#biztech

New episode of the DataCafé is out now!

"Entrepreneurship in Data Science"

Hear us talk to Freddie Odukomaiya about the skills a data scientist needs to be an entrepreneur & The Lean Startup approach to get your product from concept to launch.

👉 https://www.buzzsprout.com/1073626/5447728

Replicated from my article on Medium (https://link.medium.com/jyp3jUzjuX)

In the classic Data Science Venn diagram a data scientist exists at the convergence of skills in mathematics & statistics, hacking and domain expertise¹. But alongside this is the importance of soft skills in a data science role². To be truly effective a data scientist should possess strong communication skills; be able to negotiate and persuade stakeholders; and ultimately lead projects and teams to success, in a range of commercial or industrial environments.

This motivated me to compile a list of book recommendations for burgeoning data scientists to consider reading as part of their personal development. In my growing responsibilities to coach and lead project teams, I realised that although data scientists are generally very conscientious about their continuous development, they often focus heavily on the technical skills of the role and neglect to balance this with dedicated learnings on the softer skills side. I’ve found it very rewarding to incorporate soft skills learning into my personal development plan, to strengthen my commercial awareness and understanding of how businesses, and more importantly people, operate. And I have encouraged that these learnings be picked up in the projects and teams that I have helped lead, with great effect.

In these recommendations I summarise the key data science learnings from each book that can be put into practice in the role. I focus on the books I think most strongly complement each other to round-out the many soft skills an effective data scientist should hold, gathered from my experiences growing from a Junior role to Senior role and as the Tech Lead on a variety of projects.

“How to Stop Worrying and Start Living” — Dale Carnegie

This was the first ‘self help’ book I read when I transitioned into a data science role, and it was an excellent introduction to the effectiveness of personal development.

Recommended for: anyone who has ever experienced imposter syndrome!

Main message: recognise the cause of your worries, then rationalise them and form a plan to conquer them.

Key learnings for data scientists: when worrying about project briefs, code bugs, presentations to stakeholders, etc. it’s helpful to identify and mitigate the risks, so that you / your team / your stakeholders can be assured things are understood and under control.

Fave quote: “Decide just how much anxiety a thing may be worth — and refuse to give it any more.”

“How to Win Friends and Influence People” — Dale Carnegie

After enjoying Dale Carnegie’s writing style I next read his book on influencing people, which is itself top-ranked as a highly influential book (so meta)! Don’t let the title put you off: the advice is rooted in being your most authentic self, with a focus on interpersonal skills and the importance of empathy.

Recommended for: sociopaths (j/k!) anyone looking to become more empathetic or personable.

Main message: be sincere, honest, positive and humble when engaging others, treating them with genuine interest and respect.

Key learnings for data scientists: various projects and teams will bring together a diverse range of people whom it is important to empathise with to understand their motivations and behaviours so you can bring them on a journey of building an ideal solution, which may or may not align with their initial expectations.

Fave quote: “The only way to get the best of an argument is to avoid it.”

“Thinking Fast and Slow” — Daniel Kahneman

Great book to become aware of the differences between our conscious and unconscious thought processes.

Recommended for: those who want to bring more reasoning and deliberation into their actions and behaviours.

Main message: by recognising our ‘fast’ and ‘slow’ ways of thinking, we can better control the use of logic in our decision making and reduce any overconfidence that stems from emotion or unconscious bias.

Key learnings for data scientists: in the various engagements with people across DS projects it’s useful to understand how both your and their decision-making happens since not everyone involved will think in the same way, so you should tailor your approach to the situation and engage ‘slow’ thinking where necessary.

Fave quote: “He had an impression, but some of his impressions are illusions.”

“The 7 Habits of Highly Effective People” — Stephen R. Covey

Introduces seven approaches for being effective at attaining your goals, especially when working with others.

Recommended for: those in project management, team leadership, or a consultancy role.

Main message: how to move from dependence to independence (self-mastery) and interdependence (working effectively with others).

Key learnings for data scientists: the effective prioritisation of tasks is demonstrated through measures of urgency and importance (priority matrix of “do”, “plan”, “delegate” or “eliminate”), which is useful for breaking down requests from stakeholders so you can focus on the most valuable parts. Methods to “synergize” a team are outlined to help ensure data scientists are aligned to the same goals and work positively towards them, relying on effective leadership to ensure a “win-win” situation.

Fave quote: “Seek first to understand, then to be understood.”

“The Personal MBA” — Josh Kaufman

A condensed version of the typical lessons covered in an MBA program. It highlights key aspects of business education and strategies with examples.

Recommended for: those with limited business background or training, especially if now working with businesspeople or in a commercial setting.

Main message: the thinking behind effective strategies and decision-making throughout a business model; from evaluating the market and measuring value to building a product and managing uncertainty.

Key learnings for data scientists: stakeholders, higher management, and 3rd party providers or consultants, will all usually have a stronger foundation and background in business than a data scientist. To understand their motivations and essentially speak their language in order to gain their trust, it’s crucial to study business theory; covering forms of value creation & delivery, marketing, finance, psychology, and the function of business models and systems.

Fave quote: “Where there’s hassle, there’s opportunity.” — (Hassle Premium)

“The Bullet Journal Method” — Ryder Carroll

A method for improving your productivity through effective note taking and to-do lists.

Recommended for: anyone who struggles to keep track of their ever-growing to-do list.

Main message: using a simple written system of symbols and rules for recording actions, events and notes, you can improve your productivity.

Key learnings for data scientists: work in tech tends to be fast paced, with various dynamic requests from the business, so it’s important to manage this on a daily level — which is where the bullet journal method can help. A data scientist might wear many hats in one day, compiling various to-do lists that it’s useful to then order and prioritise, so you know what to action now, schedule for the future, or just eliminate if unimportant (see “7 habits…” above).

Fave quote: “Track the past, order the present, design the future.”

The following books hold a variety of logic, reasoning and insight on the softer side of data science & biz-tech skills.

“Freakonomics”, “Super Freakonomics”, “Think Like A Freak” and “When To Rob A Bank” — Steven D. Levitt & Stehpen J. Dubner

These are a fascinating read that show how economics is at the root of so many parts of society, highlighting its importance and influence.

“Adapt: why success always starts with failure”, “The Logic Of Life” and “The Undercover Economist” — Tim Harford

The psychology of people in society is explored through examples of economics at play in business, marketing, strategy and innovation.

“The Four: the hidden DNA of Amazon, Apple, Facebook and Google” — Scott Galloway “Are you smart enough to work at Google?” — William Poundstone “Outliers: the story of success” — Malcolm Gladwell “The Lean Startup: how constant innovation creates radically successful businesses” — Eric Reis

These books highlight how the ability to think creatively and act on innovation are what current tech companies thrive on and seek to add/grow in their employ.

“The Leader’s Mindset: how to win in the age of disruption” — Terence Mauri “The Five Minute Coach” — Lynne Cooper & Mariette Castellino “How to Develop Self-Confidence and Influence People by Public Speaking” — Dale Carnegie “Getting Things Done: how to achieve stress-free productivity” — David Allen

These four books introduce aspects of good leadership, from having the right mindset and the ability to coach others, to being confident in yourself and able to influence people so they become highly productivity without becoming stressed.

“Nudge: improving decisions about health, wealth and happiness” — Richard H. Thaler & Cass R. Sunstein “A Field Guide to Lies and Statistics: a neuroscientist on how to make sense of a complex world” — Daniel Levitin “The Return of the Economic Naturalist: how economics helps make sense of your world” — Robert H. Frank “Predictably Irrational: the hidden forces that shape our decisions” — Dan Ariely “The Signal and the Noise: the art and science of prediction” — Nate Silver “Struck By Lightning: the curious world of probabilities” — Jeffrey S. Rosenthal “How Not To Be Wrong: the hidden maths of everday life” — Jordan Ellenberg “Coincidences, Chaos, and All That Math Jazz: making light of weighty ideas” — Edward B. Burger & Michael Starbird

These are some great reads on the use of maths & stats to help understand the behaviours of people and society, how we misunderstand and misjudge our biases, and how to better use probabilities to inform our decisions and the way we evaluate risk and the predictability of outcomes.

“Wake Up! Escaping a life on autopilot” — Chris Baréz-Brown “The Rules Of Life” — Richard Templar “Happiness by Design: finding pleasure and purpose in everyday life” — Paul Dolan “The Algebra of Happiness ” — Scott Galloway

Last, but by no means least, are some books about the importance of a positive and happy mindset; which is a good balance to have in the biz-tech industry because, as much as we work with logic and metrics and efficiency measures, we are not robots and should always invest in our mental health and wellbeing.

I’m curious and interested in anyone’s further recommendations on biz-tech., leadership or soft skills resources for data scientists. These and other resources are gathered at my datascienceunicorn.com blog³. Please hit me up!

P.S. As I’ve compiled this list I’ve realised the dominance of male authors in it and have made a mental note to seek out more female-authored books in these areas. All recommendations welcome.

References

¹ http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram ² https://towardsdatascience.com/soft-skills-will-make-or-break-you-as-a-data-scientist-7b9c8c47f9b ³ http://www.datascienceunicorn.com

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