Image Source: http://on.thestar.com/2jcpuYI

Have you seen the movies: The Minority Report, The Terminator Series, The Net, and Live Free or Die Hard (Die Hard 4.0). This book is a print version of all those movies and numerous other flicks rolled into one. Sure, its written by the head honchos of Google, so I guess it deserves (or rather expects) to be read by people all over. But it could have just as well been written by someone anonymous down the street. The reason I say that is unless you have been living under a rock, it is common knowledge that there are no disconnected devices or disconnected individuals, at least in the developed world. And in the developing world, generations of people are leap frogging into the connected era with smart phones by the virtue of having entirely skipped the personal computer revolution. We are already in the era of smart money (bitcoins), smart homes, smart phones, smart cars, hyper-loop and Mars colonization in the horizon, Amazon echo, Apple Siri, and whatever else Schmidt and his team at Google are thinking up.

Don’t get me wrong, it is a decent book to understand the inherent dualities when it comes to everything around us going digital. Each chapter of the book examines how the many facets of our lives will be fundamentally transformed: ourselves, the  people around us, institutions, and governments. Schmidt and Cohen also theorize on how the digitized world  would influence terrorism and counter-terrorism efforts, how it can influence repressive regimes and the people who would rebel. There is also a dedicated chapter about how environmental and man-made catastrophes in the digitized world can unleash innovation to speed up the reconstruction efforts. A chapter that stood out was the one on the “Future of Revolution.” It discusses how ordinary citizens in the Arab Spring used technology to spread the message of freedom and brotherhood, and to coordinate peaceful protests despite technological and physical oppression by their respective regimes.

Each chapter in the book examines the pros and cons of the digital world. Each chapter has a “protagonist:” ourselves, or governments, good people, and bad people. By the end of Chapter 2 or 3, it gets rather repetitive and quite frankly, a little depressing. I am sure the the book was intended to be thought-provoking, as we step into the connected digital future, and it did its job! At the end of the book, I was wondering if I should relocate to a small village in a serene corner of the world, disconnect from the internet, grow my own food, and live out a simpler life with my family.

My rating is 3.0/5.0. I just had to finish it since I started it. Was not a compelling read.

 

Originally written in 2016, edited in Jan 2017.

RANT ALERT!!

I cannot turn a page in newspapers or browse for news on the internet without reading (and rolling my eyes) about the emergence, reemergence, take over, new era, new age, and deluge, of Big Data Analytics or how machine learning algorithms like deep learning or some other cognitive, neuro, learning are going to save the world. We all have heard some banalities being bandied about quite a bit…

• Big data is the next oil, the next soil, etc.
• Data matures like wine and applications like fish
• IoT is going to disrupt the way we live, it is a lot bigger than Big Data
• Artificial intelligence is going to take over the planet and all our jobs (Terminator style…well not quite, I made that one up, the other three are real by the way.)

My personal favorite rebuttal for all these is “Not everything that counts can be counted, and not everything that can be counted counts.” This quote was said to be hanging in Einstein’s office in Princeton (not sure if this is true or not, but the saying makes sense). With all due respect to data scientists and other analytics professionals (I am one of them) can we all please go easy on the hype and not make it all sound so cheesy. It’s like the dotcom bubble dejavu all over again. Every startup I hear about is using some fancy ‘new’ algorithm, every company is talking about how analytics will change the world. Sure, some will and should..for the better.

Let’s get a few things in order. Analytics and data science have been there for a decades. They were just known with different non-appealing names: statistics, optimization, computer science, algorithms, etc. Clearly, none of them sound as appealing as “Data Science” or “Analytics.” We should all be thankful that industry as well as academia woke up and took notice about “smart decision-making,” and I guess some amount of branding was necessary for it to be taken seriously. Duly noted.

Now, can we get back to doing good work and not sell snake oil. All of us end up sounding ridiculous, naïve, and quite frankly a little annoying. The field runs the risk of being turned into a sham by some used-car salesmen (no offense to them). Let me give you a personal anecdote. I approached a conference organizer (in India) about submitting a proposal to speak at a conference. He unabashedly sent me a brochure with a detailed price list of how I can buy slots to talk about my ideas. Never once did he talk about my proposal, what the idea was, or even what the model / algorithm / application was. All he cared about was $$$.

The brochure even said I can pay extra to talk more (buy an entire session that is). This is what knowledge in our world has come to. Who can sell the snake oil better…who can market things and make them sound better… who can come up with more cool sounding jargon…who can create entire fake conferences where people pay to talk and ideas go to die. Sure conferences cost money, but it has to have a rigorous review process, such as KDD or most of the IEEE conferences.

So how do we stop this madness? I have a few pointers that some of you may agree with. I have already spoken to a few serious data scientists and they share my views.

• Refrain from saying and posting stuff unless it makes scientific sense (do not do it just to get more likes and shares on your social media feeds)
• Reputed websites should have strict editorial and review processes and not publish garbage
• Serious data scientists should refrain from giving talks where you have to pay to simply buy a slot without any formal review process
• When recruiting for your teams, consider hiring analytics professionals who are certified or those who have demonstrable skills

If we do not give any value to our own profession, trust me no one else will. We will all end up looking like used car salespeople.

 

The key take-home message that Altman delivers through this book is that we live in an interconnected world and we neglect this axiomatic truth at our economic peril. Altman does a nice job of explaining some very complex concepts (credit default swaps, currency futures, and trading among several other complex financial jargon) in terms that any educated layperson can understand. The author essentially picks a random day in 2005–June 15th– and discusses how business transactions around the world are all interlinked and how a ripple in East-Timor’s energy economy has an impact in Italy or India and vice-versa.

What is conspicuously missing is any stargazing about the then impending global economic depression. A chapter discussing the subprime mortgages would have been prescient. Of course, that wasn’t the intent of the book, and with all due credit to the author–hindsight is 20-20, but it did feel like a miss by the author.

image source: http://images.macmillan.com/

The most interesting chapter of the book is the one on credit markets and currencies. With simple yet illustrative examples the author paints a great picture of how truly the connected the world has become. If there is inflation in UK, people around the world choose to buy products from elsewhere. With reduced demand the value of the Pound falls further. It is a slippery slope from there on. Such phenomena were seen around the world in countries such as Turkey. The last chapter about how disruptive shocks could sometimes strengthen the economy is quite interesting as well.

As the author admits, it is a piecing together of some events around the globe on that random chosen day. I was not entirely riveted to the book, its a decent read nevertheless. My rating is 2.5/5

While this was not the first book I started to read about to understand the Bitcoin, I am glad I gravitated towards it. I started with “Mastering the Bitcoin” by Andreas Antonopolous, but it turned out to be a heavy hitter and with all the code in the book– I was lost (I will certainly get back to it soon). I browsed through Amazon and the web for other books on the bitcoin and was led to this listing which I had tweeted about in December 2016, and I picked up the book by Dominic Frisby (see <<). [image source: https://www.cryptocompare.com/coins/guides/the-best-bitcoin-blockchain-and-crypto-books-our-top-picks/]

Some people on Amazon complained about it being Libertarain rambling, but I beg to differ. Frisby’s book read less like non-fiction and more like a crime-scene thriller and I was hooked. I am glad I read the book and now I am on to other books on the Bitcoin. Frisby does a very nice job of introducing readers like me who were interested and had read about Bitcoins on blogs and articles but never read a complete book about the topic. It left me wanting to delve deeper into the world of Bitcoins (Satoshi, your paper is next on my reading list: https://bitcoin.org/bitcoin.pdf).

Frisby, I believe leverages his varied life experiences as comedian, sports commentator, among others to make this book a great read. It begins with explaining what Bitcoins are and how Bitcoins are made / mined. One of the most interesting phrases of the book appears toward the beginning where Frisby says “In ordinary life…no one can create money, we only earn it. Bitcoin is different and it is possible to make them yourself…” This is perhaps the most fascinating aspect of the Bitcoin, which has gotten millions around the world excited about its potential. Frisby then goes on to explain how Bitcoins are mined. He does spare us the intricacies of the code and algorithms involved, but does keep the reader engaged about Bitcoins and the entire ecosystem and all the “organisms” it has spawned.

There is a fair bit of detective feel to the entire book with Frisby (among others) trying to uncover who the real Satoshi is. He describes linguistic analyses in the book with respect to coding styles of the people closest to being considered to be potential Satoshis. There are no firm conclusions drawn though, only some hints. There are also his interesting interactions with Ethereum’s Charles Hoskinson who likens the Bitcoin revolution to the one that was unleashed by the Internet. Bitcoin at the end of the day is a decentralized monetary system, disconnected and independent from the boundaries (and to some extent control of governments), borders, and the bureaucracy. That should enthrall everyone isn’t it?

Read it I am sure you will thank me for the recommendation…

 

 

This quick piece is about leading, motivating, and coaching data science teams. What I have below is a combination of what worked and what I wished I had done and some of what I intend to do in the future. Since our time for reading long articles has dwindled considerably in recent times, and you are probably reading this on your way to work or worse…at work, I will keep it short and sweet and stick to bullet items that we are so used to seeing.

My Top 10 intangibles for Coach, motivate, and Retain

1. Always hire for attitude, not skills, skills can be taught. This is a cliché, but it seems to work. One bad apple can ruin the team morale. Be ruthless in weeding them out if you can.
2. Focus on their development, not yours, as they grow-you grow.
3. Encourage them to attend conferences, meetups, external and internal events. You need to deal with your organizational bureaucracy so that they have the approvals and budgets in place to do so. Let them shine.
4. Give them an opportunity to present the work they did (under your guidance as appropriate). It shows that you trust them and it can be a game changer.
5. Pay them on par or more than the market (sometimes this is not in your control). This market is very competitive. People will leave if they know your competition pays them twice as much.
6. Insulate them from the bureaucratic mess and internal politics. You don’t want your team worrying about squabbles that large organizations typically have.
7. Do bi-weekly review sessions with each of them…take them out on a walk or a cup of coffee and connect with them at a personal level. There’s nothing worse than a boss who cares only about what you can do for them. And please don’t ask the same old..how’s the family question. Get to know them better.
8. Set the right tone for the team. Positive. Positive. Positive.
9. Your team is looking up to you for directions, ideas, and you better step up your game technically and otherwise.
10. Quoting from one of IBMs core principles, there are always ‘moments of impact.’ These are moments when a team member may have a personal challenge and how you react is something they will carry with them for the rest of the their lives..so be nice, seriously nice. Put yourself in their shoes and see how you may want your manager to react and do exactly that.

My Top 10 from the nerdy side of things for data science teams:

1. Challenge them constantly on the way the models have been developed.
2. Lead review sessions where ideas are presented and insist that they need to be taken seriously with rigorous prep work before they attend.
3. If you have the ability, get into the nitty-gritty’s of things. Else, get help and have them review along with you. Dive deep into the mathematical and statistical details. If you are an experienced data science / analytics person, this should be straightforward for you. Ban power-point slides and force your team to go to the whiteboard.
4. Be ready to conduct trainings yourself. Nothing demonstrates leadership better than doing it your self.
5. Be ready to roll up your sleeves and write that R / Python / SQL code. This will earn you respect and trust.
6. Encourage them to create new R packages. This helps in reducing redoing elements of work across engagements.
7. Encourage / require them to contribute to stackoverflow, not just seek help from this great resource.
8. Encourage them to participate on Kaggle competitions
9. Encourage the senior members of your team to publish technical articles
10. Encourage patenting, it encourages application-oriented thinking.

Doing all of the above I think will serve you well. I encourage you to comment below. I want to make this a top100 list of things that work for Data Science leaders.

Part I- BUILD

This is a two-part series about my thoughts in 1) building, 2) leading, motivating, coaching, and retaining data science teams. Part-I focuses on building successful data science teams from scratch. As someone who has built teams in academic research settings as well as in the industry, here are a few tips that worked for me and some pitfalls to avoid. Obviously, there is no silver bullet and I am sure readers may have many different opinions / thoughts. Please chime-in in the comments section below.

My premise here is that you already have a leader who has the expertise to interact with the C-suite, senior execs, experts from non-data science domains, marketing and sales leaders, and of course, most importantly-the clients. Granted, this is easier said than done. Nevertheless, assuming you have all that squared away, let’s start! A good data science team should have the following “personas/roles:”

1. The Modeler and Deep Thinker: This person is your statistics, optimization, and machine-learning Guru who can break down complex business questions and create mathematical models to tackle them. She/he does not need to be an expert coder/programmer. If you do find one who also has these skills, then you better hire them fast.
2. The Coder: This person is your expert coder in Big Data Analytics algorithms; he/she does not need to understand all the math and stats behind the scenes but must know how to write very, very, efficient code. This person must have handled numerous multi-million / billion record datasets. This person is typically an expert in multiple languages: Python, C++, R, Scala etc.
3. The Big Data IT Expert: This is your Hadoop / Spark expert with serious data engineering and database skills. He/she should be able to build your Hadoop / Spark cluster if needed, manage it, and ideally have experience in IT architecture and its best practices.
4. The Visualization Expert: You will need this person to sell your ideas. This person needs to be an expert in Tableau / Watson Analytics / Qlik / Power BI / Kibana etc. He/she needs to be able to translate the results from complex models to visually intuitive charts and graphics.
5. The Experts-of-all-Trades: This is your Unicorn who does it all!! As the market for data science matures, some Experts-of-all-Trades seem to be sprouting up here and there. Look out for these superstars.
6. The Enthusiastic Bunch—Potential Future Leaders: These are the indispensable, new college grads from sciences and/or engineering. They are the engines of a good data science team. They can / should be mentored based on their interests to pursue any one of the above roles.

Of course, bear in mind that many times, these personas are like sets in a Venn diagram, but the boundaries are fuzzy. A simple way to check if your team is ‘ready’ is by asking yourself this: “will my team be able to take a data science engagement from data ingestion, data quality assessment, data modeling to successful analytics operationalization?” If the answer is a resounding yes; congratulations, you have your team. Remember, data science is a team-sport. You better have a good team in place before you jump in the deep-end!