This document outlines the agenda and content for Week 2 of a course on predictive analytics. The week will cover an introduction to predictive analytics, types of data including quantitative and qualitative data, and two activities for students to practice categorizing different types of data and creating infographics from sample data. Examples of quantitative data include test scores, weights, and temperatures. Qualitative data examples include colors, smells, and names. Predictive analytics uses past data to anticipate future events, and Netflix is presented as a case study of a company that improved recommendations using these techniques.

1. DSA – 105 Introduction to
Data Science
Week 2 – Predictive Analytics
Ferdin Joe John Joseph, PhD
Faculty of Information Technology
Thai‐Nichi Institute of Technology

2. Week 2
Agenda
• Predictive Analytics – Primer
• Types of Data
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3. Fortune Tellers of ??
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4. Predictive Analytics
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5. Video https://youtu.be/7LHH7Oog2Wg
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7. Data Science inspired in movies
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8. For Movie Industry
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9. Challenges to Decision Makers
• 1 in 3 business leaders frequently make critical decisions without the
information they need
• 1 in 2 don’t have access to the information across their organization
needed to do their jobs
• 19+ Hours spent by knowledge workers each week just searching for
and understanding information
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10. Trend of Predictive Analytics
• A lot of companies want to do predictive analytics, but have yet to
master basic reporting – Deloitte Consulting's Miller
• Predictive analytics is forward looking using past events to anticipate
the future
• A set of Business Intelligence technologies that uncovers relationships
and patterns within large volumes of data that can be used to predict
behavior and events.
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11. Netflix Case Study
• Netflix asked engineers and scientists around the world to solve what
might have seemed like to improve its ability to predict what movies
users would like by a modest 10%.
• From $5 million revenue in 1999, Netflix reached $3.2 billion in 2011
• This was done by using recommender systems
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13. Types of Data
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14. Quantitative Data
Key characteristics of quantitative data:
• It can be quantified and verified.
• Data can be counted.
• Data type: number and statistics.
• It answers questions such as “how many, “how much” and “how often”.
Examples of quantitative data:
• Scores on tests and exams e.g. 85, 67, 90 and etc.
• The weight of a person or a subject.
• The number of hours of study.
• Your shoe size.
• The square feet of an apartment.
• The temperature in a room.
• The volume of a gas and etc.
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15. Types of quantitative data
• Discrete data – a count that involves integers. Only a limited number
of values is possible. The discrete values cannot be subdivided into
parts. For example, the number of children in a school is discrete
data. You can count whole individuals. You can’t count 1.5 kids.
• Continuous data – information that could be meaningfully divided
into finer levels. It can be measured on a scale or continuum and can
have almost any numeric value. For example, you can measure your
height at very precise scales — meters, centimeters, millimeters and
etc.
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16. Qualitative Data
Key characteristics of qualitative data:
• It cannot be quantified and verified.
• Data cannot be counted.
• Data type: words, objects, pictures, observations, and symbols.
• It answers questions such as “how this has happened” or and “why this has happened”.
Examples of qualitative data:
• Your socioeconomic status
• Colors e.g. the color of the sea
• The Smell e.g. aromatic, buttery, camphoric and etc.
• Your favorite holiday destination such as Hawaii, New Zealand and etc.
• Names as John, Patricia,…..
• Sounds like bang and blare.
• Ethnicity such as American Indian, Asian, etc.
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20. Activity 1
Categorize Qualitative and quantitative data. Work in groups. Take data
online and show 5 examples each for both types of data.
Start using google!
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21. Activity 2
• Download file in https://bit.ly/2BhLiQK
• Using excel graphs, provide infographics on the data
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22. Next Week…
• Steps Involved in Data Science
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