Tetiana Ivanova: How to become a Data Scientist in 6 months | PyData London 2016

Tetiana Ivanova: How to become a Data Scientist in 6 Months, a Hacker's Approach to Career Planning PyData London 2016 This talk outlines my journey from complete novice to machine learning practitioner. It started in November 2015 when I left my job as a project manager, and by April 2016 I was hired as a Data Scientist by a startup developing bleeding edge deep learning algorithms for medical imagery processing. SHORT INTRO Who I am, my background and a short summary of my story. Here I will list the steps I personally took to achieve the goal I had. HOW DID I DO IT? Why I chose a “hacky” way to enter this career path. The first mover advantage, why getting a degree doesn’t always improve your career prospects. Possibly a rant on the signaling function of formal education and how that is rarely aligned with a relevant practical skill set. Some stats to back it up (best career success predictors). Examples of hacking bureaucracies/social hierarchies from my experience and elsewhere. List of things not to do and common cognitive pitfalls. Networking for nerds - how to do it right. Time management for chronic procrastinators - how to plan a self-guided project. Some notes on psychology of time discounting and need for external reinforcement, with autobiographical examples. CONCLUSION You don’t need a Ph.D. or even a master's to do machine learning. On taking calculated risks and especially calculated exits from one’s comfort zone. Some notes on soul searching and how to choose a career that is also a passion. Reading list. Slides available here: https://www.slideshare.net/TetianaIvanova2/how-to-become-a-data-scientist-in-6-months www.pydata.org PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases. 0:00 Introduction 4:56 Higher education 13:02 Things not to do 17:55 What did I do in the end? 21:51 Time discounting and willpower 26:27 Time management techniques 31:37 Networking 37:18 Resources for Data Science transition 44:34 Don’t get started 45:51 Q&A S/o to https://github.com/anzelpwj for the video timestamps! Want to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps