### [ COVER OF THE WEEK ]

**Human resource** Source

### [ FEATURED COURSE]

### [ FEATURED READ]

**Data Science from Scratch: First Principles with Python**

### [ 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:How do you assess the statistical significance of an insight?**

A: * is this insight just observed by chance or is it a real insight?

Statistical significance can be accessed using hypothesis testing:

– Stating a null hypothesis which is usually the opposite of what we wish to test (classifiers A and B perform equivalently, Treatment A is equal of treatment B)

– Then, we choose a suitable statistical test and statistics used to reject the null hypothesis

– Also, we choose a critical region for the statistics to lie in that is extreme enough for the null hypothesis to be rejected (p-value)

– We calculate the observed test statistics from the data and check whether it lies in the critical region

Common tests:

– One sample Z test

– Two-sample Z test

– One sample t-test

– paired t-test

– Two sample pooled equal variances t-test

– Two sample unpooled unequal variances t-test and unequal sample sizes (Welchs t-test)

– Chi-squared test for variances

– Chi-squared test for goodness of fit

– Anova (for instance: are the two regression models equals? F-test)

– Regression F-test (i.e: is at least one of the predictor useful in predicting the response?)

### [ VIDEO OF THE WEEK]

@JohnNives on ways to demystify AI for enterprise #FutureOfData #Podcast

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### [ QUOTE OF THE WEEK]

Data are becoming the new raw material of business. Craig Mundie

### [ PODCAST OF THE WEEK]

#BigData #BigOpportunity in Big #HR by @MarcRind #JobsOfFuture #Podcast

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### [ FACT OF THE WEEK]

39 percent of marketers say that their data is collected ‘too infrequently or not real-time enough.’

### Sourced from: Analytics.CLUB #WEB Newsletter

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