Architecture: Getting Started with Data Science using Python

Getting Started with Data Science using Python

IBM predicts that demand for data scientists will soar by 28% by 2020, but what exactly is data science? What do data scientists do? In this workshop I'd like to talk about data science as a field, how its growing, the skills required to succeed, and how to build a portfolio through Github projects, writing, and kaggle competitions. We'll mostly be exploring examples using Python (Highlighting Numpy, scipy, pandas, SciKit Learn, Tensor Flow, Keras, Matplotlib) as well as tools such as Colaboratory by Google, Jupyter notebook, and Crestle (infrastructure for deep learning).

Bio - Andrei Lyskov has experience working for global companies such as Soylent in LA and IBM in Beijing. He's currently a Microsoft Student Partner at Queen's University, where he's specializing in data science and machine learning. He's previously built a a real-time personal dashboard that aggregates and visualizes personal data about his life (https://qself-dashboard.herokuapp.com/). He also contributes to an open source automated Crypto Trading & Technical Analysis (TA) bot which has over 750 stars on Github (https://github.com/AbenezerMamo/crypto-signal). When he's not coding, you can usually find Andrei reading books on Buddhism and Stoicism, listening to podcasts, writing on Quora, or playing an online game of blitz chess. You can learn more about him on his personal website - http://www.andreilyskov.com/