October 2020 – AnalyticsCube

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

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[ COVER OF THE WEEK ]

Productivity Source

[ FEATURED COURSE]

Introduction to Apache Spark

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

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 doesnt 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 ones shoes on is strongly correlated with waking up with a headache. Correlation-implies-causation fallacy: therefore, sleeping with ones 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

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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

iTunes GooglePlay

[ 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

Originally Posted at:

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Oct 22, 20: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[ COVER OF THE WEEK ]

Accuracy check Source

[ TIPS & TRICKS OF THE WEEK]

Data Have Meaning
We live in a Big Data world in which everything is quantified. While the emphasis of Big Data has been focused on distinguishing the three characteristics of data (the infamous three Vs), we need to be cognizant of the fact that data have meaning. That is, the numbers in your data represent something of interest, an outcome that is important to your business. The meaning of those numbers is about the veracity of your data.

[ DATA SCIENCE Q&A]

Q:What is a decision tree?
A: 1. Take the entire data set as input
2. Search for a split that maximizes the ‘separation of the classes. A split is any test that divides the data in two (e.g. if variable2>10)
3. Apply the split to the input data (divide step)
4. Re-apply steps 1 to 2 to the divided data
5. Stop when you meet some stopping criteria
6. (Optional) Clean up the tree when you went too far doing splits (called pruning)

Finding a split: methods vary, from greedy search (e.g. C4.5) to randomly selecting attributes and split points (random forests)

Purity measure: information gain, Gini coefficient, Chi Squared values

Stopping criteria: methods vary from minimum size, particular confidence in prediction, purity criteria threshold

Pruning: reduced error pruning, out of bag error pruning (ensemble methods)

Source

[ VIDEO OF THE WEEK]

Understanding #Customer Buying Journey with #BigData

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[ QUOTE OF THE WEEK]

You can use all the quantitative data you can get, but you still have to distrust it and use your own intelligence and judgment. Alvin Tof

[ PODCAST OF THE WEEK]

Nick Howe (@Area9Nick @Area9Learning) talks about fabric of learning organization to bring #JobsOfFuture #Podcast

iTunes GooglePlay

[ FACT OF THE WEEK]

2.7 Zetabytes of data exist in the digital universe today.

Sourced from: Analytics.CLUB #WEB Newsletter

Source: by

Source

Oct 15, 20: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[ COVER OF THE WEEK ]

statistical anomaly Source

[ FEATURED COURSE]

Statistical Thinking and Data Analysis

This course is an introduction to statistical data analysis. Topics are chosen from applied probability, sampling, estimation, hypothesis testing, linear regression, analysis of variance, categorical data analysis, and n… more

[ FEATURED READ]

The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World

In the world’s top research labs and universities, the race is on to invent the ultimate learning algorithm: one capable of discovering any knowledge from data, and doing anything we want, before we even ask. In The Mast… more

[ TIPS & TRICKS OF THE WEEK]

Fix the Culture, spread awareness to get awareness
Adoption of analytics tools and capabilities has not yet caught up to industry standards. Talent has always been the bottleneck towards achieving the comparative enterprise adoption. One of the primal reason is lack of understanding and knowledge within the stakeholders. To facilitate wider adoption, data analytics leaders, users, and community members needs to step up to create awareness within the organization. An aware organization goes a long way in helping get quick buy-ins and better funding which ultimately leads to faster adoption. So be the voice that you want to hear from leadership.

[ DATA SCIENCE Q&A]

Q:Explain what resampling methods are and why they are useful?
A: * repeatedly drawing samples from a training set and refitting a model of interest on each sample in order to obtain additional information about the fitted model
* example: repeatedly draw different samples from training data, fit a linear regression to each new sample, and then examine the extent to which the resulting fit differ
* most common are: cross-validation and the bootstrap
* cross-validation: random sampling with no replacement
* bootstrap: random sampling with replacement
* cross-validation: evaluating model performance, model selection (select the appropriate level of flexibility)
* bootstrap: mostly used to quantify the uncertainty associated with a given estimator or statistical learning method

Source

[ VIDEO OF THE WEEK]

@RCKashyap @Cylance on State of Security & Technologist Mindset #FutureOfData #Podcast

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[ QUOTE OF THE WEEK]

The data fabric is the next middleware. Todd Papaioannou

[ PODCAST OF THE WEEK]

@JohnTLangton from @Wolters_Kluwer discussed his #AI Lead Startup Journey #FutureOfData #Podcast

iTunes GooglePlay

[ FACT OF THE WEEK]

According to estimates, the volume of business data worldwide, across all companies, doubles every 1.2 years.

Sourced from: Analytics.CLUB #WEB Newsletter

How To Calculate Lifetime Value [Infographic]

Click to enlarge
How To Calculate Lifetime Value â The Infographic

Source: MyCustomer.com

Source

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Oct 08, 20: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[ COVER OF THE WEEK ]

Ethics Source

[ FEATURED COURSE]

Introduction to Apache Spark

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

[ FEATURED READ]

Rise of the Robots: Technology and the Threat of a Jobless Future

What are the jobs of the future? How many will there be? And who will have them? As technology continues to accelerate and machines begin taking care of themselves, fewer people will be necessary. Artificial intelligence… more

[ TIPS & TRICKS OF THE WEEK]

Data aids, not replace judgement
Data is a tool and means to help build a consensus to facilitate human decision-making but not replace it. Analysis converts data into information, information via context leads to insight. Insights lead to decision making which ultimately leads to outcomes that brings value. So, data is just the start, context and intuition plays a role.

[ DATA SCIENCE Q&A]

Q:Examples of NoSQL architecture?
A: * Key-value: in a key-value NoSQL database, all of the data within consists of an indexed key and a value. Cassandra, DynamoDB
* Column-based: designed for storing data tables as sections of columns of data rather than as rows of data. HBase, SAP HANA
* Document Database: map a key to some document that contains structured information. The key is used to retrieve the document. MongoDB, CouchDB
* Graph Database: designed for data whose relations are well-represented as a graph and has elements which are interconnected, with an undetermined number of relations between them. Polyglot Neo4J

Source

[ VIDEO OF THE WEEK]

Understanding How Fitness Tracker Works via @SciThinkers #STEM #STEAM

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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]

Jeff Palmucci @TripAdvisor discusses managing a #MachineLearning #AI Team

iTunes GooglePlay

[ FACT OF THE WEEK]

In 2015, a staggering 1 trillion photos will be taken and billions of them will be shared online. By 2017, nearly 80% of photos will be taken on smart phones.

[ COVER OF THE WEEK ]

[ FEATURED COURSE]

[ FEATURED READ]

[ TIPS & TRICKS OF THE WEEK]

[ DATA SCIENCE Q&A]

[ VIDEO OF THE WEEK]

[ QUOTE OF THE WEEK]

[ PODCAST OF THE WEEK]

[ FACT OF THE WEEK]

Sourced from: Analytics.CLUB #WEB Newsletter

Originally Posted at:

[ COVER OF THE WEEK ]

[ AnalyticsWeek BYTES]

[ FEATURED COURSE]

[ FEATURED READ]

[ TIPS & TRICKS OF THE WEEK]

[ DATA SCIENCE Q&A]

[ VIDEO OF THE WEEK]

[ QUOTE OF THE WEEK]

[ PODCAST OF THE WEEK]

[ FACT OF THE WEEK]

Sourced from: Analytics.CLUB #WEB Newsletter

Source: by

[ COVER OF THE WEEK ]

[ FEATURED COURSE]

[ FEATURED READ]

[ TIPS & TRICKS OF THE WEEK]

[ DATA SCIENCE Q&A]

[ VIDEO OF THE WEEK]

[ QUOTE OF THE WEEK]

[ PODCAST OF THE WEEK]

[ FACT OF THE WEEK]

Sourced from: Analytics.CLUB #WEB Newsletter

Source:

[ COVER OF THE WEEK ]

[ FEATURED COURSE]

[ FEATURED READ]

[ TIPS & TRICKS OF THE WEEK]

[ DATA SCIENCE Q&A]

[ VIDEO OF THE WEEK]

[ QUOTE OF THE WEEK]

[ PODCAST OF THE WEEK]

[ FACT OF THE WEEK]

Sourced from: Analytics.CLUB #WEB Newsletter