The Curious Engineer

All blogs / Math

Engora Data Blog

blog.engora.com

Blog on management, statistics, and data.

MathAI / MLScience

’20’21’22’23’24’25’26
Posts
211 from 11 Jan 2020 to 22 Sep 2026
Posting
weekly (30 in the last year)
Kind
personal blog
Feed
https://blog.engora.com/feeds/posts/default

2026 17

  1. Combining risks III: characteristic functions of standard probability distributions
  2. Combining risks II: zero-inflated distributions
  3. Combining risks I: summing random variables
  4. Fourier Transforms: way more fundamental than you think
  5. The decline and fall of LinkedIn
  6. AI Winters
  7. Ways of getting to space
  8. Rotating space habs!
  9. Ralph for beginners
  10. Arthur C. Clarke and AI
  11. Rendezvous with Rama
  12. Data is the new Lego
  13. The perceptron
  14. Learning by hand is better than learning by AI
  15. Replit vs. Cursor - who wins?
  16. The Siren Song
  17. I've got the power: what statistical power means

2025 62

  1. Whiskey prices!
  2. Why are weather forecasting sites so bad?
  3. Using Cursor for data science: a talk
  4. Small adventures with small language models
  5. The Skellam distribution
  6. Poisson to predict football results?
  7. Ozymandias
  8. Data sonification: a curious oddity that may have some uses
  9. Some musings on code generation: kintsugi
  10. Caching and token reduction
  11. Data scientists need to learn JavaScript
  12. How to rapidly build and deploy data science apps using code gen
  13. How to get data analysis very wrong: sample size effects
  14. Context 7: code generation using the most recent libraries
  15. Competency porn
  16. Regression to the mean
  17. More money means more goals
  18. A better approach to internal reporting
  19. Building server-less web apps
  20. Where does federal government money go In the US?
  21. Old & experienced vs. young and energetic: mean age in English football
  22. Em dash = AI slop?
  23. The sisters "paradox" - counter-intuitive probability
  24. Squad size and goals in English football
  25. Foreigners and goals in English football
  26. Goals and tenure in English football
  27. Probability playground: a great app/website
  28. Yes, the referee might be biased. Discipline in English football.
  29. English league football mini app
  30. Attendance at English football: a tale of tragedy and recovery
  31. Police and sharks: how not knowing how your data is processed can lead you badly astray
  32. Visualizing multi-dimensional data: score distributions in English football
  33. Vanishing home field advantage in English football
  34. G-O-A-L! Goals in English football
  35. Win, lose, or draw: trends in English football match results
  36. AI networking in the Boston area
  37. Logistic regression - a simple briefing
  38. Tell me on a Sundai.club – something novel in Boston?
  39. Don’t stop till you get enough – sample size in machine learning
  40. How to Thrive in an AI-Driven Future
  41. Google's ADK for agentic AI development - and some general thoughts
  42. Cursor for data science - a scorecard
  43. Recommendations for rolling out generative AI to data science and technical coding teams
  44. What is Model Context Protocol?
  45. What is a random variable?
  46. You need to use Manus
  47. The Importance of Logistic Regression
  48. I can’t believe they said that! How to speak difficult truths
  49. The basics of regularization in machine learning
  50. The parakeets of Belfast
  51. Imagination in Action: The Good, the Bad, and the Ugly
  52. Why a lot of confidence intervals are wrong
  53. Identifying people by how they type - a unique "fist"
  54. Platypus are weird
  55. How to improve data quality
  56. Compliance!
  57. Data science jokes
  58. Everything you wanted to know about the normal distribution but were afraid to ask
  59. Outliers have more fun
  60. Why assuming independence can be very, very bad for business
  61. Using AI (LLM) to generate data science code
  62. Python formatting

2023 14

  1. How to run webinars
  2. Using ChatGPT for real to interpret text
  3. Essential business knowledge: the Central Limit Theorem
  4. ChatGPT and code generation: be careful
  5. Isolated track vocals; hear who really can sing
  6. The bad boy of bar charts: William Playfair
  7. The Coronation and visions of the future
  8. Coworking spaces: the challenge for company loyalty
  9. You can be too clever with visualizations
  10. I'm curious if you want to buy from me
  11. How to improve your business writing
  12. Is it OK to lie in business for the right reason?
  13. The longest journeys in the world
  14. My advice to students looking for a job

2022 15

  1. Is this coin biased?
  2. The London weighting
  3. London and New York - different but similar
  4. How the post-hoc fallacy can cost businesses millions
  5. A small revolution happened when I wasn't looking
  6. Understanding Asia better
  7. Compost!
  8. All about pens
  9. Propaganda and public relations
  10. Plagiarism and blog posts
  11. What brown M&Ms can tell you about a company
  12. W.E.B. Du Bois - data scientist
  13. RCT bingo!
  14. Cultural add or fit?
  15. Prediction, distinction, and interpretation: the three parts of data science

2021 38

  1. COVID and the base rate fallacy
  2. What's a violin plot and how to make one
  3. Mouse jiggling: the good, the bad, and the ugly
  4. Ice ice baby
  5. Football crazy: predicting English Premier League football match results
  6. Why conditional probability screwiness matters for business
  7. Battle leadership: some lessons for managers from World War I
  8. The seven dysfunctionalities of management books
  9. Criminal innovations: narco-subs
  10. Poleaxed opinion polls: the ongoing 2020 disaster
  11. Reconstructing an unlabelled chart
  12. We don't need no education: England's poor educational performance
  13. What is beta in statistical testing?
  14. Spritely fraud detection
  15. Unknown Pleasures: pulsars, pop, and plotting
  16. Confidence, significance, and p-values
  17. Management and technical career tracks: separate but not equal
  18. Why A/B tests don't add up
  19. Counting on Poisson
  20. Soldiers on the moon: Project Horizon
  21. How to hire well
  22. How to be creative like David Bowie, R.E.M. and Coldplay
  23. A/B testing basics: ways of being right and wrong (frequentist version)
  24. Goodies and baddies: how a poor model of history lets us down
  25. Wicked problems in data science
  26. Why we have runs on toilet paper
  27. Know your rights: assignment of rights
  28. Forecasting the 2020 election: a retrospective
  29. A masterclass in information visualization: the tube map
  30. Simpson's paradox: a trap for the naive analyst
  31. The amazing gamma function
  32. Management degrees - how I went from a C to an A: buzzword bingo
  33. Frequency hopping and the most beautiful woman in the world
  34. What do Presidential approval polls really tell us?
  35. 3D plotting: how hard can it be?
  36. Dinosaurs and time-travel: the wrong kind of air
  37. How to grow a market segment
  38. COVID and soccer home team advantage - winning less often

2020 65

  1. I won an award! How to lose by winning
  2. The $10 screwdriver: a cautionary management tale
  3. What's a probability distribution?
  4. How to (maybe) win at dice
  5. Am I diseased? An introduction to Bayes theorem
  6. Chance encounters of the third kind: understanding probability
  7. Geese or enemy aircraft? Receiver Operating Characteristic curves in machine learning
  8. Dazed and confused: the confusion matrix and getting it right and wrong
  9. The null hypothesis test
  10. Frankenstein, vampire, and volcano: dinner at Lake Geneva
  11. Stylish Pandas in the frame
  12. Fundamentally wrong? Using economic data as an election predictor
  13. Opinion polling blog posts
  14. Faster Python BI app development through code generation
  15. Blame free learning
  16. The gamblers' fallacy
  17. The datasaurus: always visualize your data
  18. Can you believe the polls?
  19. Speaking in voices: pitch, volume, and speed
  20. Everything stops for tea
  21. President Hilary Clinton: what the polls got wrong in 2016 and why they got it wrong
  22. Poll-axed: disastrously wrong opinion polls
  23. Who will win the election? Election victory probabilities from opinion polls
  24. Serial killer! How to lose business by the wrong serial numbers
  25. Sampling the goods: how opinion polls are made
  26. The Electoral College for beginners
  27. Sad! How not to create a career structure: good intentions gone wrong
  28. The art of persuasion: pathos, logos, and ethos
  29. Managers must actively listen: foundational skills
  30. Drunk and disorderly: not funny at company events
  31. Being more persuasive in speech: anaphora
  32. Death by rounding
  33. (De)motivational speakers
  34. Personally Identifiable Information: avoid, avoid, avoid!
  35. Inventory: your job may depend on how it's managed
  36. Finding electoral fraud - the democracy data deficit
  37. The Emperor's new objects: a two-year failed project
  38. Florence Nightingale, data analyst
  39. It's a mugs' game: corporate failures, ceramics, and t-shirts
  40. The worst technical debt ever
  41. How to be more persuasive when you speak: using ‘catchphrases’
  42. Sherlock Holmes, business books, Nazi fighters, and survivor bias
  43. A sorry tale of software quality: what went wrong and some lessons
  44. John Snow, cholera, and the origins of data science
  45. Contributing to open-source software
  46. Niche knowledge and power - knowledge hoarding
  47. Benford's Law: finding fraud and data oddities
  48. How to be a compelling speaker: creating applause and tricolon
  49. Cheating charts: the axes of evil
  50. Pie charts are lie charts
  51. What I learned from a day in the woods
  52. The Monty Hall Problem
  53. Urban myths are poor motivators
  54. Coin tossing: more interesting than you thought
  55. Presentation advice from a professional comedian
  56. The Anna Karenina bias
  57. Reverse engineering a sensor
  58. Company culture and the hall of mirrors
  59. Future directions for Python visualization software
  60. Clayton Christensen, RIP
  61. How to lie with statistics
  62. The Python plotting ecosystem
  63. How to be a good candidate for analytics and data science jobs
  64. Correlation does not imply causation
  65. Choropleth maps - pretty but misleading