Jan 23, 20: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

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Convincing  Source

[ AnalyticsWeek BYTES]

>> May 29, 2017 Health and Biotech analytics news roundup by pstein

>> Part 8 of 9, Big Data/Data Lake Platforms: Removing Silos & Operationalizing Your Data by analyticsweekpick

>> The Modern Data Warehouse – Enterprise Data Curation for the Artificial Intelligence Future by analyticsweek

Wanna write? Click Here

[ FEATURED COURSE]

Master Statistics with R

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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]

The Signal and the Noise: Why So Many Predictions Fail–but Some Don’t

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People love statistics. Statistics, however, do not always love them back. The Signal and the Noise, Nate Silver’s brilliant and elegant tour of the modern science-slash-art of forecasting, shows what happens when Big Da… more

[ 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:Provide examples of machine-to-machine communications?
A: Telemedicine
– Heart patients wear specialized monitor which gather information regarding heart state
– The collected data is sent to an electronic implanted device which sends back electric shocks to the patient for correcting incorrect rhythms

Product restocking
– Vending machines are capable of messaging the distributor whenever an item is running out of stock

Source

[ VIDEO OF THE WEEK]

#FutureOfData Podcast: Conversation With Sean Naismith, Enova Decisions

 #FutureOfData Podcast: Conversation With Sean Naismith, Enova Decisions

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

Big Data is not the new oil. – Jer Thorp

[ PODCAST OF THE WEEK]

Harsh Tiwari talks about fabric of data driven leader in Financial Sector #FutureOfData #Podcast

 Harsh Tiwari talks about fabric of data driven leader in Financial Sector #FutureOfData #Podcast

Subscribe 

iTunes  GooglePlay

[ FACT OF THE WEEK]

We are seeing a massive growth in video and photo data, where every minute up to 300 hours of video are uploaded to YouTube alone.

Sourced from: Analytics.CLUB #WEB Newsletter

Jan 16, 20: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

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Data shortage  Source

[ AnalyticsWeek BYTES]

>> Visualizing taxi trips between NYC neighborhoods with Spark and Microsoft R Server by analyticsweek

>> The Wonders of Effectual Metadata Management: Automation by jelaniharper

>> Unraveling the Mystery of Big Data by v1shal

Wanna write? Click Here

[ FEATURED COURSE]

Artificial Intelligence

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This course includes interactive demonstrations which are intended to stimulate interest and to help students gain intuition about how artificial intelligence methods work under a variety of circumstances…. more

[ FEATURED READ]

Hypothesis Testing: A Visual Introduction To Statistical Significance

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Statistical significance is a way of determining if an outcome occurred by random chance, or did something cause that outcome to be different than the expected baseline. Statistical significance calculations find their … 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:Explain selection bias (with regard to a dataset, not variable selection). Why is it important? How can data management procedures such as missing data handling make it worse?
A: * Selection of individuals, groups or data for analysis in such a way that proper randomization is not achieved
Types:
– Sampling bias: systematic error due to a non-random sample of a population causing some members to be less likely to be included than others
– Time interval: a trial may terminated early at an extreme value (ethical reasons), but the extreme value is likely to be reached by the variable with the largest variance, even if all the variables have similar means
– Data: “cherry picking”, when specific subsets of the data are chosen to support a conclusion (citing examples of plane crashes as evidence of airline flight being unsafe, while the far more common example of flights that complete safely)
– Studies: performing experiments and reporting only the most favorable results
– Can lead to unaccurate or even erroneous conclusions
– Statistical methods can generally not overcome it

Why data handling make it worse?
– Example: individuals who know or suspect that they are HIV positive are less likely to participate in HIV surveys
– Missing data handling will increase this effect as it’s based on most HIV negative
-Prevalence estimates will be unaccurate

Source

[ VIDEO OF THE WEEK]

@CRGutowski from @GE_Digital on Using #Analytics to #Transform Sales #FutureOfData #Podcast

 @CRGutowski from @GE_Digital on Using #Analytics to #Transform Sales #FutureOfData #Podcast

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

Hiding within those mounds of data is knowledge that could change the life of a patient, or change the world. – Atul Butte, Stanford

[ PODCAST OF THE WEEK]

#BigData @AnalyticsWeek #FutureOfData #Podcast with @ScottZoldi, @FICO

 #BigData @AnalyticsWeek #FutureOfData #Podcast with @ScottZoldi, @FICO

Subscribe 

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

Jan 09, 20: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

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Accuracy check  Source

[ AnalyticsWeek BYTES]

>> AI Now 2019 report slams government use of facial recognition, biased AI by administrator

>> Talent Analytics: Old Wine In New Bottles? by analyticsweekpick

>> 12 Drivers of BigData Analytics by v1shal

Wanna write? Click Here

[ FEATURED COURSE]

Tackle Real Data Challenges

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Learn scalable data management, evaluate big data technologies, and design effective visualizations…. more

[ FEATURED READ]

Hypothesis Testing: A Visual Introduction To Statistical Significance

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Statistical significance is a way of determining if an outcome occurred by random chance, or did something cause that outcome to be different than the expected baseline. Statistical significance calculations find their … 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:You are compiling a report for user content uploaded every month and notice a spike in uploads in October. In particular, a spike in picture uploads. What might you think is the cause of this, and how would you test it?
A: * Halloween pictures?
* Look at uploads in countries that don’t observe Halloween as a sort of counter-factual analysis
* Compare uploads mean in October and uploads means with September: hypothesis testing

Source

[ VIDEO OF THE WEEK]

Decision-Making: The Last Mile of Analytics and Visualization

 Decision-Making: The Last Mile of Analytics and Visualization

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

The most valuable commodity I know of is information. – Gordon Gekko

[ PODCAST OF THE WEEK]

Future of HR is more Relationship than Data - Scott Kramer @ValpoU #JobsOfFuture #Podcast

 Future of HR is more Relationship than Data – Scott Kramer @ValpoU #JobsOfFuture #Podcast

Subscribe 

iTunes  GooglePlay

[ FACT OF THE WEEK]

Distributed computing (performing computing tasks using a network of computers in the cloud) is very real. Google GOOGL -0.53% uses it every day to involve about 1,000 computers in answering a single search query, which takes no more than 0.2 seconds to complete.

Sourced from: Analytics.CLUB #WEB Newsletter

Jan 02, 20: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

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Complex data  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]

Data Science from Scratch: First Principles with Python

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Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they’re also a good way to dive into the discipline without actually understanding data science. In this book, you’ll learn … more

[ TIPS & TRICKS OF THE WEEK]

Save yourself from zombie apocalypse from unscalable models
One living and breathing zombie in today’s analytical models is the pulsating absence of error bars. Not every model is scalable or holds ground with increasing data. Error bars that is tagged to almost every models should be duly calibrated. As business models rake in more data the error bars keep it sensible and in check. If error bars are not accounted for, we will make our models susceptible to failure leading us to halloween that we never wants to see.

[ DATA SCIENCE Q&A]

Q:Explain what a false positive and a false negative are. Why is it important these from each other? Provide examples when false positives are more important than false negatives, false negatives are more important than false positives and when these two types of errors are equally important
A: * False positive
Improperly reporting the presence of a condition when it’s not in reality. Example: HIV positive test when the patient is actually HIV negative

* False negative
Improperly reporting the absence of a condition when in reality it’s the case. Example: not detecting a disease when the patient has this disease.

When false positives are more important than false negatives:
– In a non-contagious disease, where treatment delay doesn’t have any long-term consequences but the treatment itself is grueling
– HIV test: psychological impact

When false negatives are more important than false positives:
– If early treatment is important for good outcomes
– In quality control: a defective item passes through the cracks!
– Software testing: a test to catch a virus has failed

Source

[ VIDEO OF THE WEEK]

@Schmarzo @DellEMC on Ingredients of healthy #DataScience practice #FutureOfData #Podcast

 @Schmarzo @DellEMC on Ingredients of healthy #DataScience practice #FutureOfData #Podcast

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

Getting information off the Internet is like taking a drink from a firehose. – Mitchell Kapor

[ PODCAST OF THE WEEK]

@AmyGershkoff on building #winning #DataScience #team #FutureOfData #Podcast

 @AmyGershkoff on building #winning #DataScience #team #FutureOfData #Podcast

Subscribe 

iTunes  GooglePlay

[ FACT OF THE WEEK]

40% projected growth in global data generated per year vs. 5% growth in global IT spending.

Sourced from: Analytics.CLUB #WEB Newsletter

Dec 26, 19: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

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Statistics  Source

[ AnalyticsWeek BYTES]

>> Talend Connect Europe 2018: Liberate your Data. Become a Data Hero by analyticsweekpick

>> Part 4 of 9, In-Memory Database/Grid Platforms: Removing Silos & Operationalizing Your Data by analyticsweekpick

>> Can Big Data Help You Plan Your Commute? by analyticsweekpick

Wanna write? Click Here

[ FEATURED COURSE]

CPSC 540 Machine Learning

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Machine learning (ML) is one of the fastest growing areas of science. It is largely responsible for the rise of giant data companies such as Google, and it has been central to the development of lucrative products, such … more

[ FEATURED READ]

The Black Swan: The Impact of the Highly Improbable

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A black swan is an event, positive or negative, that is deemed improbable yet causes massive consequences. In this groundbreaking and prophetic book, Taleb shows in a playful way that Black Swan events explain almost eve… more

[ TIPS & TRICKS OF THE WEEK]

Save yourself from zombie apocalypse from unscalable models
One living and breathing zombie in today’s analytical models is the pulsating absence of error bars. Not every model is scalable or holds ground with increasing data. Error bars that is tagged to almost every models should be duly calibrated. As business models rake in more data the error bars keep it sensible and in check. If error bars are not accounted for, we will make our models susceptible to failure leading us to halloween that we never wants to see.

[ DATA SCIENCE Q&A]

Q:Is it better to spend 5 days developing a 90% accurate solution, or 10 days for 100% accuracy? Depends on the context?
A: * “premature optimization is the root of all evils”
* At the beginning: quick-and-dirty model is better
* Optimization later
Other answer:
– Depends on the context
– Is error acceptable? Fraud detection, quality assurance

Source

[ VIDEO OF THE WEEK]

The History and Use of R

 The History and Use of R

Subscribe to  Youtube

[ 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 Podcast: Peter Morgan, CEO, Deep Learning Partnership

 #FutureOfData Podcast: Peter Morgan, CEO, Deep Learning Partnership

Subscribe 

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[ FACT OF THE WEEK]

Brands and organizations on Facebook receive 34,722 Likes every minute of the day.

Sourced from: Analytics.CLUB #WEB Newsletter

Dec 19, 19: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

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Correlation-Causation  Source

[ AnalyticsWeek BYTES]

>> Why I stopped practicing law? Because data is king. by analyticsweekpick

>> Charts Vs. Tables – Choosing the Right Visualization by analyticsweek

>> Where Big Data Projects Fail by analyticsweekpick

Wanna write? Click Here

[ FEATURED COURSE]

Lean Analytics Workshop – Alistair Croll and Ben Yoskovitz

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Use data to build a better startup faster in partnership with Geckoboard… more

[ FEATURED READ]

Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython

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Python for Data Analysis is concerned with the nuts and bolts of manipulating, processing, cleaning, and crunching data in Python. It is also a practical, modern introduction to scientific computing in Python, tailored f… more

[ TIPS & TRICKS OF THE WEEK]

Grow at the speed of collaboration
A research by Cornerstone On Demand pointed out the need for better collaboration within workforce, and data analytics domain is no different. A rapidly changing and growing industry like data analytics is very difficult to catchup by isolated workforce. A good collaborative work-environment facilitate better flow of ideas, improved team dynamics, rapid learning, and increasing ability to cut through the noise. So, embrace collaborative team dynamics.

[ DATA SCIENCE Q&A]

Q:Is mean imputation of missing data acceptable practice? Why or why not?
A: * Bad practice in general
* If just estimating means: mean imputation preserves the mean of the observed data
* Leads to an underestimate of the standard deviation
* Distorts relationships between variables by “pulling” estimates of the correlation toward zero

Source

[ VIDEO OF THE WEEK]

Data-As-A-Service (#DAAS) to enable compliance reporting

 Data-As-A-Service (#DAAS) to enable compliance reporting

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

Data is the new science. Big Data holds the answers. – Pat Gelsinger

[ PODCAST OF THE WEEK]

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

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

Subscribe 

iTunes  GooglePlay

[ FACT OF THE WEEK]

Every second we create new data. For example, we perform 40,000 search queries every second (on Google alone), which makes it 3.5 searches per day and 1.2 trillion searches per year.In Aug 2015, over 1 billion people used Facebook FB +0.54% in a single day.

Sourced from: Analytics.CLUB #WEB Newsletter

Dec 12, 19: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

image
Conditional Risk  Source

[ AnalyticsWeek BYTES]

>> Customer centric fix to save Indian Maharaja (Air India) from financial mess by v1shal

>> Best Practices For Building Talent In Analytics by analyticsweekpick

>> Logi Tutorial: How to Set up Spark on Amazon EMR for Use with Logi Analytics by analyticsweek

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[ FEATURED COURSE]

Lean Analytics Workshop – Alistair Croll and Ben Yoskovitz

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Use data to build a better startup faster in partnership with Geckoboard… more

[ FEATURED READ]

The Misbehavior of Markets: A Fractal View of Financial Turbulence

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Mathematical superstar and inventor of fractal geometry, Benoit Mandelbrot, has spent the past forty years studying the underlying mathematics of space and natural patterns. What many of his followers don’t realize is th… more

[ TIPS & TRICKS OF THE WEEK]

Analytics Strategy that is Startup Compliant
With right tools, capturing data is easy but not being able to handle data could lead to chaos. One of the most reliable startup strategy for adopting data analytics is TUM or The Ultimate Metric. This is the metric that matters the most to your startup. Some advantages of TUM: It answers the most important business question, it cleans up your goals, it inspires innovation and helps you understand the entire quantified business.

[ DATA SCIENCE Q&A]

Q:How do you take millions of users with 100’s transactions each, amongst 10k’s of products and group the users together in meaningful segments?
A: 1. Some exploratory data analysis (get a first insight)

* Transactions by date
* Count of customers Vs number of items bought
* Total items Vs total basket per customer
* Total items Vs total basket per area

2.Create new features (per customer):

Counts:

* Total baskets (unique days)
* Total items
* Total spent
* Unique product id

Distributions:

* Items per basket
* Spent per basket
* Product id per basket
* Duration between visits
* Product preferences: proportion of items per product cat per basket

3. Too many features, dimension-reduction? PCA?

4. Clustering:

* PCA

5. Interpreting model fit
* View the clustering by principal component axis pairs PC1 Vs PC2, PC2 Vs PC1.
* Interpret each principal component regarding the linear combination it’s obtained from; example: PC1=spendy axis (proportion of baskets containing spendy items, raw counts of items and visits)

Source

[ VIDEO OF THE WEEK]

Andrea Gallego(@risenthink) / @BCG on Managing Analytics Practice #FutureOfData #Podcast

 Andrea Gallego(@risenthink) / @BCG on Managing Analytics Practice #FutureOfData #Podcast

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

The most valuable commodity I know of is information. – Gordon Gekko

[ PODCAST OF THE WEEK]

Solving #FutureOfWork with #Detonate mindset (by @steven_goldbach & @geofftuff) #JobsOfFuture #Podcast

 Solving #FutureOfWork with #Detonate mindset (by @steven_goldbach & @geofftuff) #JobsOfFuture #Podcast

Subscribe 

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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

Dec 05, 19: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

image
Complex data  Source

[ AnalyticsWeek BYTES]

>> A Gentle Introduction to Linear Regression With Maximum Likelihood Estimation by administrator

>> Jun 28, 18: #AnalyticsClub #Newsletter (Events, Tips, News & more..) by admin

>> Human-Centric Artificial Intelligence: What and Why? by analyticsweekpick

Wanna write? Click Here

[ FEATURED COURSE]

R, ggplot, and Simple Linear Regression

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Begin to use R and ggplot while learning the basics of linear regression… more

[ FEATURED READ]

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

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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]

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:What is the life cycle of a data science project ?
A: 1. Data acquisition
Acquiring data from both internal and external sources, including social media or web scraping. In a steady state, data extraction and routines should be in place, and new sources, once identified would be acquired following the established processes

2. Data preparation
Also called data wrangling: cleaning the data and shaping it into a suitable form for later analyses. Involves exploratory data analysis and feature extraction.

3. Hypothesis & modelling
Like in data mining but not with samples, with all the data instead. Applying machine learning techniques to all the data. A key sub-step: model selection. This involves preparing a training set for model candidates, and validation and test sets for comparing model performances, selecting the best performing model, gauging model accuracy and preventing overfitting

4. Evaluation & interpretation

Steps 2 to 4 are repeated a number of times as needed; as the understanding of data and business becomes clearer and results from initial models and hypotheses are evaluated, further tweaks are performed. These may sometimes include step5 and be performed in a pre-production.

5. Deployment

6. Operations
Regular maintenance and operations. Includes performance tests to measure model performance, and can alert when performance goes beyond a certain acceptable threshold

7. Optimization
Can be triggered by failing performance, or due to the need to add new data sources and retraining the model or even to deploy new versions of an improved model

Note: with increasing maturity and well-defined project goals, pre-defined performance can help evaluate feasibility of the data science project early enough in the data-science life cycle. This early comparison helps the team refine hypothesis, discard the project if non-viable, change approaches.

Steps 2 to 4 are repeated a number of times as needed; as the understanding of data and business becomes clearer and results from initial models and hypotheses are evaluated, further tweaks are performed. These may sometimes include step5 and be performed in a pre-production.

Deployment

Operations
Regular maintenance and operations. Includes performance tests to measure model performance, and can alert when performance goes beyond a certain acceptable threshold

Optimization
Can be triggered by failing performance, or due to the need to add new data sources and retraining the model or even to deploy new versions of an improved model

Note: with increasing maturity and well-defined project goals, pre-defined performance can help evaluate feasibility of the data science project early enough in the data-science life cycle. This early comparison helps the team refine hypothesis, discard the project if non-viable, change approaches.

Steps 2 to 4 are repeated a number of times as needed; as the understanding of data and business becomes clearer and results from initial models and hypotheses are evaluated, further tweaks are performed. These may sometimes include step5 and be performed in a pre-production.

Deployment

Operations
Regular maintenance and operations. Includes performance tests to measure model performance, and can alert when performance goes beyond a certain acceptable threshold

Optimization
Can be triggered by failing performance, or due to the need to add new data sources and retraining the model or even to deploy new versions of an improved model

Note: with increasing maturity and well-defined project goals, pre-defined performance can help evaluate feasibility of the data science project early enough in the data-science life cycle. This early comparison helps the team refine hypothesis, discard the project if non-viable, change approaches.

Steps 2 to 4 are repeated a number of times as needed; as the understanding of data and business becomes clearer and results from initial models and hypotheses are evaluated, further tweaks are performed. These may sometimes include step5 and be performed in a pre-production.

Deployment

Operations
Regular maintenance and operations. Includes performance tests to measure model performance, and can alert when performance goes beyond a certain acceptable threshold

Optimization
Can be triggered by failing performance, or due to the need to add new data sources and retraining the model or even to deploy new versions of an improved model

Note: with increasing maturity and well-defined project goals, pre-defined performance can help evaluate feasibility of the data science project early enough in the data-science life cycle. This early comparison helps the team refine hypothesis, discard the project if non-viable, change approaches.

Source

[ VIDEO OF THE WEEK]

#FutureOfData Podcast: Peter Morgan, CEO, Deep Learning Partnership

 #FutureOfData Podcast: Peter Morgan, CEO, Deep Learning Partnership

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

We chose it because we deal with huge amounts of data. Besides, it sounds really cool. – Larry Page

[ PODCAST OF THE WEEK]

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

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

Subscribe 

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[ FACT OF THE WEEK]

Every second we create new data. For example, we perform 40,000 search queries every second (on Google alone), which makes it 3.5 searches per day and 1.2 trillion searches per year.In Aug 2015, over 1 billion people used Facebook FB +0.54% in a single day.

Sourced from: Analytics.CLUB #WEB Newsletter

Nov 28, 19: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

image
Weak data  Source

[ AnalyticsWeek BYTES]

>> Google Offers ‘Preemptible’ Virtual Machines by analyticsweekpick

>> Creating Real-Time Anomaly Detection Pipelines with AWS and Talend Data Streams by analyticsweekpick

>> Feb 22, 18: #AnalyticsClub #Newsletter (Events, Tips, News & more..) by admin

Wanna write? Click Here

[ FEATURED COURSE]

Master Statistics with R

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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]

Big Data: A Revolution That Will Transform How We Live, Work, and Think

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“Illuminating and very timely . . . a fascinating — and sometimes alarming — survey of big data’s growing effect on just about everything: business, government, science and medicine, privacy, and even on the way we think… 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: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 (Welch’s 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?)

Source

[ VIDEO OF THE WEEK]

Ashok Srivastava(@aerotrekker @intuit) on Winning the Art of #DataScience #FutureOfData #Podcast

 Ashok Srivastava(@aerotrekker @intuit) on Winning the Art of #DataScience #FutureOfData #Podcast

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

Data is the new science. Big Data holds the answers. – Pat Gelsinger

[ PODCAST OF THE WEEK]

@AmyGershkoff on building #winning #DataScience #team #FutureOfData #Podcast

 @AmyGershkoff on building #winning #DataScience #team #FutureOfData #Podcast

Subscribe 

iTunes  GooglePlay

[ FACT OF THE WEEK]

Brands and organizations on Facebook receive 34,722 Likes every minute of the day.

Sourced from: Analytics.CLUB #WEB Newsletter

Nov 21, 19: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

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Data analyst  Source

[ AnalyticsWeek BYTES]

>> Getting to Love: Customer Word Clouds by bobehayes

>> Automating Invoice Processing with OCR and Deep Learning by administrator

>> Making Sense of BI Software Reviews by analyticsweek

Wanna write? Click Here

[ FEATURED COURSE]

The Analytics Edge

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This is an Archived Course
EdX keeps courses open for enrollment after they end to allow learners to explore content and continue learning. All features and materials may not be available, and course content will not be… more

[ FEATURED READ]

Data Science from Scratch: First Principles with Python

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Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they’re also a good way to dive into the discipline without actually understanding data science. In this book, you’ll learn … more

[ TIPS & TRICKS OF THE WEEK]

Finding a success in your data science ? Find a mentor
Yes, most of us dont feel a need but most of us really could use one. As most of data science professionals work in their own isolations, getting an unbiased perspective is not easy. Many times, it is also not easy to understand how the data science progression is going to be. Getting a network of mentors address these issues easily, it gives data professionals an outside perspective and unbiased ally. It’s extremely important for successful data science professionals to build a mentor network and use it through their success.

[ DATA SCIENCE Q&A]

Q:What are the drawbacks of linear model? Are you familiar with alternatives (Lasso, ridge regression)?
A: * Assumption of linearity of the errors
* Can’t be used for count outcomes, binary outcomes
* Can’t vary model flexibility: overfitting problems
* Alternatives: see question 4 about regularization

Source

[ VIDEO OF THE WEEK]

Ashok Srivastava(@aerotrekker @intuit) on Winning the Art of #DataScience #FutureOfData #Podcast

 Ashok Srivastava(@aerotrekker @intuit) on Winning the Art of #DataScience #FutureOfData #Podcast

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

I’m sure, the highest capacity of storage device, will not enough to record all our stories; because, everytime with you is very valuable da

[ PODCAST OF THE WEEK]

Understanding #FutureOfData in #Health & #Medicine - @thedataguru / @InovaHealth #FutureOfData #Podcast

 Understanding #FutureOfData in #Health & #Medicine – @thedataguru / @InovaHealth #FutureOfData #Podcast

Subscribe 

iTunes  GooglePlay

[ FACT OF THE WEEK]

Decoding the human genome originally took 10 years to process; now it can be achieved in one week.

Sourced from: Analytics.CLUB #WEB Newsletter