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

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