of free changelog reading, with theLesson + quiz
  184. 184EXAMPLELesson + quiz
  185. 185goes. Read the sheet once, run the ritual forever, and the course has done itsLesson + quiz
Mastering Data Analytics: From First Steps to Expert
Smart information skills take the guesswork out of school, work, and business growth. This program teaches you how to collect messy details, uncover hidden patterns, design interactive reports, and use metrics to drive successful outcomes.

It is perfect for ambitious minds ready to start without any prior experience, gain advanced technical capabilities, and command total control over numbers.

Ready to start?

Enroll and learn at your pace.