Anybody heard of this new thing called data science?

• Can anyone tell me what it is about?
• Why is it so big? What is so great about it?
• Is wrangling and exploring data a new thing?
• Social media buzz
• Dashboards everywhere!
• Obvious data is obvious
  • The true power of data science for me is to help oblivious people see the truth that’s right in front of them

• Is data wrangling exciting?
• Is it safe? Can you protect personal information?
• Are you really cleaning up the data or just making it look clean by sweeping the rubbish into bins?
  • Binning is not a good thing in general, by the way. It’s only useful in special cases, not a magic solution to messy data.
• If you keep digging long enough you’ll find something eventually.
  • The biggest problem with data science is that it finds a lot of coincidences.
  • Testing and validation help a lot, but are not foolproof.

• Put up your hand if you have made something that works great when you use it; but fails when you give it to someone else
• Good products are ones that work for the customer
• and keep working into the future (generalisation)
• even after you’ve moved on from the project
• In time the needs of the customer will be different but a really good product will still meet them

• Robust, Extensible, Intelligent, Adaptable, Stylish
• Nice to have but useless without the basics:
  • Does what the client needs
  • Works
  • Easy to use
  • Readable source
• It doesn’t matter how stylish your product looks, if the next person that has to work on it can’t make sense of what you did then your work will be disregarded

• The truth has a tendency to reveal itself eventually
• No matter how much marketing hype is attached
• Marketers are quick to market the importance of marketing
  • But what is the impact of marketing really?
  • How do you measure it?
  • Will it be the same on the next project?
• Everybody who invents something new thinks everybody in the world needs it, but do they really?

The scientific method:

• You observe (desire) something interesting
• You come up with a theory (product) for that thing
• You devise an experiment to prove yourself wrong (product bad)
• You perform the experiment objectively
• If the experiment succeeds you learn from it and then you start over
• If the experiment fails you share your theory (product) widely as something that can be trusted

The other way round is much easier; but less convincing

• When ordinary people think web design they think aesthetics
• They don’t think your website looks as good as you do
• Resources are available to help you if you just want to quickly review your work
• Overview of web design evaluation
• Website design checklist

• Official international standards for software product development
• Even if you only care about aesthetics you can go very very deep
• Focus groups are extremely popular in web development
• Focus groups are easy
  • You can ask you mom, your friends, etc.
• But focus groups will lie to you
  • They lie less if it’s impersonal, so get someone else to do your focus groups for you
  • You have to ask the right questions
  • It’s nearly impossible to get a representative sample

• Paying people to test your products will reveal flaws en mass
• Helps make your work more robust
  • But not fool proof (fools can be really ingenious in their foolishness)
• Usually can’t tell you if you met the clients’ objectives

• Surveys are really easy with templates readily available
• People who choose to respond to surveys may be systematically different from the general population.
• See the Wikipedia page on Response Bias for more information.
• But if done well then they can be really useful and reliable

• To show that A causes B you must:
  • Show that B happens after A happens (effect over time)
  • Show that B doesn’t happen if A doesn’t happen (control)
  • Show that your experiment generalises (not just a lucky once off)
• The absolute best way to test something is the randomised controlled trial
• In the case of medicines it’s really expensive
• In web products it’s much easier

• Create (at least) two groups that are identical in all ways that matter and representative of the wider audience (randomisation helps a lot here)
• Treat one group one way and the other differently (or not at all)
• Measure the effect over time for both groups
• Contrast the effects
• Discuss

• Assume the world is boring
• See something possibly interesting
• Work out how likely it is to see something at least that interesting under the assumption that the world is boring
  • This is called a p-value
• If the p-value is large (say more than 0.05) then we keep assuming the world is boring
• If the p-value is small (say less than 0.05) then we reject our assumption that the world is boring and conclude there’s at least one interesting thing going on

• The Central Limit Theorem says that averages follow a Gaussian (Normal) distribution
• So if we care about the average then we can make judgements using this theorem

• The t-test is the most popular tool in statistics after summary statistics and graphs
• Assuming you are interested in testing some hypothesized average \((\mu)\)
  • Usually “No difference”, implying an average difference of zero \((\mu=0)\)
• IF your sample size is large enough then
  • \(t=\)(The average minus \(\mu\))*\(\sqrt{n}\)/standard deviation
  • Follows a \(t_{n-1}\) distribution in a boring world, where \(n\) is the number of observations
• So if \(|t|\) is large (>2 is a good rule of thumb) then we reject the boring hypothesis

• If you keep doing tests you will ‘find’ something sooner or later
• But practical significance is what really matters
• Statistical significance should come first, to help avoid analysing coincidences
• But once you find something statistically significant you have to ask what the practical meaning is
• The key is the effect size, which is the measure of the practical effect of one thing on another
• The best effect sizes are so big that anyone can see the impact, and the fancy stats becomes irrelevant!

• Point estimates are always wrong
• A much better way is to report results with uncertainty
• A basic 95% interval for the average is \(\bar{x} \pm t_{0.95;n-1}*s/\sqrt{n}\), where \(\bar{x}\) is the average and \(s\) is the standard deviation (how far the observations deviate from the average on average)
  • It says that if we were to repeat the experiment an infinite number of times the general average for the whole population would be within the generated bounds 95% of the time; or you could try the Bayesian perspective that there is a 95% probability that the the true average is between those two numbers (but only if you are willing to consider that there is no true average)
• The closer your data pattern is to that Gaussian density graph the smaller sample you need for these results to hold

• What if you don’t want to make assumptions, or you’re not working with an average?
  • You could ask a statistician for help, just saying
• You can try bootstrapping / non-parametrics / permutation testing
  • It only assumes that your sample is representative of the population (very few are)
• The term comes from the idea of pulling yourself up by your own boots
• It means you randomly take new samples from your current sample, the same size as your original sample, with replacement
  • Some values will be picked twice and others not at all

• Take a lot of bootstrap samples
• For every bootstrap sample you work out your statistic
  • Like the median, which is better than the average for skew data in some ways
• Sort those lots of statistics
  • Take the values in roughly positions 0.025 and 0.975 of the way through the list as your interval
  • Or try every interval with 95% of the sorted observations between them and find the shortest one
• These intervals give you a good idea of how certain you are about the thing you are measuring
• If your hypothesis doesn’t fit in the interval then you reject it

• Statistically you are not going to learn more from 11000 observations versus 10000 observations
• For a lot of data just take samples from the data
• Then when you need to check your results then just take new samples from the data
• Cross-validation also follows the same principles