Here is the study guide for the third (final) exam.
Yes, data scientist is the hot career of the moment, but when someone asked on Quora what the downsides were the answers were pretty telling. Here’s a look at what data scientists had to say.
Just a reminder that your final exam will be on Monday, May 8 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.
Here are some additional links for the people analytics chat we’ll have in class. Beyond this week’s reading it’s worth checking out how analytics will be used for HR via these links. It’s not required reading, but since all of you may be applying to an algorithm for a gig you might as well know the game.
- LinkedIn: People Analytics Takes Off: Ten Things We’ve Learned
- McKinsey: People analytics reveals three things HR may be getting wrong
- How Walmart uses Tableau for people 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?
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.
Here is the link for the driver download
Leave your response to the question below as a comment on this post by the beginning of class on April 24, 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?
Some quick instructions:
- You must complete the quiz by the start of class on April 24, 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.
Here is the exercise