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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