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As one of the most popular conferences for data scientists in India, The Fifth Elephant (organized by HasGeek) is an incredible opportunity to interact with data science practitioners, thought leaders and organizations from the data ecosystem.

The Atlan Team spoke with some of the speakers and participants to put together insightful messages for aspiring data scientists. We’ve compiled the responses we got in a video. Watch the complete interview here.

Cannot watch the video interview? No worries, we’ve got you covered. Go through the transcript of the same interview below.

Video transcript

Peter Wang, Co-founder and CTO at Anaconda Inc.

You cannot separate the ethics of the machine learning from the ethics of the business. If the business is fundamentally exploitative, there’s no such thing as ethical AI to augment and accelerate the business. So I think this is something—it’s a sort of a new articulation I’m trying to drive as an awareness in the industry, but I will say here to you all: you know everywhere I can I’m trying to reiterate to people: Don’t allow the question to be framed in this way—Oh we’ll do a bunch of ethical ML, a bunch of ethical AI and we will sprinkle this on top of our deeply unethical business model. That can never work.

Tulshekar Gangireddy, Senior Engineering Leader at Walmart Labs India

The best thing is to find a mentor or a guide who are can actually direct you in the direction, with the specific will give your direction.

Upendra Singh, Lead Big Data Architect at Clustr

My suggestion is always that—don’t stick to tools, stick to fundamentals. If you want to survive long in this industry, stick to fundamentals, don’t stick to tools. But youngsters in their hurry. they say you know what, if I learn these tools, I will be fine. I say you’ll be fine for a few years but that’s it. 

Ishita Mathur, Data Scientist at Gojek

Don’t just focus on machine learning, try to read up and learn about different exploratory data analysis techniques, try to focus on how you can experiment and try to gain experience.  Even if it’s not on the job, you can use publicly available data sets on say Kaggle or Google Cloud.

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Editor and Content Lead, Humans of Data

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