Recently updated, is the March 2019 Machine Learning Study Path. It contains links and resources to learn Tensorflow and Scikit-Learn.
If you are interested in details on the study path and how to best use the resources. There is a livestream on Facebook, Sunday March 17 on the Math for Data Science Facebook page.
1. Be Honest
Try not to exaggerate your skills. If the job sounds more engineering focused than you are wanting, be honest and say that. Data Science is getting very broad and you don’t want to get in a position that is a bad fit.
You often sound worse when you try to explain something you do not understand. Just be honest and say, “I have not needed to use that yet, can you explain to me when you have done that?”
Find the job you are looking for, not just any position someone is trying to fill.
2. Tell Stories of Your work
Talk about things you have built. If you built something as part of a team or project, tell about why and your involvement.
If you have a side project, talk about that. This is why side-projects are so important. They help you learn a lot, plus they give you something exciting to talk about, which only you can talk about.
Interviews are intimidating. There is really no way to avoid that. The best you can do is be prepared for the interview. Pramp provides a platform for practicing data science interviews. Practice makes perfect.
Machine Learning for Kids is a site for children and teachers to explore machine learning with the Scratch Programming language. It includes numerous lessons and tutorials for building fun programs which incorporate machine learning.
University can be a great way to learn data science. However, many universities are very expensive, difficult to get admitted, or not geographically feasible. Luckily, a few of them are willing to share data science, machine learning and deep learning materials online for everyone. Here is just I small list I have come across lately.
Do you have any favorite university resources? If so, please leave a comment.
Brandon Rohrer (along with others) created an excellent resource for academic programs, Industry recommendations for academic data science programs. The resource is authored by a number of industry data scientists and university faculty. It is collection of useful information for college data science programs. Here are some of the topics:
Plus, the site is growing, and new information is frequently being added. If your college/university is launching a data science program, this resource is a must read.