Oct 29, 20: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

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Productivity  Source

[ FEATURED COURSE]

Introduction to Apache Spark

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Learn the fundamentals and architecture of Apache Spark, the leading cluster-computing framework among professionals…. more

[ FEATURED READ]

The Future of the Professions: How Technology Will Transform the Work of Human Experts

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This book predicts the decline of today’s professions and describes the people and systems that will replace them. In an Internet society, according to Richard Susskind and Daniel Susskind, we will neither need nor want … more

[ TIPS & TRICKS OF THE WEEK]

Winter is coming, warm your Analytics Club
Yes and yes! As we are heading into winter what better way but to talk about our increasing dependence on data analytics to help with our decision making. Data and analytics driven decision making is rapidly sneaking its way into our core corporate DNA and we are not churning practice ground to test those models fast enough. Such snugly looking models have hidden nails which could induce unchartered pain if go unchecked. This is the right time to start thinking about putting Analytics Club[Data Analytics CoE] in your work place to help Lab out the best practices and provide test environment for those models.

[ DATA SCIENCE Q&A]

Q:What is root cause analysis? How to identify a cause vs. a correlation? Give examples
A: Root cause analysis:
– Method of problem solving used for identifying the root causes or faults of a problem
– A factor is considered a root cause if removal of it prevents the final undesirable event from recurring

Identify a cause vs. a correlation:
– Correlation: statistical measure that describes the size and direction of a relationship between two or more variables. A correlation between two variables doesn’t imply that the change in one variable is the cause of the change in the values of the other variable
– Causation: indicates that one event is the result of the occurrence of the other event; there is a causal relationship between the two events
– Differences between the two types of relationships are easy to identify, but establishing a cause and effect is difficult

Example: sleeping with one’s shoes on is strongly correlated with waking up with a headache. Correlation-implies-causation fallacy: therefore, sleeping with one’s shoes causes headache.
More plausible explanation: both are caused by a third factor: going to bed drunk.

Identify a cause Vs a correlation: use of a controlled study
– In medical research, one group may receive a placebo (control) while the other receives a treatment If the two groups have noticeably different outcomes, the different experiences may have caused the different outcomes

Source

[ VIDEO OF THE WEEK]

@AnalyticsWeek #FutureOfData with Robin Thottungal(@rathottungal), Chief Data Scientist at @EPA

 @AnalyticsWeek #FutureOfData with Robin Thottungal(@rathottungal), Chief Data Scientist at @EPA

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[ QUOTE OF THE WEEK]

It’s easy to lie with statistics. It’s hard to tell the truth without statistics. – Andrejs Dunkels

[ PODCAST OF THE WEEK]

#FutureOfData with @CharlieDataMine, @Oracle discussing running analytics in an enterprise

 #FutureOfData with @CharlieDataMine, @Oracle discussing running analytics in an enterprise

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[ FACT OF THE WEEK]

The data volumes are exploding, more data has been created in the past two years than in the entire previous history of the human race.

Sourced from: Analytics.CLUB #WEB Newsletter

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