Sep 21, 17: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Data analyst  Source

[ AnalyticsWeek BYTES]

>> Linkage Analysis for Your VoC Program – Free Paper by bobehayes

>> Encouraging Girls in STEM field by d3eksha

>> The Practice of Customer Experience Management: Paper for a Tweet by bobehayes

Wanna write? Click Here

[ NEWS BYTES]

>>
 Quoniam selects Axioma Risk – Finextra – Finextra Under  Risk Analytics

>>
 Gathering of winter statistics underway – Gunnison Country Times Under  Statistics

>>
 IoT spend to smash through $1trn mark soon, despite all the failures – Siliconrepublic.com 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]

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]

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:How would you define and measure the predictive power of a metric?
A: * Predictive power of a metric: the accuracy of a metric’s success at predicting the empirical
* They are all domain specific
* Example: in field like manufacturing, failure rates of tools are easily observable. A metric can be trained and the success can be easily measured as the deviation over time from the observed
* In information security: if the metric says that an attack is coming and one should do X. Did the recommendation stop the attack or the attack never happened?

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]

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]

#FutureOfData Podcast: Conversation With Sean Naismith, Enova Decisions

 #FutureOfData Podcast: Conversation With Sean Naismith, Enova Decisions

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[ FACT OF THE WEEK]

29 percent report that their marketing departments have ‘too little or no customer/consumer data.’ When data is collected by marketers, it is often not appropriate to real-time decision making.

Sourced from: Analytics.CLUB #WEB Newsletter

Sep 14, 17: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Data Accuracy  Source

[ FEATURED COURSE]

Data Mining

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Data that has relevance for managerial decisions is accumulating at an incredible rate due to a host of technological advances. Electronic data capture has become inexpensive and ubiquitous as a by-product of innovations… more

[ FEATURED READ]

Research Design: Qualitative, Quantitative, and Mixed Methods Approaches, 4th Edition

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The eagerly anticipated Fourth Edition of the title that pioneered the comparison of qualitative, quantitative, and mixed methods research design is here! For all three approaches, Creswell includes a preliminary conside… 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: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]

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

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

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[ QUOTE OF THE WEEK]

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

[ PODCAST OF THE WEEK]

#BigData @AnalyticsWeek #FutureOfData #Podcast with Joe DeCosmo, @Enova

 #BigData @AnalyticsWeek #FutureOfData #Podcast with Joe DeCosmo, @Enova

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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 07, 17: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Pacman  Source

[ AnalyticsWeek BYTES]

>> The intersection of analytics, social media and cricket in the cognitive era of computing by analyticsweekpick

>> Analytic Exploration: Where Data Discovery Meets Self-Service Big Data Analytics by jelaniharper

>> Two Underutilized Heroes of Data & Innovation: Correlation & Covariance by v1shal

Wanna write? Click Here

[ NEWS BYTES]

>>
 Security in the Age of Hybrid Cloud – CSO Australia Under  Cloud Security

>>
 Suncorp New Zealand welcomes Campbell Mitchell as head of Customer Experience – The National Business Review Under  Customer Experience

>>
 5 Predictions About the Future of IoT for Medical Devices – Geektime Under  IOT

More NEWS ? Click Here

[ FEATURED COURSE]

Process Mining: Data science in Action

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Process mining is the missing link between model-based process analysis and data-oriented analysis techniques. Through concrete data sets and easy to use software the course provides data science knowledge that can be ap… 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]

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 a few “rules of thumb” used in statistical or computer science? Or in business analytics?

A: Pareto rule:
– 80% of the effects come from 20% of the causes
– 80% of the sales come from 20% of the customers

Computer science: “simple and inexpensive beats complicated and expensive” – Rod Elder

Finance, rule of 72:
– Estimate the time needed for a money investment to double
– 100$ at a rate of 9%: 72/9=8 years

Rule of three (Economics):
– There are always three major competitors in a free market within one industry

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]

Data matures like wine, applications like fish. – James Governor

[ PODCAST OF THE WEEK]

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

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

Subscribe 

iTunes  GooglePlay

[ FACT OF THE WEEK]

73% of organizations have already invested or plan to invest in big data by 2016

Sourced from: Analytics.CLUB #WEB Newsletter

Big Data Not Doping: How The U.S. Olympic Women’s Cycling Team Competes On Analytics

Sports and data analytics are becoming fast friends, and their relationship is a topic I’ve explored before. Another example I recently came across is how the U.S. women’s cycling team used analytics to leap from underdog status to silver medalists at the 2012 London Olympics.

The team was struggling when it turned to Sky Christopherson for help. Christopherson was a former Olympian cyclist and broke a world record in the over 35s 200m velodrome sprint a decade after retiring as a professional athlete. He had done this using a training regime he designed himself, based on data analytics and originally inspired by the work of cardiologist Dr. Erik Topol.

Christopherson developed the Optimized Athlete program after becoming disillusioned with doping in the sport, putting the phrase “data not drugs” at the core of the philosophy. He put together a set of sophisticated data-capture and monitoring techniques to record every aspect affecting the women athletes’ performance, including diet, sleep patterns, environment and training intensity. However, he soon realized the data was growing at an unmanageable rate.

This prompted him to contact San Francisco’s data analytics and visualization specialists Datameer, which helped to implement the program. Datameer’s CEO, Stefan Groschupf, himself a former competitive swimmer at a national level in Germany, immediately saw the potential of the project. Christopherson said “They came back with some really exciting results – some connections that we hadn’t seen before. How diet, training and environment all influence each other – everything is interconnected and you can really see that in the data.”

The depth of the analytics meant that tailored programs could be tweaked for each athlete to get the best out of every team member. One insight which came up was that one cyclist – Jenny Reed – performed much better in training if she had slept at a lower temperature the night before. So she was provided with a temperature water-cooled mattress to keep her body at an exact temperature throughout the night. “This had the effect of giving her better deep sleep, which is when the body releases human growth hormone and testosterone naturally,” says Christopherson.

Big Data enables high performance sports teams to quantify the many factors that influence performance, such as training load, recovery, and how the human body regenerates. Teams can finally measure all these elements and establish early warning signals that, for example, stop them from pushing athletes into overtraining, which often results in injury and illness. The need to train hard while avoiding the dangers of injury and illness is, in Christopherson’s opinion, the leading temptation for athletes to use the performance-enhancing drugs (PEDs) which have blighted cycling and other sports for so long.

Christopherson’s system has not been put through rigorous scientific testing but it seems to work fairly well based his personal success as well as the success of the team he coached. The key is finding balance during training. “It’s manipulating the training based on the data you have recorded so that you are never pushing into that danger zone, but also never backing off and under-utilizing your talent. It’s a very fine line and that’s what Big Data is enabling us to finally do.”

When used accurately and efficiently, it is thought that Big Data could vastly extend the careers of professional athletes and sportsmen well beyond the typical retirement age of 30, with the right balance of diet and exercise, and avoiding injury through over-exertion. Christopherson spoke to me from Hollywood, where he is trying to finalize a distribution deal for a new documentary called ‘Personal Gold,’ that tells this amazing story in much more detail. The Optimized Athlete program has also been turned into an app (OAthlete), which will be made available to early adopters from June 18th.

To read the original article on Forbes, click here.

Source: Big Data Not Doping: How The U.S. Olympic Women’s Cycling Team Competes On Analytics