Zipfian Academy has graduated more than 50 alumni, placing graduates into data science roles at Facebook, Twitter, Airbnb, Tesla, Uber, Square, Coursera, and many more Silicon Valley companies. Participants in our program come from backgrounds in engineering, data analysis, statistics, and occasionally professional poker. Here, we share an interview with Alex Mentch, a graduate from our Winter 2014 Cohort. Alex hails originally from Idaho, and studied electrical engineering at Washington University in St. Louis. Looking for a career transition into data science, Alex attended our Winter 2014 cohort where he built a search engine for state legislation.

Statistics.com, a provider of online education in statistics and analytics, announces a partnership with CrowdANALYTIX, a predictive modeling "managed crowdsourcing" company, offering a new online course, "Applied Predictive Analytics in partnership with CrowdANALYTIX", which will run from Oct. 11 to Nov 8, 2013. The goal of this course is to teach users (who have basic knowledge of R programming, predictive analytics and statistics) to apply machine learning techniques in real world case studies. This course provides a hands on approach, presenting the opportunity to participate in a private educational competition hosted by CrowdANALYTIX. Business Case Study: We will study data from the "daily deals" industry (consisting of websites like Groupon, Living Social etc. which source local deals to offer each day). The daily deals industry is emerging and highly competitive.

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.