A FREE Live Online Conference for Aspiring Data Scientists, Data-Focused Business Leaders and Practitioners
Sign up to receive access to recordings of all 22 talks and workshops.
During 2 action-packed days, experience 16 interactive data science talks from industry-leading speakers and 6 in-depth workshops led by experts on the Metis data science and career support teams.
10AM - 2PM ET: INTERACTIVE TALKS
8 speakers will demystify data science and discuss the training, tools, and career path to one of the most in-demand jobs in the world.
2:30PM - 5PM ET: WORKSHOPS
3 Metis experts will lead in-depth workshops on introductory level technical topics and career-acceleration guidance.
10AM - 2PM ET: INTERACTIVE TALKS
8 speakers will provide best practices to successfully integrate data science into an organization and demystify its business impact.
2:30PM - 5PM ET: WORKSHOPS
3 Metis experts will lead in-depth workshops on business-oriented and technical topics with practical applications.
Demystifying Data Science is designed to be equal parts informative and interactive. All registrants will have access to the presentation recordings after the conference - but you have to attend live for the full experience!
LIVE TALKS
Talks and workshops are all presented live, followed by Q&A.
INTERACTIVITY
Real-time chat, with opportunities to ask questions, answer polls, and share socially.
REPLAY
Receive post-conference access to recordings of all presentations.
“Interesting, insightful, and each speaker taught me something highly valuable.”
“#DemystifyingDS was better than I expected this year! It's my second year attending & I'm planning to attend next year!”
“One of the greatest data science events I've attended so far.”
Co-founder, CEO and AI Neuroscience Researcher
deepkapha.ai
General Manager of Machine Learning
Cloudera
Co-Founder and Head of Strategic Initiatives
BrightHive, Inc
Founder & CEO
Analytics Vidhya
VP – Global Emerging Practices | AI & Deep Learning
Teradata
Principal Data Scientist
Nolis, LLC
Associate Professor
UCLA
Director of Analytics
KPMG
Sr Developer Advocate/Sr. Engineering & Data Science Manager
IBM
Director of Data Science
Bayer
Knight Chair in Visual Journalism
University of Miami
Data Scientist
DataCamp
Data Visualization Specialist
Story by Data
Chairman
Open Insights
North American Chief Data Scientist
Accenture
Director of Advanced Analytics
Nationwide Building Society
Senior Data Scientist
Digitas
Senior Applied AI Researcher and Developer Relations, Healthcare
NVIDIA
Tarry Singh is CEO, Co-founder and AI researcher of deepkapha.ai, a leading AI in The Netherlands. He has over 20 years of experience working with CxOs of global companies to transform into a data-driven, AI companies of the future. He speaks regularly at global AI leadership summits worldwide and conducts AI workshops with his experienced team of AI researchers who have PhD in biomedicine, bioinformatics, applied mathematics and robotics. He occasionally co-supervises PhD projects related to AI and also teaches regularly at UTD Dallas, Texas, Utrecht University of Applied Sciences, Netherlands, University of Catalunya, Barcelona to name a few.
Tarry is thought leader and he writes regularly for Forbes magazine about "impact of AI on Business and Society”. He was also chosen by LinkedIn as also Linkedin’s Top Voices for “Data Science and Analytics”. He is author of various publications and has been interviewed regularly by leading newspaper such as Al Jazeera / Associated Press and also specific industry vertical magazines worldwide. He is a seasoned entrepreneur and startup coach and he regularly mentors startups at Hult Prize Foundation and AI Med.
Hilary Mason is the General Manager of Machine Learning at Cloudera. Previously, she founded Fast Forward Labs, an applied machine learning research and advisory company, which was acquired by Cloudera in 2017. Hilary is the Data Scientist in Residence at Accel Partners, and is on the board of the Anita Borg Institute. Formerly, she co-founded HackNY.org, a non-profit that helps engineering students find opportunities in New York's creative technical economy, served on Mayor Bloomberg's Technology Advisory Council, and was the Chief Scientist at Bitly. Hilary can be reached on Twitter @hmason and on LinkedIn.
Natalie is a sought-after thought leader on the ethical and responsible use of data after nearly 20 years advancing the public sector’s strategic use of data, including a 16 year career at the National Security Agency, and 18 months with Obama Administration. Known for working with a broad network of academic institutions, data science organization, application developers, and foundations to advance the responsible use of data standards, APIs, and ethical algorithms to directly benefit people, Natalie co-founded and currently serves as Head of Strategic Initiatives of BrightHive, a data trust platform delivering a suite of smart data collection, integration, and governance products to social services providers for improved access to and usability of social sector data. She founded the Community-driven Principles for Ethical Data Sharing (CPEDS) community of practice with over 1200 active members focused on strengthening ethical practices in the data science community through crowd- sourcing of a Data Science Code of Ethics. Now the Global Data Ethics Project (GDEP) she serves as a Strategic Advisor in advancing the adoption of tools, techniques, and practices developed by the community. As a Senior Policy Advisor to the US Chief Technology Officer in the Obama Administration, she founded The Data Cabinet - a federal data science community of practice with over 200 active members across more than 40 federal agencies.
Kunal Jain is the Founder & CEO of Analytics Vidhya. Analytics Vidhya (AV) aims to build next generation data science ecosystem across the globe. They have helped millions of people realize their data science dreams through hackathons, competitions, trainings & conferences and help companies find the right data science talent.
Before starting Analytics Vidhya, Kunal completed his graduation & post-graduation from IIT Bombay and worked with companies like Capital One & Aviva Life Insurance across different geographies and responsibilities. Kunal can be reached on LinkedIn.
Atif Kureishy is a VP – Global Emerging Practices | AI & Deep Learning at Teradata, a business outcome-led global analytics consultancy. Based in San Diego, Atif specializes in enabling clients across all major industry verticals, through strategic partnerships to deliver complex analytical solutions built on machine and deep learning. His teams are trusted advisors to the world’s most innovative companies to develop next-generation capabilities for strategic data-driven outcomes in areas of artificial intelligence, deep learning & data science.
Atif has more than 18 years in strategic and technology consulting working with senior executive clients. During this time, he has both written extensively and advised organizations on numerous topics; ranging from improving the digital customer experience, multi-national data analytics programs for smarter cities, cyber network defense for critical infrastructure protection, financial crime analytics for tracking illicit funds flow, and the use of smart data to enable analytic-driven value generation for energy & natural resource operational efficiencies.
Dr. Jacqueline Nolis has over a decade of experience in the data science industry, working with companies ranging from DSW and Union bank to Microsoft and Airbnb. Her academic research covered optimization under uncertainty with a specialization in electric vehicle routing, which yielded a PhD in Industrial Engineering from ASU - and provided a solid basis for artificial intelligence, data science, and machine learning. Previously, Jacqueline was the Director of Insights and Analytics at Lenati and a Lead of Advanced Analytics at Promontory.
Dr. Safiya Umoja Noble is an Associate Professor at UCLA in the Departments of Information Studies and African American Studies, and a visiting faculty member to the University of Southern California’s Annenberg School of Communication. She is also the author of a best-selling book on racist and sexist algorithmic bias in commercial search engines, entitled Algorithms of Oppression: How Search Engines Reinforce Racism (NYU Press). The recipient of several awards, her academic research focuses on the design of digital media platforms on the internet and their impact on society. Her work is both sociological and interdisciplinary, marking the ways that digital media impacts and intersects with issues of race, gender, culture, and technology. Dr. Noble holds a Ph.D. and M.S. in Library & Information Science from the University of Illinois at Urbana-Champaign, and a B.A. in Sociology from California State University, Fresno where she was recently awarded the Distinguished Alumni Award for 2018.
Tom Schenk Jr. is a researcher and author on applying technology, data, and analytics to make better decisions. He’s currently the director of analytics at KPMG where he leads the smart city and government analytics practice. He’s authored several publications, including a book on data visualization, book chapters on education research, and academic articles on a variety of subjects. Tom has previously served as Chief Data Officer for the City of Chicago, led education research for the State of Iowa, and has held a variety of positions within academia. Tom is the co-founder of the Civic Analytics Network at Harvard University’s Ash Center for Democratic Governance and Innovation. He is also the current co-organizer of the Chicago Data Visualization Group. His work has been featured in The Economist and Wall Street Journal while he’s been featured in television programs on PBS NewsHour and National Geographic Channel.
Gabriela de Queiroz is a Sr. Developer Advocate/Sr. Engineering & Data Science Manager at IBM where she leads the CODAIT Machine Learning Team. She works in different open source projects and is actively involved with several organizations to foster an inclusive community. She is the founder of R-Ladies, a worldwide organization for promoting diversity in the R community with more than 150 chapters in 45+ countries. She has worked in several startups where she built teams, developed statistical models and employed a variety of techniques to derive insights and drive data-centric decisions. She likes to mentor and shares her knowledge through mentorship programs, tutorials and talks.
Adrian Cartier is the Director of Data Science for Bayer. In this role, Adrian partners with business executives and IT strategy leads to enable a digital-driven mindset across the company. He serves as a member of Bayer Data Science Center of Excellence leadership team and is an Associate Fellow within the Science Fellows program, a distinct honor.
Previously, Adrian held a tenure-track position as a Mathematics professor in Alabama and has operated his own analytics consulting firm. Adrian holds a doctorate degree in Mathematics from the University of Mississippi. where his research focused on the intersection between Topology, Group Theory, Category Theory, and Graph Theory.
Adrian has served as co-chair for the St. Louis United Way giving campaign for the last 3 years. In his free time, he enjoys golfing, playing soccer, beer tasting, and watching the Ole Miss Rebels take the football field every fall.
Alberto Cairo began his career in journalism and visualization in 1997, and has been a head of infographics and multimedia at media organizations in Spain (El Mundo online) and Brazil (Editora Globo). He is now the Knight Chair in visual journalism at the University of Miami, and also director of visualization at UM's Center for Computational Science. He is also a freelancer, trainer, and consultant for organizations and institutions such as Google News Initiative and the Congressional Budget Office. He is the author of the books 'How Charts Lie: Getting Smarter about Visual Information' (W.W. Norton, to be published in October 2019), 'The Truthful Art: Data, Charts, and Maps for Communication (2016), and 'The Functional Art: an Introduction to Information Graphics and Visualization (2013). His website is www.albertocairo.com, his weblog is www.thefunctionalart.com, and his Twitter handle is @Alberto Cairo
Emily works as a data scientist at DataCamp on their growth team, working with them to design, implement, and analyze experiments/ Previously, she was a data analyst at Etsy. She holds a Master’s degree in Management (specialization in Organizational Behavior) and a bachelor’s in Decision Sciences. Follow her on twitter at @robinson_es or on her blog, hookedondata.org, where she writes about A/B testing, career advice, and programming in R.
Kate Strachnyi is the author of Journey to Data Scientist and The Disruptors: Data Science Leaders. She is also the founder and host of Humans of Data Science (HoDS) - a project that works on showing the human side of data science (housed on her Story by Data YouTube channel). Kate also manages the Datacated Weekly project on the Storybydata.com site.
Kate current works in the data visualization & reporting space. She previously served as an insight’s strategy manager and research analyst, where she was responsible for enabling the exchange of information in an efficient and timely manner. Prior to working with data, she focused on risk management, governance, and regulatory response solutions for financial services organizations.
E-mail: [email protected]
LinkedIn: https://www.linkedin.com/in/kate-strachnyi-data/
Twitter: @StoryByData
Blog: http://storybydata.com/
Usama was the first person to hold the Chief Data Officer title when Yahoo! acquired his startup in 2004. At Yahoo! Usama built the Strategic Data Solutions group and founded Yahoo! Research Labs where much of the early work on BigData made it to open source and led to Hadoop and other open source contributions.
Four years later he founded Open Insights, a technology and consulting firm which enables enterprises to get value out of their data assets. Usama is also Co-Founder & CTO at OODA Health, Inc., a VC-funded company aiming to liberate the healthcare system from administrative waste by leveraging AI/automation to create real-time/retail-like experience in payments in healthcare.
Usama has held leadership roles at Microsoft and founded the machine learning systems group at NASA’s Jet Propulsion Laboratory where his work on machine learning resulted in the top Excellence in Research award from Caltech, and a U.S. Government medal from NASA. He has also published over 100 technical articles on data mining, data science, AI/ML, and databases. He holds over 30 patents and is a Fellow of both the AAAI and the ACM.
Usama earned his Ph.D. in Engineering in AI/Machine Learning from the University of Michigan, Ann Arbor. He has edited two influential books on data mining/data science and served as editor-in-chief on two key industry journals. He also served on the boards/advisory boards of private and public companies. He is an active angel investor and advisor in many early-stage tech startups across the U.S., Europe and the Middle East.
Peter Guerra has over 15 years experience building analytics and data science businesses with innovative solutions. He is currently the North America Chief Data Scientist for Accenture leading the strategy and delivery for data science, machine learning engineering, and artificial intelligence for Fortune 500 clients solving client challenges in Pharmaceutical, Retail, Energy, Transportation, Financial, Products, and other sectors. He was the architect for numerous US Federal solutions to address healthcare, national defense, and homeland security missions. He has spearheaded strategic partnerships with Cloudera, Microsoft, Elastic, AWS, Google, and NVIDIA. In the past, he built one of the largest data science businesses and lead the next generation of innovation in machine intelligence in US Federal market. His technical expertise ranges from artificial intelligence/machine learning, large-scale distributed systems, cyber security, and data science. He has written thought pieces with O’Reilly and has had the privilege of speaking at many client and industry events, including NVIDIA’s GTC Conference, AWS Summit, Blackhat, Hadoop Summit, Strata+Hadoop, and more. He earned a BA in English and a BS in Computer and Information Science from the University of Maryland, an MBA from Loyola University, and is currently working on his PhD in Artificial Intelligence from UMBC.
Aubrey HB is the Director of Advanced Analytics for Nationwide Building Society. Since graduating with a PhD in Pure Mathematics, she has focused her career on designing and delivering data solutions throughout a number of different industries. Prior to joining Nationwide, Aubrey has worked with many types of data within a wide variety of contexts and technologies to deliver production systems that have advanced analytics techniques at their core. She has developed Data Science solutions for AT&T, Scholastic, SiriusXM, and FAIR Health, so her relevant experience spans the Telecommunications, Publishing, Entertainment, Healthcare, and Financial Services industries.
A background in digital journalism brought Bryan to the NYC Metis Data Science Bootcamp, where he graduated in 2016. Since then Bryan has transformed his career, focusing on high-powered, easily learned data science automation tools. His experience with management, data science, and emerging technologies are driving change in the analytics department at Digitas, a marketing agency in NYC. Using the skills he learned at Metis, Bryan consults with and trains 150+ analysts in seven offices as they learn to leverage new technology for statistical analysis, data processing, and data science. Outside of work he likes weightlifting, Thai food and the underground music scene in Brooklyn.
Michelle Gill is a Senior Applied AI Researcher and Developer Relations in Healthcare at NVIDIA, where she performs research and guides strategy at the intersection of pharmaceutical development and machine learning. Previously, she was a Senior Machine Learning Engineer at BenevolentAI. She holds a PhD in Molecular Biophysics and Biochemistry from Yale University and completed a postdoctoral research fellowship at Columbia University Medical School, where she developed and applied biophysical methods to study the function of cancer-associated enzymes. Michelle is also a graduate of the Metis Data Science Bootcamp in New York and a former Metis Senior Data Scientist and bootcamp instructor. Her scientific and machine learning work has been published in peer reviewed journals and covered by the press.
Senior Data Scientist and Head of Corporate Training Executive Programs
Metis
Senior Data Scientist, Bootcamp
Metis
Career Advisor
Metis
Director of Bootcamp Curriculum
Metis
Data Science Instructor
Metis
Senior Data Scientist, Bootcamp
Metis
Kerstin is passionate about bringing data science from the edge of business to the center of it. She has data science experience in all three sectors: for-profit, non-profit, and government. Currently, she is a Senior Data Scientist and Head of Corporate Training Executive Programs at Metis where develops and delivers curriculum to accelerate data science learning for teams. As Director of Data Science, she founded the Guidestar data science team and brought machine learning to the largest nonprofit data warehouse. At Postmates she used her broad data science toolkit to support marketing, growth, finance, and fleet team needs. As a University of Chicago Data Science for Social Good Fellow she helped uncover early signals for delays in education. She holds graduate degrees in statistics, mathematical statistics, and mathematical computer science from Cornell University and University of Illinois at Chicago. As an undergraduate she studied psychology and anthropology at Yale University.
Check out more from Kerstin on LinkedIn.
Jonathan Balaban is an instructor at Metis’s San Francisco bootcamp, but he has crossed the country with Metis as a previous instructor at the Seattle and Chicago campuses as well. He enjoys teaching the art of impact-focused, practical data science and helping students find amazing careers with top-tier companies like Apple, Tesla, and Amazon.
As a data scientist, he has worked at McKinsey and Booz Allen Hamilton and consulted for numerous companies. He has led teams to design bespoke data science solutions that have driven revolutionary changes in client operations. Jonathan - sometimes successfully - leverages data science solutions in his personal life: on friends, racing, and training.
Ashley is a passionate Career Advisor who is proud to be a founding member of the Metis Chicago team, having helped every student and graduate at the campus identifying what they want out of a career and helping them find the career they are looking for.
With over 5 years of experience as an employment professional, she enjoys helping job seekers realize their full professional potentials and assisting them with finding their dream careers. She started her career as a technical recruiter, and most recently worked at DeVry University as a Career Advisor supporting the technical programs. Working in both a recruiting and advising capacity has given her a strong understanding of the recruitment process and what employers seek in a quality candidate, especially in the technical industry.
Check out more from Ashley on LinkedIn and the Metis blog.
Sophie is the Director of Bootcamp Curriculum at Metis where she leads Metis' bootcamp curriculum development. Sophie works in deep learning and data science ethics. Through t4tech she helps provide free trans-centered classes in programming and data science. She holds masters degrees in Electrical and Computer Engineering and Psychology, and her writing has appeared in Information Week. Sophie is passionate about teaching, both in theory and in practice, and about making sure that data science is primarily a tool that is used to improve people's lives.
Check out more from Sophie on her LinkedIn and InformationWeek.
Damien has experience bringing esoteric subjects down to Earth. Currently, he is a Data Scientist at Stitch Fix focusing on Inventory Algorithms and a Parttime Data Science Instructor for the Corporate Training team at Metis. Previously, he was a Senior Data Scientist at Metis who helped develop and deliver curriculum to accelerate data science learning for teams. After completing a PhD in cosmology, he spent his time developing project-based learning in physics, math and computer science at small liberal arts colleges. He loves developing projects that are relevant and interesting, while still highlighting the important concepts. After leaving the classroom, Damien worked as a curriculum designer and data scientist for a small San Francisco recruiting startup for people looking for coding jobs. When he's not working on lectures, Damien can be found studying Wing Tsun, playing Go, or on a photo hike.
Check out more from Damien on LinkedIn and the Metis blog.
Kimberly Fessel is an instructor at Metis’s NYC bootcamp. She joins Metis from MRM//McCann, a leading digital advertising agency, where she focused on helping clients understand their customers by leveraging unstructured data with modern NLP techniques.
She is passionate about data storytelling and the power of compelling data visualizations to challenge pre-conceived assumptions. Kimberly's enthusiasm for teaching comes from her days as an academic. She holds a Ph.D. in applied mathematics from Rensselaer Polytechnic Institute and completed a postdoctoral fellowship in math biology at the Ohio State University.
For Aspiring
Data Scientists
For Business Leaders,
Managers & Practitioners
Data science is the most coveted jobs in the industry today, yet 80-90% of aspiring professionals tend to get stuck inside the launch pad unable to unleash their full potential as proficient data science professionals. In this talk, Tarry will walk through a real-world project example to illustrate fundamental techniques to help aspiring professionals confidently move from experimentation phase into a full-fledged data scientist professional role.
The data scientist role is considered to be lucrative; it attracts talent with the promise of high demand for the skill-set, attractive salaries, as well as the potential of working on interesting projects. In writing the Journey to Data Scientist, and The Disruptors: Data Science Leaders; as well as interviewing data scientists for the Humans of Data Science video-podcast, Kate Strachnyi uncovered specific qualities of exceptional data scientists. Since it's difficult to find all of these qualities in a single person, companies build data science teams. Watch this presentation to learn about the findings from the in-depth interviews conducted.
Too often, data science aspirants focus a lot on learning the tools and the techniques involved in data science. They learn R / Python, undergo courses, but feel lost at the very first encounter with any real life data science problem. This talk focuses on the importance of structured thinking and problem solving for data scientists and provides a few frameworks which people can use.
Data visualization is a powerful tool to explore data, and also to communicate with other people. However, visualization can be misleading if we believe that is intuitive or if we embrace myths such as “a picture is worth a thousand words”, "data speaks for itself”, or “we should show, not tell”. This presentation, based on my upcoming book 'How Charts Lie', explains how we can approach data visualization more critically, and take advantage of it by becoming more attentive readers.
As a Data Scientist (or aspiring Data Scientist) we are overwhelmed by the amount of knowledge we need to have and acquire. Every day there is a new technique, a new framework, a new state of the art model. For the last few years, Deep Learning has become a hot topic and it is the main driver of many applications. But how can we start our Deep Learning journey? Which of the several deep learning frameworks should we use? Where can I find examples of code that work and that I can use without worrying about the license?
In this talk, I will show you how you can start with Deep Learning without any previous Deep Learning knowledge and how you can have a basic ready-to-use deep learning “service” running in less than five minutes.
Have you heard that you need to “network” if you want to get ahead in your career? Have you wondered if you could get to know data scientists at companies you admire? In this presentation, I’ll start by addressing some of the barriers and misconceptions about building your data science network, share motivation, help you get started, and give you tips on how to network most effectively.
Critical information scholars continue to demonstrate how technology and its narratives are shaped by and infused with values, that is, that it is not the result of the actions of impartial, disembodied, unpositioned agents. Technology consists of a set of social practices, situated within the dynamics of race, gender, class, and politics. This talk, stemming from the new book, Algorithms of Oppression: How Search Engines Reinforce Racism (NYU Press), addresses the issues of Internet search, and how language and meaning are derived in ways that pose particular harms to various publics who are increasingly reliant upon commercial technologies.
People are always willing to tell you about the fancy different modeling techniques in data science, and suggest they are the key to success. As a practicing data scientist, I am here to say that models are only a small part of the complexity of a corporate data science project. Enterprises contain massive amounts of data, but this data is hard to find and harder to clean. Business stakeholders aren’t informed enough about machine learning to understand the level of difficulty of the tasks they’re asking, which leads to analysis after analysis because of additional requests and tweaks from upstream. Almost always these complexities outside of the model are what cause projects to fail, not the fact that a model wasn’t using a cutting-edge approach. In this talk, I’ll walk through the end-to-end lifecycle of a data science project in industry. I’ll demonstrate how organizational pressures can cause solid data science to fail and poor data science to succeed—and what you can do to maximize the chances of success. The goal of this talk is for you to leave with a new awareness of all the non-academic pieces of doing data science at scale.
What does good ML look like? While leading companies inspire us by inventing new business with ML and AI, many on the outside don’t realize that those innovations rest on the successes and failures of projects that tackle mundane goals like cost savings and efficiency. In her keynote, Hilary Mason will discuss emerging best practices for machine learning and data products as well as a few hard truths about what teams might encounter as they build out their own capabilities over time.
This talk will detail examples of different journeys to scaling AI -- the joys and perils for each journey.
Technology evolves far faster than the Darwinian variation of the word. Companies with a legacy data estate face Brobdingnagian challenges catching up with their post-.com era competitors when it comes to using data science and becoming data driven. While a legacy company’s longer history may mean it has richer historical data stores and industry knowledge that could be leveraged to provide it with a comparative advantage over its less well-established peers, my experience is that it is often difficult to mine these resources effectively because such efforts are fatally undermined by the legacy company’s inevitably haphazard history of technological evolution. Invariably, a legacy company’s data ecosystem is underpinned by a massive technological mess that was inadvertently created when disparate decisions were made to put technology in place in different areas of the organization over time without any overarching roadmap for how those different technologies and datasets might one day provide deeper, more valuable insights through their integration and the subsequent revelation of intricate connections and hidden patterns that can drive innovation. Because data is the language through which technologies speak to each other, it is important to understand the historical choices made to generate the entanglement of the technology ecosystem that underwrites a company’s many processes. Only then can one appreciate the steps necessary to bring together pertinent data that will allow a company to strategically excel at using data to drive business outcomes and benefits from blending the current data being captured across different arms of the business with the enduring repositories that already reside on premise.
Through collective power, data can support transforming the human experience. One of the greatest challenges standing in the way, however, is finding the right balance between maximum social impact and also the protection of individual rights. Natalie Evans Harris will explore the critical role that public-private collaboration plays in this balance, and how building an ethical, equitable and sustainable framework for data governance can help organizations move beyond the limitations of traditional approaches to data sharing.
Are you confused about what it takes to be a data scientist? Curious about how companies recruit, train and manage analytics resources? You are not alone. Many employers, educators, and managers are struggling with these issues. In fact, tremendous resources are being wasted by employers on interviewing candidates who claim knowledge of Data Science that are not even qualified for such positions. This presentation covers insight from the most comprehensive research effort to-date on the data analytics profession, proposes a framework for standardization of roles in the industry and methods for assessing skills.
We have been running an industry initiative named: Initiative for Analytics and Data Science Standards (IADSS) to support the development of standards regarding analytics role definitions, required skills and career advancement paths. The initiative kicked off a research study including a detailed survey for analytics executives and professionals, in-depth interviews with industry leaders and academicians as well as an extensive literature review. We will present our initial findings from the research and provide case studies of how bad this confusion and why it is important for the field, for practitioners and for employers and educators to have clarity on this front.
Adrian Cartier, Ph.D., Director of Data Science, Bayer, will demystify data science and unpack its tangible business value. Using his many years of experience building a data-driven culture across Monsanto and now Bayer Crop Science, Adrian highlights the focal points for not only creating a data strategy, but more importantly driving a sustainable digital (and business!) transformation.
In his presentation, Adrian will walk you through the imperative steps to get there. Foremost, you must understand the key high value decisions across your enterprise. What is your mission and vision, your challenge? There are many tools in the toolbox for data science. If you have a nail, use a hammer. But don’t buy a hammer, and then look for a nail. That is, apply data to key business decisions, which ultimately will drive substantial outcomes for the company.
Next, create a data strategy that centers around data as an asset for the company with data science unlocking its value. Get buy-in and create a community. After all, it takes a community to build sustainable robust data science products, including data scientists, software engineers, data engineers and business partners. They don’t all have to be data scientists, but they should be able to access, understand and apply the data to solve their challenges and develop solutions. This community can also support user-centered data science, that is, identify and improve the user experience and user adoption to get the most value out of the output, whether it’s a robust model, a critical insight or a digital product.
Be ready to fail fast and learn from it. That’s a good lesson. Always remember, data science is science: it should be tracked, measured, and repeatable. Also remember to identify quick wins to show measurable value.
And think how your enterprise can make an impact now, as well as the potential that lies in the future. While once a cottage industry, data science unleashed will become a value-added force in any industry.
We will explore emerging approaches for improving retail operations. By utilizing existing camera and network infrastructure, Retailers can better understand detailed information on the movements and interactions of customers and associates. The ability to bring this new “observational” data set into a larger context that includes product sales, promotions, and inventory unlocks new retail value. We will also discuss how capabilities are delivered while ensuring the preservation of customer privacy standards and regulations.
Technology and data have begun to be embedded in the fundamental structure and processes of the cities and states in which we live. Smart cities are looking to incorporate data and technology that improve the quality of life for their residents and help governments be more efficient. But what does this mean for you and how can we learn best practices in data science? We’ll explore practical, real-life ways that data science, open data, and open source are being used to help cities become more livable and healthy. From this, we can also learn how to deploy data science projects to be successful and make an impact in any organization.
Launch your data science journey by skilling up in Python, one of the world’s most popular programming languages. This workshop, which requires no prior coding knowledge, aims to introduce beginner programmers to the fundamentals of Python.
Attendees of this workshop will:
Note: There are no prerequisites for this workshop.
Looking for a data science job and interviewing can be stressful and difficult. It can be hard to know what skills a company is looking for, and “do I really need 5 years of experience?”, or how you can apply online and actually get an email back. Not to mention, data science interviews can be uniquely challenging in that they typically include behavioral, technical, and business focus interviews. All of this can be daunting and lead to frustrations when you are trying to pivot or advance your career in data science. Luckily there are some great strategies to help you put your best foot forward in the job search. Metis Career Advisor, Ashley Purdy, will walk you through:
Advance your data science skills by exploring powerful Python packages, including Pandas, numpy, Sci-Kit Learn, and visualization tools. Attendees of this workshop will:
Note: The following are prerequisites for attendees of this workshop:
With the advent of the data-driven corporation, it’s more essential than ever to be comfortable, competent, and confident making decisions with data. Yet, it’s rare to ever be properly taught how to do so. This crash course will cover the most critical aspects of and tackle the most common mistakes in data literacy. We’ll formulate specific, informative questions for analysis. We’ll create metrics that actually track the performance we care about. And we’ll design visuals that drive a point home. Then, we’ll weave it all together with data storytelling--and get others excited to listen to our data.
Attendees of this workshop will learn:
We will discuss ethical frameworks and regulations for data science and related fields including FATE (fairness, accountability, transparency, and ethics), Weapons of Math Destruction, and GDPR. These frameworks will be presented through case studies where the audience will learn to recognize the ethical impact of data projects and apply those frameworks according to individual business circumstances.
Attendees of this workshop will learn to:
Python's scikit-learn library makes building a machine learning model simply with a consistent interface: create the model, fit the model, and score the model. The difficult parts of modeling are ensuring that the preprocessing steps are done consistently, metrics for scoring are chosen appropriately, and features are engineered to capture the nuance in the data. This workshop will look at some of the issues that occur when trying to build models, as well as best practices.
During this workshop, Damien will dive into: