Nov 15, 18: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Statistics  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ NEWS BYTES]

>>
 Why Big Data and Machine Learning are Essential for Cyber Security – insideBIGDATA Under  Big Data Analytics

>>
 Amazon’s Big Step Into IoT – Seeking Alpha Under  IOT

>>
 Hitachi and Tencent team up on ‘internet of things’ – Nikkei Asian Review Under  Internet Of Things

More NEWS ? Click Here

[ FEATURED COURSE]

Learning from data: Machine learning course

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This is an introductory course in machine learning (ML) that covers the basic theory, algorithms, and applications. ML is a key technology in Big Data, and in many financial, medical, commercial, and scientific applicati… 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]

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 a long-tailed distribution is and provide three examples of relevant phenomena that have long tails. Why are they important in classification and regression problems?
A: * In long tailed distributions, a high frequency population is followed by a low frequency population, which gradually tails off asymptotically
* Rule of thumb: majority of occurrences (more than half, and when Pareto principles applies, 80%) are accounted for by the first 20% items in the distribution
* The least frequently occurring 80% of items are more important as a proportion of the total population
* Zipf’s law, Pareto distribution, power laws

Examples:
1) Natural language
– Given some corpus of natural language – The frequency of any word is inversely proportional to its rank in the frequency table
– The most frequent word will occur twice as often as the second most frequent, three times as often as the third most frequent…
– The” accounts for 7% of all word occurrences (70000 over 1 million)
– ‘of” accounts for 3.5%, followed by ‘and”…
– Only 135 vocabulary items are needed to account for half the English corpus!

2. Allocation of wealth among individuals: the larger portion of the wealth of any society is controlled by a smaller percentage of the people

3. File size distribution of Internet Traffic

Additional: Hard disk error rates, values of oil reserves in a field (a few large fields, many small ones), sizes of sand particles, sizes of meteorites

Importance in classification and regression problems:
– Skewed distribution
– Which metrics to use? Accuracy paradox (classification), F-score, AUC
– Issue when using models that make assumptions on the linearity (linear regression): need to apply a monotone transformation on the data (logarithm, square root, sigmoid function…)
– Issue when sampling: your data becomes even more unbalanced! Using of stratified sampling of random sampling, SMOTE (‘Synthetic Minority Over-sampling Technique”, NV Chawla) or anomaly detection approach

Source

[ VIDEO 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 to  Youtube

[ QUOTE OF THE WEEK]

Everybody gets so much information all day long that they lose their common sense. – Gertrude Stein

[ 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 

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 08, 18: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Data security  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ AnalyticsWeek BYTES]

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

>> Analyzing Big Data: A Customer-Centric Approach by bobehayes

>> Analytics Implementation in 12 Steps: An Exhaustive Guide (Tracking Plan Included!) by analyticsweek

Wanna write? Click Here

[ NEWS BYTES]

>>
 Why Cloud (By Default) Gives You Security You Couldn’t Afford Otherwise – Forbes Under  Cloud Security

>>
  Under  Big Data Security

>>
 Make it so: Red River Mill Employees FCU rebrands as Engage – Credit Union Journal Under  Talent Analytics

More NEWS ? Click Here

[ FEATURED COURSE]

Baseball Data Wrangling with Vagrant, R, and Retrosheet

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Analytics with the Chadwick tools, dplyr, and ggplot…. 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]

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: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]

Surviving Internet of Things

 Surviving Internet of Things

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

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

[ PODCAST OF THE WEEK]

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

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

Subscribe 

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[ FACT OF THE WEEK]

94% of Hadoop users perform analytics on large volumes of data not possible before; 88% analyze data in greater detail; while 82% can now retain more of their data.

Sourced from: Analytics.CLUB #WEB Newsletter

Nov 01, 18: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Trust the data  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ AnalyticsWeek BYTES]

>> Can Police Use Data Science to Prevent Deadly Encounters? by analyticsweekpick

>> SDN and network function virtualization market worth $ 45.13 billion by 2020 by analyticsweekpick

>> Digital Marketing 2.0 – Rise of the predictive analytics by analyticsweekpick

Wanna write? Click Here

[ NEWS BYTES]

>>
 The Rise of Illiberal Artificial Intelligence – National Review Under  Artificial Intelligence

>>
 2018-2023 Prescriptive Analytics Market Overview, Growth, Types, Applications, Market Dynamics, Companies … – Stock Analysis Under  Prescriptive Analytics

>>
 KBI releases Kansas crime statistics for 2017 – WIBW News Now Under  Statistics

More NEWS ? Click Here

[ FEATURED COURSE]

CS229 – Machine Learning

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

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: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]

@AnalyticsWeek: Big Data at Work: Paul Sonderegger

 @AnalyticsWeek: Big Data at Work: Paul Sonderegger

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 

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

Oct 25, 18: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Trust the data  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ AnalyticsWeek BYTES]

>> The convoluted world of data scientist by v1shal

>> Ten Guidelines for Clean Customer Feedback Data by bobehayes

>> Why Cloud-native is more than software just running on someone else’s computer by analyticsweekpick

Wanna write? Click Here

[ NEWS BYTES]

>>
 How the Windows 10 October 2018 update will impact your enterprise IoT deployments – TechRepublic Under  IOT

>>
 Global Advanced Analytics Market research report 2018: Techniques, Region, Feature Analysis, Study Methodology … – IDA Report Under  Social Analytics

>>
 Hybrid cloud data specialist Datrium nabs $60M led by Samsung at a $282M valuation – TechCrunch Under  Hybrid Cloud

More NEWS ? Click Here

[ FEATURED COURSE]

Machine Learning

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6.867 is an introductory course on machine learning which gives an overview of many concepts, techniques, and algorithms in machine learning, beginning with topics such as classification and linear regression and ending … 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]

Keeping Biases Checked during the last mile of decision making
Today a data driven leader, a data scientist or a data driven expert is always put to test by helping his team solve a problem using his skills and expertise. Believe it or not but a part of that decision tree is derived from the intuition that adds a bias in our judgement that makes the suggestions tainted. Most skilled professionals do understand and handle the biases well, but in few cases, we give into tiny traps and could find ourselves trapped in those biases which impairs the judgement. So, it is important that we keep the intuition bias in check when working on a data problem.

[ DATA SCIENCE Q&A]

Q:What does NLP stand for?
A: * Interaction with human (natural) and computers languages
* Involves natural language understanding

Major tasks:
– Machine translation
– Question answering: “what’s the capital of Canada?”
– Sentiment analysis: extract subjective information from a set of documents, identify trends or public opinions in the social media

– Information retrieval

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]

Torture the data, and it will confess to anything. – Ronald Coase

[ PODCAST OF THE WEEK]

@DrewConway on fabric of an IOT Startup #FutureOfData #Podcast

 @DrewConway on fabric of an IOT Startup #FutureOfData #Podcast

Subscribe 

iTunes  GooglePlay

[ 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

Oct 18, 18: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Data shortage  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ AnalyticsWeek BYTES]

>> Apr 19, 18: #AnalyticsClub #Newsletter (Events, Tips, News & more..) by admin

>> Dec 07, 17: #AnalyticsClub #Newsletter (Events, Tips, News & more..) by admin

>> The ultimate customer experience [infographic] by v1shal

Wanna write? Click Here

[ NEWS BYTES]

>>
 Nikkei: Japan probes Facebook data security – Seeking Alpha Under  Data Security

>>
 How Is Artificial Intelligence Changing The Business Landscape? – Forbes Under  Artificial Intelligence

>>
 Global Risk Analytics Market report provides the data on the past progress, ongoing market scenarios and future … – The Business Investor Under  Risk Analytics

More NEWS ? 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]

Superintelligence: Paths, Dangers, Strategies

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The human brain has some capabilities that the brains of other animals lack. It is to these distinctive capabilities that our species owes its dominant position. Other animals have stronger muscles or sharper claws, but … 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:How to clean data?
A: 1. First: detect anomalies and contradictions
Common issues:
* Tidy data: (Hadley Wickam paper)
column names are values, not names, e.g. 26-45…
multiple variables are stored in one column, e.g. m1534 (male of 15-34 years’ old age)
variables are stored in both rows and columns, e.g. tmax, tmin in the same column
multiple types of observational units are stored in the same table. e.g, song dataset and rank dataset in the same table
*a single observational unit is stored in multiple tables (can be combined)
* Data-Type constraints: values in a particular column must be of a particular type: integer, numeric, factor, boolean
* Range constraints: number or dates fall within a certain range. They have minimum/maximum permissible values
* Mandatory constraints: certain columns can’t be empty
* Unique constraints: a field must be unique across a dataset: a same person must have a unique SS number
* Set-membership constraints: the values for a columns must come from a set of discrete values or codes: a gender must be female, male
* Regular expression patterns: for example, phone number may be required to have the pattern: (999)999-9999
* Misspellings
* Missing values
* Outliers
* Cross-field validation: certain conditions that utilize multiple fields must hold. For instance, in laboratory medicine: the sum of the different white blood cell must equal to zero (they are all percentages). In hospital database, a patient’s date or discharge can’t be earlier than the admission date
2. Clean the data using:
* Regular expressions: misspellings, regular expression patterns
* KNN-impute and other missing values imputing methods
* Coercing: data-type constraints
* Melting: tidy data issues
* Date/time parsing
* Removing observations

Source

[ VIDEO 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 to  Youtube

[ QUOTE OF THE WEEK]

If you can’t explain it simply, you don’t understand it well enough. – Albert Einstein

[ 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 

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[ FACT OF THE WEEK]

Facebook stores, accesses, and analyzes 30+ Petabytes of user generated data.

Sourced from: Analytics.CLUB #WEB Newsletter

Oct 11, 18: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Conditional Risk  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ AnalyticsWeek BYTES]

>> Nick Howe (@Area9Nick) talks about fabric of learning organization to bring #JobsOfFuture #podcast by v1shal

>> How do we cut through the jumble of Business Analytics? -Janet Amos Pribanic by analyticsweek

>> Why Your Company Should Use Data Science to Make Better Decisions by analyticsweekpick

Wanna write? Click Here

[ NEWS BYTES]

>>
 Deliver desktop and app virtualization for mobile devices – TechTarget Under  Virtualization

>>
 Continental Protects Vehicles From Cyber Attacks – Modern Tire Dealer Under  cyber security

>>
 Cyber security unit has no strategic plan, C&AG finds – Irish Times Under  cyber security

More NEWS ? Click Here

[ FEATURED COURSE]

Statistical Thinking and Data Analysis

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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 Industries of the Future

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The New York Times bestseller, from leading innovation expert Alec Ross, a “fascinating vision” (Forbes) of what’s next for the world and how to navigate the changes the future will bring…. 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:Give examples of bad and good visualizations?
A: Bad visualization:
– Pie charts: difficult to make comparisons between items when area is used, especially when there are lots of items
– Color choice for classes: abundant use of red, orange and blue. Readers can think that the colors could mean good (blue) versus bad (orange and red) whereas these are just associated with a specific segment
– 3D charts: can distort perception and therefore skew data
– Using a solid line in a line chart: dashed and dotted lines can be distracting

Good visualization:
– Heat map with a single color: some colors stand out more than others, giving more weight to that data. A single color with varying shades show the intensity better
– Adding a trend line (regression line) to a scatter plot help the reader highlighting trends

Source

[ VIDEO OF THE WEEK]

Using Topological Data Analysis on your BigData

 Using Topological Data Analysis on your BigData

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

Information is the oil of the 21st century, and analytics is the combustion engine. – Peter Sondergaard

[ PODCAST OF THE WEEK]

@AlexWG on Unwrapping Intelligence in #ArtificialIntelligence #FutureOfData #Podcast

 @AlexWG on Unwrapping Intelligence in #ArtificialIntelligence #FutureOfData #Podcast

Subscribe 

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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

Oct 04, 18: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Trust the data  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ AnalyticsWeek BYTES]

>> Can Analytics Improve your Game? by bobehayes

>> @DrewConway on creating socially responsible data science practice #FutureOfData #Podcast by v1shal

>> Avoiding a Data Science Hype Bubble by analyticsweek

Wanna write? Click Here

[ NEWS BYTES]

>>
 Why It’s Time For Retail To Stop Experimenting With IoT, And Start Implementing It – PYMNTS.com Under  Internet Of Things

>>
 Panzura Barreling Toward IPO In $68B Cloud Data Management Market – Forbes Under  Cloud

>>
 As Hadoop landscape evolves, Hortonworks CEO plots future in hybrid cloud and IoT – SiliconANGLE News (blog) Under  Hadoop

More NEWS ? 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]

Storytelling with Data: A Data Visualization Guide for Business Professionals

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Storytelling with Data teaches you the fundamentals of data visualization and how to communicate effectively with data. You’ll discover the power of storytelling and the way to make data a pivotal point in your story. Th… 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 local optimum is and why it is important in a specific context,
such as K-means clustering. What are specific ways of determining if you have a local optimum problem? What can be done to avoid local optima?

A: * A solution that is optimal in within a neighboring set of candidate solutions
* In contrast with global optimum: the optimal solution among all others

* K-means clustering context:
It’s proven that the objective cost function will always decrease until a local optimum is reached.
Results will depend on the initial random cluster assignment

* Determining if you have a local optimum problem:
Tendency of premature convergence
Different initialization induces different optima

* Avoid local optima in a K-means context: repeat K-means and take the solution that has the lowest cost

Source

[ VIDEO OF THE WEEK]

#BigData @AnalyticsWeek #FutureOfData #Podcast with Juan Gorricho, @disney

 #BigData @AnalyticsWeek #FutureOfData #Podcast with Juan Gorricho, @disney

Subscribe to  Youtube

[ 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]

#DataScience Approach to Reducing #Employee #Attrition

 #DataScience Approach to Reducing #Employee #Attrition

Subscribe 

iTunes  GooglePlay

[ FACT OF THE WEEK]

YouTube users upload 48 hours of new video every minute of the day.

Sourced from: Analytics.CLUB #WEB Newsletter

Sep 27, 18: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Data interpretation  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ AnalyticsWeek BYTES]

>> The Blueprint for Becoming Data Driven: Data Quality by jelaniharper

>> The Role of Bias In Big Data: A Slippery Slope by analyticsweekpick

>> How to Win Business using Marketing Data [infographics] by v1shal

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]

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

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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]

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:Explain likely differences between administrative datasets and datasets gathered from experimental studies. What are likely problems encountered with administrative data? How do experimental methods help alleviate these problems? What problem do they bring?
A: Advantages:
– Cost
– Large coverage of population
– Captures individuals who may not respond to surveys
– Regularly updated, allow consistent time-series to be built-up

Disadvantages:
– Restricted to data collected for administrative purposes (limited to administrative definitions. For instance: incomes of a married couple, not individuals, which can be more useful)
– Lack of researcher control over content
– Missing or erroneous entries
– Quality issues (addresses may not be updated or a postal code is provided only)
– Data privacy issues
– Underdeveloped theories and methods (sampling methods…)

Source

[ VIDEO OF THE WEEK]

Making sense of unstructured data by turning strings into things

 Making sense of unstructured data by turning strings into things

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

The data fabric is the next middleware. – Todd Papaioannou

[ PODCAST OF THE WEEK]

@DrewConway on creating socially responsible data science practice #FutureOfData #Podcast

 @DrewConway on creating socially responsible data science practice #FutureOfData #Podcast

Subscribe 

iTunes  GooglePlay

[ FACT OF THE WEEK]

Poor data can cost businesses 20%–35% of their operating revenue.

Sourced from: Analytics.CLUB #WEB Newsletter

Sep 20, 18: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Data security  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ AnalyticsWeek BYTES]

>> Leveraging Virtualization to Streamline Data Management by analyticsweekpick

>> How to Successfully Incorporate Analytics Into Your Growth Marketing Process by analyticsweek

>> Benefits of IoT for Hospitals and Healthcare by analyticsweek

Wanna write? Click Here

[ NEWS BYTES]

>>
 DC BLOX Plans $785M Data Center Investment in AL – Commercial Property Executive Under  Data Center

>>
 How Legacy Systems Stifle Marketing Analytics – eMarketer Under  Marketing Analytics

>>
 How artificial intelligence will transform sales – Raconteur Under  Sentiment Analysis

More NEWS ? Click Here

[ FEATURED COURSE]

Probability & Statistics

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This course introduces students to the basic concepts and logic of statistical reasoning and gives the students introductory-level practical ability to choose, generate, and properly interpret appropriate descriptive and… more

[ FEATURED READ]

On Intelligence

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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]

Keeping Biases Checked during the last mile of decision making
Today a data driven leader, a data scientist or a data driven expert is always put to test by helping his team solve a problem using his skills and expertise. Believe it or not but a part of that decision tree is derived from the intuition that adds a bias in our judgement that makes the suggestions tainted. Most skilled professionals do understand and handle the biases well, but in few cases, we give into tiny traps and could find ourselves trapped in those biases which impairs the judgement. So, it is important that we keep the intuition bias in check when working on a data problem.

[ DATA SCIENCE Q&A]

Q:How would you come up with a solution to identify plagiarism?
A: * Vector space model approach
* Represent documents (the suspect and original ones) as vectors of terms
* Terms: n-grams; n=1 to as much we can (detect passage plagiarism)
* Measure the similarity between both documents
* Similarity measure: cosine distance, Jaro-Winkler, Jaccard
* Declare plagiarism at a certain threshold

Source

[ VIDEO OF THE WEEK]

@SidProbstein / @AIFoundry on Leading #DataDriven Technology Transformation #FutureOfData #Podcast

 @SidProbstein / @AIFoundry on Leading #DataDriven Technology Transformation #FutureOfData #Podcast

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[ QUOTE OF THE WEEK]

You can have data without information, but you cannot have information without data. – Daniel Keys Moran

[ PODCAST OF THE WEEK]

#BigData @AnalyticsWeek #FutureOfData #Podcast with Eloy Sasot, News Corp

 #BigData @AnalyticsWeek #FutureOfData #Podcast with Eloy Sasot, News Corp

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[ FACT OF THE WEEK]

In that same survey, by a small but noticeable margin, executives at small companies (fewer than 1,000 employees) are nearly 10 percent more likely to view data as a strategic differentiator than their counterparts at large enterprises.

Sourced from: Analytics.CLUB #WEB Newsletter

Sep 13, 18: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Data security  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ NEWS BYTES]

>>
 Top 10 Cloud Computing Challenges – Datamation Under  Cloud Security

>>
 Statistics Show More Signs Of The Tourism Slowdown – Reykjavík Grapevine Under  Statistics

>>
 IoT In Action – Introducing Azure Sphere – Microsoft – Channel 9 Under  IOT

More NEWS ? 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]

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]

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:Give examples of data that does not have a Gaussian distribution, nor log-normal?
A: * Allocation of wealth among individuals
* Values of oil reserves among oil fields (many small ones, a small number of large ones)

Source

[ VIDEO OF THE WEEK]

@DrewConway on fabric of an IOT Startup #FutureOfData #Podcast

 @DrewConway on fabric of an IOT Startup #FutureOfData #Podcast

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[ QUOTE OF THE WEEK]

Without big data, you are blind and deaf and in the middle of a freeway. – Geoffrey Moore

[ PODCAST OF THE WEEK]

@EdwardBoudrot / @Optum on #DesignThinking & #DataDriven Products #FutureOfData #Podcast

 @EdwardBoudrot / @Optum on #DesignThinking & #DataDriven Products #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