Here are some important things for preparing for the 4th week:
Week 4 quiz due (you can click the highlights to get the related content)
- please read the reading materials for Week 4 (one reading)
- please finish the week 4 quiz before the class on 9/13 (Tuesday).
Review sessions and office hour re-determination
- I have received some suggestions for holding review sessions. I will do that in the class before the exam day.
- At the same time, please click the following link to select all of your available times for an office hour. I will make the final decision based on your common selections.
- Link: https://fox.az1.qualtrics.com/jfe/form/SV_9BxknEnNQ8HOS34
- You can also leverage the office hour to address your questions regarding the class content/concepts.
Some explanations for the class so far:
MIS community website and Gradebook
- Holding the class content on a website is not convenient for students who heavily use Canvas, but it is the department’s policy. Sorry for that.
- You can find your grade in the grade book, being put under the “About–>Gradebook” of the website.
- You can click “About–>Gradebook” and then select our course (“Fall 2022 – MIS 0844 -003 – Data Science”) and click “VIEW GRADES” (I plan to demonstrate in the class)
In-class quiz and activities
- I normally decide to set the extra points one week before the class lecturing.
- I have two general principles: 1> I think it is necessary to test whether the concept delivered in the class is understood via class quiz or activity; 2> I think it is time to assign some extra points. Anyway, my principle is to help you guys.
Week 3 key takeaways
- How to Get Data?
- Collect ourselves
- For example, we can collect the library visit data by sitting in front of the library. Then, we can know when is the visiting peak, which can be used for better managing the library.
- Advantage: we know how the data were produced
- Drawback: it is laborious
- Get access to the existing data
- Directly downloading
- Using reporting tool
- Scraping
- Using API
- Benefit: convenient
- Weakness: difficult to know whether the data represents the real phenomenon we care about
- Open data
- Different organizations have different motivations to open-source their data. However, there are some reasons for not sharing data
- Difficult to gain value from sharing data
- Afraid of competition and possible liabilities
- Data breaches
- Laborious to prepare data
- Collect ourselves
- Biases in (Big) Data
- Survivorship bias
- Intentional bias (e.g., fake reviews and ratings)
- Confirmation bias
- Assessing the trustworthiness of data
- What are your hypotheses?
- What are your biases?
- What is the sample (size)?
- What is the data source?
- How good are your (customer) measures?
I will release your grade in the morning of this Saturday (this should be our regular grade releasing time). My principle here is to help students who are urgent and extremely busy for a week to have some grades for a particular quiz or assignment.
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