These talks cover a wide range of topics: from showcasing your work to connecting with data leaders, from telling a persuasive data story to debugging myths in data science. I took some detailed notes of all the talks and decided to use this post to share the main takeaways.
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.
Data mining is the process where one structures the raw data and formulate or recognize the various patterns in the data through the mathematical and computational algorithms. This helps to generate new information and unlock various insights. In this article, I want to share the 10 mining techniques that I believe any data scientists should learn to be more effective while handling big datasets.
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.