Presented By O’Reilly and Cloudera
Make Data Work
21–22 May 2018: Training
22–24 May 2018: Tutorials & Conference
London, UK

In-Person Training
Data science for managers

Jean Innes (ASI Data Science), Matthew Ward (ASI Data Science)
Monday, 21 May & Tuesday, 22 May, 9:00 - 17:00
Location: London Suite 2

Participants should plan to attend both days of this 2-day training course. Platinum and Training passes do not include access to tutorials on Tuesday.

Jean Innes, Matthew Ward, Emanuele Haerens, and Alli Paget lead a condensed introduction to key data science and machine learning concepts and techniques, showing you what is (and isn't) possible with these exciting new tools and how they can benefit your organization.

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

  • Explore key data science and machine learning concepts and techniques

Jean Innes, Matthew Ward, Emanuele Haerens, and Alli Paget lead a condensed introduction to key data science and machine learning concepts and techniques, showing you what is (and isn’t) possible with these exciting new tools and how they can benefit your organization. You’ll learn a language and framework to talk to both technical experts and executives in order to better oversee the practical application of data science in your organization.

Outline

Introduction to data science

  • Course objectives: AI for everyone
  • Definitions: Data science, machine learning, big data, data analytics, customer science, etc.
  • Historical context: How data science started, integration with computing, etc.
  • Present-day usage
  • Data science within the corporate context
  • Significance and urgency: Why now, and why you?

Data science in industry today: Practical applications and benchmark leaders

  • A selection of practical applications
  • Strategic approaches: Commissioning versus procurement (build or buy?)
  • Data science maturity models and the enabling preconditions within your organization to
    use data science well (data, skills, tools, etc.)

Data in your organization: What are your raw materials?

  • What data assets do you have, where does it live, who owns those systems, where are the
    gaps, and how could you fill them by (e.g., by buying data or installing sensors)?
  • What skills do you have access to from your part of the business, and what gaps might there be?
  • What tools do you use, where are the gaps, what other options are there?

Project selection: What makes a good data science project?

  • Overview of the four key criteria for successful project selection and how to assess them
  • Group exercise: Create a project longlist (often categorized broadly within marketing,
    operational efficiency, operational effectiveness, and commercial optimization)
  • Scoring longlist against key criteria and plenary discussion

Managing a data science project: How is it is different from other projects?

  • Similarities and differences
  • Project lifecycle and timescales
  • Project delivery methodology
  • Typical staffing profiles
  • Data scientist culture
  • Cost estimates for external suppliers (for data science as a service and for consulting)
  • Performance metrics
  • Operations and maintenance
  • Governance and risk management

Leading or supporting a data transformation

  • What is data transformation (as opposed to gradual reform), and when might it be
    necessary?
  • The ABCs of data transformation
  • Common pitfalls facing successful data transformation initiatives
  • Different operational models for data science within a modern organization
  • Next steps

About your instructors

Photo of Jean Innes

Jean Innes is Director of Transformation and Strategy at ASI. Before joining ASI Jean was Director of Consumer Data at Rightmove, the UK’s top online property search website, where she identified the objectives and then built a team to deliver the company’s first machine learning capability, delivering new commercial tools from terabytes of unstructured data. Jean worked at Amazon as Head of Commercial Relationships in the UK retail business, and also has experience in the public sector at HM Treasury. Jean is advisor to the board of HouseMark, with a focus on data and technology.

Matthew Ward is a Commercial Principal at ASI. He has more than seven years experience in advising energy and utility companies, helping to transform their customer engagement strategies using data solutions. Prior to joining ASI, Matthew was responsible for Client Success at Opower (acquired by Oracle in 2016), leading customer success, implementation engineering and project management teams. He holds a Bachelors in Climatology from McGill University and a Masters of International Energy Policy from the Middlebury Institute.

Conference registration

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

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Comments

Picture of Sophia DeMartini
Sophia DeMartini | SENIOR SPEAKER MANAGER
21/06/2018 19:36 BST

Hi Trui,

You should have been emailed the slides about two weeks ago. If you cannot find them in your email (they would have been coming from confreg@oreilly.com), please let me know. You can reach me at speakers@oreilly.com.

Thank you,
Sophia

Trui Lanckriet |
21/06/2018 18:02 BST

Dear,

i followed this training and i was told that we would receive the slides. untill now i am not able to retreive those slides on the website. as those slide will make it possible for me to get more practical with the training in my day to day work … can you please send them to me/make them available.

thank you

Picture of Sophia DeMartini
Sophia DeMartini | SENIOR SPEAKER MANAGER
7/05/2018 22:09 BST

Hi Daniel,

There are still spaces available for this training, but only a few, so it you’d like to attend, please make sure to sign up soon.

Best,
Sophia

Daniel Altman |
7/05/2018 22:07 BST

Hello Angie, I would like to know if the training is full or I can still assist.
Thanks!

Picture of Angie Ma
Angie Ma | COO
4/04/2018 5:30 BST

Hi Daniele,

The training is interactive, mostly involves group activities. We’ve a fun technical hands-on at the end, depending on time. If so, it’d just require an internet browser as we provide the group access to our platform SherlockML for machine learning and data science tooling. I’d suggest to bring along your laptop. Thanks and look forward to meeting in person then.

Best,
Angie

Daniele Bonacorsi | RESEARCHER
28/03/2018 15:11 BST

Dear Angie,

is there any hands-on technical part foresees in your training? And if so, which tools will you be using?

Or will the material be 100% slides-based?

Thanks!