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IGTC01DA - Data Analytics for Business Professionals (EN)
01 - Introduction
02 - What you should know (1:00)
01 - Making data more useful (1:27)
Ex_Files_Data_Analytics_for_Business_Professionals
Ex_Files_Data_Analytics_for_Business_Professionals
02 - 1. Data Analytics in the Business World
02 - Introduction to WearOne (1:17)
01 - Business leaders and data analytics (2:27)
03 - Types of data (1:51)
04 - Case study one Performance at Miami locations (0:38)
05 - Case study one Explanation (5:05)
06 - Challenge Calculate descriptives (0:24)
07 - Solution Calculate descriptives (4:07)
03 - 2. Predictive and Prescriptive Analytics
01 - Predictive analytics (2:14)
02 - Challenge Make predictions (0:29)
03 - Solution Make predictions (2:22)
04 - Prescriptive analytics (2:03)
04 - 3. Asking the Right Question
02 - Crafting better questions (1:01)
01 - Guidelines for formulating questions (3:31)
03 - Case study two What is the right question (2:36)
04 - Role of business acumen (1:39)
05 - 4. Unlocking the Data Within
01 - Data collection issues (2:22)
02 - Case study three Unclean data (0:38)
03 - Case study three Explanation (2:57)
04 - Data fail When data is just wrong (3:17)
06 - 5. Understanding Averages
02 - Case study four Conversion rates and benchmark (0:55)
01 - Nature of averages (0:31)
03 - Case study four Explanation (2:58)
04 - Context is everything (1:12)
07 - 6. Sampling
02 - Case study five Social media survey (0:40)
01 - Pros and cons (2:33)
03 - Case study five Explanation (0:44)
04 - Case study five Statistical deep dive (3:00)
08 - 7. Cherry-Picking
01 - What is cherry-picking (2:34)
02 - Case study six Revenue (1:02)
03 - Case study six Explanation (2:44)
09 - 8. Forecasting
02 - Case study seven Forecasting customer complaints (1:23)
01 - Hurricane Matthew (2:33)
03 - Case study seven Explanation (2:32)
04 - Issues to consider (1:06)
10 - 9. Correlation versus Causation
02 - Case study eight Boston revenue (0:44)
01 - Cause and effect (1:53)
03 - Case study eight Explanation (2:45)
04 - Causal questions (0:38)
11 - Conclusion
01 - Next steps (0:49)
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04 - Prescriptive analytics
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