Our friends over at The Data Incubator just released a new series of data-driven ranking reports that showcase the quantitative methodologies the data science fellowship, hiring and training company uses to actively teach their fellows. The idea was to develop a more data-driven approach to what the company should be teaching in their data science corporate training and their free fellowship for masters and PhDs looking to enter data science careers in industry.
It’s a familiar dilemma. You’ve done your research, read some books, taken some online classes – and at long last, you’re finally ready to get real-life work experience as a Data Scientist. In this contributed article, Dan Saber, Data Science Hiring Manager at Coursera, offers three important steps for successfully transitioning into a data science career.
I recently caught up with Emily Glassberg Sands, Data Science Manager at Coursera, to talk about applying machine learning, neural networks, natural language processing, and big data analytics to the retail industry. Emily leads an awesome team of data scientists and data science managers working on growth, discovery, the learning experience, and a new enterprise offering. The team’s job is to help build a better Coursera through data-driven decisions and products.
In this special guest feature, Devavrat Shah, professor in MIT’s Department of Electrical Engineering and Computer Science, discusses the type of training data scientists need in order to glean the most value from big data.
I don’t usually get excited about a new book for the field in which I’ve been deeply involved for quite a long time, but a timely and useful new resource just came out that provided me much anticipation. “Deep Learning” by three experts in the field – Ian Goodfellow, Yoshua Bengio, and Aaron Courville is destined to considered the AI-bible moving forward.
In this special guest feature, Lauren Willison, Director of Admissions at Florida Polytechnic University, takes a critical look at the value of an advanced degree in big data in terms of job prospects and expected salary.
Our friends over at DataCamp have produced the “Become a Data Scientist in 8 Steps” infographic providing a view of the eight steps that you need to to through to learn data science. Some of these eight steps will be easier for some than for others, depending on background and personal experience, among other factors.
With the continued upward trajectory of interest in getting on board with a data science career, our friends over at Simplilearn Solutions put together the compelling infographic below.
Last year, I wrote a review of a useful book that got students up to speed with a key mathematical ingredient of machine learning – linear algebra: The Manga Guide to Linear Algebra. No Starch Press (an excellent source of technical books) just came out with a follow-up title: The Manga Guide to Regression Analysis.
A fantastic new book just landed on my desk, “The Book of R: A First Course in Programming and Statistics” by Tilman M. Davies from No Starch Press. I’ve been looking for a book like this for some time – to use with the introductory data science and machine learning course I teach.