Sep 23–26, 2019

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SOLD OUT: Big data for managers

Michael Li (The Data Incubator), Gonzalo Diaz (The Data Incubator)

9:00am—5:00pm Monday, September 23—Tuesday, September 24

Location: 1A 01/02

Strata Business Summit

SOLD OUT

Average rating:

(2.50, 4 ratings)

Participants should plan to attend both days of training course. Note: to attend training courses, you must be registered for a Platinum or Training pass; does not include access to tutorials on Tuesday.

Michael Li and Gonzalo Diaz provide a nontechnical overview of AI and data science. Learn common techniques, how to apply them in your organization, and common pitfalls to avoid. You’ll pick up the language and develop a framework to be able to effectively engage with technical experts and use their input and analysis for your business’s strategic priorities and decision making.

What you'll learn, and how you can apply it

Learn to identify potential pitfalls in projects before they start, identify and prioritize which projects a company should pursue, and translate data science insights for business professionals and decision makers
Discover how to communicate business objectives to data professionals
Understand the business implications of technical decisions and assess the risk-reward trade-offs of different projects

Who is this presentation for?

You're a business professional who wants to learn about big data.
You work with data scientists or analysts regularly.
You manage teams or projects with a significant data component.
You find yourself translating between data and management.

Outline

Introduction to AI and data science

Terms and definitions: What does machine learning mean?
Historical context and present day
Drivers for AI and data science
What’s so different about big data?
“AI is eating the world.”
Making AI practical

Algorithms and techniques

Data formats, databases, and schemas
Evaluating model performance and validating models
Terminology: Regression, classification, supervised, and unsupervised
Advanced models: Random forests, support vector machines, deep learning, and neural networks

Industry use cases

Finance
Healthcare
Industrial
Technology

AI within the organization

Maturity levels for AI
Evaluating good projects for AI
Build versus buy and hire versus train
Skills, tools, and platforms needed for AI
Structuring data and AI initiatives within your organization: Successful and cautionary tales

Common pitfalls and fallacies in AI and data science

AI and data science in the headlines: The good, the bad, and the ugly
Legal and regulatory implications
Litigation and liabilities of bad data science
Common fallacies in data science and AI
Lying with statistics and how to spot it

About your instructors

Tianhui Michael Li is the founder and president of the Data Incubator, a data science training and placement firm. Michael bootstrapped the company and navigated it to a successful sale to the Pragmatic Institute. Previously, he headed monetization data science at Foursquare and has worked at Google, Andreessen Horowitz, JPMorgan, and D.E. Shaw. He’s a regular contributor to the Wall Street Journal, TechCrunch, Wired, Fast Company, Harvard Business Review, MIT Sloan Management Review, Entrepreneur, VentureBeat, TechTarget, and O’Reilly. Michael was a postdoc at Cornell, a PhD at Princeton, and a Marshall Scholar in Cambridge.

Gonzalo Diaz is a data scientist in residence at the Data Incubator, where he teaches the data science fellowship and online courses; he also develops the curriculum to include the latest data science tools and technologies. Previously, he was a web developer at an NGO and a researcher at the IBM TJ Watson Research Center. He has a PhD in computer science from the University of Oxford.

Conference registration

Get the Platinum pass or the Training pass to add this course to your package.

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Comments

Tsun Tsai | EPM

09/25/2019 7:56am EDT

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Gonzalo Diaz | Data Scientist in Residence

09/24/2019 2:12pm EDT

Thanks everyone for attending! It was a pleasure working with you. If you have a moment, please visit the link we shared during the course and fill out the post-course survey! Your feedback would be very much appreciated. Good luck with your big data projects!

Sophia DeMartini | Senior Speaker Manager

09/20/2019 7:25pm EDT

HI Bijal,

The class is currently sold out, and we do not have a waitlist. You can contact our Conference Registration department at confreg@oreilly.com and if a spot opens up (if anyone cancels), they might be able to get you into the class.

Thank you,
Sophia

Bijal Shah | Manager, Risk Analytics & Reporting

09/20/2019 6:56pm EDT

I am trying to register myself for this class. I see its sold out. Is there any possibility to include myself for this session?

Gonzalo Diaz | Data Scientist in Residence

09/16/2019 9:53am EDT

Hello Yahya, laptops are not required for the workshop, although you are more than welcome to bring yours along for note-taking.

I’m looking forward to meeting you there!

Yahya Al-Abri | Developer

09/16/2019 9:48am EDT

Do we have to bring our labtops?