MIS 0855: Data Science Fall 2017

Section 004, Instructor: Larry Dignan

Final Exam Reminder

Just a reminder that your final exam will be on Monday, Dec. 18 at 5:30pm in the same room as class.  Please be on time.  Students will not be permitted to enter late. Please make sure that all missing assignments, quizzes and weekly questions are done before the start of the exam.  Grades will be submitted after the exam.

Weekly Question #11: Complete by Dec. 11, 2017

Leave your response to the question below as a comment on this post by the beginning of class on Dec. 11, 2017. It only needs to be three or four sentences.

What was the most important takeaway (from your perspective) from this course? If you had to explain to a future MIS0855 scholar what this course was about, what would you say?

Primer on descriptive, prescriptive, predictive analytics

I found the reading this week–beyond the people analytics one–to be a bit lacking on what we’re talking about on Monday. So I aggregated some items that better define descriptive, prescriptive, and predictive.

The semi wonky textbook definition of the flavors of analytics go like this:

  • Descriptive analytics is a preliminary stage of data processing that creates a summary of historical data to yield useful information and possibly prepare the data for further analysis. … Diagnostic analytics is a deeper look at data to attempt to understand the causes of events and behaviors.
  • Prescriptive analytics is the area of business analytics (BA) dedicated to finding the best course of action for a given situation. Prescriptive analytics is related to both descriptive and predictive analytics.
  • Predictive analytics is the branch of the advanced analytics which is used to make predictions about unknown future events. Predictive analytics uses many techniques from data mining, statistics, modeling, machine learning, and artificial intelligence to analyze current data to make predictions about future.

But this post explains it a bit better.

  • Descriptive Analytics, which use data aggregation and data mining to provide insight into the past and answer: “What has happened?”
  • Predictive Analytics, which use statistical models and forecasts techniques to understand the future and answer: “What could happen?”
  • Prescriptive Analytics, which use optimization and simulation algorithms to advice on possible outcomes and answer: “What should we do?

As does Quora.

And finally, this post from my site has a handy Gartner chart that sums of the continuum. Most companies are in the descriptive and diagnostic phase of analytics and trying to get to the predictive and prescriptive part (think Watson, AI etc) where there will be some technology/model/AI that tells you what will happen and how to act.

 

Reading Quiz #10: Complete by Dec. 4, 2017

Some quick instructions:

  • You must complete the quiz by the start of class on Dec. 4, 2017.
  • When you click on the link, you may see a Google sign in screen. Use your AccessNet ID and password to sign in. It will then take you to the quiz.
    If it says you don’t have access, make sure you’re signed out of your regular Gmail (non-TUMail) account!
  • You can only do the quiz once. If you submit multiple times, I’ll only use the first (oldest) one.
  • This is “open book” – you can use the articles to answer the questions – but do not get help from anyone else.

Ready? Take the quiz by clicking this link.

Weekly Question #10: Complete by December 4, 2017

Leave your response as a comment on this post by the beginning of class on Dec. 4, 2017. Remember, it only needs to be three or four sentences. For these weekly questions, I’m mainly interested in your opinions, not so much particular “facts” from the class!

Here is the question:

Think about a data-driven service that you use regularly (i.e., Blackboard, Amazon.com, Facebook). Imagine you want to store the data for that service in a spreadsheet – what would each row in the spreadsheet represent? What would some of the data columns be?

(For example, Yelp.com stores restaurant reviews. A row would be an individual review, and some columns would be the name of the restaurant, the type of food they serve, the address, the star rating, and the name of the reviewer.)

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Office Hours

Larry Dignan lawrence.dignan@temple.edu Alter Hall 232 267.614.6467 Class time: 5:30-8pm, Mondays Office hours: Monday half hour before class, half hour after class or by appointment. ITA: Nathan Pham. Contact via email at Nathan.Pham@temple.edu