English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 55 Lessons (6h 16m) | 277 GB

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

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

## Table of Contents

**Introduction**

1 Product Analytics for Data-Driven Decisions Introduction

**Part 1 Theory Building Techniques in Product Analytics**

2 Theory Building Techniques in Product Analytics

**Lesson 1 Explore the Data-Generating Process**

3 Learning objectives

4 The Data-Generating Process

5 The Characteristics of a Social System

6 Types of Inference

7 Pitfalls for Analysis

8 Actionable Insights

**Lesson 2 Theory Building**

9 Learning objectives

10 Theory Creation Process

11 Elements of Theory Building

12 Conceptualization and Measurement

13 Example Theory Building for Web Products

**Lesson 3 Behavior Change**

14 Learning objectives

15 Understanding Behavior

16 Psychological Theories of Behavior Change

17 Neurological Theories of Behavior Change

18 Behavior Change for Web Products

**Part 2 Testing Theories in Product Analytics FeatureMetric Development**

19 Testing Theories in Product Analytics FeatureMetric Development

**Lesson 4 Learn Basic Statistical Techniques and Common Pitfalls**

20 Learning objectives

21 Distributions

22 Mean, Mode and Variance

23 Skew, Kurtosis

24 Sampling

25 Other Types of Distributions

26 Calculating Linear Correlations

**Lesson 5 Conceptualization, Operationalization and Metric Development**

27 Learning objectives

28 Period — Age — Cohort

29 Cohort and Period Metrics

30 The Denominator Problem

31 Period Person Years

32 Standardization

33 Re-weighting

**Lesson 6 Metric Development Process**

34 Learning objectives

35 Common Metrics–Part 1

36 Common Metrics–Part 2

37 Funnel Metrics

38 Progression Metrics

39 Survival Metrics

40 Pitfalls of Metric Development

**Lesson 7 Index Creation**

41 Learning objectives

42 Measuring Complex Concepts

43 Basic Survey Design Best Practices

44 User Segmentation vs. Typing

45 Modelling PreferencesChoice

46 Principal Components Analysis (PCA)

47 Example Using PCAFactor Analysis for Indicator Creation

**Lesson 8 AB Testing**

48 Learning objectives

49 Set-Up AB Tests–Part 1

50 Set-Up AB Tests–Part 2

51 Understand Randomization

52 Interpret the Results of AB Tests–Part 1

53 Interpret the Results of AB Tests–Part 2

54 Pitfalls of AB Testing

**Summary**

55 Product Analytics for Data-Driven Decisions Summary

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