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Mastering Data Science at Enterprise Scale

How to design and implement machine-learning solutions that improve your organization

Jerry Overton

Some people just seem to know how to use data to measurably improve an enterprise—they can explain the vision, make it real, and affect change in their organization.The combination of skills this requires is uncommon, but these people aren’t unicorns. They’re professional data scientists and engineers with the ability to make a real impact on the enterprise. It could be you.

If you want to get better at visualizing data-driven improvements, making them real, and driving change in an organization, join Jerry Overton for this invaluable online course. In just 2 days, he’ll walk you through how to build and execute a data strategy, how to write algorithms, and how to experiment on an enterprise-scale. You’ll learn how to turn agile experimentation, hypothesis testing, and disciplined hacking into machine learning solutions that have significant impact on your organization.

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

By the end of this live, hands-on, online course, you'll understand:

  • Techniques for building and using data pipelines to make sure you always have enough data to do something useful

  • Best practices for running experiments in very short sprints, discovering insights, and making improvements to the enterprise in small, meaningful chunks

And you’ll be able to:

  • Select tools and technologies that can deliver critical business insights
  • Build and execute a successful data strategy
  • Deploy professional-grade data pipelines
  • Create data utilities capable of generating continuous business insight
  • Quickly build machine-learning algorithms

This training course is for you because...

  • You are a data scientist working in the field, and you want to hone your skills in working with industrial machine learning solutions.
  • You are a data engineer, who wants to increase your skill at communicating with analysts and management about the needs and plans for using data to improve your organization.
  • You are a technical manager, who wants to get the most out of your data science investments by applying those insights to new business initiatives

Prerequisites

  • Familiarity with R programming, and basic machine-learning algorithms
  • A laptop with a web browser and an Internet connection
  • A Twitter account is recommended, but not required

Recommended Preparation:

Learning Path: Introduction to Data Science with R

Machine Learning with R Cookbook

Building Data Science Teams

About your instructor

  • Jerry Overton is a Data Scientist and Distinguished Technologist in DXC’s Analytics group. He is the Principal Data Scientist for the strategic alliance between DXC and Microsoft known as Industrial Machine Learning— enterprise-scale applications across six different industries: banking and capital markets, energy and technology, insurance, manufacturing, healthcare, and retail.

    Jerry is the author of the O'Reilly Media eBook Going Pro in Data Science: What It Takes to Succeed as a Professional Data Scientist. He teaches the Safari Live Online training course Mastering Data Science at Enterprise Scale: How to design and implement machine-learning solutions that improve your organization. In his blog, Doing Data Science, Jerry shares his experiences leading open research and transforming organizations using data science.

Schedule

The timeframes are only estimates and may vary according to how the class is progressing

Day One

Part 1: How to get started on the journey to pro

Going pro in data science

  • Introduction
  • The pro’s journey
  • What it means to go pro

Forget about the stack

  • The stack
  • Stack thinking
  • The utility
  • Utility thinking

Think utility instead

  • The big picture for utility thinking
  • The scientific method is key
  • The reality of the scientific method
  • And that’s why you start with a pipeline

Anatomy of the pipeline

  • Ingest, clean and transform, monitor, automate
  • Example

Break and learning activities

Part 2: How to build (and execute) a data strategy

What’s a data strategy?

  • Strategy is more than rules
  • The principles of strategic game play
  • You need a map

Building a data strategy

  • Business questions
  • The business question value chain
  • Identifying systems
  • Picking out strategic targets
  • Taking action
  • Example

    Learning activities, Q&A session

Day Two


Part 3: How to write algorithms like a pro


Why an algorithm is to a data scientist as a microscope is to a biologist

Tools of the pro

  • Foundational concepts in machine learning and data science

Things the pros know

  • What you need to write good algorithms
  • How algorithms are really created
  • Resources for learning the language and basic principles of algorithms
  • Why you need a hypothesis
  • The art of the hack

Break and learning activities


Part 4: How to experiment on an enterprise scale

The dimensions of industrialized machine learning

  • Building pipelines
  • Running experiments
  • Generating insights
  • Predicting hospital lengths of stays (a case study)

Break and learning activities


Part 5: The top five habits of a professional data scientist or engineer

  • 5: Put aside the technology stack

  • 4: Keep data lying around

  • 3: Have a strategy

  • 2: Hack

  • 1: Experiment

Learning activities, Q&A session