Global Data Science Forum

Data Science in Action: Join us for Community Day Sessions!

By Christina Howell posted Thu July 19, 2018 05:00 PM

  


Next week, thousands of practitioners and leaders in the data science and developer space will converge for IBM's Virtual Community Day, a free, online event, which will feature 25 industry-leading speakers presenting live talks, coding tutorials and 1:1 meetings. Six of those speakers are our very own Data Science Community members. Make sure to register for the event, taking place next Tuesday, July 24 from 9am-6pm PT and mark our Community talks on the agenda to get notifications: 

Shadi Copty - "Mega Trends in Data Science"
9:00am PT - IBM Sessions
Data Science continues to evolve in importance, adoption and impact across industries. In this talk we will go over the mega trends that are shaping this landscape, from the algorithmic and computational all the way to the people and cultural.

Victor Terpstra - "Getting Started with Decision Optimization for DSX"

9:00am PT - IBM Code
Walk-through of how to develop and evaluate a decision optimization model in DSX. How to use the CPLEX Python API in a Jupyter notebook to code an optimization model. Use the DO for DSX add-on to create, run, do what-if analysis and compare multiple scenarios using dashboards. Scenario management. Discussion of best practices.

More articles by Victor: DSX, Github and Forking, Github for DSX Projects, Using Excel to manage scenarios in Decision Optimization for DSX

Ted Fischer - "Data Science Best and Worst Practices"

10:00am PT - IBM Sessions
In this session, a data science leader will provide best practices for your data science project as well as tips to avoid landmines that could cause significant problems. 

Steve Barbee - "Tips on Generalizing Feature Creation and Assessing Imbalanced-Data Trees"

6:00am PT - IBM Sessions
Successful deployments in machine learning require that models generalize well to new data. When building predictive work flows it is easy to overlook where bias may enter when creating features thereby leading to an over-optimistic expectation of a success deployment. The correct placement and use of the Partition Node when creating targeted features can help to avoid this problem. When trees are built using sampling methods to address imbalanced data the coverage and accuracy reported for the resulting rules are based on the balanced training data. A method starting with the Rule Trace Node is shown that finds the coverage and accuracy of individual tree rules on the test data. This will indicate how each rule generalizes to new data.

Srini Kadamati
 - "How To Stop Worrying And Break Into Data Science"
11:00am PT - Partner Sessions
Data science is a massive field that encompasses hundreds of different titles and positions. Newcomers to the field get overwhelmed by the opaqueness of titles, the seemingly large list of requirements, vast array of tools, and large number of new trends that people feel they need to be experts in. I’ve spent the last 3 years teaching data science to hundreds of thousands of students online at Dataquest Labs, personally interacting with many of them in a meaningful way.
 
In this talk, I want to outline the full cycle of breaking into data science, spanning the learning, practice, portfolio building, networking, and interviewing processes. I want to discuss the most common roadblocks that learners face in the journey and some suggestions for how to overcome them. This talk is going to be jam-packed with insights, observations, diagrams, examples, and frameworks. 

Gabriela de Queiroz - "Statistics for Data Science: What you Should Know and Why"
11:00am PT - IBM Code

Data science is not only about machine learning. To be  a successful data person, you also need a significant understanding of statistics. Gabriela de Queiroz walks you through the top five statistical concepts every Data Scientist should know to work with data. 

Gabriela is co-founder of R-Ladies Global
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Also, be sure to follow our community forum for exclusive follow-up discussions and Q&A with our speakers. As a member of the Data Science Community, you'll also have access to talk replays and future virtual and in-person events. Thank you to all of our community members who have already registered,  contributing to over 6,000 total registrations (and climbing) for this special virtual event. 

  • What sessions are you most looking forward to? 
  • What topics would you have liked to see at this event? 
  • Are you interested in becoming a speaker for future events or contributor to the IBM Data Science Community? Reach out in the forum to inquire. 

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