Data Analytics for Business Professionals (2022)

Course details

What can data analytics do for your business? Take a lesson from companies like Xerox and UPS, with qualitative and quantitative examples along the way. Data analytics can help cut costs, speed up delivery, generate forecasts, and improve outcomes for your business over time. In this introductory course, economist and author John Johnson shows you how to use analytics to make data-driven decisions and gain competitive advantage.

Explore examples of real-life analytics in action, distinguishing between predictive and prescriptive approaches, and learning how to formulate and pose your own questions. Find out how to collect, clean, and aggregate data from different sources across your organization, and identify when data is flawed. John gives you pointers on planning and deploying an analytics strategy that fits the specific needs of your business, covering a variety of simple techniques: averages, sampling, cherry picking, forecasting, correlation, causality, and more.

Concepts

0. Introduction

01 - Making data more useful
02 - What you should know

1. Data Analytics in the Business World

03 - Business leaders and data analytics
04 - Introduction to WearOne
05 - Types of data
06 - Case study one - Performance at Miami locations
07 - Case study one - Explanation
08 - Challenge - Calculate descriptives
09 - Solution - Calculate descriptives

2. Predictive and Prescriptive Analytics

10 - Predictive analytics
11 - Challenge - Make predictions
12 - Solution - Make predictions
13 - Prescriptive analytics

3. Asking the Right Question

14 - Guidelines for formulating questions
15 - Crafting better questions
16 - Case study two - What is the right question
17 - Role of business acumen

4. Unlocking the Data Within

18 - Data collection issues
19 - Case study three - Unclean data
20 - Case study three - Explanation
21 - Data fail - When data is just wrong

5. Understanding Averages

22 - Nature of averages
23 - Case study four - Conversion rates and benchmark
24 - Case study four - Explanation
25 - Context is everything

6. Sampling

26 - Pros and cons
27 - Case study five - Social media survey
28 - Case study five - Explanation
29 - Case study five - Statistical deep dive

7. Cherry-Picking

30 - What is cherry-picking
31 - Case study six - Revenue
32 - Case study six - Explanation

8. Forecasting

33 - Hurricane Matthew
34 - Case study seven - Forecasting customer complaints
35 - Case study seven - Explanation
36 - Issues to consider

9. Correlation versus Causation

37 - Cause and effect
38 - Case study eight - Boston revenue
39 - Case study eight - Explanation
40 - Causal questions

Conclusion

41 - Next steps