Paid FoundationCertificate

Describing Vessel Data

How to look at a shipboard signal before you do anything to it: the measures that describe it honestly, the plots that expose what the measures hide, and the normality question that quietly decides which methods you are allowed to use next.

Lessons
8
Quiz questions
67
Exercises
1 (10 min)
Calculators
1
Time
About 91 min
Price
€189+ VAT
Certificate
Graded final exam, then a PDF certificate with a number anyone can verify
Who it's for
Performance analysts, superintendents and engineers who receive shipboard data and have to say something defensible about it: anyone whose spreadsheet starts with an average and whose conclusions deserve better.
Assumed knowledge
School-level arithmetic. No statistics and no programming are assumed: every method is worked in numbers in the text, and every one of them can be reproduced with standard spreadsheet functions.
Taught by

Christos Papandreou

CEO & Founder, Blue Autonomy

  • Naval architect and marine engineer, National Technical University of Athens
  • 15 years of experience in fleet performance and operational data
  • Chief Scientific Officer, NEMO, previously DeepSea Technologies, StormGeo, Athenian Sea Carriers, PwC

Part of the Performance Data Science specialisation.

What you will be able to do

By the end of the course. Each outcome names the lessons that deliver it, and the final exam samples every one of them.

  1. Classify a shipboard dataset before touching it: its place on the descriptive-to-prescriptive ladder, the variable types it contains, and why a year of high-frequency records is a far smaller sample than it appears. Lesson 1
  2. Choose measures of location and spread on the evidence of the distribution (mean, median, mode, trimmed mean; variance, IQR, MAD), and recognise the duty-cycle signals on which every single-number summary misleads. Lessons 2, 3
  3. Read skewness and kurtosis as statements about tail direction and weight, avoid the two-conventions kurtosis reporting trap, and use the classical-to-robust spread ratio as a first diagnostic of shape. Lessons 3, 4
  4. Choose a histogram bin width by rule (Sturges, Scott, Freedman-Diaconis), stating what each assumes and what coarse binning hides, and pick between box plot, violin and ECDF for the comparison at hand. Lessons 5, 6
  5. Build and read quantile, Q-Q and P-P plots, translating Q-Q curvature into statements about skew and tail weight. Lesson 7
  6. Run and interpret the Kolmogorov-Smirnov, Shapiro-Wilk and D'Agostino normality tests, avoiding the estimated-parameter trap, and explain why a normality test on a very large sample answers a question nobody asked. Lesson 8

An average is the fastest way to be wrong about a ship. One of the three diesel generators on a real VLCC, logged once a minute for two years, has a mean load of 258 kW, which sounds like a machine ticking over steadily at a modest fraction of its 1,520 kW rating. Its median is zero. It is running in 40.4 per cent of its records, at a median load of 620 kW when it runs, so the one statistic that reported a busy machine was averaging "running" and "stopped" and describing neither.

This course is about the step everybody skips: describing a signal properly before analysing it. What is inside: the four questions data can answer and which of them a given dataset is entitled to; measures of location, spread and shape, and the shipboard signals that break each one; histograms and the binning rules that change the story you see; box plots, violins and empirical distribution functions for comparing many channels at once; and the normality question, answered first by eye and then by test, together with the awkward truth about what those tests do when the sample handed to them is very large.

The opening lesson is free to read as a sample. By the end you should be able to open an unfamiliar shipboard dataset, produce a description of it that a sceptical reader cannot fault, and know which of the methods in the rest of this specialisation that description has just licensed you to use.

What the course covers

  1. What a Ship's Data Actually Looks Like 11 min
  2. Location: The Mean, the Median and the Machine That Breaks Both 12 min
  3. Spread: Variance, the IQR and the Robust Alternatives 20 min + exercise
  4. Shape: What Skewness and Kurtosis Warn You About 9 min
  5. Histograms and the Binning Question 10 min
  6. Box Plots, Violins and ECDFs: Comparing Many Signals 9 min
  7. Is It Normal? The Graphical Answers 8 min
  8. Is It Normal? The Tests, and What They Are For 12 min

€189 + VAT

€234.36 including 24% VAT, which is what an individual in the EU pays. Outside the EU no VAT is added. A company in another EU country pays the net price once its VAT number is confirmed in VIES, the EU's register; a company in Greece pays the 24%. Checkout shows the total before you pay.

Paid once, for one person, for as long as the Academy runs: every lesson, the Excel workbook, the final examination and the certificate. Part of the Performance Data Science specialisation, which costs less than its courses bought one at a time.

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All prices · Refunds: 7 days, unless you have started

Common questions

What exactly is included?

8 lessons (about 91 minutes in total), 67 quiz questions with explained answers, and 1 working Excel calculator: Spread: classical and robust, on thirty records.

How much does it cost?

€189 plus VAT, paid once: €234.36 with 24% VAT, which is what an individual in the EU pays; outside the EU no VAT is added, and a company in another EU country with a VAT number valid in VIES pays the net price. That buys every lesson, the Excel workbook, the final examination and the certificate, for one person, for as long as the Academy runs. The course is also part of the Performance Data Science specialisation, which costs less than its courses bought one at a time. Everything else is on the pricing page.

Is there work to do, or is it only reading?

1 lesson carries a required exercise, about 10 minutes of hands-on work in total. You download that lesson's Excel workbook, follow the numbered steps against the numbers the lesson states, then change one input and watch the answer move. The exercises are where the method stops being something you have read and becomes something you can run; the stated course time includes them.

Do I get a certificate?

Yes. The course ends with a graded final exam, marked on our server rather than in your browser: you see your score and which questions you got wrong, but not the answers, because the questions are drawn from a pool. Pass it and you get a PDF certificate in your name, with a certificate number on it.

That number is the point. Anyone you show the certificate to can check it themselves at academy.blueautonomy.gr/verify, with no account and no email to us: the page states the name, the course and the date we issued it. A certificate nobody can verify is a decorated PDF, and we would rather issue one that stands up.

Can I preview the course?

Yes: the first lesson, What a Ship's Data Actually Looks Like, is free to read in full. It is a real lesson, not a trailer: same depth, same quiz.

Do I need prior knowledge?

School-level arithmetic. No statistics and no programming are assumed: every method is worked in numbers in the text, and every one of them can be reproduced with standard spreadsheet functions.