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Category Archives: Data Science

Logical and Creative Thinking

22 Thursday Apr 2021

Posted by Max Hemingway in 21st Century Human, Data Science, Tools

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21st Century Human, Data Science, Tools

Right Side vs Left Side of brain

Our brain is an amazing organ of that learns, remembers, controls, moves, repairs a complex body. It is in control of lots of functions and as part of that it is also responsible for our Logical and Creative Thinking. There are lots of articles that talk about the left side of the brain being responsible for Logical and the right side for Creativity. This was first researched by Roger Wolcott Sperry with his work on the split brain.

There are lots of articles that talk about people being left or right dominant on the brain, hence being more logical or creative, however more recently published articles and research show that the activity in the brain is similar on both sides of the brain regardless of how dominant they are “An Evaluation of the Left-Brain vs. Right-Brain Hypothesis with Resting State Functional Connectivity Magnetic Resonance Imaging“.

Either way the brain is still an amazing thing and you can learn to use both Logical and Creative Thinking techniques, you just need to apply a growth mindset.

“We cannot solve our problems with the same thinking we used when we created them.” – Albert Einstein

Logical Thinking

Logical thinking helps us to make “sense” of things, coming up with solutions and in decision making.

The five W’s and 1 H are commonly used as questioning to help form logical thinking. These are

  • Who
  • When
  • Why
  • What
  • Where
  • How

Some add another H – How Much to the list as cost can play an important factor in decisions.

Creative Thinking

Creative thinking helps us approach things with an out of the box approach and an ability to look at things through different lenses to discover new solutions.

Balanced View

Taking a balanced view across Logical and Creative thinking, the Six Thinking Hats written by Dr. Edward de Bono starts to provide a balanced view by using the idea of parallel thinking to plan and use thinking more effectively. This can include logical and creative thinking.

Blue Hat – Process

  • manage process
  • action plans
  • next steps
  • reviewing thinking
  • summary

White Hat – Facts

  • data
  • facts
  • information needed
  • information available

Red Hat – Feelings

  • feelings
  • hunches
  • instinct
  • intuition

Green Hat – Creativity

  • creativity
  • solutions
  • ideas
  • alternatives
  • possibilities

Yellow Hat – Benefits

  • positives
  • brightness and optimism
  • value
  • benefits

Black Hat – Cautions

  • difficulties
  • potential problems
  • weaknesses

Build on the Skills

Learn different ways of thinking

Learn some new ways of thinking that you have not used before.

Practice and mix it up

As the phrase goes “Practice makes perfect”. Using different methods of thinking can bring different views and possibly different solutions to the problem/challenge.

Personally I have created my own set of cards based on several ways and methods of thinking that I use when I am looking at a problem. See my blog post Playing a Game with Innovation and Thinking.

Work with others

There is nothing better than working with others to bring in different views and ways of thinking that you may not have thought of previously. This is a great way of seeing how other people approach the problem/challenge and help identify if there are areas you can improve/learn on.

Be creative

Spend some time on creative hobbies that will help you build you creative thinking.

Learning a new skill

Learning a new skill will help you develop your thinking.

Further Reading

  • Being Logical: A Guide to Good Thinking
  • Six Thinking Hats
  • De Bono’s Thinking Course
  • Gamestorming: A Playbook for Innovators, Rulebreakers, and Changemaker
  • Thinking, Fast and Slow Paperback

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Experimental Mindset

10 Wednesday Feb 2021

Posted by Max Hemingway in 21st Century Human, Data, Data Science, Mindset

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21st Century Human, Data, Data Science, Mindset

We have all at sometime done some sort of experiment, from maybe from a young age as to see which cry and actions resulted in the reward of milk to test driving cars to find which is best suited to your needs before you buy it. These are experiments that produced results from things we have tried and may not have thought about it as developing an Experimental Mindset. In this article I am concentrating on how this applies to data.

Here are my notes from my research into the topic.

The main areas for an Experimental Mindset are:

  • Learning
  • Testing
  • Evaluating

In order to constantly learn you need to be open to learning and develop your Growth Mindset. I have covered this in another blog so wont repeat here: Having the Right Digital Mindset: Business (Change, Agility and a Growth Mindset).

Having an Experimental Mindset is one of the key traits in being a Data Analyst or Data Scientist and it is not a new term. This has been around as long as the field of science and research has. These arena have developed methodologies that have been adopted and taken forward by many other areas such as business and computing that can be used for testing and evaluating.

At a high level this methodology can be shown as:

Observations –> Hypothesis –> Scientific Law

Overlaid with the areas for data this can be shown as:

Observations (Learning) –> Hypothesis (Testing) –> Scientific Law (Evaluating)

or as:

Observations (Data) –> Hypothesis (Product/Service) –> Scientific Law (Predictive Model)

Using this methodology, one of the more common types of Hypothesis Testing is A/B Testing. This sets out a framework for a simple controlled experiment against two versions (A and B) to look at the impact of changes to a thing or product. Some useful articles on A/B Testing are listed below that go into the details of it:

  • A/B Testing
  • A Beginner’s Guide To A/B Testing: An Introduction
  • A Refresher on A/B Testing

Udacity host a course by Google on A/B testing.

There are some risks to A/B Testing that should be considered when reviewing the results:

  • Sampling Bias
  • Study Population
  • Target Population
  • Segmentation
  • World Time Zones
  • Target Population
  • Data/Privacy Laws

I will go further into the realms of A/B testing in a later blog post.

Further Reading

  • 5 Benefits of Adopting an Experimental Mindset
  • A/B Testing
  • A Beginner’s Guide To A/B Testing: An Introduction
  • A Refresher on A/B Testing
  • Comparison of Segmentation Approaches
  • Design Thinking Mindsets for Human-Centered Design
  • Embracing an Experimental Mindset
  • Sampling Bias
  • Sampling Bias
  • Sampling bias: What is it and why does it matter?
  • Simpson’s Paradox and segmentation: why analysis is crucial
  • The Upside of an Experimental Mindset

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Data Storytelling

08 Monday Feb 2021

Posted by Max Hemingway in Data, Data Science

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Data, Data Science

Humans have been using the medium of storytelling since the begining, but only really recording it from the moment a wet painted hand went onto a cave wall. These days we read stories in books or access stories over the internet on our tablets and other devices.

Photo by Suzy Hazelwood on Pexels.com

The main key to all of storytelling is data in one form or another. From 1 x wooly mammoth and 3 x hunters (thats 4 items of data) in a cave painting to the complexity of how many bits and bytes are in an online book.

For a good explanation on What is data? – Cassie Kozyrkov, Head of Decision Intelligence,@ Google has written some great posts and videos on the subject.

So when we have data, we use stories to explain what it is telling us – hopefully not through 1000’s of powerpoint slides…….Make it Stop!!. What are you going to put in those slides that will keep the audience hooked and focused.

Stories are normall based around a simple concept of beginning, middle and end, however there is more to it that that if you want to tell a good story.

The first thing through before getting to the story is to make sure you understand what the data is telling you. If you don’t understand the data and your asked a question, will you be able to answer it or further illustrate your point. Keep in mind – EVALUATE – LEARN – PRACTICE. Then maybe practice some more until you are confident with what your about to talk about.

Decluttered and simple visuals help to tell the story and keep the audience focused on what you are telling them, rather than they spend the time trying to understand what all that text and facts are on the screen. Information is Beautiful is a site that shows some ways to display data visually in easy to understand ways by David McCandless. Here is his TED talk:

Stories normally follow a Heroes Journey which takes the plot line through a series of steps to keep the audience wanting more and to continue to read the rest or listen until the end. When storytelling about data, as similar construct can be used using the Heroes Journey:

SequenceHeroes Storytelling StepData Storytelling Step
1Status QuoWhats the current normal
2Call to AdvetureThe Question (What is being asked of the data)
3AssistanceWhat are the Sources
4DepatureTurn the data into something understandable
5TrailsData Analysis
6ApproachMethods used
7CrisisData Modelling / Wrangling
8TreasureThe Findings
9ResultResult
10ReturnPresentation
11New LifeNew normal
12ResolutionReview
13EndEnd or maybe a different question?
Data Storytelling using a Heroes Journey

There is a good explanation of the different styles of Heroes Journey on Wikipedia. the above table is change a bit. Heres a video that goes through a format:

Now we have a structure, how you tell the story is just as important. How can you pursuade the audience about the data and point of view that you are presenting?

There are, then, these three means of effecting persuasion. The man who is to be in command of them must, it is clear, be able (1) to reason logically, (2) to understand human character and goodness in their various forms, and (3) to understand the emotions–that is, to name them and describe them, to know their causes and the way in which they are excited.

Aristotle

Aristotle set out his Powers of Persuasion in four areas:

  • Ethos – Author/Speaker (Character, Credibility, Authority, Truthfulness)
  • Pathos – Howtopic effects you – connect and bridge the gap (Current emotional state, Target emotional state)
  • Logos – Why it effects you – story / proposal (Reasonablenss, Consistancy, Clarity)
  • Karios – Time and place

Ethos – ‘It is not true, as some writers assume in their treatises on rhetoric, that the personal goodness revealed by the speaker contributes nothing to his power of persuasion; on the contrary, his character may almost be called the most effective means of persuasion he possesses.’

Pathos ‘persuasion is effected through the speech itself when we have proved a truth or an apparent truth by means of the persuasive arguments suitable to the case in question.’

Logos ‘persuasion may come through the hearers, when the speech stirs their emotions. Our judgements when we are pleased and friendly are not the same as when we are pained and hostile.’

Rhetoric, Aristotle

Karios is an Ancient Greek word meaning the right, critical, or opportune moment.

How we can use these areas is illustrated in this example:

When preparing for the Storytelling session its worth checking that you are not going to fall into the trap of the “echo chamber effect”.  From my post on the subject I have created the following term to help me remember – STACK

  • Step Back
  • Think
  • Absorb other views
  • Challenge your thinking
  • communicate your Knowledge

Storytelling is more trustworthy than just presenting data on its own. One to consider when you create your next PowerPoint Presentation.

Further Reading

  • Data Storytelling: The Essential Data Science Skill Everyone Needs
  • 5 Data Storytelling Tips for Creating More Persuasive Charts and Graphs
  • Data Storytelling: What It Is, Why It Matters
  • What is data storytelling? Plus 5 great examples

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Data Fellowship

08 Monday Feb 2021

Posted by Max Hemingway in Data, Data Science

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Data, Data Science

Data, it’s everywhere and there are thousands, millions, billions…… lets just say “lots” of data created evry second of the day, from articles and discussions on the internet, to texts and whats apps, to cars, to well anything with a chip in it really. It goes a huge way to ruling our lives and telling us how to live, from what to eat to the carbon footprint of the world. so when I was given an opportunity to undertake an apprenticeship in Data Analytics on a Data Fellowship Apprenticeship over the next 18 months. Of course Im going to jump at that!

A great way to check my understanding and knowledge on things and learn many new things and more importantly for me provide a qualification at Data Analyst Level 4 standard.

So what is the So What? At the moment the programme is starting, so not much to report back so far, however I have started to document some of my journey and bits in my GitHub repo and will use this and my blog to record my thoughts and learnings going forward. Watch this space as they say.

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Getting to grips with Probability

14 Monday Sep 2015

Posted by Max Hemingway in Data Science

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Data Science, learning

calcWhen learning Data Science one area to learn is that of probability.

William Chen has created a good 10 page Probability Cheat Sheet to help guide you through. The content is based on “Harvard’s Introduction to Probability”.

The cheatsheet summarizes important probability probability concepts, formulas, and distributions, with figures, examples, and stories.

There are also 16 Data Science books listed on his site. A couple of which cover statistics and probability.

http://www.wzchen.com/data-science-books/

Some other Probability resources to get you started:

  • Basic Probability – BBC Skillwise
  • Bitesize Probability – BBC Bitesize
  • Probability and Statistics
  • Coursera – Probability

Source: http://www.wzchen.com/probability-cheatsheet

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Visual Introduction to Machine Learning

03 Monday Aug 2015

Posted by Max Hemingway in Data Science, Machine Learning

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Tags

Data, Data Science, Machine Learning

I came across this “Visual Introduction to Machine Learning” in a forum. This is an experimental site showing statistical thinking with an interactive web page. The page builds as you scroll down and takes you through a journey of Machine Learning.

It provides a high level graphical view of:Machine

  • Nuance
  • Drawing boundaries
  • Machine learning
  • Forks
  • Tradeoffs
  • Best splits
  • Recursion
  • Trees
  • Making predictions

The URL in the Web page indicates that this is part 1 so hopefully there will be more to follow with the first page indicated further posts on “overfitting, and how it relates to a fundamental trade-off in machine learning”

You can follow this project on Twitter @r2d3us

Other posts on Machine Learning

  • In-depth Introduction to Machine Learning
  • Learning Data Science

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Learning Data Science – Useful References

14 Tuesday Jul 2015

Posted by Max Hemingway in Big Data, Data Science, Machine Learning, Open Source

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Tags

Big Data, Data, Data Science, Knowledge, Machine Learning

Firstly thanks to Tim Osterbuhr who prompteLearningd me to create this list of resources that I have found useful in learning about Data Science after he read my blog post on Learning Data Science. Tim has also provided some of the likes below as well.

Here is the list of Useful References for Learning Data Science. (This list is be no means exhaustive)

From my Blog

  • Learning Data Science
  • Data Science in the Cloud ebook
  • Data Science and Information Theory
  • Data Mining Courses
  • Open Source, Open Human, Open Data, Open Sesame!
  • Data Scientist Skill Set
  • R {swirls} – Learning R by doing
  • Correlation does not imply causation
  • Statistical Inference Resources

From Around the Web

  • 6 checkpoints to ensure regression model validity for analytics
  • Algorithms: Design and Analysis
  • Analyzing Big Data with Twitter
  • Big Data Analytics: Descriptive Vs. Predictive Vs. Prescriptive
  • Data Analysis
  • Data Mining for the Masses
  • Data Science Course
  • Google Visualization API Reference
  • k-means clustering
  • Occam’s Razor
  • PCA Step by Step
  • Regression Equation: What it is and How to use it
  • Using JavaScript visualization libraries with R

Public Data Sets

  • http://www.cs.cmu.edu/~./enron/
  • http://www.secviz.org/content/the-davix-live-cd
  • http://www.caida.org/data/overview/
  • http://www.secviz.org/content/visual-analytics-workshop-with-worlds-leading-security-visualization-expert-0
  • http://snap.stanford.edu/data/
  • http://analytics.ncsu.edu/
  • https://code.google.com/p/google-refine/

Data Science Books

  • 9 Free Books for Learning Data Mining & Data Analysis
  • 16 Free Data Science Books
  • 27 free data mining books

Happy to add other links from readers to this list.

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The R Consortium

01 Wednesday Jul 2015

Posted by Max Hemingway in Data Science, Open Source, Programming

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Coding, Data Science, Development, Open Source

RThe R Consortium has been founded and recently launched under an Open Source Governance and Foundation model. This is in response to the growing use of R and its communities.

The initial members of the R Consortium include:

  • Microsoft
  • R Studio
  • TIBC Analytics
  • Alteryx
  • Google
  • HP
  • Ketchum Trading LLC
  • Mango Solutions
  • Oracle

The mission statement is listed as:

The central mission of the R Consortium is to work with and provide support to the R Foundation and to the key organizations developing, maintaining, distributing and using R software through the identification, development and implementation of infrastructure projects.

They have also listed a potential number of projects they will be involved with:

  • strengthening the R Forge infrastructure;
  • assisting the Stanford University group running user!R 2016;
  • developing documentation; and
  • encouraging increased communication and collaboration among users and developers of the R language.

One to watch going forward for influencing the R Community.

Source https://www.r-consortium.org

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Getting to Grips with Git

08 Monday Jun 2015

Posted by Max Hemingway in Cloud, Data Science, DevOps/OpsDev, Open Source, Programming

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Cloud, Coding, Data Science, DevOps, Open Source, OpsDev, Programming

If you are new to GIT or want to refresh your skills/knowledge a good way of learning is through the Learning Git Branching simulator with it taking you through the commands and techniques.

Welcome to Learn Git Branching

Interested in learning Git? Well you’ve come to the right place! “Learn Git Branching” is the most visual and interactive way to learn Git on the web; you’ll be challenged with exciting levels, given step-by-step demonstrations of powerful features, and maybe even have a bit of fun along the way.

After this dialog you’ll see the variety of levels we have to offer. If you’re a beginner, just go ahead and start with the first. If you already know some Git basics, try some of our later more challenging levels.

The simulator covers:

Introduction SequenceGithub
– Introduction to the majority of Git commands

Ramping Up
– Additional Git commands

Moving Work Around
– Modifying the source tree

A Mixed Bag
– Git techniques tricks and tips

Advanced Topics

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Open Data Handbook Revised

21 Thursday May 2015

Posted by Max Hemingway in Data Science, IoT, Open Source

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Data Science, IoT, Open Source

openOriginally published in 2012 the “Open Data Handbook” has been revamped to inspire Open Data Newcomers.

The new version of the online site allows access to:

  • The Open Data Book
  • Value Stories
  • Resource Library

For those who have not yet been introduced to the Open Data Handbook, the below is a good overview of what its about.

This handbook discusses the legal, social and technical aspects of open data. It can be used by anyone but is especially designed for those seeking to open up data. It discusses the why, what and how of open data – why to go open, what open is, and the how to ‘open’ data.

There are already a lot of “Open” sources of such as Open Source Web Crawlers and Data Sets, which is set to grow in the wake of the Internet of Things (IoT) and other data creation solutions.

The Handbook/Guide provides a good place to start if you are considering making your data Open, however the data created will also be a huge revenue generator for companies who produce the IoT devices.  The amount of data that will made Open remains to be seen more things come online

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