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

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[  COVER OF THE WEEK ]

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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

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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

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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]

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 

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[ 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

Visualization’s Twisted Path

Visualization is not a straight path from vision to reality. It is full of twists and turns, rabbit trails and road blocks, foul-ups and failures. Initial hypotheses are often wrong, and promising paths are frequently dead ends. Iteration is essential. And sometimes you need to change your goals in order to reach them.

We are as skilled at pursuing the wrong hypotheses as anyone. Let us show you.

We had seen the Hierarchical Edge Bundling implemented by Mike Bostock in D3. It really clarified patterns that were almost completely obfuscated when straight lines were used. 

Edge Bundling

We were curious if it might do the same thing with geographic patterns. Turns out Danny Holten, creator of the algorithm, had already done something similar. But we needed to see it with our own data.

We grabbed some state-to-state migration data from the US Census Bureau, then found Corneliu Sugar’s code for doing force directed edge bundling and got to work.

To start, we simply put a single year’s (2014) migration data on the map. Our first impression: sorrow, dejection and misery. It looked better than a mess of straight lines, but not much better. Chin up, though. This didn’t yet account for how many people were flowing between each of the connections — only whether there was a connection or not. 

Unweighted edge bundled migration

Unweighted edge bundled migration

With edge bundling, each path between two points can be thought to have some gravity pulling other paths toward it while itself being pulled by those other paths. In the first iteration, every part of a path has the same gravity. By changing the code to weight the bundling, we add extra gravity to the paths more people move along.

Weighted edge bundled migration

Weighted edge bundled migration

Alas, things didn’t change much. And processing was taking a long time with all those flows. When the going gets tough, simplify. We cut the data into two halves, comparing westward flows to eastward flows.

East to west migration

East to west migration

West to east migration

West to east migration

Less data meant cleaner maps. We assumed there would be some obvious difference between these two, but these maps could be twins. We actually had to flip back and forth between them to see that there was indeed a difference.

So our dreams of mindblowing insight on a migration data set using edge bundling were a bust. But, seeing one visualization regularly leads to ideas about another. We wondered what would happen if we animated the lines from source to destination? For simplicity, we started with just eastward migration. 

Lasers

Lasers

Cool, it’s like laser light leisurely streaming through invisible fibre optic cables. But there’s a problem. Longer flows appear to indicate higher volume (which is misleading as their length is not actually encoding volume, just distance). So we tried using differential line lengths to represent the number of people, sticking with just eastward flows. 

Star Wars blasters

Star Wars blasters

Here we get a better sense of the bigger sources, especially at the beginning of the animation, however, for some paths, like California to Nevada, we end up with a solid line for most of the loop. The short geographic distance obscures the large migration of people. We wondered if using dashed lines would fix this—particularly in links like California to Nevada.

Machine gun bursts

Machine gun bursts

This gives us a machine gun burst at the beginning with everything draining into 50 little holes at the end. We get that sense of motion for geographically close states, but the visual doesn’t match our mental model of migration. Migrants don’t line up in a queue at the beginning of the year, leaving and arriving at the same time. Their migration is spread over the year.

What if instead we turn the migration numbers into a rate of flow. We can move dots along our edge bundled paths, have each dot represent 1000 people and watch as they migrate. The density of the dots along a path will represent the volume.  This also has the convenience of being much simpler to explain.

Radar signals

Radar signals

We still have a burst of activity (like radar signals) at the beginning of the loop, so we’ll stagger the start times to remove this pulsing effect.

Staggered starts

Staggered starts

Voilà. This finally gives us a visual that matches our mental model: people moving over the period from one state to another. Let’s add back westward movement.

Ants

Ants

Very cool, but with so much movement it’s difficult to tell who’s coming and who’s going. We added a gradient to the paths to make dots appear blue as they leave a state and orange as they arrive.

Coloured ants

Coloured ants

Let’s be honest, this looks like a moderately organized swarm of ants. But it is a captivating swarm that people can identify with. Does it give us any insight? Well not any of the sort we were originally working for. No simple way to compare years, no clear statements about the inflows and outflows. If we want to make sense of the data and draw specific conclusions… well other tools might be more effective.

But it is an enchanting overview of migration. It shows the continuous and overwhelming amount of movement across the country and highlights some of the higher volume flows in either direction. It draws you in and provides you with a perspective not readily available in a set of bar charts. So we made an interactive with both.

Each dot represents 1,000 people and the year’s migration happens in 10 seconds. Or if you’d prefer, each dot can represent 1 person, and you can watch the year play out in just over 2 hours and 45 minutes. If you’re on a desktop you can interact with it to view a single state’s flow. And of course for mobile and social media, we made the obligatory animated gif.

And just when we thought we’d finished, new data was released and were were obliged to update things for 2015.

Glowing ants

Glowing ants

Building a visualization that is both clear and engaging is hard work. Indeed, sometimes it doesn’t work at all. In this post we’ve only highlighted a fraction of the steps we took.  We also fiddled with algorithm settings, color, transparency and interactivity.  We tested out versions with net migration. We tried overlaying choropleths and comparing the migration to other variables like unemployment and birth rate. None of these iterations even made the cut for this blog post.

An intuitive, engaging, and insightful visualization is rare precisely because of how much effort it takes. We continue to believe that the effort is worthwhile.

Originally Posted at: Visualization’s Twisted Path by analyticsweek

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

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[  COVER OF THE WEEK ]

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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

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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]

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

Voices in AI – Episode 86: A Conversation with Amir Husain

[voices_in_ai_byline]

About this Episode

Episode 86 of Voices in AI features Byron speaking with fellow author Amir Husain about the nature of Artificial Intelligence and Amir’s book The Sentient Machine.

Listen to this one-hour episode or read the full transcript at www.VoicesinAI.com

Transcript Excerpt

Byron Reese: This is Voices in AI brought to you by GigaOm, and I’m Byron Reese. Today my guest is Amir Husain. He is the founder and CEO of SparkCognition Inc., and he’s the author of The Sentient Machine, a fine book about artificial intelligence. In addition to that, he is a member of the AI task force with the Center for New American Security. He is a member of the board of advisors at UT Austin’s Department of Computer Science. He’s a member of the Council on Foreign Relations. In short, he is a very busy guy, but has found 30 minutes to join us today. Welcome to the show, Amir.

Amir Husain: Thank you very much for having me Byron. It’s my pleasure.

You and I had a cup of coffee a while ago and you gave me a copy of your book and I’ve read it and really enjoyed it. Why don’t we start with the book. Talk about that a little bit and then we’ll talk about SparkCognition Inc. Why did you write The Sentient Machine: The Coming Age of Artificial Intelligence?

Byron, I wrote this book because I thought that there was a lot of writing on artificial intelligence—what it could be. There’s a lot of sci fi that has visions of artificial intelligence and there’s a lot of very technical material around where artificial intelligence is as a science and as a practice today. So there’s a lot of that literature out there. But what I also saw was there was a lot of angst back in 2015, 2014. I actually had a personal experience in that realm where outside of my South by Southwest talks there was an anti-AI protest.

So just watching those protesters and seeing what their concerns were, I felt that a lot of the sort of philosophical questions, existential questions around the advent of AI, if AI indeed ends up being like Commander Data, it has sentience, it becomes artificial general intelligence, then it will be able to do the jobs better than we can and it will be more capable in let’s say the ‘art of war’ than we are and therefore does this mean that we will lose our jobs. We will be meaningless and our lives will be lacking in meaning and maybe the AI will kill us?

These are the kinds of concerns that people have had around AI and I wanted to sort of reflect on notions of man’s ability to create—the aspects around that that are embedded in our historical and religious tradition and what our conception of Man vs. he who can create, our creator—what those are and how that influences how we see this age of AI where man might be empowered to create something which can in turn create, which can in turn think.

There’s a lot of folks also that feel that this is far away, and I am an AI practitioner and I agree I don’t think that artificial general intelligence is around the corner. It’s not going to happen next May, even though I suppose some group could surprise us, but the likely outcome is that we are going to wait a few decades. I think waiting a few decades isn’t a big deal because in the grand scheme of things, in the history of the human race, what is a few decades? So ultimately the questions are still valid and this book was written to address some of those existential questions lurking in elements of philosophy, as well as science, as well as the reality of where AI stands at the moment.

So talk about those philosophical questions just broadly. What are those kinds of questions that will affect what happens with artificial intelligence?

Well I mean one question is a very simple one of self-worth. We tend to define ourselves by our capabilities and the jobs that we do. Many of our last names in many cultures are literally indicative of our profession. You know goldsmiths as an example, farmer as an example. And this is not just a European thing. Across the world you see this phenomenon of last names just reflecting the profession of a woman or a man. And it is to this extent that we internalize the jobs that we do as essentially being our identity, literally to the point where we take it on as a name.

So now when you de-link a man or a woman’s ability to produce or to engage in that particular labor that is a part of their identity, then what’s left? Are you still, the human that you were with that skill? Are you less of a human being? Is humanity in any way linked to your ability to conduct this kind of economic labor? And this is one question that I explored in the book because I don’t know whether people really contemplate this issue so directly and think about it in philosophical terms, but I do know that subjectively people get depressed when they’re confronted with the idea that they might not be able to do the job that they are comfortable doing or have been comfortable doing for decades. So at some level obviously it’s having an impact.

And the question then is: is our ability to perform a certain class of economic labor in any way intrinsically connected to identity? Is it part of humanity? And I sort of explore this concept and I say “OK well, let’s sort of take this away and let’s cut this away let’s take away all of the extra frills, let’s take away all of what is not absolutely fundamentally uniquely human.” And that was an interesting exercise for me. The conclusions that I came to—I don’t know whether I should spoil the book by sharing it here—but in a nutshell—this is no surprise—that our cognitive function, our higher order thinking, our creativity, these are the things which make us absolutely unique amongst the known creation. And it is that which makes us unique and different. So this is one question of self worth in the age of AI, and another one is…

Just to put a pin in that for a moment, in the United States the workforce participation rate is only about 50% to begin with, so only about 50% of people work because you’ve got adults that are retired, you have people who are unable to work, you have people that are independently wealthy… I mean we already had like half of adults not working. Does it does it really rise to the level of a philosophical question when it’s already something we have thousands of years of history with? Like what are the really needy things that AI gets at? For instance, do you think a machine can be creative?

Absolutely I think the machine can be creative.

You think people are machines?

I do think people are machines.

So then if that’s the case, how do you explain things like the mind? How do you think about consciousness? We don’t just measure temperature, we can feel warmth, we have a first person experience of the universe. How can a machine experience the world?

Well you know look there’s this age old discussion about qualia and there’s this discussion about the subjective experience, and obviously that’s linked to consciousness because that kind of subjective experience requires you to first know of your own existence and then apply the feeling of that experience to you in your mind. Essentially you are simulating not only the world but you also have a model of yourself. And ultimately in my view consciousness is an emergent phenomenon.

You know the very famous Marvin Minsky hypothesis of The Society of Mind. And in all of its details I don’t know that I agree with every last bit of it, but the basic concept is that there are a large number of processes that are specialized in different things that are running in the mind, the software being the mind, and the hardware being the brain, and that the complex interactions of a lot of these things result in something that looks very different from any one of these processes independently. This in general is a phenomenon that’s called emergence. It exists in nature and it also exists in computers.

One of the first few graphical programs that I wrote as a child in basic [coding] was drawing straight lines, and yet on a CRT display, what I actually saw were curves. I’d never drawn curves but it turns out that when you light a large number of pixels with a certain gap in the middle and it’s on a CRT display there there are all sorts of effects and interactions like the Moire effect and so on and so forth where what you thought you were drawing was lines, and it shows up if you look at it from an angle, as curves.

So I mean the process of drawing a line is nothing like drawing a curve, there was no active intent or design to produce a curve, the curve just shows up. It’s a very simple example of a kid writing a few lines of basic can do this experiment and look at this but there are obviously more complex examples of emergence as well. And so consciousness to me is an emergent property, it’s an emergent phenomenon. It’s not about the one thing.

I don’t think there is a consciousness gland. I think that there are a large number of processes that interact to produce this consciousness. And what does that require? It requires for example a complex simulation capability which the human brain has, the ability to think about time, to think about objects, model them and to also apply your knowledge of physical forces and other phenomena within your brain to try and figure out where things are going.

So that simulation capability is very important, and then the other capability that’s important is the ability to model yourself. So when you model yourself and you put yourself in a simulator and you see all these different things happening, there is not the real pain that you experience when you simulate for example being struck by an arrow, but there might be some fear and a why is that fear emanating? It’s because you watch your own model in your imagination, in your simulation suffer some sort of a problem. And now that is a very internal. Right? None of this has happened in the external world but you’re conscious of this happening, so to me at the end of the day it has some fundamental requirements. I believe simulation and self modeling are two of those requirements, but ultimately it’s an emergent property.

Listen to this one-hour episode or read the full transcript at www.VoicesinAI.com

[voices_in_ai_link_back]

Byron explores issues around artificial intelligence and conscious computers in his new book The Fourth Age: Smart Robots, Conscious Computers, and the Future of Humanity.

Originally Posted at: Voices in AI – Episode 86: A Conversation with Amir Husain

Better Recruiting Metrics Lead to Better Talent Analytics

metrics

According to Josh Bersin in Deloitte’s 2013 report, Talent Analytics: From Small Data to Big Data, 75% of HR leaders acknowledge analytics are important to the success of their organizations. But 51% have no formal talent analytics plans in place. Nearly 40% say they don’t have the resources to conduct sound talent analytics. Asked to rate their own workforce analytics skills, another 56% said poor.

As Bersin further noted in a recent PeopleFluent article, HR Forecast 2014, “Only 14% of the companies we studied are even starting to analyze people-related data in a statistical way and correlate it to business outcomes. The rest are still dealing with reporting, data cleaning and infrastructure challenges.”

There’s a striking gap between the large number of companies that recognize the importance of metrics and talent analytics and the smaller number that actually have the means and expertise to put them to use.

Yes, we do need to gather and maintain the right people data first, such as when and where applicants apply for jobs, and the specific skills an employee has. But data is just information captured by recruiting system or software already in place. It doesn’t tell any story.

Compare data against goals or thresholds and it turns into insight, a.k.a workforce metrics — measurements with a goal in mind, otherwise known as Key Performance Indicators (KPIs), all of which gauge quantifiable components of a company’s performance. Metrics reflect critical factors for success and help a company measure its progress towards strategic goals.

But here’s where it gets sticky. You don’t set off on a cross-country road trip until you know how to read the map.

For companies, it’s important to agree on the right business metrics – and it all starts with recruiting. Even with standard metrics for retention and attrition in place, some companies also track dozens of meaningless metrics— not tied to specific business goals, not helping to improve business outcomes.

I’ve seen recruiting organizations spend all their time in the metrics-gathering phase, and never get around to acting on the results — in industry parlance, “boiling the ocean.” You’re far better off gathering a limited number of metrics that you actually analyze and then act upon.

Today many organizations are focused on developing recruiting metrics and analytics because there’s so much data available today on candidates and internal employees (regardless of classification). Based on my own recruiting experience and that of many other recruiting leaders, here are what I consider the Top 5 Recruiting Metrics:

1. New growth vs. attrition rates. What percentage of the positions you fill are new hires vs. attrition? This shows what true growth really looks like. If you are hiring mostly due to attrition, it would indicate that selection, talent engagement, development and succession planning need attention. You can also break this metric down by division/department, by manager and more.

2. Quality of hires. More and more, the holy grail of hiring. Happily, all measurable: what individual performances look like, how long they stay, whether or not they are top performers, what competencies comprise their performance, where are they being hired from and why.

3. Sourcing. Measuring not just the what but the why of your best talent pools: job boards, social media, other companies, current employees, etc. This metric should also be applied to quality of hire: you’ll want to know where the best candidates are coming from. Also, if you want to know the percentage rate for a specific source, divide the number of source hires by the number of external hires. (For example, total Monster job board hires divided by total external hires.)

4. Effectiveness ratio. How many openings do you have versus how many you’re actually filling?  You can also measure your recruitment rate by dividing the total number of new hires per year by the total number of regular headcount reporting to work each year. Your requisitions filled percent can be tallied by dividing the total number of filled requisitions by the total number of approved requisitions.

5. Satisfaction rating. An important one, because it’s not paid much attention to when your other metrics are in good shape. Satisfaction ratings can be gleaned from surveys of candidates, new hires and current employees looking for internal mobility. While your overall metrics may be positive, it’s important to find out how people experience your hiring process.

As your business leaves behind those tedious spreadsheets and manual reports and moves into Talent Analytics, metrics are going to be what feeds those results. Consider which metrics are the most appropriate for your business — and why. And then, the real analysis can begin, and help your organization make better talent-related decisions.

Article originally appeared HERE.

Source by analyticsweekpick

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

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[ 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]

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]

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

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[ 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

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[ 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

Building Long-Term Business Relationships Through BI

The best business relationships have a certain synergy, and that synergy is especially important for sustaining long-term business relationships that stand the test of time. A long-term business relationship should feel secure but expansive, with room to grow into new opportunities and address changing needs.

It’s therefore critical that business solutions providers and consultants never miss an opportunity to expand their services into an area that complements their current offerings and supports the continued growth of their clients. And in an age of Big Data, your clients don’t just want the solutions you’re providing. They want the data transparency necessary to calibrate those solutions, test their effectiveness, and scale. For the most strategic providers, partnering with a scalable BI platform committed to your success is the answer—giving you the power to nurture your long-term business relationships with data transparency and actionable insights.

Expanding Offerings to Grow Business Partnerships

YourDMS was founded in 2007 as a document management and solutions consultancy. Our teams consult and implement document management solutions for growing businesses to streamline administration and accounts payable departments. Over the years, we’ve expanded to provide workflow automation and outsourcing services, and one of our longest-standing clients grew right alongside us. With the support, consultancy, and solutions provided by YourDMS, Pet Family expanded beyond a chain of successful pet food stores to include dog day-care centers, pet grooming facilities, pet clinics, and multiple brands of natural pet food.

And it was this 10 year business relationship that helped YourDMS discover a new paradigm in business intelligence, data, and analytics. Pet Family was scaling, and their data was growing faster than they could handle.

More Data, More Problems

As business expands, data expands. YourDMS was covering the document and workflow solutions required to manage the entire Pet Family umbrella of businesses, from procurement to sales, logistics, warehousing, accounts, HR, and beyond. But unlike the solutions and services offered by YourDMS, their BI platform wasn’t scalable. And when it came to business problems, Pet Family was paying us to have the solutions.

Their legacy business intelligence software could only spit out massive Excel files. And with the sheer volume of their growing data, they would have needed a team of data scientists working full time to extract actionable insights. Pet Family simply couldn’t expand any further without a single source of truth and full data transparency across their multiple businesses. They needed their employees focused on creating new products and dreaming up new expansions—not wrestling with massive data dumps.

Building a BI Partnership with Sisense

We knew it was time to expand our suite of solutions to include a robust BI platform that could handle big, complex data from multiple sources. But it was critical to me that the solution we selected wasn’t a band-aid for a single client. I wanted a long-term business partnership with a BI platform that I could easily scale and provide to additional clients.

The success of YourDMS has always hinged on the quality of the solutions we provide and the relationships we build with our OEM suppliers. I require that every solution and software we endorse, tailor, and provide to our clients gives me that same feeling of synergy and mutual commitment to success that I feel with my own clients. I wanted a platform flexible and scalable enough to serve the Pet Family empire as a test case and one that I was confident could expand with us to serve our other growing clients as well.

Looking Toward the Future with a BI Partner

For Pet Family, Sisense was the boost they needed to continue their expansion with confidence. Not only are documents and workflows streamlined, but all of the data from their different businesses and branches funnel into key dashboards, allowing management to view a single source of truth on any given branch, business, or the entire Pet Family empire.

Meetings with senior business management used to start with, “How are we doing with X?” Now they start with, “We’ve all seen what’s happening with X, let’s discuss action steps.” Senior management have never felt more secure or empowered to make the kinds of business decisions that used to go unattended for months while they waited on the data.

For the team at YourDMS, a successful Sisense rollout to a high-value client is just the beginning. Pet Family’s request for better BI served as a catalyst for us to identify and partner with a BI platform committed to our success and the success of our clients. Because Pet Family is only the first of many YourDMS clients who will benefit from the power of Sisense.

About the Author


Stewart Wright has over 25 years in the Document and Data Management industry offering extensive senior management experience, delivering high-quality products and services. Working with companies such as Invu, Abbyy, Sisense, Cumulus Pro, Draycir, Microsoft and Fujitsu to deliver business-critical software and solutions to organisations from 3 to 3000 users, YourDMS provides tailored, efficient document management, process management, and business intelligence solutions to companies seeking to reduce costs by improving their data capture, data analysis and reporting, workflows, accounts payable processing, and email management.

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

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[  COVER OF THE WEEK ]

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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

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[ 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

Advanced analytics for a new era

Analytics is serious business. I don’t care what buzz words are being used in today’s day’s stylized reinterpretation of the data business that we are in but analytics has indeed come a long way. Both, in the way it is done and the increasing incidence of companies doing it to stay competitive and grow. Thing is, in an odd sort of way, analytics is often done in tentative spurts of frenzy and the business of providing analytic solutions that can create a sustained analytics practice and one that is fully beneficial to the business is, well, rarely the norm in companies today.

Challenges that impede a sane analytics practice

So, let’s first answer the question of what challenges companies face in creating this competitively differentiating analytics practice on a sustained basis. But before I get into this, a quick note. Typically, in the spirit of shameless confessionals, my blog posts are prolix, long-winded affairs. In the interest of judiciously using your spare time I will be brief and if or when you may have the desire to engage in a larger relaxed conversation, reach out to me. Now, for the challenges:

First, businesses today generate vast gobs of data of all types, shapes, sizes, looks, across different times, and at various speeds. Consequently, analytics solutions need to be repositories of these diverse data. One cannot have bespoke storage solutions for each data type and confront an array of infrastructure that requires all kinds of physical and mental gymnastics just to get all the data in one place.

Second, the diversity of data alludes to the diversity of the business. For example, when customer data is collected through CRM systems, web servers, call center notes, and images, it means that customers engage with the business via the store, through online portals, via the call center, and on social media. To understand the multi-channel experience, we need to analyze these diverse data through multiple techniques. Unfortunately, to do that businesses have been forced to buy multiple analytic solutions, each with a unique set of algorithmic implementations and none easily interacting with the others.

Third, let’s assume that the ability and purpose of doing advanced analytics is there and well established. Now comes the challenge of purchasing a solution that can neatly fit into the budgetary constraints of the business. Not for the first time can I recall a customer who has expressed an inability to transact business with a vendor not because they do not have a desperate need for that vendor’s solution but because they are likely locked into a solution purchase that inevitably restricts their flexibility of deploying it. For example, a customer may first desire to kick the tired with a solution by purchasing a limited time cloud subscription before they are able to commit more resources to it. This is only a fair ask. Once they are successful in a minimal-risk purchase, they can up the ante by buying something more substantial depending on their needs. Analytic solutions that cannot fulfil this primal customer need will fast recede as a prickly memory of the past, regardless of how good or versatile they can be.

Teradata Analytics Platform

Now that I have outlined the challenges, how about providing a rational fix for these? Fortunately, and not too coincidentally, this is a pleasant enough occasion for me to introduce the Teradata Analytics Platform. At a high level, the Teradata Analytics Platform is an advanced analytics solution that comes in multi-deployment form factors with the capability of ingesting diverse data types at scale upon which native analytic engines can be invoked to implement multi-genre analytic functions that deliver and operationalize critical business insights. There are six core capabilities that are likely to provide a unique and significant competitive edge to customers of this solution. They are:

  • Evolution of the Teradata Database to include multi-data type storage capabilities (e.g., time series, text) and pre-built analytics functions (e.g., sentiment extraction, time series modelling).
  • Aster Analytics analytic engine with over 180 pre-built advanced analytics functions that span multiple genres such as Graph, Statistics, Text and Sentiment, Machine and Deep Learning, and Data Preparation.
  • A customer friendly deployment and pricing option set (in-house, hybrid cloud, managed cloud, term and perpetual licensing) that ensures flexibility in accommodating changing customer preferences without impacting any current investments.
  • A Data Science friendly analytic environment that includes a variety of development tools and languages (e.g., Hadoop) that aims to provide a customized solution that dovetails with a customer’s current investments and desired ecosystems mix.
  • A highly performant solution where the insights delivery operationalization are tightly coupled in the same environment without having to artificially separate them.

Addressing the Analytics Challenges 

So, now that you know what the Teradata Analytics Platform is it behooves me to close the loop and discuss briefly how it fixes the challenges that I had outlined earlier. Fortunately, for me, this is an easy and delightful exercise. For one thing, the features above clearly speak to the solution’s capability to ingest and process data of all types (our first challenge). The fact that it comes with a mind-boggling array of analytic capabilities, not to mention the capability to leverage open source analytic engines such as Tensorflow clearly indicates that Data Scientists and other analytics professionals have the ease and flexibility to choose from a colorful palette of techniques to effectively do their work. And what’s more, they can do their work using development tools and languages that they’re most comfortable with (our second challenge). Finally, given that the Teradata Analytics Platform was conceived with a “customer first” mentality – a hall-mark of the Teradata way of doing business – it is available for deployment in ways that suit the customer’s unique business needs. Customers who prefer to analyze their data on the public cloud will have the option of buying a subscription to this solution. Alternatively, those that prefer an in house implementation can have their choice fulfilled as well. Customers who choose one deployment option to begin with and decide to change to something else won’t have the worry of a repriced solution as the pricing unit (TCore) is the same across all deployments (our third challenge).

Teradata Analytics Platform, the smart choice

Clearly, and honestly, my conclusion is not likely to culminate in a dramatic denouement. Be that as it may, it is a logical choice to opt for the Teradata Analytics Platform that puts the power of analytics in the hand of the customer and delivers a unique purchasing experience that is quite revolutionary in the market.

The post Advanced analytics for a new era appeared first on Think Big.

Originally Posted at: Advanced analytics for a new era

Data Driven Innovation: A Primer

Data Driven Innovation A Primer
Data Driven Innovation A Primer

We are hearing all the hoopla about Big-data. How it is radically changing the way we look at company data and provide data driven reasoning for better and less risky decision making. Innovation is one such area. Big-data could provide a real lift to DDI. Having a data driven approach will help in better, targeted and relevant innovations that are craved by the clients/ customers. This bottom-up approach at its most effective form could be easily conceived by a good data driven innovation strategy.

Now let’s get into the primer on DDI- What is entails and how could one leverage that. Here is the Who, What, Where, Why and When of Data Driven innovation.

Why use DDI?
Let us address why do we actually need to use DDI- what will it buy us. Consider a situation where you have to come up with your next best product/feature/innovation. Where would that come from? From your gut based on some hunch or from hardcore actual data from right sources. Hunch based discoveries are great but their failure rate is higher. Also, they are difficult to validate as the implementer has to do various focus groups which in themselves are flawed to certain extent. Now consider a case where your product-customer interactions, operations fill you up on what is relevant and you can use data to understand its impact on the organizations. This helps you identify what matters most and helps you choose the idea with substantial data to back up the theory. So, there is no need for spending money on focus groups, but there is a need to leverage real interactions with the real customers/people leading to real results. This ultimately means lesser chance of failure and cost effective way to find the next big thing that is most craved by your customer or organization. This reduces the risk of failure substantially and puts you at ease. So, DDI is important and it could provide a sustainable and continuous way to innovate, iterate and improve.

What is DDI?
Data driven innovation, as name suggests is the way through which data is used for learning about new features, modifications, product ideas that is most cherished by your current customers, market landscape. However, its usage and manifestation in an organization could be different based on its structure, maturity, usage and implementation. Its definition could very well incorporate the application and purpose it is set to achieve. For some organization DDI is a way for finding process improvements, for others it is way to learn from customers and how they use products to learn about next features and/or products, for some it is a sustained source of learning about people, process and technology. But, I would put it in generic and call it “A method to innovate/iterate/improve using sustainable & continuous ways using data based decision process, where data is sourced to help learn about people, process and technology critical to your organization”.

Who could use DDI?
Data driven innovation is not everyone’s play. Not that it is too difficult to implement or it requires too much investment, but it requires certain maturity in your data handling capability before getting started. If you are diligent about using data to learn about your processes and its effectiveness, it will be easier. If you are not yet focused towards using data around your product and processes, you still might have some distance to travel before you delve into data driven methodologies. It is never late to start planning and executing strategies to introduce and leverage data points that go beyond your traditional direct customer & product data. So, in short, DDI could be used by any organization that is serious about learning from data. In Fact, smaller the firm, the better it could be implemented and lesser it would cost. The more silos, more complicated product/process structure, the more it is going to cost, to execute. In short, you could safely tag your DDI initiative on your management, the more selling your management requires for a data driven project, the farther you are from pursuing a full scale DDI. So, first get the leadership buyin on its value and then start shaping your organization to implement DDI.

Where will DDI take place?
Yes, you could figure out that DDI is a system that runs on data driven insights and data is everywhere in an organization so, it could show up anywhere. But, it is a bit trickier than that. The toughest part is not when data driven decision making is running in an organization’s DNA but the time when organizations decides to get started. DDI requires some careful understanding of how data works and how it could be used to get insights. Therefore, place where it should start is important. The best starting place for DDI could be around project managers, or if your organization is big enough to accommodate project management office, for agile companies, it should be around group leads. In short, DDI should start from a place that is not a stranger to data and understands how to handle it. So, in short, it could exist everywhere but it should start from a place that provides the most amiable surroundings required by a data driven project. Project managers, supervisors, PMOs are meant to keep a tap on the progress, so they possess some basic skills to function as data driven professionals and therefore, could help the best in understanding and executing a good DDI strategy.

When is the time to delve into DDI?
In short, the sooner the better. DDI requires substantial amount of preplanning and dedication. The sooner organizations delve into data driven innovation, the better will be its execution and value to the organization. A good data driven innovation implementation requires some practice and iterations on data models, validations, analysis and reporting. So, a successful implementation will rarely emerge from first implementation and would require some iteration. Also, the sooner the organization will start in direction of implementing DDI, the better it is because organizations will start acting in ways to facilitate smart data handling, which will have its own benefits. But, one caveat is that organization should have data to play with. Doing DDI sooner when data handling capabilities are not established could confuse the processes and steer the implementation in wrong directions. So, we could reword our sooner as “the sooner the organizations have started embracing data based decision making process, the better”.

To summarize, DDI is important and beneficial to any organization. It has the tendency to make any organization grow sustainably without having to invest too much into research and development. It support continuous improvements and that too without investing too much money and it could re-utilize the same infrastructure for sustainable leanings.

As a treat, here is a video on Big-Data and Innovation:

Source: Data Driven Innovation: A Primer