Building Skills: From PDE to Machine Learning, Academia to Industry
Explore a comprehensive guide for transitioning from academia to industry in the field of machine learning. Learn about online courses, resources, bootcamps, job search strategies, resume tips, interview preparation, and more to enhance your skills and secure a job in the industry. Discover a structured learning path covering basic coding skills, machine learning techniques, and essential background knowledge. Additionally, get insights into popular conferences and specialized bootcamps like Insight Data Science.
Download Presentation
Please find below an Image/Link to download the presentation.
The content on the website is provided AS IS for your information and personal use only. It may not be sold, licensed, or shared on other websites without obtaining consent from the author. Download presentation by click this link. If you encounter any issues during the download, it is possible that the publisher has removed the file from their server.
E N D
Presentation Transcript
From PDE to Machine Learning; From Academia to Industry KO-SHIN CHEN UNIVERSITY OF CONNECTICUT
Outline Background and Motivation Building Skills Online courses/resources Bootcamps Looking for Industry Jobs Resume Job search sites All About Interview Preparation resources Procedures and experiences
Online Courses and Resources Courses Coursera: https://www.coursera.org/ (verified certificate) o Single course/ Specialization (series of courses + capstone project) edX: https://www.edx.org/ (verified certificate) Udemy: https://www.udemy.com/ Online degrees UIUC CS/DS (via Coursera) Georgia Tech OMS CS Free resources MIT Open Course: https://ocw.mit.edu/index.htm YouTube
Learning Path of ML Basic Coding Skills (Coursera) An Introduction to Interactive Programming in Python 1,2 Fundamentals of Computing Principles of Computing 1,2 Algorithmic Thinking 1,2 Java Programming: Object-Oriented Design of Data Structures Object Oriented Programming in Java Data structures: Measuring and Optimizing Performance Advanced Data Structures in Java R Programming Getting and Cleaning Data (R) Machine Learning by Andrew Ng (MatLab) Inferential Statistics
Learning Path of ML Machine Learning Background Knowledge Videos Machine Learning Foundations by Hsuan-Tien Lin (YouTube) Machine Learning Techniques by Hsuan-Tien Lin (YouTube) MIT 6.S094: Deep Learning for Self-Driving Cars (https://selfdrivingcars.mit.edu/) Books Numerical Optimization by Jorge Nocedal and Stephen J. Wright The Elements of Statistical Learning by Jerome H. Friedman, Robert Tibshirani, and Trevor Hastie
Learning Path of ML Techniques Online Courses Functional Programming in Scala Specialization (Coursera) Complete Guide to TensorFlow for Deep Learning with Python (Udemy) SQL Advanced (Udemy) UConn: CSE 5304-001 High-Performance Computing Conferences Neural Information Processing Systems (NIPS) Knowledge Discovery and Data Mining (SIGKDD) International Conference on Machine Learning (ICML)
Bootcamps (Data Science) Insight Data Science/ Data Engineering/ Health Data/ AI/ Data PM (new) Postdoctoral training Locations: Silicon Valley, New York, Boston, Seattle, and Remote 7 weeks The Data Incubator (Data Science Fellowship) Master and PhD Locations: New York City, San Francisco Bay Area, Seattle, Boston, and Washington DC 8 weeks Must intend to get hired full-time after the program
Resume Styles: academic positions v.s. industry jobs Additional Elements GitHub: sample code/ projects Linkedin: build network with recruiters
Sits for Job Search Indeed: https://www.indeed.com/ Monster: https://www.monster.com/ See what your resume looks like in application tracking system AngelList (startup): https://angel.co/ Flexjobs (remote jobs): https://www.flexjobs.com/
Prepare for an Interview Books Cracking the Coding Interview by Gayle Laakmann McDowell Cracking the PM Interview by Gayle Laakmann McDowell Coding Practice LeetCode: https://leetcode.com/ HackerRank: https://www.hackerrank.com/ Pramp: https://www.pramp.com/ Company Research Culture, mission, and values Clients, products, and services The team and person interviewing you
The Interview Process HR phone screen Company and job description Resume and past experiences Technical phone interview Background knowledge Live coding without IDE CEO/ team leader phone interview (startup) Behavioral questions Details in projects and skills Onsite interview
