Data Analytics for Business Professionals

Data Analytics for Business Professionals

English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 1h 17m | 382 MB

What can data analytics do for your business? Take a lesson from companies like Xerox and UPS. Data analytics can help cut costs, speed up delivery, generate forecasts, and make better decisions. In this introductory overview, economist and author John Johnson shows leaders and executives how to use analytics to make data-driven decisions and gain competitive advantage. First, see examples of real-life analytics in action. Then explore the differences between predictive and prescriptive analytics, and find out how to formulate questions—a process that can be almost as revealing as finding the answers. John then shows how to collect, clean, and aggregate data from different sources across your organization, and identify when data is flawed. Then learn how to plan and deploy an analytics strategy for your business, starting with a variety of simple techniques: averages, sampling, cherry picking, forecasting, and correlation and causality. Finally, John closes with some resources and next steps to advance your analytics knowledge.

Topics include:

  • Qualitative vs. quantitative data
  • Data analytics success stories
  • Making predictions
  • Asking the right questions
  • Collecting data
  • Understanding averages
  • Sampling: pros and cons
  • Forecasting
  • Cause and effect
Table of Contents

Introduction
1 Welcome
2 What you should know

Data Analytics in the Business World
3 Business leaders and data analytics
4 Introduction to Wear One
5 Types of data
6 Case study 1_ Performance at Miami locations
7 Case study 1_ Explanation
8 Challenge_ Calculate descriptives
9 Solution_ Calculate descriptives

Predictive and Prescriptive Analytics
10 Predictive analytics
11 Challenge_ Make predictions
12 Solution_ Make predictions
13 Prescriptive analytics

Asking the Right Question
14 Guidelines for formulating questions
15 Crafting better questions
16 Case study 2_ What is the right question
17 Role of business acumen

Unlocking the Data Within
18 Data collection issues
19 Case study 3_ Unclean data
20 Case study 3_ Explanation
21 Data fail_ When data is just wrong

Understanding Averages
22 Nature of averages
23 Case study 4_ Conversion rates and benchmark
24 Case study 4_ Explanation
25 Context is everything

Sampling
26 Pros and cons
27 Case study 5_ Social media survey
28 Case study 5_ Explanation
29 Case study 5_ Statistical deep dive

Cherry Picking
30 What is cherry picking
31 Case study 6_ Revenue
32 Case study 6_ Explanation

Forecasting
33 Hurricane Matthew
34 Case study 7_ Forecasting customer complaints
35 Case study 7_ Explanation
36 Issues to consider

Correlation versus Causation
37 Cause and effect
38 Case study 8_ Boston revenue
39 Case study 8_ Explanation
40 Causal questions

Conclusion
41 Next steps