[ COVER OF THE WEEK ]
[ NEWS BYTES]
[ FEATURED COURSE]
In this Specialization, you will learn to analyze and visualize data in R and created reproducible data analysis reports, demonstrate a conceptual understanding of the unified nature of statistical inference, perform fre… more
[ FEATURED READ]
Storytelling with Data teaches you the fundamentals of data visualization and how to communicate effectively with data. You’ll discover the power of storytelling and the way to make data a pivotal point in your story. Th… more
[ TIPS & TRICKS OF THE WEEK]
Strong business case could save your project
Like anything in corporate culture, the project is oftentimes about the business, not the technology. With data analysis, the same type of thinking goes. It’s not always about the technicality but about the business implications. Data science project success criteria should include project management success criteria as well. This will ensure smooth adoption, easy buy-ins, room for wins and co-operating stakeholders. So, a good data scientist should also possess some qualities of a good project manager.
[ DATA SCIENCE Q&A]
Q:You have data on the durations of calls to a call center. Generate a plan for how you would code and analyze these data. Explain a plausible scenario for what the distribution of these durations might look like. How could you test, even graphically, whether your expectations are borne out?
A: 1. Exploratory data analysis
* Histogram of durations
* histogram of durations per service type, per day of week, per hours of day (durations can be systematically longer from 10am to 1pm for instance), per employee
2. Distribution: lognormal?
3. Test graphically with QQ plot: sample quantiles of log(durations)log?(durations) Vs normal quantiles
[ VIDEO OF THE WEEK]
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[ QUOTE OF THE WEEK]
The world is one big data problem. – Andrew McAfee
[ PODCAST OF THE WEEK]
[ FACT OF THE WEEK]
In late 2011, IDC Digital Universe published a report indicating that some 1.8 zettabytes of data will be created that year.