• Brainstorm on a Data Science Product

    Venue SF

    • What we'll do I've built a working prototype of a data science passion project and I would love some feedback from the community! It's just for fun (at the moment), but I can't be the only one that likes to imagine where data science can take things. Here's my proposed agenda: 6:00p - 6:30p: Arrive & chat with others in a casual setting while eating chips + dip. 6:30p - 7:15p: I'll give a presentation on the why, what and how, and listen to your thoughts and questions. 7:15p - 7:30p: We can brainstorm for a little while together as a group. 7:30p: Anyone that wants to continue the conversation can walk over to Spark ( http://sparksocialsf.com ), a cool food truck area, and get food / keep chatting. • What to bring Just your thoughts, and cash or credit if you'd like to buy something for dinner at Spark. • Important to know I've got a cool data science product idea - and I want YOUR feedback and thoughts! (I'll even describe how I built the prototype). You have to buy your own dinner! We can walk over to Spark (food truck area) for eats and conversation at ~7:30p!

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  • Data Science Study Group: T-Tests & Z-Tests!

    Glen Park Branch Library - meeting room

    Are you teaching yourself data science? Looking for a group of peers to bounce ideas off of or work on projects with? Curious about exploring data science as a career? Then this group is for you! Come join us at the Glen Park Library this Thursday, from 12:00 to 3:00, and hang out with other data science learners, collaborate, or work independently on your own course or project. We're an informal group, and all levels of experience are welcome. This week we'll take a quick look at the theory behind t-tests and z-tests, and apply them to a real data set using Python. We'll look at street condition data in San Francisco from SF OpenData (https://data.sfgov.org/) to determine if the quality of streets in San Francisco increased, or decreased, in 2016. NOTE: If you plan to follow along with the tutorial, please have jupyter notebooks installed before you arrive. Also have the following python modules installed: scipy, pandas, matplotlib, seaborn, numpy, and sklearn. I recommend installing anaconda (https://www.continuum.io/downloads), which has python, jupyter notebooks, and all of the python modules that we'll be using installed with it. If you would like to install the jupyter notebooks seperately, you can download it here (http://jupyter.org/). For downloading python modules seperately, you can use pip (https://pypi.python.org/pypi/pip). Modules we commonly use are: Pandas, Numpy, Matplotlib, Seaborn, Scikit-Learn, statsmodels. I recommend this textbook (http://www-bcf.usc.edu/~gareth/ISL/) for learning the basics of statistical analysis (it's the text I referenced at the meetup). Members are welcome to join in on the discussion or work on individual projects. We’ve worked on the following so far: - Multiple Logistic Regression using the Titanic Data set (Kaggle.com) - Introduction to Data Science in Python (https://www.coursera.org/learn/python-data-analysis/home) (Coursera course) - Data Science Specialization in R (https://www.coursera.org/specializations/jhu-data-science) (Coursera course) This is a weekly, repeating "study group" event. Anyone with an interest in Data Science is welcome. Hope to see you there!

  • Data Science Study Group: Logistic Regression using Python

    Glen Park Branch Library - meeting room

    Are you teaching yourself data science? Looking for a group of peers to bounce ideas off of or work on projects with? Curious about exploring data science as a career? Then this group is for you! Come join us at the Glen Park Library this Thursday, from 12:00 to 2:00, and hang out with other data science learners, collaborate, or work independently on your own course or project. We're an informal group, and all levels of experience are welcome. This week I'll be leading you guys through a multiple logistic regression with Python and applying it to predict who survived on the Titanic (https://www.kaggle.com/c/titanic). We'll try to start the walkthrough at 12:10, and should wrap up by 12:40, so we can have the rest of the time to work on solo/collaborative projects. You'll need jupyter notebooks installed, and I recommend installing anaconda (https://www.continuum.io/downloads), which has python, jupyter, and a bunch of python modules installed with it. We’ve worked on the following so far: - Introduction to Data Science in Python (https://www.coursera.org/learn/python-data-analysis/home) (Coursera course) - Data Science Specialization in R (https://www.coursera.org/specializations/jhu-data-science) (Coursera course) This is a weekly, repeating "study group" event. Anyone with an interest in Data Science is welcome. Hope to see you there!

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  • Data Science Study Group

    Glen Park Branch Library - meeting room

    Are you teaching yourself data science? Looking for a group of peers to bounce ideas off of or work on projects with? Curious about exploring data science as a career? Then this group is for you! Come join us at the Glen Park Library this Thursday, from 12:00 to 3:00, and hang out with other data science learners, collaborate, or work independently on your own course or project. We're an informal group, and all levels of experience are welcome. We’ve worked on the following so far: - The Titanic: Machine Learning from Disaster competition (https://www.kaggle.com/c/titanic) - Introduction to Data Science in Python (https://www.coursera.org/learn/python-data-analysis/home) - Data Science Specialization in R (https://www.coursera.org/specializations/jhu-data-science) We intend to have this be a weekly, repeating "study group" event. Anyone with an interest in Data Science is welcome. Hope to see you there!

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  • Data Science Study Group

    Glen Park Branch Library - meeting room

    Are you teaching yourself data science? Looking for a group of peers to bounce ideas off of or work on projects with? Curious about exploring data science as a career? Then this group is for you! Come join us at the Glen Park Library this Thursday, from 12:00 to 3:00, and hang out with other data science learners, collaborate, or work independently on your own course or project. We're an informal group, and all levels of experience are welcome. We’ve worked on the following so far: - The Titanic: Machine Learning from Disaster competition (https://www.kaggle.com/c/titanic) - Introduction to Data Science in Python (https://www.coursera.org/learn/python-data-analysis/home) - Data Science Specialization in R (https://www.coursera.org/specializations/jhu-data-science) We hope to make this a repeating, weekly "study group" event. Anyone with an interest in Data Science is welcome. Hope to see you there!

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  • Kaizen Data Conference (Paid Event) - 40% Off

    As a member of the Data Science Professional Development Meetup, you get 40% off Kaizen Data (http://kaizendata.io/)! When buying your ticket, enter the discount code: DATAPRO Register here (http://www.eventbrite.com/e/kaizen-data-conference-tickets-26625511622?discount=DATAPRO) *The Early Bird Gets the Worm: Today (9/8/2016) is the last day that tickets will be available at the "early bird" price. Register (http://www.eventbrite.com/e/kaizen-data-conference-tickets-26625511622?discount=DATAPRO) before end of day 9/8. ----- Who Will be at Kaizen Data? Uber, Instacart, Netflix, Slack, Clover Health, Stitch Fix, Pinterest… the top data scientists from the top companies will be sharing their knowledge at Kaizen Data (http://kaizendata.io/). See speakers here: https://kaizendata.io/#speakers See participating companies here: https://kaizendata.io/#sponsors

  • Last Mile to DS - Bootcamp & Masters Alumni Panel

    How does entering a bootcamp or Masters program help you land that dream job in data science? How does it help you network, acquire new skills, or build confidence and credibility in DS? What impact do the programs have on participants? If these questions interest you or you have experiences to share, please join us at if(we) (http://www.ifwe.co/) for a panel discussion and interactive session! Food and drink generously provided by if(we)! This is an opportunity to listen to and ask questions of data scientists who have graduated from data science bootcamps & data science Masters programs. After our panel discussion and Q&A, there will be breakout sessions with discussions of specific data science career paths, and an opportunity to learn more about Open Data Science Conference too. Panelists: Luba Gloukhova is a data scientist supporting the Digital Business Initiative at the Stanford Graduate School of Business—a small team of world-renowned faculty members who are investigating competitive strategy and entrepreneurship in digital platform markets, including online advertising, electronic payments, and media. She received her master's degree in analytics from University of San Francisco in 2015 and bachelor’s degrees in applied math and economics from UC Berkeley in 2009. Luba has experience in analytics consulting, high frequency trading analysis, catastrophe risk modeling, and online marketing analysis. Ike Okonkwo is a data scientist at 6sense, a B2B Predictive Intelligence Engine for Marketing and Sales. His educational background is in Physics, Electrical Engineering and Industrial & Systems Engineering. Prior to joining 6sense, Ike has held data scientist roles at other startups. He is a graduate of Zipfian Academy (now Galvanize). He is excited about all things data and data science education and regularly shares his thoughts on his blog : http://yet-another-data-blog.blogspot.com/ Erin Burnside is a data scientist at Asana, where she works closely with the product team helping them make data-driven decisions. She is a graduate of Zipfian Academy (now Galvanize). In her spare time, she enjoys cooking with butter and building Ikea furniture. TJ Torres received his PhD in Theoretical High Energy Physics from UC Santa Cruz, where he worked on inflationary models of quantum gravity. After graduating, he attended the Insight Data Science Fellowship Program, a 7-week bootcamp aiming to transition academics into industry data science positions. He now works at Stitch Fix on the Data Labs team using deep learning to develop style models through image data. Dan Morris is a data scientist at Radius Intelligence, where he builds machine learning models to compile an accurate index of all of the small businesses in the US. After graduating from Duke University with degrees in Mathematics and Mechanical Engineering, he embarked on a successful 8-year career as a professional poker player and instructor (with brief detours to found startups and produce documentary films). He recently transitioned from poker to data science with a year of self-study and Coursera classes and twelve intense weeks at Zipfian Academy. When he isn't playing with data, he enjoys bikes, burritos, and ultimate frisbee. Schedule: 6:00p doors open and networking begins 6:45p opening remarks 7:00p panel discussion begins 7:40p brief break and then breakout sessions 8:15p conclusion of main program and networking 9:00p progression to your next event Sponsors: if(we) is a platform and incubator for social technology and apps. Open Data Science runs meetups, workshops, and conferences that support learning open source tools and languages for data science. Please consider attending or volunteering at the SF conference (http://opendatascicon.com/) on November 14-15. DS ProD members get 25% off the already discounted early-bird rate at Open Data Science Conference (http://opendatascicon.com/) by using promo code DSPROD here (https://www.eventbrite.com/e/open-data-science-conference-west-tickets-17678395557?discount=DSPROD)

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  • Building/Scaling Your Data Science Team: Best Practices Panel

    A panel discussion on best practices for growing a Data Science team. Panelists will focus on keys to success at their companies as well as respond to a prospective case study. Pizza and drinks generously provided by PagerDuty. Panel Discussion Topics: How can a company know if it is ready for data science? How might they put the right team together? What else should they know before starting or expect along the road? Panel questions will be provided ahead of time, and will cover panelists' past experiences, perceptions of best practices, and questions relevant to the case study. The Meetup will enable both participants and attendees to learn from each other's experiences on what is required to create and maintain a successful Data Science team. Current Panelists: • Raaid Ahmad from Weebly • Parvez Ahammad from Instart Logic • Eric Colson from StitchFix (formerly VP of DS at Netflix) • Zach Kagin from Dropbox • Jeremy Schiff from OpenTable Panelist Bios: Raaid is the head of analytics and data science at Weebly where he is focused on scaling his team and creating best practices for the new function. Before Weebly he spent 3 years at Kiwi, a mobile gaming company where he oversaw the centralized BI function, quantitative game economy development, and built ensemble learning models to target users based on behavioral patterns. Earlier in his career he enjoyed 5 years at Bridgewater Associates, the world's biggest hedge fund, where he led the high risk trades team and developed proprietary trading algorithms for new markets and exceptionally large trades. Prior to his time in an office, Raaid played poker professionally from[masked] and developed a unique "barbell" play strategy as a countermeasure for poker tracking software, nearly doubling his win-rate against opponents using the software. Raaid holds an MBA from Stanford's Graduate School of Business and a BS in Computer Science, Applied Mathematics & Statistics from Johns Hopkins University's School of Engineering. Parvez Ahammad currently leads the data science and machine learning group at Instart Logic Inc. He earned his PhD in computer vision and machine learning from UC-Berkeley. He has 15+ years of experience in computer vision (CV), machine learning (ML), statistics and signal processing. His work spans diverse application domains such as web application delivery, camera sensor networks, bioinformatics and neuroscience. He is the creator of novel algorithmic technologies such as smartVision (patent pending) @ Instart Logic, OpSIN and Salient Watershed @ HHMI-Janelia, to name a few. Eric Colson is the Chief Algorithms Officer at Stitch Fix, where he specializes in social algorithms. He is also an advisor at several companies: Earnest Inc (consumer lending), Data Elite (Big Data incubator), Mortar Data (Big Data Platform). Previously, he was VP of Data Science & Engineering at Netflix and has held analytics positions at Yahoo!, Blue Martini, Proxicom and Information Resources. He holds a B.A. in Economics, a M.S. in Information Systems, and a M.S. in Management Science & Engineering. Zach Kagin is the Head of Data Science and Product Analytics at Dropbox. Prior to that, he was a product manager driving growth efforts for Dropbox for Business and Dropbox's mobile apps. Before joining Dropbox, Zach worked at The Boston Consulting Group as a management consultant focused on tech and product strategy. Zach has a BS in Physics and Economics from Yale University, and in his free time, he likes to write comics and build snow forts. Jeremy Schiff earned an undergraduate in Electrical Engineering and Computer Science from the University of California in 2005, and a Ph.D. in Electrical Engineering and Computer Science in 2009, with a focus on applying machine learning and statistical inference to robotics. In 2006, Jeremy co-founded FotoFlexer.com, an online photo editing company that powered companies such as MySpace and Photobucket. In 2009, Jeremy joined Ness Computing, a Personalized Search and Recommendation company. As VP of Machine Learning, he oversaw the efforts around Personalized Recommendations, and other data-driven features. Ness sold to OpenTable in 2014, where Jeremy now leads Data Science. Case Study: PagerDuty (PD) provides alerting, on-call scheduling, escalation policies and incident tracking to increase uptime on customer apps, servers, website and databases. As the focal point between monitoring tools and the people resolving system incidents, PD maintains an extensive dataset with the potential to optimize the on-call process. PD intends to grow out a Data Science team that will enable it to trace seemingly unrelated incidents back to a single real-world cause, find patterns in how incidents are triggered, and help PD differentiate the optimal customer experience by learning from customers that churn.

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  • Kaggle's CEO on Ramping up your Data Science Skills

    Get advice for ramping up your Data Science skills from Anthony Goldbloom, CEO of Kaggle! Kaggle organizes predictive analytics competitions with cash prizes and hundreds / thousands of participants. Why consider Kaggle if you’re just getting started in your career or switching into data science? In one word: collaboration. Somewhat unexpectedly (for a competition), Kaggle has a vibrant and beginner-friendly community forum and basic productivity tools for sharing scripts, collaborating, and testing proposed competition solutions. Competition problems are particularly good learning exercises for predictive analytics, because the problems have been meticulously ‘framed’ by the sponsors. (It takes a ton of effort to get things to that stage in 'real world' data science). Anthony, as CEO of Kaggle, is uniquely positioned to observe the evolution of the data science ecosystem. Come join him as he shares his thoughts on getting started in data science. Kaggle has generously offered to provide some food for the event. Rough Schedule: 6:30 - 6:55p - Chatting, Eating, Forming Kaggle Groups, and Networking 6:55 - 7:45p - Anthony on DS 7:45 - 8:15p - Anthony, Jeremy Schiff and Aaron (Organizer) on Skills, ProD, and Hiring 8:15 - 9:00p - More Chatting, Forming Kaggle Groups, and Networking

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