Course Review – Big Data – Capstone Project (Ilkay Altintas, Amarnath Gupta)

Here is my review for Big Data Capstone Project course offered on Coursera in Jul 2016. The course represents the final project for the Big Data specialization, it does not have separate rankings, while I passed with 98.2% score.
Technologies/Material: As a final project, the course does not have lectures, but rather brief descriptions of relevant project parts each week. The project is about making suggestions on how to increase revenue of a company promoting a fictional game “Catch the Pink Flamingo”. A lot of simulated game data is made available to the learners. The part assigned each week represents a separate area of big data analytics: data exploration, classification, clustering, and graph analysis. The suggested technologies are: Splunk, KNIME, Apache Spark, and Neo4j, respectively. As usual within the specialization instead of free exploration a “correct” path is given along with substantial help on the way. The assignment each week is peer graded with the ability to submit multiple times and get regraded. Grading asks to compare learners’ numbers with the correct numbers, which means that almost everyone gets correct answers on their second attempt. Unfortunately, many people slack off on their first attempt or simply submit an empty report. At the end of the course a final report with a powerpoint presentation are submitted and also peer graded.
Instructor/lectures: the task instructions are given by Amarnath Gupta and Ilkay Altintas. The course offers a realistic view of a job of a Data Scientist: analyze all available data to increase revenue of a company, improve retention rates, suggest the ways of development, and, most importantly, make presentations to the management. The instructors emphasize each week that the company’s bottom line is of the utmost importance. Even though the specialization is called Big Data, there is no emphasize on especially large volumes of data or on distributed computations, thus we are in the Data Science realm.

Leave a Reply

Your email address will not be published. Required fields are marked *