First, I don't know if this question is suitable for this site. I tried posting to Quora, but the length was too long. If it's not an acceptable question, would someone please suggest a more appropriate place?
I'm a student currently finishing up an MS in electrical engineering. In the past few months I've gotten really interested in data science and machine learning, and I've decided that I'd like to try and pursue this field as a career. There's a tremendous amount of resources available for learning these topics, which is a great thing, but to the beginner it is slightly overwhelming to try and figure out which resources I should focus on first and what's the best way to make use of my time. My goal is to be prepared to interview for junior data science/entry level machine learning engineer positions in about 6-8 months time. I know that this is a small timeframe and I'll need to work very hard for this to be possible. I'm currently taking an introductory course in machine learning that's supposed to loosely follow the book Machine Learning: a Probabilistic Perspective by Kevin P. Murphy, along with a course in image processing which I hope will be useful later on for feature extraction and other topics. I went a bit (a lot?) overboard in the past few weeks and purchased several textbooks related to data science and machine learning. I also started the Kaggle-Udacity machine learning engineer nanodegree program.
Would someone who has some experience in the field mind suggesting a rough order of how I should direct my studies with respect to the resources I've compiled? The books/resources I currently have at my disposal are:
Dataquest.io data scientist path (25% completed)
Kaggle-Udacity machine learning engineer nanodegree program
Make Your Own Neural Network (100% completed)
An Introduction to Statistical Learning with R (50% completed)
Deep Learning (25% completed)
Python Machine Learning (25% completed)
The Elements of Statistical Learning
Machine Learning: A Probabilistic Perspective
Doing Bayesian Data Analysis
Practical Data Science with R
Data Science for Business
Probability Theory The Logic of Science
At this point I've read the introductory chapters to all of these books, but am beginning to feel a bit haphazard in my approach. I know that it will be difficult to complete all of these in 6-8 months, so which would be the most important to focus on in order to appear competent to hiring managers?