Course #:TP2805

Managing Data-Driven Projects: Building a Creative Data Science Team Training

Since 2003, the world produces more data every two days than in the previous two thousand years combined.  Credit card transactions, web-clicks, GPS locations and other bits of data can be collected and analyzed using increasingly massive data sets.  The organization that best understands their data will more easily anticipate needs, create new products, overcome challenges and win elections.
This course gives project managers a foundation for leading data-driven projects.  It is an introduction to the major concepts of data-driven teams and data specific project challenges. This is not a data analytics or programming course.  This is a project management course.  The coursework will rely on general skills and requires no prior knowledge of data analytics or statistics.  The focus of the course will be on starting or managing a data-driven team and gaining insights from your data.

Objectives

The goals for the course are very practical:

  • Introduce project managers to big data terms, tools and the team
  • Introduce project managers to a data-science lifecycle
  • Understand challenges that are specific to data-driven projects
  • Apply business values to craft a clear and actionable data strategy

Audience

Project Managers, Business Analysts, Managers, Directors

Prerequisites

Some knowledge of databases is beneficial

Duration

Two days.

Outline of Managing Data-Driven Projects: Building a Creative Data Science Team Training

Chapter 1. Databases

  • Database Tables
  • Relational Databases
  • SQL & CRUD
  • RDBMS
  • Data Warehouses & ETL
  • NoSQL

Chapter 2. Introduction to Big Data

  • Big Data History
  • What is Big Data?
  • Big Data Definition
  • What Big Data Isn’t
  • Big Data Example

Chapter 3. The Data-Science Lifecycle

  • A Typical Data Science Product
  • What are Big Data Projects?
  • Applying the SDLC to Data Driven Products
  • A Data Science Lifecycle (DSLC)

Chapter 4: The Data-Science Team

  • Traditional Project Team Roles
  • The Data Science Team Roles
  • The Knowledge Explorer
  • Analysis Versus Reporting
  • Asking Questions
  • Learning

Chapter 5: Data-Science Team Tools

  • Insight Board
  • Creating an Insight Board

Chapter 6: Statistics

  • Descriptive Statistics
  • Probability
  • Correlation
  • Regression Analysis
  • Samples Versus Populations

Chapter 7: Types of Data

  • Structured Data
  • Semi-Structured Data
  • Unstructured Data
  • Big Garbage

Chapter 8: Big Data Platforms

  • The Data Bottleneck
  • Hadoop
  • MapReduce Basics
  • Hive and Pig
  • Hadoop’s Strengths and Weaknesses

Chapter 9: Privacy and Business Values

  • Privacy Rights
  • Privacy Norms
  • Crafting your Privacy Policy
  • Business Values
  • Ethics
We regularly offer classes in these and other cities. Atlanta, Austin, Baltimore, Calgary, Chicago, Cleveland, Dallas, Denver, Detroit, Houston, Jacksonville, Miami, Montreal, New York City, Orlando, Ottawa, Philadelphia, Phoenix, Pittsburgh, Seattle, Toronto, Vancouver, Washington DC.