Saurabh Bhatnagar is the ex-Principal Data Scientist at Rent The Runway. He created the big-data analytics and recommendation platforms at RTR and scaled it to 30m users. RTR is now valued at $800m.
Leni Krsová is social media data analyst from the Czech Republic, currently based in Prague. At the beginning of her career, she worked for Czech TV, a public broadcaster in the country, as a social media editor but switched quickly from marketing to the path of a data analyst. Since then she is interested more and more in data analysis of social media and online news media data with R, data privacy and academic research in these fields. She is planning to start her Ph.D. studies in near future.
Deep Narain Singh is Data Scientist with specialization in machine learning and deep learning. He has extensive work experience in building NLP/Computer Vision products using AI/ML/DL. He has spent 12 years in industry working with startups and large scale companies. He holds a Master’s degree in Data Science from the University Of New Haven/Galvanize and completed his undergraduate in Civil Engineering from NIT Jaipur.
The major advantage of using decision trees is that they are intuitively very easy to explain. They closely mirror human decision-making compared to other regression and classification approaches. They can be displayed graphically, and they can easily handle qualitative predictors without the need to create dummy variables.
As you see, you should make a habit of thinking about the time complexity of algorithms as you design them. Asymptotic analysis is a powerful tool, but use it wisely. Sometimes optimizing runtime may negatively impact readability or coding time. Whether you like it or not, an effective engineer knows how to strike the right balance between runtime, space, implementation time, maintainability, and readability.
Dr. Jonathan Leslie obtained his Ph.D. in Biology from the University of London, studying blood vessel formation at the Cancer Research UK London Research Institute. After 20 years of researching the molecular processes underlying cancer, he turned to data science and founded a freelance consultancy business. He is passionate about promoting open-source software and routinely volunteers as a mentor in the R-programming and data science communities.
I would highly recommend you to read The Data Science Handbook. The data scientists in the book have helped create the very industry that is now having such a tremendous impact on the world. They discuss the mindset that allowed them to create this industry, address misconceptions about the field, share stories of specific challenges and victories, and talk about what they look for when building their teams.
At the moment, data scientists are getting a lot of attention, and as a result, books about data science are proliferating. While searching for good books about the space, it seems to me that the majority of them focus more on the tools and techniques rather than the nuanced problem-solving nature of the data science process. That is until I encountered Brian Godsey’s “Think Like a Data Scientist.”
From machine translation that connects humans across cultures, to conversational chatbots that help with customer service; from sentiment analysis that deeply understands a human’s mood, to attention mechanisms that can mimic our visual attention, the field of NLP is too expansive to cover completely, so I’d encourage you to explore it further, whether through online courses, blog tutorials, or research papers.
NLP is certainly one of the most important technologies of the information age. Understanding complex language utterances is also a crucial part of artificial intelligence. Fully understanding and representing the meaning of language is an extremely difficult goal. Why? Because the human language is quite special.
Computer Vision is one of the hottest research fields within Deep Learning at the moment. It sits at the intersection of many academic subjects, such as Computer Science (Graphics, Algorithms, Theory, Systems, Architecture), Mathematics (Information Retrieval, Machine Learning), Engineering (Robotics, Speech, NLP, Image Processing), Physics (Optics), Biology (Neuroscience), and Psychology (Cognitive Science).
Systems thinking is a way of seeing the world as a series of interconnected and interdependent systems rather than lots of independent parts. As a thinking tool, it seeks to oppose the reductionist view — the idea that a system can be understood by the sum of its isolated parts — and replace it with expansionism, the view that everything is part of a larger whole and that the connections between all elements are critical.
I just spent the past month finishing “Tribe of Mentors”, the latest book by the legendary Tim Ferriss. It is packed with wisdom and tools that will change your life. The book contains more than 100+ interviews with people around the world. I made my notes, did some highlights and will be referring back to it on the need per basis. After all, I learned this trick from Tim himself.