Product Analytics for Data-Driven Decisions: Derive Insights from Web Analytics Data

Video description

7+ Hours of Video Instruction

Learn How to Work with Real-World Data to Derive Actionable Business Insights

Overview
Product Analytics for Data-Driven Decisions: Derive Insights from Web Analytics Data will explore core concepts that will help viewers work with their data, identify bias in data sets, differentiate good data from bad data, and ultimately derive insights to help make actionable business decisions. Learners will see real-world examples of successful product analytics and learn how to utilize qualitative and quantitative measures for desirable outcomes.

Instructor Joanne Rodrigues is an accomplished data scientist, enterprise manager, and entrepreneur who applies machine learning/statistical algorithms to business strategy. Through eight unique video lessons, Rodrigues will provide in-depth training in the data generating process, psychological and neurological theories of behavior, implementing statistical tools in survey design and psychometric techniques, and much more.

What You Will Learn

  • Identify and create good metrics and KPIs to drive growth
  • Avoid common pitfalls in understanding your data
  • Move from raw data to inference and strategy
  • Who Should Take This Course?

    • Product, Consumer, or User Data Scientists
    • Product, Marketing, Research or Business Analysts
    • Entrepreneurs or Business Owners

    Course Requirements

    • There is no prior knowledge or requirements for this course.

    About Pearson Video Training
    Pearson publishes expert-led video tutorials covering a wide selection of technology topics designed to teach you the skills you need to succeed. These professional and personal technology videos feature world-leading author instructors published by your trusted technology brands: Addison-Wesley, Cisco Press, Pearson IT Certification, Sams, and Que Topics include: IT Certification, Network Security, Cisco Technology, Programming, Web Development, Mobile Development, and more. Learn more about Pearson Video training at http://www.informit.com/video.

Table of contents

  1. Introduction
    1. Product Analytics for Data-Driven Decisions: Introduction
  2. Part 1: Theory Building Techniques in Product Analytics
    1. Theory Building Techniques in Product Analytics
  3. Lesson 1: Explore the Data-Generating Process
    1. Learning objectives
    2. 1.1 The Data-Generating Process
    3. 1.2 The Characteristics of a Social System
    4. 1.3 Types of Inference
    5. 1.4 Pitfalls for Analysis
    6. 1.5 Actionable Insights
  4. Lesson 2: Theory Building
    1. Learning objectives
    2. 2.1 Theory Creation Process
    3. 2.2 Elements of Theory Building
    4. 2.3 Conceptualization and Measurement
    5. 2.4 Example: Theory Building for Web Products
  5. Lesson 3: Behavior Change
    1. Learning objectives
    2. 3.1 Understanding Behavior
    3. 3.2 Psychological Theories of Behavior Change
    4. 3.3 Neurological Theories of Behavior Change
    5. 3.4 Behavior Change for Web Products
  6. Part 2: Testing Theories in Product Analytics: Feature/Metric Development
    1. Testing Theories in Product Analytics: Feature/Metric Development
  7. Lesson 4: Learn Basic Statistical Techniques and Common Pitfalls
    1. Learning objectives
    2. 4.1 Distributions
    3. 4.2 Mean, Mode and Variance
    4. 4.3 Skew, Kurtosis
    5. 4.4 Sampling
    6. 4.5 Other Types of Distributions
    7. 4.6 Calculating Linear Correlations
  8. Lesson 5: Conceptualization, Operationalization and Metric Development
    1. Learning objectives
    2. 5.1 Period -- Age -- Cohort
    3. 5.2 Cohort and Period Metrics
    4. 5.3 The Denominator Problem
    5. 5.4 Period Person Years
    6. 5.5 Standardization
    7. 5.6 Re-weighting
  9. Lesson 6: Metric Development Process
    1. Learning objectives
    2. 6.1 Common Metrics--Part 1
    3. 6.2 Common Metrics--Part 2
    4. 6.3 Funnel Metrics
    5. 6.4 Progression Metrics
    6. 6.5 Survival Metrics
    7. 6.6 Pitfalls of Metric Development
  10. Lesson 7: Index Creation
    1. Learning objectives
    2. 7.1 Measuring Complex Concepts
    3. 7.2 Basic Survey Design Best Practices
    4. 7.3 User Segmentation vs. Typing
    5. 7.4 Modelling Preferences/Choice
    6. 7.5 Principal Components Analysis (PCA)
    7. 7.6 Example Using PCA/Factor Analysis for Indicator Creation
  11. Lesson 8: A/B Testing
    1. Learning objectives
    2. 8.1 Set-Up A/B Tests--Part 1
    3. 8.2 Set-Up A/B Tests--Part 2
    4. 8.3 Understand Randomization
    5. 8.4 Interpret the Results of A/B Tests--Part 1
    6. 8.5 Interpret the Results of A/B Tests--Part 2
    7. 8.6 Pitfalls of A/B Testing
  12. Summary
    1. Product Analytics for Data-Driven Decisions: Summary

Product information

  • Title: Product Analytics for Data-Driven Decisions: Derive Insights from Web Analytics Data
  • Author(s): Joanne Rodrigues
  • Release date: June 2022
  • Publisher(s): Addison-Wesley Professional
  • ISBN: 0137907745