Organizations like Insight Data science founded by Jake Klamka is specifically designed for helping PhD's transition into industry. At the other end of the spectrum, aspiring data scientists, who have enough domain expertise and are keen to pursue this art can take umbrage from the example of Clare Corthell who has embarked on a self crafted journey to embrace the art of data science purely on online learning MOOCs. In Fact she has herself come out with a curriculum for data science with the Open Source Data Science Masters--OSDSM- program. These courses can help you to bridge the gap in your learning and practicing the craft. The OSDSM is a collection of open source resources that will help you to acquire skills necessary to be a competent entry level data scientist. You can access the curriculum here . You have to be adept at learning and upgrading on the job and on the fly. Kunal Punera the Co founder / CTO at Bento labs talks about this aspect when he says.. I spent two years at RelateIQ. I worked on building the data mining system from scratch -- and by the time I left I had built most of the data products deployed in RelateIQ.

I've just finished Week 5 of the Coursera/Stanford Machine Learning course. It has been a mixture of refreshing, relearning, and new for me. I had already been using, building, and researching/evaluating machine learning algorithms for a number of years. I therefore felt like I'knew' a lot of the concepts, particularly the introductory ones. I put'knew' in quotes, however, since I've always had a feeling that I don't know them well enough, no matter how many times I've used them.

I will start by giving some background information. Currently I am a final year (graduation year) CS student who got interested in machine learning about 6 months ago. I started with the Andrew NG course from Coursera which I recently finished (about 3 weeks ago). When I finished the Coursera course I saw a suggestion that if you'd like to continue to learn more about machine learning you could follow the online Coursera specialization from the Washington university. In this AMA he suggested that if you'd like to learn more about machine learning one of the things you could do was to follow and complete the Coursera course from Andrew NG and their specialization course.

Scatterings of completed buildings, sporting new plantings of drought-tolerant grasses, are already occupied; other buildings are going up quickly, including a new fire station. There's Nissan's new Silicon Valley research center, a well-financed medical device startup called Spiracur, a digital cash startup called Quisk, and a biotech startup incubator. And there is Baidu's Silicon Valley AI Lab--my destination along this dusty road crowded with construction vehicles. It's good to spend time in a new research lab; there's not only fresh paint and hip decor--like living walls of plants--there are fresh, excited faces, and empty desks waiting to be filled. In mid-2014, I spent a morning on just the other side of nearby Moffett Field watching a far more somber group of researchers moving out of a suddenly closed division of Microsoft Research.

Some time ago I started a journey into one of the most exciting fields in Computer Science -- Machine Learning. This is my subjective guide for anyone who would like to explore this topic, but don't know how to start. Your first steps should lead to Stanford Machine Learning class at Coursera by Andrew Ng. This course is simply brilliant! Along a way, you will be given everything you need to know, including algebra review.

John Hennessy joined Stanford in 1977 right after receiving his Ph.D. from the State University of New York at Stony Brook. He soon became a leader of Reduced Instruction Set Computers. This research led to the founding of MIPS Computer Systems, which was later acquired for 320 million. There are still nearly a billion MIPS processors shipped annually, 30 years after the company was founded. Hennessy returned to Stanford to do foundational research in large-scale shared memory multiprocessors. In his spare time, he co-authored two textbooks on computer architecture, which have been continuously revised and are still popular 25 years later. This record led to numerous honors, including ACM Fellow, election to both the National Academy of Engineering and the National Academy of Sciences. Not resting on his research and teaching laurels, he quickly moved up the academic administrative ladder, going from the CS department chair to Engineering college dean to provost and finally to president in just seven years. He is Stanford's tenth president, its first from engineering, and he has governed it for an eighth of its existence. Since 2000, he doubled Stanford's endowment, including a record 6.2 billion for a single campaign. He used those funds to launch many initiatives--which often cross departmental lines--along with new buildings to house them. Undergraduate applications also doubled, for the first time making Stanford even more selective than Harvard.

Artificially intelligent conversational agents have been demonstrated to positively impact team based learning in classrooms and hold even greater potential for impact in the now widespread Massive Open Online Courses (MOOCs) if certain challenges can be overcome. These challenges include team formation, coordination and management of group processes in teams working together while distributed both in time and space. Our work begins with an architecture for orchestrating conversational agent based support for group learning called Bazaar, which has facilitated numerous successful studies of learning in the past including some early investigations in MOOC contexts. In this paper, we briefly describe our experience in designing, developing and deploying agent supported collaborative learning activities in 3 different MOOCs in three iterations. Findings from this iterative design process provide an empirical foundation for a reusable framework for facilitating similar activities in future MOOCs.

Peer grading is the process of students reviewing each others' work, such as homework submissions, and has lately become a popular mechanism used in massive open online courses (MOOCs). Intrigued by this idea, we used it in a course on algorithms and data structures at the University of Hamburg. Throughout the whole semester, students repeatedly handed in submissions to exercises, which were then evaluated both by teaching assistants and by a peer grading mechanism, yielding a large dataset of teacher and peer grades. We applied different statistical and machine learning methods to aggregate the peer grades in order to come up with accurate final grades for the submissions (supervised and unsupervised, methods based on numeric scores and ordinal rankings). Surprisingly, none of them improves over the baseline of using the mean peer grade as the final grade. We discuss a number of possible explanations for these results and present a thorough analysis of the generated dataset.

We propose a general framework for studying adaptive regret bounds in the online learning setting, subsuming model selection and data-dependent bounds. Given a data- or model-dependent bound we ask, “Does there exist some algorithm achieving this bound?” We show that modifications to recently introduced sequential complexity measures can be used to answer this question by providing sufficient conditions under which adaptive rates can be achieved. In particular each adaptive rate induces a set of so-called offset complexity measures, and obtaining small upper bounds on these quantities is sufficient to demonstrate achievability. A cornerstone of our analysis technique is the use of one-sided tail inequalities to bound suprema of offset random processes.Our framework recovers and improves a wide variety of adaptive bounds including quantile bounds, second order data-dependent bounds, and small loss bounds. In addition we derive a new type of adaptive bound for online linear optimization based on the spectral norm, as well as a new online PAC-Bayes theorem.

We consider a sequential learning problem with Gaussian payoffs and side information: after selecting an action $i$, the learner receives information about the payoff of every action $j$ in the form of Gaussian observations whose mean is the same as the mean payoff, but the variance depends on the pair $(i,j)$ (and may be infinite). The setup allows a more refined information transfer from one action to another than previous partial monitoring setups, including the recently introduced graph-structured feedback case. For the first time in the literature, we provide non-asymptotic problem-dependent lower bounds on the regret of any algorithm, which recover existing asymptotic problem-dependent lower bounds and finite-time minimax lower bounds available in the literature. We also provide algorithms that achieve the problem-dependent lower bound (up to some universal constant factor) or the minimax lower bounds (up to logarithmic factors).