My freewheeling conversation on Data Science
A few weeks back I had setup up an Ask-Me-Anything session where anyone could raise any question on Data science. Having started this as an experiment, I nervously waited to see the reaction and the kind of questions that will be thrown in.
Answering over 40 questions across 5 days, this turned out to be an exhilarating experience and I enjoyed every bit of it. There were diverse queries ranging from how to enter the analytics industry, setting up data science teams, Gramener’s value proposition, demystifying latest technologies such as deep learning, to some probing questions on my personal journey.
The questions had me explain some technical terms in simple english, made me lookup to learn aspects I wasn’t sure about and also forced me to reflect on some deeper aspects I hadn’t spent as much time thinking about.
A quick word on the platform: AMAFeed is gaining traction as a forum where interesting people across fields come in to have open conversations on a variety of topics, on the lines of the popular Reddit AMA pages. I reproduce here some of the favorite questions from my session.
My top 3 favorite questions, with the answers:
YANINE: Do you believe there is a winning formula for becoming a successful entrepreneur? Which is yours?
GANES KESARI: There are 3 key attributes of successful entrepreneurs, and from my journey I’ve seen how critical they are:
- Passion: An exceptional interest to get something done. This needn’t be always in a single idea, but can also be to experiment in a chosen area or fight tooth-and-nail for a cause. Passion fuels the drive to take risks and step out of comfort zones.
- Resilience: Entrepreneurship is a stormy journey and things tend to get rought often, at times on multiple fronts. There are days one feels accomplished and on top of the world, while the very next day could turn depressing with one cursing themselves on why they got started. A temperament to balance and weather these keeps one sane and sailing.
- Vision: One needn’t be a visionary, but should definitely be able to look several steps ahead. A foresight to see where the market is headed, and an ability to spot opportunities when things are muddled is a key trait. This helps in taking the crucial call on whether one must persist and keep pushing, or drop and move on.
EVOH: What was your key driving force to launch your own startup and become an entrepreneur?
GANES KESARI: An urge to have a bigger canvas and greater freedom to learn & experiment was a key driver. The decision was a little easier for me since the other co-founders were senior colleagues whom I had spent a bulk of my workex with, and could trust blindly.
While we didn’t have a rock-solid idea at inception, we were able to freely explore multiple avenues and could pour our heart into the opportunities that came our way. While some didn’t work out, some did click and one thing led to another.
LEA ROMANO: What are the most important ethical concerns about the use of machine learning methods?
GANES KESARI: ML usecases are still getting mainstream, but the ethical concerns around them is already a long list. In my opinion, the top 3 are:
- Bias in algorithms: An algorithm is as good as its training data. Data from a biased human sample would lead to reinforcement of the same bias in a model-driven world. This can multiply the effects of racism, and other stereotypes, and hence could get scary.
- Privacy concerns: Each of us is a real-time source of data, generating data whether we shift, sit or sleep. With so much of data at disposal, algorithms can tease out uncanny insights, at times even before we become conscious about it. With very little regulations, this can escalate into an alarming privacy issue.
- Fake content: The deep learning techniques getting invented by the day can put fake news creators to shame. These can be remarkably powerful at creating video/image content which could look way more realistic than the original. All the recent buzz around fake news may be reduced to just a teaser before the big bad act.
For more reading, I came across this article that delves into such issues in more detail, and may be worth a look.
Few more questions of interest
- يس سليمان: Where do you see the data science industry going in the next five years and how should business prepare for the upcoming changes?
- SHREYA: What are your thoughts on data science, machine learning, and deep learning as a driving force for the creation of smart cities?
- DAYAMEJIAS: What is the difference between deep learning and machine learning?
- ANONYMOUS: How does coming from tier 4 university(undergrad) in India with a good profile get someone to even look at their resume for a Data Science role.
- PAOLA CUBER: What are the things that must be avoided in a CV aimed to get jobs in Data Science?
If you found these interesting, check out the AMAFeed below for the complete conversation. And if you’d want to do some introspection and engage in a freewheeling conversation with a curious audience, I’d urge you to host your own session and throw it open for questions!
