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T Level Digital Data Analytics: the chapters

Every lesson of the T Level Technical Qualification in Digital Data Analytics, written in plain English. Read the first page of any lesson and have a go at its activities, free and with no account.

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Each has its own chapters and lessons, free to read the first page of and try, with no account.

Problem solvingCore, Paper 1 · 13 lessons
  1. AC 1.1: Computational thinking for data work40 minutes
  2. AC 1.1: Splitting a data problem into parts45 minutes
  3. AC 1.1: Spotting patterns in sales data40 minutes
  4. AC 1.1: Keeping what matters in a report45 minutes
  5. AC 1.1: Choosing the right thinking part for data35 minutes
  6. AC 1.2: Writing a data check as clear steps45 minutes
  7. AC 1.2: Sequence, choice and repeat in data work50 minutes
  8. AC 1.2: Trace a report, find the fault, fix it50 minutes
  9. AC 1.3: Three ways to build a data solution40 minutes
  10. AC 1.3: Finding why a report is wrong again55 minutes
  11. AC 1.3: Six steps to solve a data problem40 minutes
  12. AC 1.3: Data incidents, problems and how to handle them55 minutes
  13. AC 1.3: Matching the strategy to the data problem40 minutes
Introduction to analyticsCore, Paper 1 · 46 lessons
  1. AC 2.1: Choosing a data format for each field55 minutes
  2. AC 2.1: Asking for rows with SELECT and WHERE55 minutes
  3. AC 2.1: Filtering with BETWEEN, AND, OR and NOT55 minutes
  4. AC 2.1: Finding patterns with LIKE and wildcards50 minutes
  5. AC 2.1: Sorting rows and counting them50 minutes
  6. AC 2.1: Grouping rows and counting each group55 minutes
  7. AC 2.1: Stacking two lists with UNION45 minutes
  8. AC 2.1: Joining tables with INNER, LEFT and RIGHT60 minutes
  9. AC 2.1: From a question to a working query60 minutes
  10. AC 2.2: Saving and loading CSV, TSV and JSON60 minutes
  11. AC 2.3: Checking each field before it is saved60 minutes
  12. AC 2.3: Catching typing slips with a check digit55 minutes
  13. AC 2.3: Interrogating a table by sorting and filtering60 minutes
  14. AC 2.3: Looking values up with VLOOKUP and HLOOKUP60 minutes
  15. AC 2.3: Making decisions with IF, AND, OR and NOT60 minutes
  16. AC 2.3: Summaries with SUMIFS, COUNTIFS and pivot tables65 minutes
  17. AC 2.3: Joining and slicing text in cells55 minutes
  18. AC 2.4: What database and spreadsheet software make easy55 minutes
  19. AC 2.4: Judging which tool suits the problem50 minutes
  20. AC 2.5: Is this data fit for its purpose?60 minutes
  21. AC 2.5: Records that cannot be used as they are60 minutes
  22. AC 2.5: Splitting data apart and putting it back55 minutes
  23. AC 2.5: Designing a process that cleans a dataset65 minutes
  24. AC 2.6: Patterns, trends and predicting the next value60 minutes
  25. AC 2.6: Regression: the line that fits the data65 minutes
  26. AC 2.6: Outliers and skewed data60 minutes
  27. AC 2.6: Finding the story in a messy dataset70 minutes
  28. AC 2.7: Mean, median, mode and weighted mean60 minutes
  29. AC 2.7: Minimum, maximum, range and frequency55 minutes
  30. AC 2.7: Variance and standard deviation65 minutes
  31. AC 2.7: Quartiles and percentiles65 minutes
  32. AC 2.7: Statistics functions in sheets, SQL and code60 minutes
  33. AC 2.7: Describing a dataset in words and numbers60 minutes
  34. AC 2.8: Showing information flow with boxes and arrows55 minutes
  35. AC 2.8: Tables, keys and the shape of a schema60 minutes
  36. AC 2.8: One-to-one, one-to-many and many-to-many60 minutes
  37. AC 2.8: Reading and drawing entity relationship diagrams65 minutes
  38. AC 2.8: The symbols of a data flow diagram55 minutes
  39. AC 2.8: Reading and completing data flow diagrams60 minutes
  40. AC 2.9: Why test each part before the whole55 minutes
  41. AC 2.9: Testing methods used while building70 minutes
  42. AC 2.9: Testing methods for people, load and change70 minutes
  43. AC 2.9: Running tests by macro or script60 minutes
  44. AC 2.9: Choosing and creating test data60 minutes
  45. AC 2.9: Writing and using a test plan60 minutes
  46. AC 2.9: How far can you trust a result?65 minutes
DataCore, Paper 1 · 28 lessons
  1. AC 3.1: From a number to a decision40 minutes
  2. AC 3.1: Where analysts get their data45 minutes
  3. AC 3.1: Is this data worth using?45 minutes
  4. AC 3.1: How organisations put data to work45 minutes
  5. AC 3.2: Manipulate, analyse, process40 minutes
  6. AC 3.3: Counted, measured and labelled values50 minutes
  7. AC 3.3: Quantitative, qualitative and structure45 minutes
  8. AC 3.4: Choosing a data type50 minutes
  9. AC 3.4: How type and structure link45 minutes
  10. AC 3.5: Letters as numbers, and file choice45 minutes
  11. AC 3.5: Text, CSV, JSON and XML files45 minutes
  12. AC 3.6: Files and folders for datasets40 minutes
  13. AC 3.6: Trees: hierarchy-based storage45 minutes
  14. AC 3.6: Metadata: the story behind the data45 minutes
  15. AC 3.7: Big Data and its six dimensions55 minutes
  16. AC 3.7: Six checks that protect data quality55 minutes
  17. AC 3.7: What it costs to keep data good45 minutes
  18. AC 3.8: Planning a data entry screen45 minutes
  19. AC 3.8: Slips when typing data in50 minutes
  20. AC 3.8: The seven jobs a data system does50 minutes
  21. AC 3.8: Wrangling raw data in five steps55 minutes
  22. AC 3.9: Choosing how to show data55 minutes
  23. AC 3.10: Choosing a data model for analysis55 minutes
  24. AC 3.10: Drawing hierarchy, network and tables55 minutes
  25. AC 3.11: RBAC, RuBAC and APIs for data50 minutes
  26. AC 3.11: Who may do what to data45 minutes
  27. AC 3.12: Mining, reports and business intelligence55 minutes
  28. AC 3.12: Warehouses, lakes and marts for analysis50 minutes
Legislation and regulationCore, Paper 2 · 12 lessons
  1. AC 4.1: The safety law behind every desk45 minutes
  2. AC 4.1: Long hours at a screen50 minutes
  3. AC 4.1: The rules for data about people60 minutes
  4. AC 4.1: Looking at data you may not see50 minutes
  5. AC 4.1: Equal treatment under the law50 minutes
  6. AC 4.1: Who owns a design or an invention45 minutes
  7. AC 4.1: When data and crime cross borders40 minutes
  8. AC 4.1: Weighing up what the law means for analysts55 minutes
  9. AC 4.2: Professional rules for people who use data45 minutes
  10. AC 4.2: Standards that data work follows45 minutes
  11. AC 4.2: The signed policy and speaking up50 minutes
  12. AC 4.2: Weighing up guidelines in data work55 minutes
Business context and emerging issuesCore, Paper 2 · 19 lessons
  1. AC 5.1: Sectors, purposes and business models45 minutes
  2. AC 5.1: Who has a stake in a data project45 minutes
  3. AC 5.2: Digital help for selling, design and people55 minutes
  4. AC 5.2: Digital help for running a firm55 minutes
  5. AC 5.2: Does the system meet its users' needs55 minutes
  6. AC 5.3: When firms rely on data systems60 minutes
  7. AC 5.4: The events that force a firm to change60 minutes
  8. AC 5.4: Choosing how to respond to change45 minutes
  9. AC 5.4: Planning a change step by step70 minutes
  10. AC 5.4: Testing and switching on a change65 minutes
  11. AC 5.4: Keeping records and a way back60 minutes
  12. AC 5.4: Can a digital project really be done60 minutes
  13. AC 6.1: How data and digital systems change work45 minutes
  14. AC 6.1: What digital data means for society60 minutes
  15. AC 6.1: Fair digital services and fair data65 minutes
  16. AC 6.1: Why analysts keep on learning30 minutes
  17. AC 6.2: Five new technologies and their effects60 minutes
  18. AC 6.2: Open source, blockchain, machines and the planet55 minutes
  19. AC 6.2: Judging new technology in data work60 minutes
Digital environmentsCore, Paper 2 · 37 lessons
  1. AC 7.1: Where analysts' data work runs40 minutes
  2. AC 7.1: Getting data in and results out40 minutes
  3. AC 7.1: Processors and the slow query45 minutes
  4. AC 7.1: Memory and storage for big files45 minutes
  5. AC 7.1: Board, slots and ports40 minutes
  6. AC 7.1: Graphics chips, heat and cooling40 minutes
  7. AC 7.2: Batch, multitasking and real-time systems50 minutes
  8. AC 7.2: Shared and pocket operating systems40 minutes
  9. AC 7.2: Tidying, shrinking and packing files50 minutes
  10. AC 7.2: Installing, guarding and copying tools45 minutes
  11. AC 7.2: Picking the right tool for the data job45 minutes
  12. AC 7.3: Why analysts connect computers40 minutes
  13. AC 7.3: Personal, local, city and wide networks40 minutes
  14. AC 7.3: Cable, glass and radio for moving data45 minutes
  15. AC 7.3: The shapes of a network50 minutes
  16. AC 7.3: Who serves and who asks45 minutes
  17. AC 7.3: Parts that carry data across a network45 minutes
  18. AC 7.3: The seven layers, the top four50 minutes
  19. AC 7.3: The seven layers, the bottom three50 minutes
  20. AC 7.3: The four layers the internet uses50 minutes
  21. AC 7.3: How a download is cut into packets50 minutes
  22. AC 7.3: Missing packets and the check value45 minutes
  23. AC 7.3: Rules for web pages and mail45 minutes
  24. AC 7.3: Rules that route and rules that serve50 minutes
  25. AC 7.3: Wide pipes and slow answers50 minutes
  26. AC 7.4: Virtual machines for a safe analysis lab45 minutes
  27. AC 7.4: Seven things a virtual environment does50 minutes
  28. AC 7.4: Is a virtual environment worth it?50 minutes
  29. AC 7.5: Private and public clouds for data50 minutes
  30. AC 7.5: Three cloud services and their owners55 minutes
  31. AC 7.5: Picking a cloud service for a warehouse50 minutes
  32. AC 7.6: What a tough system gives a data team50 minutes
  33. AC 7.6: Patching, upgrading and retiring kit55 minutes
  34. AC 7.6: Locking down a laptop that holds data50 minutes
  35. AC 7.6: Copies you can restore from50 minutes
  36. AC 7.6: Hot, warm and cold standby sites45 minutes
  37. AC 7.6: Written procedures and training50 minutes
SecurityCore, Paper 2 · 19 lessons
  1. AC 8.1: What a data team must keep secret45 minutes
  2. AC 8.1: The cost of a leak45 minutes
  3. AC 8.2: Malware, botnets and floods of traffic60 minutes
  4. AC 8.2: Break-in methods against data systems60 minutes
  5. AC 8.2: Tricks that fool the person60 minutes
  6. AC 8.2: Wrong addresses and open Wi-Fi45 minutes
  7. AC 8.2: Weak and old parts of a data system45 minutes
  8. AC 8.2: Mistakes, insiders and bad habits45 minutes
  9. AC 8.2: Locks, screens and storms45 minutes
  10. AC 8.2: Five harms a threat can do40 minutes
  11. AC 8.3: Settings, scanners and alarms50 minutes
  12. AC 8.3: Rules for people and logins50 minutes
  13. AC 8.3: Scrambling data: hash, one key, two keys55 minutes
  14. AC 8.3: Backups, patches and unplugged copies55 minutes
  15. AC 8.3: Trusted APIs, VPN tunnels and test attacks55 minutes
  16. AC 8.3: Firewall rules, split networks and watching55 minutes
  17. AC 8.4: Three goals that hold each other up45 minutes
  18. AC 8.4: Say who you are, then prove it50 minutes
  19. AC 8.4: Permission and the audit trail50 minutes
Sourcing and preparing dataOccupational Specialism · 33 lessons
  1. DA 1.1: Internal and external data sources45 minutes
  2. DA 1.1: Open, private and licensed data45 minutes
  3. DA 1.1: Select, prepare, extract, transform50 minutes
  4. DA 1.2: How each sector uses data systems50 minutes
  5. DA 1.3: What information systems store and do55 minutes
  6. DA 1.4: Sorting data into groups50 minutes
  7. DA 1.5: Primary and secondary data45 minutes
  8. DA 1.5: How organisations and people use data50 minutes
  9. DA 1.6: Three shapes of data50 minutes
  10. DA 1.7: Choosing sources for a business question50 minutes
  11. DA 1.8: Collecting data lawfully from many places55 minutes
  12. DA 1.9: Finding trends and patterns60 minutes
  13. DA 1.10: Storing sourced data safely55 minutes
  14. DA 1.11: The data types an analyst meets50 minutes
  15. DA 1.11: Big data and why types must agree50 minutes
  16. DA 1.12: Changing and formatting data types55 minutes
  17. DA 1.13: Spotting poor quality data55 minutes
  18. DA 1.13: Why quality matters and how to cleanse55 minutes
  19. DA 1.14: What makes data trustworthy50 minutes
  20. DA 1.14: Tidy tables: normal forms60 minutes
  21. DA 1.14: Auditing data with checks60 minutes
  22. DA 1.15: Blending sources into one table60 minutes
  23. DA 1.16: Cleaning data to meet a need60 minutes
  24. DA 1.17: Checking a dataset and saving it safely55 minutes
  25. DA 1.18: Is this dataset right for the job?50 minutes
  26. DA 1.19: Writing a statistical summary60 minutes
  27. DA 1.20: Showing that data law was followed55 minutes
  28. DA 1.21: The data life cycle: prepare and run55 minutes
  29. DA 1.21: The end of the data life cycle45 minutes
  30. DA 1.22: Choosing tools to find patterns60 minutes
  31. DA 1.23: Moving data between storage systems60 minutes
  32. DA 1.24: Why data architecture matters45 minutes
  33. DA 1.24: What data architecture does day to day50 minutes
Blending dataOccupational Specialism · 29 lessons
  1. DA 2.1: Relational and non-relational stores45 minutes
  2. DA 2.1: Warehouses, marts and spreadsheets45 minutes
  3. DA 2.2: Keys that join tables45 minutes
  4. DA 2.2: Indexes and partitions45 minutes
  5. DA 2.2: Linking sheets, moving files, pivot ideas45 minutes
  6. DA 2.2: Standardising formats and cross-checking45 minutes
  7. DA 2.3, 2.11: Logical queries find the right rows50 minutes
  8. DA 2.3: SQL, NoSQL and macros as query tools45 minutes
  9. DA 2.4: Inner, left and right joins50 minutes
  10. DA 2.4: Full joins and union joins50 minutes
  11. DA 2.4, 2.8: Fuzzy matching and spatial matching55 minutes
  12. DA 2.4: Consolidating, merging and linking data50 minutes
  13. DA 2.5: Blending a sheet, a file and a table60 minutes
  14. DA 2.6: Joining tables to meet a business need55 minutes
  15. DA 2.7: Linking datasets to a shared repository50 minutes
  16. DA 2.8: Blending several sources for one report60 minutes
  17. DA 2.9: Planning a dataset to fit its job45 minutes
  18. DA 2.9: Setting up a dataset with CREATE TABLE50 minutes
  19. DA 2.10: Importing and exporting data safely55 minutes
  20. DA 2.11: From question to query50 minutes
  21. DA 2.12: Designing and testing a pivot table55 minutes
  22. DA 2.13: Choosing which data to blend55 minutes
  23. DA 2.14: Automating sheet work with macros55 minutes
  24. DA 2.15: Why sample, and how random sampling works50 minutes
  25. DA 2.15: Stratified, cluster and systematic sampling55 minutes
  26. DA 2.16: Auditing the results of a blend55 minutes
  27. DA 2.17: Gathering data to design a workflow45 minutes
  28. DA 2.17: Input, transformation and output in a workflow50 minutes
  29. DA 2.17: Analysing a process and drawing conclusions55 minutes
Analysing dataOccupational Specialism · 20 lessons
  1. DA 3.1: What data is worth to a business45 minutes
  2. DA 3.2: Understanding a data problem50 minutes
  3. DA 3.2: From plan to review in a data project55 minutes
  4. DA 3.3: OLAP: looking at data from every side55 minutes
  5. DA 3.3: Spreadsheets, dashboards and KPIs50 minutes
  6. DA 3.3, 3.4: Time series: trends and seasons55 minutes
  7. DA 3.3: Exploring data before you model it60 minutes
  8. DA 3.3: AI and learning from labelled examples60 minutes
  9. DA 3.3: Data mining and simulation55 minutes
  10. DA 3.3, 3.4: Clusters, groups and places60 minutes
  11. DA 3.4: Spread, correlation and regression70 minutes
  12. DA 3.4: Chi-square: are two categories linked?70 minutes
  13. DA 3.4, 3.5: Applying methods and showing results55 minutes
  14. DA 3.5: Forecasting and its assumptions65 minutes
  15. DA 3.6: Finding the data a problem needs55 minutes
  16. DA 3.7: Preparing data so it can be used45 minutes
  17. DA 3.7: Conceptual, logical and physical models60 minutes
  18. DA 3.7, 3.9: Testing a dataset against requirements60 minutes
  19. DA 3.8: Gather, cleanse and standardise60 minutes
  20. DA 3.8: Blend sources and build a dataset60 minutes
Communicating resultsOccupational Specialism · 19 lessons
  1. DA 4.1: Who is the message for?45 minutes
  2. DA 4.1: Purpose, size and complexity50 minutes
  3. DA 4.1: Did the message work?50 minutes
  4. DA 4.2: Seven kinds of chart and table70 minutes
  5. DA 4.2: Pictures, XR and 3D models45 minutes
  6. DA 4.2: Choosing the right picture55 minutes
  7. DA 4.3: From a messy log to a chart70 minutes
  8. DA 4.3: Charts that tell the truth55 minutes
  9. DA 4.4: Writing a communication specification65 minutes
  10. DA 4.5: Finding the numbers that matter60 minutes
  11. DA 4.5: Telling a data story60 minutes
  12. DA 4.6: From data to a decision60 minutes
  13. DA 4.7: Written, spoken and shown45 minutes
  14. DA 4.7: Seven ways to get a message across55 minutes
  15. DA 4.7: Reports, dashboards, video and more45 minutes
  16. DA 4.8: Reviewing results together65 minutes
  17. DA 4.9: What to measure in digital marketing55 minutes
  18. DA 4.9: Working out email and social results60 minutes
  19. DA 4.9: Predicted against actual60 minutes
Law and ethics for data workOccupational Specialism · 26 lessons
  1. DA 5.1: The seven rules for personal data55 minutes
  2. DA 5.1: Copyright, licences and who owns data50 minutes
  3. DA 5.2, 5.1: Controller, processor and officer roles55 minutes
  4. DA 5.3: Planning to share data well55 minutes
  5. DA 5.3: Sharing data with other organisations55 minutes
  6. DA 5.4, 5.5: Where the data rules are found60 minutes
  7. DA 5.6: Reading and applying data handling rules55 minutes
  8. DA 5.7: Using data safely for a purpose60 minutes
  9. DA 5.8: Finding and scoring data risks60 minutes
  10. DA 5.8: Four ways to respond to a risk55 minutes
  11. DA 5.8: Monitoring and communicating risks60 minutes
  12. DA 5.9: Choosing a response to an attack risk60 minutes
  13. DA 5.10: Personal data, erasure and impact checks60 minutes
  14. DA 5.10: Aggregating data safely with SQL60 minutes
  15. DA 5.10, 5.14: Anonymising and pseudonymising in practice60 minutes
  16. DA 5.11: Ethics of consent, access and reuse60 minutes
  17. DA 5.11: Bias, privacy, impact and ownership60 minutes
  18. DA 5.12: When technology does the work for us55 minutes
  19. DA 5.12: Constant access, addiction and privacy55 minutes
  20. DA 5.13: Who attacks a business and how55 minutes
  21. DA 5.14: Plans, policies and standards that protect60 minutes
  22. DA 5.14: Controls that protect data itself60 minutes
  23. DA 5.15: What a cyber-attack costs people55 minutes
  24. DA 5.16: Marketing consent and the five tests55 minutes
  25. DA 5.16: Recording, withdrawing and renewing consent60 minutes
  26. DA 5.17: Keeping data safe, correct and available60 minutes
Sources of knowledgeOccupational Specialism · 22 lessons
  1. DA 6.1: Money and bookings leave a trail55 minutes
  2. DA 6.1: Footprints, sensors and smart places60 minutes
  3. DA 6.2: Eight places knowledge comes from50 minutes
  4. DA 6.3: Who made it, and can you trust them?55 minutes
  5. DA 6.3: Evidence, citations and fake data60 minutes
  6. DA 6.3: Right data, right reader, right year55 minutes
  7. DA 6.3: Four kinds of bias in a source55 minutes
  8. DA 6.3: Partiality, prejudice and omission55 minutes
  9. DA 6.4: Five steps of critical thinking55 minutes
  10. DA 6.4: Ways to evaluate: ask, count, compare60 minutes
  11. DA 6.4: Gap analysis, KPIs and score cards60 minutes
  12. DA 6.4: Diaries, maps and maturity55 minutes
  13. DA 6.5: Pictures, dashboards, sound and XR55 minutes
  14. DA 6.5: One message, many channels55 minutes
  15. DA 6.6: Plan a data search50 minutes
  16. DA 6.6: Safe searching and cross-checking60 minutes
  17. DA 6.7: Run an evaluation for a business need60 minutes
  18. DA 6.8: Compare actions that keep sources sound65 minutes
  19. DA 6.9: Is this sample fair?65 minutes
  20. DA 6.9, 6.3: How bias gets in, and stopping it65 minutes
  21. DA 6.10: Think it through and write it up65 minutes
  22. DA 6.11: Choose and vet three sources65 minutes
ProjectsEmployer Set Project · 27 lessons
  1. AESP pre-task: Research how finance uses data45 minutes
  2. AESP pre-task: Five tasks, one data project35 minutes
  3. AESP task 1: Plan a data job on a Gantt chart55 minutes
  4. AESP task 1: Choose who does what and in what order55 minutes
  5. AESP task 1: Cost a data project and judge it60 minutes
  6. AESP task 1: Explain why you planned it that way50 minutes
  7. AESP task 2: Keep a test log for a dataset60 minutes
  8. AESP task 2: Find repeated rows with SQL55 minutes
  9. AESP task 2: Find missing, wrong and impossible values60 minutes
  10. AESP task 2: Fix the data, explain why, test again65 minutes
  11. AESP task 2: Clean records with small Python functions65 minutes
  12. AESP task 2: Spot faults in a data model65 minutes
  13. AESP task 3: Break a brief into entities and keys60 minutes
  14. AESP task 3: Draw an entity relationship diagram60 minutes
  15. AESP task 3: Write a data dictionary with rules65 minutes
  16. AESP task 3: Split a flat table into three tables80 minutes
  17. AESP task 3: ACID: keeping changes safe55 minutes
  18. AESP task 3: Define every step of a data process55 minutes
  19. AESP task 3: Write design notes anyone can build from60 minutes
  20. AESP task 4a: Pick and shape the data you need65 minutes
  21. AESP task 4a: Clear names, comments and exact logic60 minutes
  22. AESP task 4a: Cell references, 3D totals and locked cells65 minutes
  23. AESP task 4a: Share only the data that is needed55 minutes
  24. AESP task 4a: Handle errors so nothing breaks60 minutes
  25. AESP task 4a: Clear, fair and readable output65 minutes
  26. AESP task 4b: Measure the solution against the brief60 minutes
  27. AESP task 4b: Reflect and say how to improve it55 minutes