An Ode to the Weird, and how to manage them

Naked, he ran through the dark streets of Syracuse. His damp footfalls and shrill screams echoing off the Sicilian architecture. While the footfalls have been forgotten, Archimedes’ alleged cries of, “Eureka!”, are still synonymous with the theory of displacement nearly 2,500 years later. Wow, he must have been a weirdo to run screaming through the streets, but undoubtedly a genius weirdo.

Naked, he ran through the dark streets of Syracuse.  His damp footfalls and shrill screams echoing off the Sicilian architecture. While the footfalls have been forgotten, Archimedes’ alleged cries of, “Eureka!”, are still synonymous with the theory of displacement nearly 2,500 years later. Wow, he must have been a weirdo to run screaming through the streets, but undoubtedly a genius weirdo.

Henry Ford was renowned for his love of carrots and once sponsored a 14-course meal with every course containing carrots. Thomas Edison, once deliberately and macabrely electrocuted an elephant called Topsy, and let’s not even address Albert Einstein’s choice of hairstyle.

Aristotle once famously stated that, “there is no great genius without some touch of madness”.

But, if this is true, how can the madness/weirdness be appropriately ‘managed’ to benefit from those who are able to see the world through different eyes and bring innovation to the corporate space?

The first thing to note is that trying to overly control a creative genius, using the same rules and policies that have been applied to the rest of the corporation, is going to ensure that you stifle any potential for positive disruption. This is not to say that rules, policies, and that demonised word, ‘governance’ do not have a place, but rather that they should be appropriate for the required outcomes, not just a generic ‘one-size-fits-all’ approach. For example, if you expect your creative geniuses to deliver quickly and maintain momentum, don’t force them to use archaic procurement processes that require completion of forms, in triplicate, with 17 signatures each. Tell them what needs to be achieved, but don’t tell them how to do it.

Next, you need to make them feel valued. It is an unfortunate reality that creative geniuses often come with an oversized ego. But this should be acknowledged, rather than ignored in a vain attempt to contain that ego. If not, they will leave the organisation.

Arguably worse than that, they may stay, but their spirits will leave, which only results in a chair being occupied, but very little more than that. If you think a genius with a large ego is hard to manage, try managing a disengaged genius, or moreover a genius hell-bent on exacting revenge on an oppressive manager. Give them the freedom to experiment and the opportunity to truly see their ideas fly.

So, in summary, you need to control that which does not like to be controlled. Easy, right?

Actually, it can be, just ensure there is a clear vision of the end state and the bare minimum of rules to support that. After that, just let your creative geniuses feel valuable. And, let’s face it, if they are geniuses then they are indeed incredibly valuable to the future of your company.

by Brad Carter

A curriculum for growing your data science skills (almost) for free

With the plethora of free (or at least reasonably priced) high-quality massive open online courses (MOOCs), free online textbooks, tutorials, the tools available for aspirant data science apprentices are many and varied. From taking courses offered by Coursera to freely available eBooks and code examples to download from Github, there are many useful resources at our disposal.

I hear and I forget. I see and I remember. I do and I understand – Confucius

With the plethora of free (or at least reasonably priced) high-quality massive open online courses (MOOCs), free online textbooks, tutorials, the tools available for aspirant data science apprentices are many and varied. From taking courses offered by Coursera to freely available eBooks and code examples to download from Github, there are many useful resources at our disposal.

Demand for data science skills remains consistently high. IBM predicts that appetite for data scientists will grow 28% by 2020. Job postings for data science skills in South Africa are rising rapidly as companies begin to realise the true value of their data initiatives.

According to IBM, the current most desirable and lucrative skills include machine learning, data science, Hadoop, Hive, Pig and MapReduce. It is interesting to note just how many data engineering type skills are in demand. I recently started to set up a data lab at the Foundery based on the Hortonworks distribution of Hadoop, and I can understand why this is true – (big) data engineering is unnecessarily complicated!

Over the last few years, I have completed (and sometimes part-completed) some data science MOOCs and tutorials. I have downloaded free eBooks and textbooks – some good and some not so good. These, along with the MOOCs, have become my primary source of knowledge and skills development in the data science domain. I am finding this form of online learning to be a very efficient and effective way to grow my knowledge and expertise. However, my choice of which courses to do has been haphazard at best and having this much choice has also made it difficult to find the right courses to pursue, often leading to me abandoning classes or not learning as well as I should.

The purpose of this blog, therefore, is twofold: to create a thoughtful and considered curriculum that I can follow to elevate my data science mastery and to share with you some of the resources that I have collated in researching this proposed curricula. Whether you are a seasoned data science expert, or an absolute beginner in the field, I believe there is value from some, if not all, of the topics in the curriculum.

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The ultimate ambition of completing this proposed curriculum is to vastly (and more efficiently) improve my mathematical, statistics, algorithmic development, programming and data visualisation skills to go from a journeyman level understanding of data science to full-on mastery of advanced data science concepts.

I want to DO so that I can better UNDERSTAND. Eventually, I’d like to understand and implement advanced machine learning and deep learning concepts (both from a theoretical and practical perspective) as well as obtain more in-depth expertise in big data technology. I also aim to improve my data visualisation skills so that I can have more impactful, interesting and valuable discussions with our business stakeholders and clients.

The day that I can have a debate with my maths colleagues about advanced mathematical concepts, compete with the computer scientists on Hackerrank coding challenges, run my models on a big data platform that I have set up, create a beautiful and insightful visualizations AND make this all understandable to my wife and daughter is the day when I know I have been successful in this endeavour.

I proposed this curriculum based on the skills that are commonly acknowledged to be required for data science as well as on course ratings, popularity, participant reviews and cost. I have tried to be as focussed as possible and my thinking is that this is the most efficient plan to get deep data science skills.

This curriculum will be based on open-source programming languages only, namely Python and R. My initial focus will be on improving my Python skills where possible as I want to get this up to a level where I can implement Python-based machine learning models in NumPy/SciPy. I do acknowledge, however, that for many of the stats and maths related courses, R is often preferred and in that event, I will switch.

Given my work commitments and the fact that we have a new (and very loud) addition to our family, I think that I would likely only be able to devote 10 hours a week to this challenge. My proposed timetable will, therefore, be based on this estimate. The current estimate to fully complete the curriculum is at 110 weeks or just over 2 years! This is going to be a long journey…

I plan to update this blog periodically as and when I complete a course. My updates will include a more detailed summary of the course, an in-depth review and score, how much it cost me as well as tracking how long the course took to complete relative to the advised timeframe provided by the course facilitators. My time estimates will be slightly more conservative relative to the time estimates for each course as, in my experience, it always takes longer than suggested.

Thank you for reading this far. If you wish to join me in growing your data science skills (almost for free) and help keep me honest and accountable in completing this curriculum, then please do read on.

Data Science Curriculum

0. Supplementary resources and setup

Sticking to the blog’s theme of finding low-cost resources for this curriculum wherever possible, I have found a few high-quality free online maths and stats textbooks. These will serve as useful reference material for the bulk of the curriculum. They are:

  • Think Stats – a freely downloadable introductory book on Probability and Statistics for Python. Code examples and solutions are provided via the book’s Github repository.
  • An Introduction to Statistical Learning with Applications in R – another freely available book that is described as the “how to” manual for statistical learning. This textbook provides an introduction to machine learning and contains code examples written in R. There is online course material that accompany this book and this can be found here as well as here. I will use this manual and potentially their associated MOOCs as a reference when I begin the machine learning component of this curriculum.
  • Introduction to Linear Algebra is the accompanying linear algebra reference book for the MIT Open Courseware MOOC. This book will also have to be purchased should the MOOC require this.
  • Although not free, Python Machine Learning by Sebastian Rashka has good reviews as a reference book for machine learning applications in Python. The book also has an accompanying code repository on Github.

1.  start by focusing on maths and stats

The first section of the curriculum will allow us to concentrate on redeveloping fundamentals in mathematics and statistics as they relate to data science. University, over a decade ago now, was the last time I did any proper maths (yes, engineering maths is ‘proper mathematics’ to all you engineering-maths naysayers).

Regarding learning the mathematics and statistics required for data science and machine learning, I will focus on the following courses.

  • Statistics with R Specialisation – There were many courses available to improve my stats knowledge. Ultimately, I settled on this Coursera specialisation by Duke University as it seemed the most comprehensive and the textbook seems a good companion book. This specialisation comprises 5 courses – Introduction to Probability and Data, Inferential Statistics, Linear Regression and Modelling, Bayesian Statistics and a capstone project written in R. Each course will take 5 weeks and will require 5-7 hours of effort per week. I will use this set of courses to improve my R skills, and I will audit courses if possible or I may have to pay for the specialisation. [Total time estimate: 250 hours]
  • Multivariable Calculus – (Ohio State University) referencing the Multivariable calculus sections of the Khan Academy where required. This highly rated course (average rating of 4.8 out of 5 stars) will provide me with a refresher of calculus and will take approximately 25 hours to watch all the videos. I think I can safely add the same amount of time to go through all the tutorials and exercises putting the length of this study at 50 hours. [Total time estimate: 50 hours]
  • Linear Algebra – (MIT Open Courseware) referencing the Linear Algebra sections of the Khan Academy where required. I don’t know how long this should take to complete, so I will base my estimate on the previous courses estimate of 50 hours. I chose this course as the lecturer of the Linear Algebra textbook, MIT Professor Gilbert Strang, conducts this MOOC. [Total time estimate: 50 hours]

2.  Time to improve my management skills of data science projects, experiments and teams

A large part of work at my previous employer and at my current job at the Foundery is to manage various data science projects and teams. I have a lot of practical experience in this domain, but I don’t think it would hurt to go back and refresh some of the core concepts that relate to effective data science project management. To this end, I managed to find an appropriate Coursera specialisation that aims to help data science project managers “assemble the right team”, “ask the right questions”, and “avoid the mistakes that derail data science project”.

  • Executive Data Science Specialization – John Hopkins University. The entire specialisation is only 5 weeks long, and requires 4-6 hours a week of effort. The courses that are on offer are titled “A Crash Course in Data Science”, “Building a Data Science Team”, “Managing Data Analysis”, “Data Science in Real Life” and “Executive Data Science Capstone”. I wasn’t able to obtain rating information for this specialisation. [Total time estimate: 50 hours]

 3.  Improve my computer science and software engineering skills

When I first started out, I managed to pick up a few Unix skills (just enough to be dangerous as evidenced when I once took out a production server with an errant Unix command). Since then, and over time, I have lost the little that I knew (luckily for production support teams).

New and exciting software engineering paradigms have emerged, such as DevOps and code repository solutions like Github are now commonly used in both the data science and development industries. As such, I thought that some study in this domain would be useful in my journey.

I would also like to increase my knowledge of data structures and algorithms from both a practical and theoretical perspective. To this end, I have found an exciting and challenging University of California San Diego Coursera specialisation called “Master Algorithmic Programming Techniques”.

The courses that I am planning to complete to improve my computer science and software engineering skills are:

  • How to use Git and GitHub – a freely available course offered by Udacity with input from Github. This course is a 3-week MOOC and is rated 4.5 out of 5 stars out of 41 student reviews and will require 6 hours of commitment per week. This course introduces Git and GitHub and will help me to learn how to use better source control, which in turn will greatly assist with project delivery of medium to large sized data science projects. [Total time estimate: 30 hours]
  • Introduction to Linux – a freely available course from edX. This is an 8-week course rated 4 out of 5 stars by 118 student reviews with over 250 000+ students enrolled. Thoroughly covering this material will take between 40-60 hours per the course notes. Gaining a firm understanding of Linux will allow me more control when using the open source data science environments and tools. [Total time estimate: 60 hours]
  • Introduction to DevOpsUdacity. This free course introduces that concept of DevOps and explains how to implement continuous integration, continuous testing, continuous deployment and release management processes into your development workflow. I am very interested to see how this could be applied to the data science world. The course does not have a rating and is 3 weeks in length requiring 2-3 hours per week of effort. [Total time estimate: 10 hours]
  • Master Algorithmic Programming Techniques – This Coursera specialisation by the University of California San Diego comprises 6 courses —Algorithmic Toolbox, Data Structures, Algorithms on Graphs, Algorithms on Strings, Advanced Algorithms and Complexity, Genome Assembly Programming Challenge Each course is 4 weeks of study, 4-8 hours per week. The individual courses were rated between 3.5 – 4.5 stars.

What excited me about this specialisation is that I would get an opportunity to learn and implement over 100 algorithms in a programming language of my choice from the ground up. I think that this would certainly improve both my knowledge about algorithms as well as my programming skills.

After looking a bit deeper at the course structure, it seems as if this specialisation is paid for at $49 per month until you complete it. So, the faster I do this, the cheaper it’ll be – nice incentive! [Total time estimate: 235 hours]

4.  Improve my base data science skills and up my Python coding abilities

At this stage of the curriculum, I would have solidified my maths and stats skills, improved my computer science and software engineering skillset, and brushed up on some data science project management theory. Before embarking on intensive machine learning material, I think that it might be a good decision to get back to basics and look at improving my base data science and visualisation skills and upping my Python coding abilities while at it.

One of my goals for this curriculum was to improve my communication skills by becoming a real data story-teller. An effective way to do this is to learn how to visualise data in a more concise, meaningful and, I guess, beautiful manner. I say beautiful because of an amazing data visualisation website called Information is Beautiful. Check it out; you won’t regret it.

  • Learning Python for Data Analysis and Visualisation – Udemy. Jose Portilla’s Udemy course is highly rated at 4.6 stars out of 5 from over 4 220 student reviews. Over 47 812 students have enrolled in the course. The length of the videos on this course is 21 hours, so until I can estimate this better, I will add 100% to my time estimate for completing the course.  The course is focussed on Python and introduces topics such as Numpy, Pandas, manipulating data, data visualisation, machine learning, basic stats, SQL and web scraping. Udemy often run specials on their courses, so I expect to pick this one up between $10 and $20. [Total time estimate: 50 hours]
  • Data Visualization and D3.js – Communicating with DataUdacity. This free course is part of Udacity’s Data Analyst nanodegree programme. The course provides a background in visualisation fundamentals, data visualisation design principles and will teach you D3.js. It is an intermediate level course that will take approximately 7 weeks to complete at 4-6 hours per week. [Total time estimate: 50 hours]
  • HackerRank challenges – HackerRank is a website that provides a very entertaining, gamified way to learn how to code. HackerRank offers daily, weekly and monthly coding challenges that reward you for solving a problem. The difficulty of the questions ranges from “Easy’ to “Hard”, and I plan to use this to test my new-and-improved Python skills. Every now and then I will use this form of learning Python as a “break” from the academic slog. [Total time estimate: n/a]

5.  Learn the basics of machine learning from both a practical and theoretical perspective

The resurgence of machine learning (the science of “teaching” computers to act without explicitly being programmed) is one of the key factors in the popularity of data science and drives many of the biggest companies today including the likes of Google, Facebook and Amazon. Machine learning is used in many recent innovations including self-driving cars, natural language processing, advances in medical diagnoses to name a few. It is a fascinating field, and as such, I want to gain a solid foundational understanding of this topic. It will also lay the foundation to understand the more advanced machine learning theory such as deep learning, reinforcement learning and probabilistic graphical models.

Machine Learning – Stanford University. Taught by Andrew Ng, this 10-week course is one of Coursera’s most popular courses and is rated 4.9 out of 5 from 39 267 student reviews. A commitment of 4-6 hours per week will be required.

Andrew Ng provides a comprehensive and beginner-friendly introduction to machine learning, data mining and pattern recognition and is based on several case studies and real-world applications. Supervised and unsupervised learning algorithms are explained and implemented from first principles, and machine learning best practices are discussed.

This course is a rite of passage for all aspirant data scientists and is a must-do. If you are on a more advanced level of machine learning understanding, look for the handouts of the CS229 Machine Learning course taught at Stanford (also by Andrew Ng) for further material. [Total time estimate: 80 hours]

Machine Learning A-Z Hands-On Python & R In Data ScienceUdemy. This course is a highly rated, practical machine learning course on Coursera. It is rated 4.5 stars out of 5 based on 11 798 student reviews. 86 456 students had signed up to this course at the time of writing. The videos total 41 hours and as before I will double this for my effort estimate.

The course is very hands on, and comprehensively covers topics such as data pre-processing, regression, classification, clustering, association rule learning, reinforcement learning, natural language processing, deep learning, dimensionality reduction and model selection. It can be completed in either R or Python. Again, I will look to pick this one up on a special for between $10 – $20. [Total time estimate: 80 hours]

6.  Our capstone project – let’s dive into the deep learning end

We have finally made it to what I regard as the curriculum’s capstone project – a practical course on deep learning:

  • Practical Deep Learning For Coders, Part 1 – Out of all the courses that I have looked at, I am probably the most excited about this one.’s deep learning course is a very different MOOC to the rest in that the content is taught top down rather than bottom up. What this means is that you are taught how to use deep learning to solve a problem in week 1 but only taught why it works in week 2.

The course is run by Jeremy Howard who has won many Kaggle challenges and is an expert in this field. The problems solved and datasets used in this course comes from previously run Kaggle challenges, which allows you to easily benchmark your solution to the best submitted entries.

A significant time commitment is required for this course – 10 hours a week for 7 weeks. The course teaches you some cool stuff such as such as how to set up a GPU server in the cloud using Amazon Web Services, and how to use the Python Keras library. As per the homepage for Keras, “Keras is a high-level neural networks AP developed with a focus on enabling fast experimentation. It is written in Python and is capable of running on top of either TensorFlow, CNTK or Theano.

As Jeremy Howard says, all you need to succeed in this course is pragmatic programming, tenacity, an open mind and high school math so good luck and well done on getting to this stage! [Total time estimate: 100 hours]

7.  Conclusion

So, we have finally made it to the end – well done! I have reviewed countless number courses in compiling this curriculum, and there were so many more that I wanted to add, including these more advanced topics:

I have also not touched on related topics such as big data, data engineering, data management, data modelling nor database theory of structured and unstructured sets of data. An understanding of these topics is nonetheless vital to understand the end-end spectrum that makes up the data analytics continuum. Nor have I chatted about the myriad data science tutorials and Kaggle-like data science challenges out there.

I intend to look at relevant tutorials and Kaggle problems where they relate to parts of this curriculum and where possible I will try implement some of these solutions on a big-data platform. While discussing this topic with one of my colleagues, he suggested also trying to build something big enough that encompasses all the above so that I can have an end-target in mind, don’t get bored and implement something that I am passionate about from the ground up. This is certainly something that I will also consider.

This challenge will start on 10 July 2017. According to my estimate, this curriculum will take 110 weeks or just over 2 years!! As daunting as this sounds, I take heart from Andrew Ng, the machine learning expert, when he said the following in an interview with Forbes magazine:

In addition to work ethic, learning continuously and working very hard to keep on learning is essential. One of the challenges of learning is that it has almost no short-term rewards. You can spend all weekend studying, and then on Monday your boss does not know you worked so hard. Also, you are not that much better at your job because you only studied hard for one or two days. The secret to learning is to not do it only for a weekend, but week after week for a year, or week after week for a decade. The time scale is measured in months or years, not in weeks. I believe in building organizations that invest in every employee. If someone joins us, I can look them in the eye and say, “If you come work with me, I promise that in six months you will know a lot more and you will be much better at doing this type of work than you are today

I hope that this quote resonates with you too and that the blog has helped or motivated you to improve your data science skills. Thank you for reading this and please keep me honest in terms of completing this challenge. Please post a comment if you think I should add to or change the curriculum in any way, and post your own course reviews — let me know if there are any other books and textbooks that I should consider. Expect updates soon!

by Nicholas Simigiannis



The changing world around programmers

In today’s ever-changing world, we find that businesses have become more concerned about what you can do rather than what qualification you have.

Gabriel blogIn today’s ever-changing world, we find that businesses have become more concerned about what you can do rather than what qualification you have. This paradigm is becoming more apparent as companies have an unbelievable shortage of decent coders who are able to deliver to their expectations. This gap in the employment market is increasing as the average university turnout of BSc Computer Science graduates is far less than actual demand.

 This situation has led the industry to change the way they look at qualifications and to focus more on a person’s ability to code and learn. If you are a self-taught coder and have an understanding of industry-relevant technology, you are in a much better position than someone who still has to go into university and learn coding there for the first time. A few companies are willing to take the risk of hiring someone without formal coding qualifications, and have reaped the rewards in taking those risks. The coders that they hire generally seem to be more aware of what new technology is available, and are more willing to learn something new in order to help them grow further.

 We are starting to see a paradigm shift in the industry and the way in which people think. The stack overflow statistics show that the proportion of self-taught developers increased from 41.8% in 2015 to 69.1% in 2016. This shows that a lot of developers are self-taught and a lot more people are teaching themselves how to code each year. People who start to code from a young age show such passion for coding and in combination with their curiosity for learning something new, their love for it speaks volumes. To have the ability to create anything that they can think of on a PC, and to manipulate a PC to behave like they want it to and have a visual representation of this, is unbelievable.

 For those interested in teaching themselves how to code there are many websites to look at. Here is a list of 10 places you can learn coding from, but I will list the top 3 places that I learnt the most from:

Those websites have their own way of teaching code and if youcombine this with some Youtube videos from CS50 and MIT OpenCourseWare you will be all set to learn at your own pace. Hackerrank is a good way to test everything you learnt and you can see how you rank against the world.

 WeThinkCode_ is an institution to learn coding, for anyone from ages 17-35 years old. Their thinking is that you do not need to have a formal qualification to be a world class coder. More institutes like this are opening across the world. Having a wide age gap illustrates that you are never too old to learn how to code. There are also more and more coding education opportunities for young people. It is really easy to learn how to code from a young age as that is when your mind is at its prime to learn new things and adjust to constant change.

 In a programmer’s world you are constantly learning new things and this is what makes our jobs exciting.

The world is ever-evolving and we all need to keep adjusting our mindsets on how we look at things, otherwise we will be left behind while everyone moves forward.

By Gabriel Groener

The Modern Programmer

IT professionals often don’t get an honest portrayal in the entertainment industry and, for better or worse, the mass perception of Computer Science has been influenced by what people see on their TV screens. Either we sit in a dingy dark room, littered with empty energy drink cans, staring at a terminal with green font flashing and passing by at light speed – with sound effects, or we are cool rich guys creating programs that become self-aware.

IT professionals often don’t get an honest portrayal in the entertainment industry and, for better or worse, the mass perception of Computer Science has been influenced by what people see on their TV screens. Either we sit in a dingy dark room, littered with empty energy drink cans, staring at a terminal with green font flashing and passing by at light speed – with sound effects, or we are cool rich guys creating programs that become self-aware. There really isn’t a middle ground and these perceptions either drive people to developing an insatiable curiosity in the field or becoming fearful and believing that they aren’t mentally fit to join the club.

The demographic of the modern programmer isn’t what it was back in the 70’s. Most IT professionals were – well…Professionals. They were mathematicians, engineers, scientists, accountants, etc. often in their 30’s or 40’s. The programming industry was almost 50% women. What on earth happened?

Well, I have a theory. Computer Science (CS) wasn’t a course at any universities at that time, so youngsters really had no way of entering the field. Not to mention the fact that what they called a computer back then isn’t what we have today. They were big, expensive and obviously fewer. There were no operating systems. They wrote code by hand which was then converted into punch cards that could be fed into the computer and you had better pray that what you wrote was correct – which, if you code, you know it often isn’t – because then you would have to start that lengthy process from scratch. Blessed are those that came before us, for they were a resilient few. By the time we had a CS course it was the 80’s and young adults could learn how to code.

The 80’s was definitely one of the most defining times in modern history. We saw technology really being embraced in the media. Back to the Future, Ghostbusters, Star Wars, Terminator and many more franchises showed us a world of technology that seemed almost impossible. In lots of ways we are still catching up the imaginations of the filmmakers and science fiction writers. But I find this time very interesting because it gave birth to the geek culture which has lasted to this day. This culture was very young and male dominated. It was a kind of cult to those who were part of it. This must have driven the women away. Women in general still don’t get the culture. Heck, even I don’t get it to the degree of hardcore followers. Now think about how we perceive these “geeks” in society. Beady eyed, brace faced, drooling, good-grade-getting teens with bad acne (is there good acne?) and thick glasses, always getting bullied by the “jocks”. Truth is, in a quest to fit in, teens only hang out with the group that they relate to and/or accepts them. Learning became the uncool thing and Disco was in. The media neatly crafted and packaged nerd culture. Being a cool kid meant you didn’t even greet the nerd – unless shoving someone into a wall counted as a greeting. And so that was that. Programmers were part of a culture that embraced creativity, logic and intelligence and frowned upon anything less, because in order to be a programmer you needed to love learning and solving problems. Being a cool kid meant you had to love partying, gossip and creating problems.

Things have changed somewhat. Programmers today come in different shapes and sizes. Still not many hourglass shapes, but we’re getting there. The next generation of teens will definitely be more in-tune with technology and the true culture of the geek or the “hacker”. Those that fail to see the power of new technologies will be left behind. Computers are so much more accessible and all schools are starting to teach coding. With innovative colleges like We Think Code and 42, the future of what we perceive as an IT professional will be completely different to what we have today.

we-think-code-banner (003)

It’s now up to us to make sure that our kids become programmers rather than the programmed. It’s in the small things that we spot the young coder. The little kid that breaks his/her toys to find out how they work. Kids are naturally curious and it’s up to us to nurture that curiosity and not reprimand or punish them for it. We interact with technology every day and we would only be empowering them by encouraging them to learn how to control that technology as creators in the same way that we might teach them how to play a musical instrument. I envision a world where the modern programmer is anyone, in a society that frowns on those that shun learning. Let’s make it happen.

by Sherwin Hulley

Our book is yet unwritten

2016 was a year of discovery, of adventure, of breaking boundaries. For many it’s been a year of unparalleled innovation – especially for those of us that live in experimental spaces. We’ve long known that innovation is for the brave – those souls who dare to speak out, the curious ones asking “But who says?”.
As I reflect on bravery or courage or heroism, it dawns on me that bravery in any of its forms is remarkably like crazy – or is this simply a matter of perspective?

2016 was a year of discovery, of adventure, of breaking boundaries.  For many it’s been a year of unparalleled innovation – especially for those of us that live in experimental spaces. We’ve long known that innovation is for the brave – those souls who dare to speak out, the curious ones asking “But who says?”

img_6768As I reflect on bravery or courage or heroism, it dawns on me that bravery in any of its forms is remarkably like crazy – or is this simply a matter of perspective? Much of our lives as innovators requires us to quiet the voices in our heads yelling out “You can’t do that! It’s crazy!”. And it’s exactly this act of changing perspective that allows us to see possibility and create a new future – to disrupt our worlds. It takes a special kind of crazy to question assumptions that are years old, to challenge ideals and concepts that work well enough, to be that person in the room asking “why?”

In Adam Grant’s “Originals” (if you haven’t read it yet, what are you waiting for? It’s incredible!), he speaks about “Vuja De” –  the obvious reverse of Déjà vu – the concept of facing something familiar but seeing it with a fresh perspective that enables new insights into old problems.

In today’s world of work, one of the biggest issues we face is creating spaces where people can bring their excellence, where the uniqueness of the individual can be expressed to create winning innovation.  How do we create that winning culture?

For years we’ve followed the rules on how “work” is, a kind of imaginary Encyclopaedia Britannica of how we work. But that imaginary book was written before “we” were working! It was written before many of “us” entered the world of work! Us being women and millennials and innovators and also closet creatives, and evening gardeners and day-time-suit-wearing-iron-men and also… well, most everyone.

Let’s face it, this book was written for a bunch of folk who are now in the minority. And don’t get me wrong, it worked really really well back then, but for “us” in the workplace now, it really does fall short. Many of us feel that our workplaces just don’t enable the way we need to work. So why then are we still using that imaginary book as our core reference guide?

That way of work was perfect for specific workplaces, for a workforce that were all very similar (or were told that they had to be) and for a time that was, well…industrial revolution. We’re in a whole new time, with a whole new workforce, and yet – there is no new book!  We have moved from a world where work was about creating consistency, to a world where work is about embracing each individual’s unique contribution and, if we wish to see that reality, it means we are going to need that bravery to change our worlds of work.

img_6779And it’s right about at this point that I hear Natasha Bedingfield belting out “I’m just beginning, the pen’s in my hand, ending unplanned” and then…a great big ol’ penny drops…it’s time to do some re-writing!

In 2017 I’m keen to see these new chapters take shape.  Let’s take the time to write “the Wikipedia of work” for our future, one that works for us, one that creates space for innovation, for creativity, one that allows every person to thrive, one that isn’t creating a whole workforce of ill-fitting pegs.

We have already rewritten the chapter on dynamic working (literally rewritten), but there are still many chapters that we haven’t even begun to write. We’ve only just started the chapters on what the world of work look could like for single moms? What about the chapters on working dads? Or insomniacs? Or those that live far from their workplaces? Or nocturnals?

And what about the chapter on success? Does it still mean becoming the CEO? Really? What is success if you believe in balancing family and sport and work and creative hobbies? What could that chapter look like?

And what is a career? Is it really a straight-line 20-year plan? What if there was a chapter on changing careers mid-way? Or one on taking a break from your career? Or one on how to come back after a break?

Now is the time for a massive cultural innovation.  It’s the time for new chapters. It’s time for all you brave crazies out there to start recreating, it’s time to get writing. Take it home Natasha… “Live your life with arms wide open, today is where your book begins, the rest is still unwritten”

by Liesl Bebb-McKay

Banking on the future

As we travel down the road to banking innovation and focus on attracting incredible talent, it’s becoming increasingly clear that we have a slight problem Houston…it seems that banks have a bad rap!

As we travel down the road to banking innovation and focus on attracting incredible talent, it’s becoming increasingly clear that we have a slight problem Houston…it seems that banks have a bad rap!

Where to lay the blame seems a little less clear…it could be the multiple crises over the last few decades, could be recent high-profile scandals, sensationalised movies and TV shows with hyper-villainous bankers, or tales of “great vampire squid wrapped around the face of humanity, relentlessly jamming its blood funnel into anything that smells like money” – whatever the cause, the reputation is certainly a little more tarnished than it was back in the day!

Now I’m not claiming that there is a whole lot of fireless smoke nor do I want to embark on a meaningless defensive diatribe, but I do feel compelled to encourage incredible talent back into the illustrious world of financial services.


So, why is banking an exciting place to forge your career?

Firstly, banking has its heart in innovation – yes, innovation. Banking by definition sits in the middle of the economy and as much as that economy changes (all the time!) so must banking adapt. It is an industry that continually has to reinvent itself, continually innovating and redefining what it provides for its clients. Now, more than ever before, banks are feeling the pressure to reinvent themselves to thrive in a digital age and i future-proof the world of financial services. This is a great opportunity for innovators in non-traditional banking spheres to participate — engineers, developers, creatives, design experts — all have roles to play in the banking innovation ecosystem of the present and of the future. Diversity is essential for innovation. Banks are seeking talent from all walks of life and cultural shifts that have been slow in the past are accelerating as the industry recognises the need for great diversity of thinking.

Secondly, it’s a challenging and dynamic working environment. Banks are notoriously competitive, but it’s exactly that type of backdrop that allows you to participate in a highly stimulating career path. The opportunities for growth are immediate and long-lasting, and the energy and buzz that the environment provides create great opportunities for individual and team outperformance. Dynamic environments also have great scope for autonomy and mastery and, importantly, an opportunity to be part of creating a new future.

The industry is also one where learning is part of the everyday experience: banks are well known for structured in-house training, but they are also supportive of formal training programmes. Continual learning is essential for if banks are to continue to be innovative and is of great value to individuals working in the space as they follow their own development and leadership journeys.

And, lastly, we have fun! As we strive for lives where work and life come together, an industry that allows you to have fun while you work and that embraces your individual excellence in a great working environment is pretty appealing!

by Liesl Bebb-McKay

Listen to Liesl talking about RMB’s Athena programme on Classic FM here.  The Athena programme won the “Women Empowerment in the Workplace” award at the 2016 Gender Mainstreaming Awards.