Demystifying Data Science Conference

A FREE Live Online Conference for Aspiring Data Scientists

SEPTEMBER 27, 2017 // 10AM - 10PM EST

Early Registration Deadline is August 31!
Register by August 31st for a chance to win a complimentary seat in our upcoming
Live Online Introduction to Data Science Professional Development Course!
Terms and Conditions Apply

Metis Demystifying Data Science Conference Sweepstakes Official Rules

NO PURCHASE NECESSARY TO ENTER OR WIN. A PURCHASE DOES NOT INCREASE YOUR CHANCES OF WINNING. VOID WHERE PROHIBITED. Metis Demystifying Data Science Conference Sweepstakes ("Promotion") begins on 8/15/17 at 12:00:00 AM Eastern Time ("ET") and ends at 11:59:59PM ET on 8/31/17 (the "Promotion Period").

EACH ENTRANT HEREBY REPRESENTS AND WARRANTS THAT SUCH ENTRANT IS AT LEAST 18 YEARS OF AGE AND THAT SUCH ENTRANT HAS READ THESE OFFICIAL RULES AND IS FAMILIAR WITH AND AGREES TO ITS CONTENTS. IF ENTRANT IS NOT AT LEAST 18 YEARS OF AGE, DO NOT PARTICIPATE IN THIS PROMOTION.

ELIGIBILITY: The Promotion is open only to legal residents of the United States and the District of Columbia (excluding Rhode Island) who are 18 years of age or older at the time of Promotion. Employees, officers and directors of Kaplan, Inc. (the "Sponsor"), and Sponsor's parent, affiliates, subsidiaries, and advertising, contest, fulfillment and marketing agencies (all of such entities (including, but not limited to, the Sponsor) are collectively referred to herein as the, "Promotion Parties"), their immediate families (parent, child, sibling and spouse and their respective spouses, regardless of where they reside) and persons living in the same households as such individuals (whether related or not) are not eligible to participate in the Promotion. By participating, you agree to these Official Rules and to the decisions of the Sponsor, which are final and binding in all respects. Void where prohibited by law, rule or regulation. All applicable federal, state and local laws and regulations apply.

HOW TO ENTER THE SWEEPSTAKES: To enter the Promotion, you must register for the Demystifying Data Science Conference at http://www.thisismetis.com/demystify by 11:59:59 PM ET of 8/31/17 during the Promotion Period. The Sponsor reserves the right to disqualify any entry from the Promotion for any reason.

PRIZES, DRAWINGS & ODDS OF WINNING: A total of two (2) prizes will be selected in electronic, randomized drawings. The drawing for the Prize (described below) will be held on 9/1/17 following the Promotion Period. The drawing will be from all eligible entries received during the Promotion Period. The drawing will be conducted by Sponsor using RandomPicker, whose decisions are final and binding in all matters relating to this Promotion. Odds of winning depend on the number of eligible entries received.

PRIZES: Two (2) Prize will be awarded for the Promotion. Each Prize will consist of the following:

One enrollment in a Metis Live Online Professional Development Course The Approximate Retail Value ("ARV") of the Prize during a Promotion is $3,800.00 * Any applicable taxes on the Prize are the sole responsibility of the winner. WINNER NOTIFICATION: Potential winner will receive Prize notification and prize claim information via email within forty-eight (48) hours after the drawing, or as soon thereafter as reasonably practicable. Sponsor is not responsible for any change in entrant's telephone number, mailing address, and/or email. Winner will have seven (7) calendar days from time of receipt of winning notification to confirm that the notification has been received in order to claim his/her Prize. A potential winner is subject to verification, including verification of age. If such potential winner cannot be contacted within a reasonable time period, if the potential winner is ineligible, if any notification is returned undeliverable, or if the potential winner otherwise fails to fully comply with these Official Rules, he/she will forfeit that Prize and, if time permits, an alternate winner will be selected from among all remaining entries for that drawing.

GENERAL PRIZE CONDITIONS: Prize will only be awarded by Sponsor upon potential winner's verification of eligibility (if requested by Sponsor), and compliance with these Official Rules and final approval by Sponsor. No prize substitution, cash equivalent of Prize, transfer or assignment of Prize is permitted, except by Sponsor which reserves the right to substitute a Prize with one of comparable or greater value, in its sole discretion. Prize is awarded "as is" with no warranty or guarantee, either express or implied. All Prize details are at Sponsor's sole discretion. Any and all warranties and/or guarantees on a Prize (if any) are subject to the respective manufacturers' terms therefore, and winner agrees to look solely to such manufacturers for any such warranty and/or guarantee. An unclaimed Prize will not be awarded. All federal, state, local, and other taxes on a Prize and any other costs and expenses associated with Prize acceptance and use not specified herein as being provided, are the sole responsibility of the applicable winner. A 1099 tax form will be issued where required. Notwithstanding anything herein, a potential Prize winner may be required to complete and return an Affidavit of Eligibility and Liability/Publicity Release form within five (5) calendar days of attempted delivery of same. Non-compliance within this time period or return of any Prize/Prize notification as undeliverable may result in disqualification. All federal, state, municipal, provincial and local laws and regulations apply.

PUBLICITY RELEASE: Except where prohibited or restricted by law and notwithstanding the fact that the winner may be required to sign a separate Affidavit of Eligibility and Liability/Publicity Release, winner's acceptance of Prize constitutes winner's agreement and consent for Sponsor to use and/or publish the winner's full name, city and state of residence, Promotion entry, and/or statements made by winner regarding the Promotion or Sponsor, winner's voice, photographs and other likeness, worldwide and in perpetuity for any and all purposes, including, but not limited to, advertising, trade and/or promotion on behalf of Sponsor, in any and all forms of media, now known or hereafter devised, including, but not limited to, print, TV, radio, electronic, cable or World Wide Web, without further limitation, restriction, compensation, notice, review or approval.

MISCELLANEOUS: Entrants who do not follow all of the instructions, provide the required information in their entry form or the like, or abide by these Official Rules or other instructions of Sponsor may be disqualified. All Promotion entries become the property of Sponsor and will not be acknowledged or returned. Online entries will be considered to be entered by the authorized account holder of the e-mail address submitted at time of entry and he/she must comply with these Official Rules. The authorized account holder is deemed as the natural person who is assigned to an e-mail address by an Internet access provider, online service provider or other organization that is responsible for assigning e-mail addresses for the domain associated with the submitted e-mail address.

PRIVACY: Personal information collected by Sponsor will be used for administration of the Promotion and awarding of a Prize. In addition, by entering, entrants and/or winner agree to Sponsor's use of entrant's/winner's personal information as described in its Privacy Policy athttp://www.thisismetis.com/privacy-policy. Please refer to Sponsor's privacy policy for important information regarding the collection, use and disclosure of personal information by Sponsor.

RELEASE: By participating in the Promotion, entrants and winner agree to release, discharge and hold harmless the Promotion Parties from any and all damages whether direct or indirect, which may be due to or arise out of participation in the Promotion or any portion thereof, or the acceptance, use/misuse or possession of any Prize (or activity related thereto). As a condition of entering the Promotion, entrants and winner agree that (1) under no circumstances will entrant and/or winner be permitted to obtain awards for, and hereby waives all rights to claim punitive, incidental, consequential or any other damages, and any claims, judgments or awards shall be limited to actual out-of-pocket expenses; (2) all causes of action arising out of or connected with this Promotion, or any Prize awarded, shall be resolved individually, without resort to any form of class action; and (3) in no event will any entrant and/or winner be entitled to receive attorneys' fees. BY ENTERING THE PROMOTION, ENTRANTS AND/OR WINNER AGREE TO RELEASE, INDEMNIFY AND HOLD HARMLESS PROMOTION PARTIES AND EACH OF THEIR RESPECTIVE PARENT, SUBSIDIARY AND AFFILIATED ENTITIES AS WELL AS THE SUCCESSORS, ASSIGNS AND LICENSEES OF EACH AND THE RESPECTIVE OFFICERS, DIRECTORS, AGENTS, EMPLOYEES, SHAREHOLDERS, CONTRACTORS AND REPRESENTATIVE OF EACH, FROM ANY AND ALL CLAIMS, EXPENSES, DAMAGES, (INCLUDING REASONABLE ATTORNEYS FEES), OR LIABILITY FOR ANY INJURY, LOSS, OR DAMAGE OF ANY KIND TO PERSONS (INCLUDING, BUT NOT LIMITED TO, DEATH), AND PROPERTY, WHETHER DIRECT OR INDIRECT, WHICH MAY BE DUE TO OR ARISE OUT OF PARTICIPATION IN THE PROMOTION OR ANY PORTION THEREOF; A BREACH OR ALLEGATION WHICH IF TRUE WOULD CONSTITUTE A BREACH OF ANY OF ENTRANT'S AND/OR WINNER'S REPRESENTATIONS, WARRANTIES OR OBLIGATIONS HEREIN; OR THE ACCEPTANCE, USE/MISUSE OR POSSESSION OF PRIZE(S), OR ANY PRIZE-RELATED ACTIVITY. ENTRANTS AND/OR WINNER WAIVE ALL WARRANTIES, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE.

DISCLAIMER: Promotion Parties are not responsible for printing or typographical errors in these Official Rules or in any Promotion-related materials. Sponsor reserves the right, in its sole discretion, to disqualify any individual that tampers with the entry process. Sponsor also reserves the right to terminate, suspend, cancel or modify the Promotion and award the Prize for the Promotion from among all eligible, non-suspect entries received (i) as of the date of termination or modification, as applicable, using the judging procedure outlined above and (ii) in a random drawing if for any reason this Promotion is not capable of running as planned due to any reason, including infection by computer virus, bugs, tampering, fraud, unauthorized intervention, technical failures or other causes that may corrupt or impair the integrity, fairness or proper play of the Promotion. Promotion Parties are not responsible or liable for any events which may cause errors and/or the Promotion to be stopped, including but not limited to any error, omission, interruption, deletion, defect, delay in operation or transmission, communications line failure, theft or destruction or unauthorized access to, or alteration of, entries, nor are they responsible for any problems or technical malfunction of any telephone, network or telephone lines, computer online systems, servers, or cable, satellite, or Internet Service Providers, computer equipment, mobile equipment, software or any other failure of any email or entry to be received by Sponsor on account of technical problems, human error or traffic congestion on the Internet or at any web site, or any combination thereof, including any injury or damage to entrant's or any other person's computer or mobile device relating to or resulting from participation in this Promotion or downloading any materials in this Promotion. Promotion Parties are not responsible for computer, mobile, mechanical, technical, electronic, network or other errors or problems, including any errors or problems that may occur in connection with the administration of the Promotion, the processing of entries, or in any other Promotion-related materials. The Promotion Parties may stop you from participating in this Promotion if you violate Official Rules or act, in Sponsor's sole discretion: (a) in a manner Sponsor determines to be not fair; (b) with an intent to annoy, threaten or harass any other entrant, winner or the Sponsor; or (c) in any other disruptive manner. Should more prizes be awarded through a computer, hardware, or software malfunction, error or failure, or for any other reason, in any prize category, than are stated for that category in the Official Rules, Sponsor reserves the right to award only the number of prizes stated in the Official Rules for that category. CAUTION: ANY ACT OR ATTEMPT BY AN ENTRANT TO DELIBERATELY DAMAGE ANY WEB SITE OR UNDERMINE THE LEGITIMATE OPERATION OF THIS PROMOTION IS A VIOLATION OF CRIMINAL AND CIVIL LAWS. SHOULD SUCH AN ATTEMPT BE MADE, PROMOTION PARTIES RESERVE THE RIGHT TO SEEK DAMAGES AND OTHER REMEDIES (INCLUDING ATTORNEYS' FEES) FROM ANY SUCH INDIVIDUAL(S) TO THE FULLEST EXTENT PERMITTED BY LAW.

WINNER'S LIST: Entrants are responsible for complying with these Official Rules. To receive the winner's list for a specific Promotion, send a self-addressed stamped envelope to: Metis, 79 Madison Avenue, New York, NY 10016. Please reference the dates of the Promotion Period and Prize with your request. SPONSOR: Kaplan, Inc.

Are you data curious?

Join more than 25 incredible speakers who will demystify data science, and discuss the training, the tools, and the career path to the "best job in the United States."*

*SOURCE: Glassdoor
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Consecutive 18-minute live presentations, each followed by Q&A

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Meet The Speakers

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Emily Robinson

Data Analyst at Etsy

Emily is a Data Analyst at Etsy where she works with the search team to design, implement, and analyze experiments on the ranking algorithm, UI changes, and new features. Emily earned her masters in Organizational Behavior from INSEAD in 2016 and her bachelor's in Decision Sciences from Rice University. Emily is also a graduate of the Metis Data Science bootcamp.

Follow Emily on Twitter at @robinson_es.

Kevin Markham

Founder of Data School

Kevin Markham is the founder of Data School, an online school that will help you to launch your data science career. He is passionate about teaching data science to people who are new to the field, regardless of their educational and professional backgrounds. He teaches machine learning and data analysis in Python to over 10,000 students each month through his popular YouTube channel. Prior to Data School, he co-founded a technology startup, worked for two national nonprofit organizations, served our country for two years through AmeriCorps, and worked for two seasons as a wildland firefighter. He has a degree in Computer Engineering from Vanderbilt University.

Follow Kevin on Twitter at @justmarkham.

Mara Averick

Consultant at TCB Analytics

Mara is a bit of a polymath and self-confessed data nerd. With a background in research in Science & Technology Studies, she has a breadth of experience in data analysis, visualization, and applications thereof. Currently, she's a Consultant at TCB Analytics. You can find her sharing R-related stuff on Twitter and turning technical subject matter into easy reading for non-technical audiences. When she's not talking data, she's diving into NBA stats, exploring weird and wonderful words, and/or indulging in her obsession with all things Archer.

Follow Mara on Twitter at @dataandme.

Claudia Perlich

Chief Data Scientist, Dstillery

Claudia Perlich is the Chief Data Scientist at Dstillery. Prior to joining Dstillery (formerly at Media6Degrees), she spent five years working at the Data Analytics Research group at the IBM T.J. Watson Research Center, concentrating on research in data analytics and machine learning for complex real-world domains and applications. She has been published in over 30 scientific publications and holds multiple patents in the area of machine learning. Claudia has won many data mining competitions, including the prestigious 2007 KDD CUP on movie ratings, the 2008 KDD CUP on breast-cancer detection, and the 2009 KDD CUP on churn and propensity predictions for telecommunication customers. Claudia received her Ph.D. in Information Systems from Stern School of Business, New York University in 2005, and holds a Master of Computer Science from Colorado University.

Follow Claudia on Twitter at @claudia_perlich.

Deborah Berebichez

Chief Data Scientist, Metis

Deborah Berebichez is a physicist, data scientist and TV host. She has expertise in scientific research and advanced analysis and she has helped automate decision-making and uncover patterns in large amounts of data. Her passion lies in merging critical thinking skills with practical coding skills. She specializes in drawing connections between the approaches used in data science and the challenges organizations face. Deborah has a Ph.D. in physics from Stanford and completed two postdoctoral fellowships at Columbia University's Applied Math and Physics Department and at NYU's Courant Institute for Mathematical Sciences. She is a frequent mentor of young women in STEM. Her work in science education and outreach has been recognized by the Discovery Channel, WSJ, Oprah, Dr. Oz, TED, DLD, WIRED, Ciudad de las Ideas and others.

Follow Deborah on Twitter at @debbiebere.

Chris Albon

Co-Host/Co-Founder, Partially Derivative

Chris Albon is a data scientist. He founded the TechStars company New Knowledge and is also the co-host of the data science podcast, Partially Derivative. Previously, he led Ushahidi's work on crisis and humanitarian data and launched CrisisNET. Prior to Ushahidi, he was Director of the Governance Project at FrontlineSMS. He is also known for his machine learning flashcards. He earned a Ph.D. in Political Science from the University of California, Davis researching the quantitative impact of civil wars on health care systems. He earned a B.A. from the University of Miami, where he triple-majored in political science, international studies, and religious studies.

Follow Chris on Twitter at @ChrisAlbon.

Kirk Borne

Principal Data Scientist, Booz Allen Hamilton

Dr. Kirk Borne is an Executive Adviser and the Principal Data Scientist at Booz Allen Hamilton (since 2015). He previously spent 12 years as Professor of Astrophysics and Computational Science at George Mason University where he taught and advised students in the graduate and undergraduate Data Science degree programs. Before that, he worked 18 years on NASA projects, developing and managing large data systems for space science research, including a term as the NASA Project Scientist for the Hubble Space Telescope Science Data Archive, and several years as contract Program Manager in NASA's Space Science Data Operations Office. He is an active contributor on social media, where he has been named consistently among the top worldwide influencers in big data and data science since 2013. In 2014 he was named an IBM Big Data and Analytics Hero, and in 2016 he was elected Fellow of the International Astrostatistics Association.

Follow him on Twitter at @KirkDBorne.

Megan Ayraud

Head of Careers, Metis

As Head of Careers for Metis, Megan has successfully coached and supported hundreds of people through their data science job search including: creating a personalized career strategy, identifying open job opportunities, providing introductions, preparing candidates for interviews, and navigating them through the salary negotiation process. Megan is passionate about helping people identify the right career choices and jobs they love. She got her Bachelor's degree in Political Science from Pepperdine University.

Follow Megan on Twitter at @missmegana22.

Claudia Perlich

Chief Data Scientist, Head of Dstillery

Claudia Perlich is the Chief Data Scientist at Dstillery. Prior to joining Dstillery (formerly at Media6Degrees), she spent five years working at the Data Analytics Research group at the IBM T.J. Watson Research Center, concentrating on research in data analytics and machine learning for complex real-world domains and applications. She has been published in over 30 scientific publications and holds multiple patents in the area of machine learning. Claudia has won many data mining competitions, including the prestigious 2007 KDD CUP on movie ratings, the 2008 KDD CUP on breast-cancer detection, and the 2009 KDD CUP on churn and propensity predictions for telecommunication customers. Claudia received her Ph.D. in Information Systems from Stern School of Business, New York University in 2005, and holds a Master of Computer Science from Colorado University.

Follow Claudia on Twitter at @claudia_perlich.

Carla Gentry

Data Scientist, Talent Analytics

Carla is an acknowledged influencer and subject matter expert in the field of advanced analytics and data science. During the past 19 years, Carla has worked with Fortune 100 and 500 companies including but not limited to, Discover Financial Services, J&J, Hershey, Kraft, Kellogg’s, SCJ, McNeil, PBA, Disney, Deloitte and Firestone. Carla has multiple degrees in Mathematics and Economics from the University of Tennessee where she graduated tops in her class while raising two boys as a single mom. Carla has joined forces with Talent Analytics to spread the word of predictive and workforce analytics for pre-hire employment and retention/goodness of fit. Carla commented that after watching the wrong people being hired into companies over the last 20 years and leaving companies due to bad managers and teams that didn’t work, she wanted to be a part of change in the hiring process.

Follow Carla on Twitter @data_nerd.

Bob Hayes

President, Business Over Broadway

Bob Hayes is a researcher, writer and consultant. He likes to solve problems using the scientific method, and his interests are at the intersection of customer experience, data science and analytics. Bob holds a PhD in industrial-organizational psychology and has written three books on the topic of customer experience, measurement and analytics and blogs regularly on these topics. He is considered a top social influencer in the data science and machine learning space.

Follow Bob on Twitter at @bobehayes.

Sebastian Gutiérrez

Data Scientist and Data Visualizer

In Data Science, Sebastian co-founded DataScienceWeekly.org to provide news and commentary in data science. The Data Science Weekly newsletter reaches tens of thousands of aspiring and professional data scientists on a weekly basis. Sebastian is the author of Data Scientist at Work, a collection of interviews with many of the world’s most influential data scientists. Sebastian is also the co-author of the "Get A Data Science Job Course," a three-part guide to getting started in data science, constructing your data science portfolio, and building a data science resume.In Data Visualization, Sebastian founded DashingD3js.com to provide online and corporate training in data visualization and D3.js to a diverse client base, including corporations like the New York Stock Exchange, the American Express Company, Intel, General Dynamics, Salesforce, Thomson Reuters, Oracle, Bloomberg Businessweek, universities (MIT, Georgia, and others), and dozens of startups. More than 1,000 people have attended his live trainings and many more have succeeded with his online D3.js training. Sebastian Gutierrez holds a BS in Mathematics from MIT and an MA in Economics from the University of San Francisco.

William Chen

Data Scientist, Quora

William Chen is a data scientist at Quora, where he leads data science efforts surrounding questions on Quora. He is also an avid writer and 4x Top Writer on Quora, where he answers questions about data science, statistics, machine learning, and how to make the career transition to data science. He has a passion for telling stories with data and sharing advice with aspiring data scientists on Quora. His data science content has been featured on VentureBeat, HuffPost, and Forbes. William is a co-author of “The Data Science Handbook” (a collection of 25 interviews) and “The Only Probability Cheatsheet You’ll Ever Need”. William holds degrees in Statistics and Applied Mathematics from Harvard University.

Follow William on Twitter at @wzchen.

Ryan Swanstrom

Director of Data Science, Matisia Consultants

Since creating the first data science specific blog on the internet in 2012, Ryan Swanstrom has been named as a thought-leader in big data and listed as one of the most influential people on the internet for data science. Ryan currently works as the Director of Data Science for Matisia Consultants, where he helps companies use data to solve problems. He lives in South Dakota with his wife and five children.

You can follow Ryan's Data Science 101 blog or follow Ryan on Twitter @ryanswanstrom.

Rumman Chowdhury

Senior Manager, Accenture Artificial Intelligence

Rumman's passion lies at the intersection of artificial intelligence and humanity. She comes to data science from a quantitative social science background. She holds two undergraduate degrees from MIT, a Masters in Quantitative Methods of the Social Sciences from Columbia University, and a Ph.D. from the University of California, San Diego. More recently, she has emerged as a thought leader in the San Francisco Data Science community and in mainstream media, she has been interviewed for the PhDivas podcast, German Public Television, Software Engineering Daily, RE-Work, StemGirls, and fashion line MM LaFleur. Her professional consulting experience includes the BBC, Capital One, the World Bank, and LACMA, among others. She is also the co-host of the podcast Studies Show, which is a critical take on data literacy in pop culture and mainstream media.

Follow Rumman on Twitter at @ruchowdh.

Camille Eddy

Mechanical Engineering Student and Tech Advocate

Camille Eddy is currently in her senior year studying Mechanical Engineering at Boise State University (BSU). She is also a robotics intern at X formerly known as Google X and has previously completed research in Augmented Reality at BSU, as well as robotics and machine learning research as an intern at HP Labs. Camille actively speaks around the country on topics about tech and diversity.

Follow Camille on Twitter at @NikkyMill.

Rachel Thomas

Founder & Researcher, fast.ai

Rachel Thomas has a math Ph.D. from Duke and was selected by Forbes as one of “20 Incredible Women Advancing AI Research”. She is co-founder of fast.ai and a researcher-in-residence at the University of San Francisco Data Institute. Her background includes working as a quant in energy trading, a data scientist + backend engineer at Uber, and a full-stack software instructor at Hackbright. Rachel’s writing about diversity and data science has made the front page of Hacker News 4x; been translated into Chinese, Spanish, & Portuguese; and been featured in newsletters from O’Reilly, Fortune, Mattermark, & others. She writes an ask-a-data-scientist advice column and is on twitter @math_rachel. Rachel co-founded fast.ai with the goal of making deep learning accessible to people outside of elite institutions, who are tackling problems in meaningful but low-resource areas. Over 50,000 students have started the FREE fast.ai course, Practical Deep Learning for Coders, and graduates from the course have gone on to become Google Brain residents, earned patents, gotten new job offers, won hackathons, and had their work featured in Forbes and on HBO’s Silicon Valley.

Follow Rachel on Twitter at @math_rachel.

Joel Grus

Research Engineer, Allen Institute for Artificial Intelligence

Joel Grus is a research engineer at the Allen Institute for Artificial Intelligence, the author of Data Science from Scratch, and co-host of the Adversarial Learning podcast. Previously he worked as a software engineer at Google and as data scientist at a variety of startups.

Aylee Nielsen

Analytics Community Manager, IBM

Aylee Nielsen is the Analytics Community Manager at IBM Hybrid Cloud, where she is responsible for overseeing and reimagining the 400K+ user group and nearly 150+ member IBM Analytics Champions advocacy program. She is committed to enriching vibrant user communities through tactics and practices that strive to truly empower all users and customers. In her current role, Aylee is exploring the full potential of brand advocacy and redesigning communities, large and small, that are in need of new leadership, purpose, and meaning. Previously, as Head of Influencer Engagement, she reshaped the way IBM Analytics envisioned and practiced thought-leadership while building a strong and enduring community of some of the world’s most influential data and analytics experts. She far exceeded targets, and broke records as she set new standards and restructured that influencer program. Aylee is also passionate about the art of digital storytelling, and fascinated by the exercise and psychology of perfecting shareability. Her background spans a variety of fields from Business Analytics and Visualizations to Data Science and Machine Learning to Hybrid Cloud and Data Management.

You can follow Aylee on Twitter at @AyleeNielsen.

Vin Vashishta

Founder and Chief Data Scientist, V-Squared

Vin is the founder and chief data scientist of V-Squared. He has been an applied data scientist for the last eight years and has worked in software development for 12 years before that. Yes, even on his bio there’s math. He’s been published on business strategy and machine learning topics. He speaks a bit and tweets out what he’s reading. He’s only as good as his next project so he’d better get back to it.

You can follow Vin on Twitter at @v_vashishta

Jessica Cox

Technology Researcher, Elsevier Labs

Jessica is a tech researcher at the scientific publishing company Elsevier, where she leverages her background as a biomedical scientist and machine learning to gain deeper insight into their published content. Jessica is also a graduate of the Metis Data Science bootcamp.

Andre Gatorano

Principal Data Scientist, Capital One

Andre is a Chicago native, but he has lived on both coasts. He is a data scientist, but took a few turns to get here. He was initially a bioinformatician, and found himself trying to do data science projects on biological topics. He enrolled in the Metis Data Science bootcamp to expand his tool set and apply himself not only to biological problems, but also to anything. He loves data, and we what can do and understand with it.

Naoya Kanai

Data Scientist at Airbnb

Naoya Kanai is a data scientist based in San Francisco. Before experiencing Metis both as a student and teaching assistant, he worked in diverse roles ranging from back-end development, marketing, and operations at local startups, in addition to advising multinational corporations at Bain & Company. A graduate of Stanford University and The Juilliard School, Naoya is active as a professional cellist and enjoys performing, teaching, contributing to open source, and watching soccer.

Lorena De La Parra

Data Scientist, CKM Advisors

Lorena is an International Development professional turned Data Scientist. Her previous experiences include serving as a Peace Corps volunteer in West Africa and South Africa, interning with the United Nations Development Program, acting as an Evaluation Consultant for a microfinance in Haiti, and working as a Research and Evaluation Analyst for a domestic violence organization. It was Lorena’s strong belief that the nonprofit and public sectors should have the same drive and capabilities as their for-profit counterparts that led her to Data Science. Lorena currently works as a Data Scientist at CKM Advisors and holds a dual Masters in Economics and International Political Economy and Development from Fordham University. Lorena is also a graduate of the Metis Data Science bootcamp.

Michael Lai

Data Scientist, Philadelphia 76ers

Michael found an interest in data science through his hobby of following basketball analytics. With a background in applied mathematics and economics, Michael decided to make a leap from his career as a trader -- his sights set being a data scientist with an NBA team. After a brief stint as a consultant at IBM, Michael eventually landed with the Philadelphia 76ers as a data scientist, building tools and analyzing data to help support basketball operations. Michael is also a graduate of the Metis Data Science bootcamp.

Brandon Rohrer

Data Scientist, Facebook

Brandon loves solving puzzles and building things. Practicing data science gives him the opportunity to do both in equal measure. Like most data scientists, he came to the field indirectly. He started by studying robotics and human rehabilitation at MIT, moved on to machine vision and machine learning at Sandia National Laboratories, then to predictive modeling of agriculture DuPont Pioneer, to cloud data science at Microsoft and finally to building models with global data at Facebook. In his spare time, he likes to rock climb, write robot learning algorithms, and go on walks with his wife and their dog, Reign of Terror.

You can follow Brandon on Twitter at @_brohrer_.

Renee Teate

Host, Becoming a Data Scientist Podcast

Renee Marie Parilak Teate is the host and creator of the Becoming a Data Scientist Podcast and works as a Data Scientist at HelioCampus, a higher ed analytics start-up. She has an undergraduate degree in Integrated Science and Technology from James Madison University, and a Masters in Systems Engineering from the University of Virginia. Before officially becoming a data scientist about 1 year ago, Renee worked with databases for over 10 years as a relational database designer, data-driven website developer, and SQL Data Analyst. At HelioCampus, Renee takes on a variety of roles, from SQL ETL development to Tableau dashboard design to Predictive Modeling with Python and scikit-learn to custom data analysis and training. Renee also created DataSciGuide, a data science learning resource directory, and loves chatting with people on Twitter about transitioning into data science careers.

Follow Renee on Twitter at @becomingdatasci.

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