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

image

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 ]

image
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

Nov 14, 19: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

image
Data Storage  Source

[ AnalyticsWeek BYTES]

>> Three Types Of Context To Make Your Audience Care About Your Data by analyticsweek

>> Surge in real-time big data and IoT analytics is changing corporate thinking by analyticsweekpick

>> 7 Steps to Start Your Predictive Analytics Journey by analyticsweek

Wanna write? Click Here

[ FEATURED COURSE]

Intro to Machine Learning

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Machine Learning is a first-class ticket to the most exciting careers in data analysis today. As data sources proliferate along with the computing power to process them, going straight to the data is one of the most stra… more

[ FEATURED READ]

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

image

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: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]

#FutureOfData with Rob(@telerob) / @ConnellyAgency on running innovation in agency

 #FutureOfData with Rob(@telerob) / @ConnellyAgency on running innovation in agency

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

With data collection, ‘the sooner the better’ is always the best answer. – Marissa Mayer

[ PODCAST 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 

iTunes  GooglePlay

[ 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.

Sourced from: Analytics.CLUB #WEB Newsletter

Nov 07, 19: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

image
Complex data  Source

[ AnalyticsWeek BYTES]

>> Fighting the Amazon Rainforest Fires with Real-Time Data [Guest Post] by analyticsweek

>> Two-Tier Data Storage: A GigaOm Market Landscape Report by analyticsweekpick

>> Cheers to Stream-based Data Integration…and to Never Losing a Byte!! by analyticsweek

Wanna write? Click Here

[ FEATURED COURSE]

Master Statistics with R

image

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]

Thinking, Fast and Slow

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Drawing on decades of research in psychology that resulted in a Nobel Prize in Economic Sciences, Daniel Kahneman takes readers on an exploration of what influences thought example by example, sometimes with unlikely wor… 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:Compare R and Python
A: R
– Focuses on better, user friendly data analysis, statistics and graphical models
– The closer you are to statistics, data science and research, the more you might prefer R
– Statistical models can be written with only a few lines in R
– The same piece of functionality can be written in several ways in R
– Mainly used for standalone computing or analysis on individual servers
– Large number of packages, for anything!

Python
– Used by programmers that want to delve into data science
– The closer you are working in an engineering environment, the more you might prefer Python
– Coding and debugging is easier mainly because of the nice syntax
– Any piece of functionality is always written the same way in Python
– When data analysis needs to be implemented with web apps
– Good tool to implement algorithms for production use

Source

[ VIDEO OF THE WEEK]

@chrisbishop on futurist's lens on #JobsOfFuture #FutureofWork #JobsOfFuture #Podcast

 @chrisbishop on futurist’s lens on #JobsOfFuture #FutureofWork #JobsOfFuture #Podcast

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

Processed data is information. Processed information is knowledge Processed knowledge is Wisdom. – Ankala V. Subbarao

[ PODCAST OF THE WEEK]

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

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

Subscribe 

iTunes  GooglePlay

[ FACT OF THE WEEK]

Facebook users send on average 31.25 million messages and view 2.77 million videos every minute.

Sourced from: Analytics.CLUB #WEB Newsletter

Oct 31, 19: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

image
Conditional Risk  Source

[ AnalyticsWeek BYTES]

>> QlikSense Set Analysis – Creating and Using Variables by analyticsweek

>> Applying Data-Driven Analytics to Sales by jelaniharper

>> The Strategic and Tactical Roles of Customer Surveys by bobehayes

Wanna write? Click Here

[ FEATURED COURSE]

Hadoop Starter Kit

image

Hadoop learning made easy and fun. Learn HDFS, MapReduce and introduction to Pig and Hive with FREE cluster access…. more

[ FEATURED READ]

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

image

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]

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 the difference between supervised learning and unsupervised learning? Give concrete examples
?

A: * Supervised learning: inferring a function from labeled training data
* Supervised learning: predictor measurements associated with a response measurement; we wish to fit a model that relates both for better understanding the relation between them (inference) or with the aim to accurately predicting the response for future observations (prediction)
* Supervised learning: support vector machines, neural networks, linear regression, logistic regression, extreme gradient boosting
* Supervised learning examples: predict the price of a house based on the are, size.; churn prediction; predict the relevance of search engine results.
* Unsupervised learning: inferring a function to describe hidden structure of unlabeled data
* Unsupervised learning: we lack a response variable that can supervise our analysis
* Unsupervised learning: clustering, principal component analysis, singular value decomposition; identify group of customers
* Unsupervised learning examples: find customer segments; image segmentation; classify US senators by their voting.

Source

[ VIDEO OF THE WEEK]

@chrisbishop on futurist's lens on #JobsOfFuture #FutureofWork #JobsOfFuture #Podcast

 @chrisbishop on futurist’s lens on #JobsOfFuture #FutureofWork #JobsOfFuture #Podcast

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

Data that is loved tends to survive. – Kurt Bollacker, Data Scientist, Freebase/Infochimps

[ PODCAST OF THE WEEK]

#BigData @AnalyticsWeek #FutureOfData #Podcast with  John Young, @Epsilonmktg

 #BigData @AnalyticsWeek #FutureOfData #Podcast with John Young, @Epsilonmktg

Subscribe 

iTunes  GooglePlay

[ FACT OF THE WEEK]

By then, our accumulated digital universe of data will grow from 4.4 zettabyets today to around 44 zettabytes, or 44 trillion gigabytes.

Sourced from: Analytics.CLUB #WEB Newsletter

Oct 24, 19: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

image
Data security  Source

[ AnalyticsWeek BYTES]

>> Data APIs: Gateway to Data-Driven Operation and Digital Transformation by analyticsweek

>> Sample Size in Usability Studies: How Well Does the Math Match Reality? by analyticsweek

>> How mobile consumers are using customer service apps [Infographics] by v1shal

Wanna write? Click Here

[ FEATURED COURSE]

Python for Beginners with Examples

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A practical Python course for beginners with examples and exercises…. more

[ FEATURED READ]

On Intelligence

image

Jeff Hawkins, the man who created the PalmPilot, Treo smart phone, and other handheld devices, has reshaped our relationship to computers. Now he stands ready to revolutionize both neuroscience and computing in one strok… 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 is star schema? Lookup tables?
A: The star schema is a traditional database schema with a central (fact) table (the “observations”, with database “keys” for joining with satellite tables, and with several fields encoded as ID’s). Satellite tables map ID’s to physical name or description and can be “joined” to the central fact table using the ID fields; these tables are known as lookup tables, and are particularly useful in real-time applications, as they save a lot of memory. Sometimes star schemas involve multiple layers of summarization (summary tables, from granular to less granular) to retrieve information faster.

Lookup tables:
– Array that replace runtime computations with a simpler array indexing operation

Source

[ VIDEO OF THE WEEK]

Big Data Introduction to D3

 Big Data Introduction to D3

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

It is a capital mistake to theorize before one has data. Insensibly, one begins to twist the facts to suit theories, instead of theories to

[ PODCAST OF THE WEEK]

@JustinBorgman on Running a data science startup, one decision at a time #Futureofdata #Podcast

 @JustinBorgman on Running a data science startup, one decision at a time #Futureofdata #Podcast

Subscribe 

iTunes  GooglePlay

[ FACT OF THE WEEK]

140,000 to 190,000. Too few people with deep analytical skills to fill the demand of Big Data jobs in the U.S. by 2018.

Sourced from: Analytics.CLUB #WEB Newsletter

Oct 17, 19: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

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Accuracy  Source

[ AnalyticsWeek BYTES]

>> Aug 01, 19: #AnalyticsClub #Newsletter (Events, Tips, News & more..) by admin

>> Oct 03, 19: #AnalyticsClub #Newsletter (Events, Tips, News & more..) by admin

>> LogicAI and Kaggle team up on Kaggle Days events in 2019 and beyond! by analyticsweekpick

Wanna write? Click Here

[ FEATURED COURSE]

CS229 – Machine Learning

image

This course provides a broad introduction to machine learning and statistical pattern recognition. … more

[ FEATURED READ]

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

image

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]

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:How to efficiently scrape web data, or collect tons of tweets?
A: * Python example
* Requesting and fetching the webpage into the code: httplib2 module
* Parsing the content and getting the necessary info: BeautifulSoup from bs4 package
* Twitter API: the Python wrapper for performing API requests. It handles all the OAuth and API queries in a single Python interface
* MongoDB as the database
* PyMongo: the Python wrapper for interacting with the MongoDB database
* Cronjobs: a time based scheduler in order to run scripts at specific intervals; allows to bypass the “rate limit exceed” error

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 beats emotions. – Sean Rad, founder of Ad.ly

[ PODCAST OF THE WEEK]

#FutureOfData Podcast: Conversation With Sean Naismith, Enova Decisions

 #FutureOfData Podcast: Conversation With Sean Naismith, Enova Decisions

Subscribe 

iTunes  GooglePlay

[ FACT OF THE WEEK]

By 2020, at least a third of all data will pass through the cloud (a network of servers connected over the Internet).

Sourced from: Analytics.CLUB #WEB Newsletter

Oct 10, 19: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

image
Conditional Risk  Source

[ AnalyticsWeek BYTES]

>> Leveraging Social Media to Showcase Your Expertise [Infographic] by v1shal

>> Connecting the Dots within the Data Science Community by mills_steven

>> Simplifying Data Warehouse Optimization by analyticsweekpick

Wanna write? Click Here

[ FEATURED COURSE]

CS109 Data Science

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Learning from data in order to gain useful predictions and insights. This course introduces methods for five key facets of an investigation: data wrangling, cleaning, and sampling to get a suitable data set; data managem… more

[ FEATURED READ]

Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking

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Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the “data-analytic thinking” necessary for e… 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:Do you know / used data reduction techniques other than PCA? What do you think of step-wise regression? What kind of step-wise techniques are you familiar with?
A: data reduction techniques other than PCA?:
Partial least squares: like PCR (principal component regression) but chooses the principal components in a supervised way. Gives higher weights to variables that are most strongly related to the response

step-wise regression?
– the choice of predictive variables are carried out using a systematic procedure
– Usually, it takes the form of a sequence of F-tests, t-tests, adjusted R-squared, AIC, BIC
– at any given step, the model is fit using unconstrained least squares
– can get stuck in local optima
– Better: Lasso

step-wise techniques:
– Forward-selection: begin with no variables, adding them when they improve a chosen model comparison criterion
– Backward-selection: begin with all the variables, removing them when it improves a chosen model comparison criterion

Better than reduced data:
Example 1: If all the components have a high variance: which components to discard with a guarantee that there will be no significant loss of the information?
Example 2 (classification):
– One has 2 classes; the within class variance is very high as compared to between class variance
– PCA might discard the very information that separates the two classes

Better than a sample:
– When number of variables is high relative to the number of observations

Source

[ VIDEO OF THE WEEK]

Discussing Forecasting with Brett McLaughlin (@akabret), @Akamai

 Discussing Forecasting with Brett McLaughlin (@akabret), @Akamai

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

War is 90% information. – Napoleon Bonaparte

[ PODCAST OF THE WEEK]

@chrisbishop on futurist's lens on #JobsOfFuture #FutureofWork #JobsOfFuture #Podcast

 @chrisbishop on futurist’s lens on #JobsOfFuture #FutureofWork #JobsOfFuture #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

Oct 03, 19: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

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Ethics  Source

[ AnalyticsWeek BYTES]

>> 6 Ways to Start Utilizing Machine Learning with Amazon Web Services and Talend by analyticsweekpick

>> Telangana government to set up India’s first data analytics park by analyticsweekpick

>> Best Practices For Building Talent In Analytics by analyticsweekpick

Wanna write? Click Here

[ FEATURED COURSE]

Statistical Thinking and Data Analysis

image

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]

How to Create a Mind: The Secret of Human Thought Revealed

image

Ray Kurzweil is arguably today’s most influential—and often controversial—futurist. In How to Create a Mind, Kurzweil presents a provocative exploration of the most important project in human-machine civilization—reverse… 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:Compare R and Python
A: R
– Focuses on better, user friendly data analysis, statistics and graphical models
– The closer you are to statistics, data science and research, the more you might prefer R
– Statistical models can be written with only a few lines in R
– The same piece of functionality can be written in several ways in R
– Mainly used for standalone computing or analysis on individual servers
– Large number of packages, for anything!

Python
– Used by programmers that want to delve into data science
– The closer you are working in an engineering environment, the more you might prefer Python
– Coding and debugging is easier mainly because of the nice syntax
– Any piece of functionality is always written the same way in Python
– When data analysis needs to be implemented with web apps
– Good tool to implement algorithms for production use

Source

[ VIDEO OF THE WEEK]

Surviving Internet of Things

 Surviving Internet of Things

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

For every two degrees the temperature goes up, check-ins at ice cream shops go up by 2%. – Andrew Hogue, Foursquare

[ PODCAST OF THE WEEK]

#BigData @AnalyticsWeek #FutureOfData with Jon Gibs(@jonathangibs) @L2_Digital

 #BigData @AnalyticsWeek #FutureOfData with Jon Gibs(@jonathangibs) @L2_Digital

Subscribe 

iTunes  GooglePlay

[ FACT OF THE WEEK]

100 terabytes of data uploaded daily to Facebook.

Sourced from: Analytics.CLUB #WEB Newsletter