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Just a reminder that your final exam will be on Monday, May 6 at 5:30 p.m. in the same room as class.  You will not be permitted to take the exam if you arrive late.

Please make sure that all missing assignments and quizzes are done before the start of the exam.  Grades will be submitted after the exam.

Here is the study guide for the third (final) exam. And here’s the more detailed overview.

Here is the link for the driver download

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.

 

Here’s your reading for the week ahead:

• Analytics Beautiful Game
• Descriptive Predictive
• Watching you at work

Sentiment Viz

StockTwits

Corporate efforts: IBM Tone Analyzer, AWS Comprehend

Here’s your reading for 12.1, 12.2 and sentiment analysis:

• Sentiment analysis
• Unstructured data
• Facebook no clue

For one point added to your final grade, here’s what I’m looking for. Read these following three Q&As and give me 100 words on one of them (your choice).

1. Death and data science: How machine learning can improve end-of-life care
2. A day in the data science life: Salesforce’s Dr. Shrestha Basu Mallick
3. The data science life: Intuit’s Ashok Srivastava on AI, machine learning, and diversity of thought

The 100 words should focus on one of the following:

• What are the challenges with this topic in regards to data science?
• How do you foresee analytics affecting the fields that are in the interviews (end of life care, sales assistance and product development)?
• What was your biggest takeaways from these data scientists?

This will be due on April 15 to me before class via email.

• Hadoop for non-geeks

A note about that first reading: It’s a bit dated and Hadoop has advanced since that article. Much of the focus in the open source community has been on side projects tied to Hadoop. One common theme is that analytics and better user interfaces are being layered onto Hadoop. Most companies would use Hadoop via companies like Cloudera and Hortonworks. These companies package Hadoop and sell services and support. To see what I mean re Hadoop and its other projects see the primary Apache page. For our purposes, we’ll keep Hadoop high level, but in the data science department, internship interviews etc you may want to know about projects like Hive, Cassandra, Pig and Spark.

• Relational databases
• Structure pivot data

Here is the study guide for the second exam. And here’s the more detailed version.

Agenda for the exam will be to:

–Form groups for last group project at the beginning.

–Take test