Dec 31, 20: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

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Pacman  Source

[ FEATURED COURSE]

Pattern Discovery in Data Mining

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Learn the general concepts of data mining along with basic methodologies and applications. Then dive into one subfield in data mining: pattern discovery. Learn in-depth concepts, methods, and applications of pattern disc… more

[ FEATURED READ]

Hypothesis Testing: A Visual Introduction To Statistical Significance

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Statistical significance is a way of determining if an outcome occurred by random chance, or did something cause that outcome to be different than the expected baseline. Statistical significance calculations find their … more

[ TIPS & TRICKS OF THE WEEK]

Winter is coming, warm your Analytics Club
Yes and yes! As we are heading into winter what better way but to talk about our increasing dependence on data analytics to help with our decision making. Data and analytics driven decision making is rapidly sneaking its way into our core corporate DNA and we are not churning practice ground to test those models fast enough. Such snugly looking models have hidden nails which could induce unchartered pain if go unchecked. This is the right time to start thinking about putting Analytics Club[Data Analytics CoE] in your work place to help Lab out the best practices and provide test environment for those models.

[ DATA SCIENCE Q&A]

Q: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]

#BigData @AnalyticsWeek #FutureOfData #Podcast with Nathaniel Lin (@analytics123), @NFPA

 #BigData @AnalyticsWeek #FutureOfData #Podcast with Nathaniel Lin (@analytics123), @NFPA

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

The world is one big data problem. – Andrew McAfee

[ PODCAST OF THE WEEK]

Scott Harrison (@SRHarrisonJD) on leading the learning organization #JobsOfFuture #Podcast

 Scott Harrison (@SRHarrisonJD) on leading the learning organization #JobsOfFuture #Podcast

Subscribe 

iTunes  GooglePlay

[ FACT OF THE WEEK]

Estimates suggest that by better integrating big data, healthcare could save as much as $300 billion a year — that’s equal to reducing costs by $1000 a year for every man, woman, and child.

Sourced from: Analytics.CLUB #WEB Newsletter

Dec 24, 20: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

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Data Mining  Source

[ AnalyticsWeek BYTES]

>> Analyze Data Faster with Google Cloud’s BigQuery Storage API by analyticsweek

>> Success Story: Adding Value to Applications with Embedded Analytics by analyticsweek

>> How to Successfully White Label Analytics by analyticsweek

Wanna write? 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]

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:What are confounding variables?
A: * Extraneous variable in a statistical model that correlates directly or inversely with both the dependent and the independent variable
* A spurious relationship is a perceived relationship between an independent variable and a dependent variable that has been estimated incorrectly
* The estimate fails to account for the confounding factor

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]

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

[ PODCAST OF THE WEEK]

Understanding #BigData #BigOpportunity in Big HR by @MarcRind #FutureOfData #Podcast

 Understanding #BigData #BigOpportunity in Big HR by @MarcRind #FutureOfData #Podcast

Subscribe 

iTunes  GooglePlay

[ FACT OF THE WEEK]

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

Sourced from: Analytics.CLUB #WEB Newsletter

How to Prepare for the Future of Construction Work Today

Throughout 2019 and even in early 2020, discussions about new technologies and the future of construction work have been framed as something to keep an out for in the 5 or 10 years. Pre-pandemic, the future of construction was seen as something that lives… well, in the future, and therefore wasn’t something to be concerned about today.

But COVID-19 shook up the industry and accelerated the need to adopt new solutions like automation, robotics, and advanced analytics. The pandemic also shed light on the importance of humans in construction projects, and how people’s roles need to shift in the new normal.

Simply put, there’s a new sense of urgency for construction firms to change and adapt. This means, to plan for the future of construction — particularly as it relates to the workforce — tomorrow’s concerns need to be addressed today.

Let’s explore how all of this plays out in the construction industry and what you can do to prepare.

Humans and Technology: More Powerful Together

Human beings and technology are often pitted against each other. There’s this “us versus them” mentality around people and tech, and many are quick to think that new gadgets or software will replace people.

And while it’s true that technology (e.g. automation, artificial intelligence, etc.) can start doing things that were previously assigned to people, that doesn’t mean humans won’t have roles to play going forward.

Sure, you can use analytics to crunch the numbers, but you still need someone to analyze the data and surface meaningful insights. Drones can play a role in site inspections, but it still takes skilled workers to operate the technology; not to mention, there are numerous tasks that require the presence of field workers.

In many cases, technology creates new opportunities for people while helping them be more productive and creative at the same time. And let’s not forget that construction is still a people-centric field. Your construction clients are humans, not robots. And for the most part, we construct buildings or renovate spaces for people, which means humans will always have a prominent role behind the scenes and on the jobsite.

The key is to figure out which roles people should own, and how they can work with technology. As Deloitte puts it:

“Organizations should evolve their thinking about technology from taking a purely substitution view (replacing humans with technology) to using technology as an augmentation or collaboration strategy. The latter view can allow organizations to not only streamline costs, but to also create value and ultimately, provide meaning to the workforce as a whole.”

We can see this in action the robot SAM (Semi-Automated Mason), whose purpose is to install bricks. SAM isn’t there to replace humans; it’s there to make their jobs easier and safer. The robot does the heavy lifting, but human masons are responsible for setting up and operating the system, as well as striking the joints and ensuring wall quality.

Scott L. Peters, the co-founder of Construction Robotics explains that SAM enables workers to continue using their knowledge and skills without having the demand on their bodies that they may have had previously.

[youtube https://www.youtube.com/watch?v=MVWayhNpHr0]

Aim for that level of thinking and action when you’re adopting new technologies and hiring workers. As you move forward with new solutions or practices, see to it that you’re seeing the relationship between people and technology as symbiotic, rather than combative.

Building a More Adaptable Workforce

When discussing COVID-19, many people express the desire to “go back to normal.” Nonetheless, things will never completely go back to the way things were; the only path is forward. The sooner firms embrace a new mentality, the more effectively they can prepare for the future of construction work.

Rather than waiting for things to “return to normal,” construction firms must forge ahead. The path likely won’t be easy and there’s a lot of uncertainty out there, which is why it’s important to build an adaptable and resilient workforce.

To thrive, employees must be able to quickly roll with unexpected situations and they should be in a position to move and change course when necessary. A big part of doing that means arming your team with the right tools — i.e., hardware and software that allow them to work and collaborate from anywhere.

It’s also important to instill the right mindsets and skills in your workers. Train them on research and critical thinking. Introduce new thought processes — ones that push them to be agile and flexible. Mindfulness exercises that encourage employees to be more aware and present may help as well. The Harvard Business Review points out that mindfulness improves cognitive flexibility, judgment accuracy, and insight-related problem-solving.

To maximize your success, incorporate these things into your construction culture. Foster an environment of collaboration and focus on continuous learning through initiatives like mentorships and upskilling. When employees are encouraged to work together and are regularly stimulated, they’ll be more agile and adaptable.

Finally, make sure you’re recruiting the right people. Expand your hiring strategies and bring in candidates from diverse technical backgrounds — even if they’re not strictly construction-focused. A diverse and well-rounded workforce gives you access to a variety of talents and skills, which are valuable when dealing with an uncertain and fast-changing environment.

Leveraging the Power of Connected Construction Data and Workflows

The things we discussed thus far (i.e., using people and tech effectively, and increasingly adaptability) will only be possible with accurate data and streamlined workflows.

In order for your operations to be productive, safe, and effective, all of your firm’s components — from your people and processes to your data and tech — should be connected. Information must flow smoothly across multiple teams and areas of the business.

How do you accomplish that?

Start with using a central construction management system powered by the cloud. Rather than giving different teams separate tools or solutions, have your staff collaborate on one platform, when possible. If you need to rely on multiple solutions, ensure you have solid data and technology integrations that can create a connected tech stack. Doing so helps you unlock several benefits including:

Automated workflows. A tightly integrated system reduces the need for manual entry, which paves the way for automated and streamlined workflows. Teams work faster and are more productive — and this critical, particularly with today’s new safety protocols and the limited number of people onsite.

Better access to insights. You get better visibility into your data. Rather than trying to retrieve information from siloed systems, the data you need lives in one place, making it easy to uncover trends and insights that can inform project decisions.

Improved collaboration. Having connected construction data and workflows keeps team members on the same page, which allows them to collaborate more effectively.

Tapping into AI and Predictive Analytics

Artificial intelligence and predictive analytics are already doing wonders for the construction industry. These technologies are helping firms plan projects, optimize schedules, and manage tasks, among other things.

And in the age of COVID-19, AI, predictive analytics, and machine learning are playing an increasingly important role: keeping jobsites and workers safe.

Needless to say, health and safety should be a priority when you’re gearing up for the future of construction work, and we highly recommend tapping into these solutions.

We can already see applications of how AI helps reduce COVID-19 risk in the workplace. The contractor Barton Malow, for instance, uses AI technologies from Smartvid.io and Autodesk BIM 360 to keep jobsites safe during the pandemic. Barton Malow uses an AI solution named Vinnie which can identify safety issues in photos. Vinnie can recognize when there’s a lack of PPE or when individuals aren’t standing six feet apart.

It’s important to note that the objective here isn’t to “catch” people who aren’t complying with guidelines. Rather, Vinnie’s goal is to assess risk and help teams take action or direct resources where they’re needed.

Aside from helping you enforce safety measures, AI can also enable you to make smarter decisions around health and safety. A BIM 360 IQ project with Autodesk found that AI algorithms were able to understand risk, which means it could figure out which problems to prioritize and what could happen if certain concerns weren’t addressed.

That can be incredibly useful when operating amidst a pandemic. You need to constantly assess the risk of spreading the virus and make quick decisions accordingly. With AI, you don’t have to go in blind. The technology can assist you with data and recommendations so you can make decisions that keep your workforce safe.

The Future of Construction Work is Here and There’s No Time to Waste

Preparing for the future is always something to keep in mind, but COVID-19 has undoubtedly brought more urgency to this task. It’s high time to prepare your workforce for the future, and doing that requires you to level up in terms of technology, adaptability, efficiency, and safety. It’s time to start building a strong construction workforce today… and for the advancement of construction’s future.

The post How to Prepare for the Future of Construction Work Today appeared first on Autodesk Construction Cloud Blog.

Originally Posted at: How to Prepare for the Future of Construction Work Today by analyticsweekpick

Are Pre-hire Talent Assessments Part of a Predictive Talent Acquisition Strategy? 

Over the past 30+ years, businesses have spent billions on talent assessments. Many of these are now being used to understand job candidates.  Increasingly, businesses are asking how (or if) a predictive talent acquisition strategy can include the use of pre-hire assessments?  As costs of failed new hires continue to rise, recruiters and hiring managers are looking for any kind of pre-hire information to increase the probability of making a great hire.

How Can You Know – If It is It a Real Predictive Solution vs. Marketing Fluff

What is a Real Predictive Solution?

For all of the marketing hype, Predictive Analytics boils down to three very simple steps.

  • 1st, a system reads “input” data – perhaps assessment scores or CV information.
  • 2nd, the system does some math to apply a “predictive model” to the input data.
  • Finally, the results of the model are shown as “output” data of the model – perhaps the likelihood of the candidate achieving a certain level of Sales Performance or another KPI. At heart, it takes “inputs” and turns them into “outputs” or predicted business outcomes.  But to build and validate a model, you need a healthy, logical set of both input and output data for that role in your company.

If you are using a talent assessment alone this is just input data.  The talent assessment is just one piece of the system.  There are 2 more pieces (see above).

For most companies, their current pre-hire talent assessments are wasted data.  Results are delivered in an individual report that cannot be analyzed or aggregated.  For most “legacy” talent assessments, it’s difficult or impossible to determine what positive (or negative) business affect the assessments are having.  It often comes down to the question of “how much the HR person believes the results”.  This is a bad measure of success.

But it doesn’t have to be that way.  At Talent Analytics, we include talent assessment data, generated from our own proprietary assessments, as an additional data point in every predictive project.  In predictive-speak, “our assessment data has proven to be a very strong independent variable for our predictive models”.  We repeatedly prove that our Talent Analytics scores, predict business performance, such as the probability of someone making their sales quota, or the probability of someone lasting in a contact center role for at least 12 months, the probability of a truck driver making accidents . . . and so on for most quantifiable KPIs.

If you’d like to begin a predictive talent acquisition project using talent assessments, it can be daunting to figure out what solutions are smoke and mirrors, and what solutions will actually deliver a predictive solution.

To help, I wanted to share important factors to consider to help you sort through “pretend predictive solutions” and “real, rigorous predictive solutions” that can deliver significant bottom line results.  This decision can dramatically affect your business’s bottom line.  It’s important.

15 Criteria for Selecting a Predictive Talent Assessment Solution

  1. The Predictive Company Itself
    Are you dealing with an assessment company, who is trying to learn how to be predictive? Or is it a predictive company that also uses assessment data?  How long have they been doing predictive work?  Are they invited to speak at predictive conferences or at basic HR conferences?
  1. Their Predictive Team
    Ideally the company will have Data Scientists on staff as well as IO Psychologists. This is important because Data Scientists tend to utilize more modern and rigorous methods for prediction and validation.  IO Psychologists tend to be focused on the instrument, while Data Scientists tend to be concerned with predictive validity and business results.
  2. Are They Predicting For Your Company, or For Everyone?
    There are companies that create “Industry Benchmarks,” that is, a general performance predictions for general industry categories – such as Retail Sales or Customer Service. These predictions are significantly less accurate, because they are based on companies different from your own, with different cultures, goals, and regions.  Not all “Customer Service” is the same.  Modern computing methods enable leading providers to create and validate predictive models for your roles in your own company alone, and to continuously update the model over time.
  1. Do They Care About Your Outcome Data?
    Generally these solutions predict attrition or performance for a candidate or employee. Has the assessment company asked you for the attrition or KPI data for your employees in your target role?  If they don’t know your employee outcomes, how can they predict your outcomes?  They can’t. Most job roles have multiple KPIs that describe performance – do they predict each of these separately?  For KPIs that naturally contradict each other, e.g. speed vs. accuracy, how does the predictive solution resolve the contradiction?  Just getting a “green light” isn’t good enough in many cases. What sample size did they ask for?  Real predictions require a reasonable sample to properly validate that you aren’t being fooled by randomness.  If they only ask for 15 top performers, your sample is too small to create a real prediction.
  1. Does the Solution Base Predictions on Outcome Data or a Job Fit, Job Match or Job Blueprint Survey?
    Data Science predicts what you ask it to predict. If you want lower attrition or higher KPIs, the models must be trained and validated with those data alone.  The process looks for fact-based patterns to drive your business.Surprisingly, many solutions don’t use this approach, but fall back to managerial bias.  These solutions ask well-meaning committees of managers to list competencies that they believe are needed for success in a role.  The resulting criteria are not predictive at all – they just find candidates that match the laundry list of beliefs and biases held by that committee.  Nowhere in this process is a connection to actual attrition or KPI outcomes.  Again, if the system doesn’t know about your outcomes, how can the process predict them?  Start with data, not bias.
  1. Does the Solution Use Machine Learning to Recalibrate Your Predictive Models? How Often?
    Business needs, role descriptions, and culture changes over time.  Local labor conditions change.  For example, Service Representatives may be incentivized to cross-sell related products, or new regulations may require new compliance to be performed.  It is important to update and re-validate your predictive models 2-4 times a year to keep up to date with seen and unseen trends.  Some solutions have not changed their models for 30 years – do you expect these to find great sales reps for you?
  1. The New Validation Question: Criterion Validation?
    HR has been taught to ask if the assessment is validated.  The first level of validation checks whether the assessment measures are self-consistent.  Continue to ask this question.But ultimately you care about whether the assessment feeds predictions that accurately correspond to improved business outcomes.  That is, are the predictions actually working?  This level is called “Criterion Validation” and is a high bar that is not commonly reached by vendors.A top tier predictive talent assessment vendor will perform Criterion Validation for the solutions several times a year – with every client.  Criterion validation is the highest level of validation possible, and is the most preferred by regulatory agencies.
  1. Can You Easily Access / Download Your Company’s Talent Assessment Data
    Talent assessment data is a critical dataset for your company. If your Talent Assessment vendor makes it difficult or impossible to access your talent assessment data – this is a good indication they are using pre-predictive technology and that they don’t appreciate that this data is your asset.True predictive solutions know that your workforce data scientists will want to use your talent assessment data to find correlations and predictions in many areas of your business. You need to insist on easy and direct access to the raw assessment scores.
  1. How Easy is it to Deploy the Solution into the Talent Acquisition Process and Use the Predictions
    How much training is required? Do your talent acquisition professionals need to read long text reports, or get out a calculator to use the predictions?  The complexity of a prediction should be kept out of the way of daily operations.
    If your team still needs to “think” about what the answer is, it is probably not a predictive solution.
  2. Is there a Different Assessment for Every Role? Or 1 Assessment with Multiple Predictive Models?
    Multiple assessments make it impossible to predict one candidate’s performance against multiple roles.  This may also be a signal that you are working with an older, legacy (less predictive) talent assessment supplier.
  1. Is There an Answer Key for their Solution on the Web?
    For many assessments, there are answer keys and guides on how to fool or pass the test. One example is here:  http://on.wsj.com/29Che0n .  When you see this, it means two things: (a) that the test is easily fooled, lacking internal controls to prevent spoofing, and (b) it means that that you are looking at an “industry benchmark” with one clear set of answers.A Data Science-driven model would be custom to your role in your company, and be continuously evolving – therefore very difficult for answer keys and spoofing to catch.
  1. Does the Company Itself (i.e. Myers Briggs) Specifically Tell You Not to Use their Solution for Hiring / Talent Acquisition?
    Some assessments, notably the Myers Briggs survey, specifically implore users to not use the tool for talent acquisition: “It is not ethical to use the MBTI instrument for hiring or for deciding job assignments.”  http://www.myersbriggs.org/my-mbti-personality-type/hiring-an-mbti-consultant/guidelines-for-hiring-an-outside-consultant.htm
  2. Ask to see their company policy on employee predictive modeling, discrimination, disparate impact and fairness.
    It is important that a predictive solution has thought through the specific outcomes of their models and how they fit into creating fair opportunity for all applicants.  In particular it is vital for the solution to satisfy or exceed any government requirements for hiring and selection.
  3. Do Your Own (Internal) Data Scientists Approve of this Predictive Solution?
    We recommend asking one of your own data scientists (from HR, marketing, or another area inside your own company) to accompany you in your evaluation. They know what is a rigorous approach and what is marketing fluff.
  4. How Does the Predictive Solution Regularly Prove to You that the Models Are Working?
    Ideally the company you select will be able to show you 2 – 4 times a year how your predictions are working (i.e. turnover is going down, sales are going up, calls are going up, errors are going down etc.,)

Only use a predictive model during talent acquisition if the predictions are accurate.  If they’re not – you should stop using the models.  You need this feedback.
Predictive Talent Assessments Can Have a Prominent Place in Your Predictive Talent Acquisition Process…but you need to be careful in choosing the real predictive solution vs. a legacy talent assessment with a predictive marketing wrapper.

ABOUT THE AUTHOR

Author photo

Greta Roberts is an influential pioneer of the emerging field of predictive workforce analytics where she continues to help bridge the gap and generate dialogue between the predictive analytics and workforce communities.

Since co-founding Talent Analytics in 2001, CEO Greta has successfully established the firm as the recognized employee predictions leader, both pre- and post-hire, on the strength of its powerful predictive analytics approach and innovative Advisor™ software platform designed to solve complex employee attrition and performance challenges. Greta has a penchant for identifying strategic opportunities to innovate and stay ahead of the curve as evident in the firm’s early direction to use predictive analytics to solve “line of business” challenges instead of “HR” challenges and model business outcomes instead of HR outcomes.

In addition to being a contributing author to numerous predictive analytics books, she is regularly invited to comment in the media and speak at high end predictive analytics and business events around the world.

Follow Greta on twitter @GretaRoberts.

Get in touch:

http://www.talentanalytics.com/

Phone: 617.864.7474

Originally Posted at: Are Pre-hire Talent Assessments Part of a Predictive Talent Acquisition Strategy? 

Dec 17, 20: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

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Productivity  Source

[ FEATURED COURSE]

Python for Beginners with Examples

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

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:Do we always need the intercept term in a regression model?
A: * It guarantees that the residuals have a zero mean
* It guarantees the least squares slopes estimates are unbiased
* the regression line floats up and down, by adjusting the constant, to a point where the mean of the residuals is zero

Source

[ VIDEO OF THE WEEK]

Dave Ulrich (@dave_ulrich) talks about role / responsibility of HR in #FutureOfWork #JobsOfFuture #Podcast

 Dave Ulrich (@dave_ulrich) talks about role / responsibility of HR in #FutureOfWork #JobsOfFuture #Podcast

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

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

[ PODCAST OF THE WEEK]

#DataScience Approach to Reducing #Employee #Attrition

 #DataScience Approach to Reducing #Employee #Attrition

Subscribe 

iTunes  GooglePlay

[ FACT OF THE WEEK]

This year, over 1.4 billion smart phones will be shipped – all packed with sensors capable of collecting all kinds of data, not to mention the data the users create themselves.

Sourced from: Analytics.CLUB #WEB Newsletter

Dec 10, 20: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

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Human resource  Source

[ AnalyticsWeek BYTES]

>> Building Data Science AI Teams by @MikeTamir / @UberATG #FutureOfData #Podcast by v1shal

>> Business Linkage Analysis: An Overview by bobehayes

>> A Gentle Introduction to Computational Learning Theory by administrator

Wanna write? 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]

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

image

This book predicts the decline of today’s professions and describes the people and systems that will replace them. In an Internet society, according to Richard Susskind and Daniel Susskind, we will neither need nor want … more

[ TIPS & TRICKS OF THE WEEK]

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:Define: quality assurance, six sigma?
A: Quality assurance:
– A way of preventing mistakes or defects in manufacturing products or when delivering services to customers
– In a machine learning context: anomaly detection

Six sigma:
– Set of techniques and tools for process improvement
– 99.99966% of products are defect-free products (3.4 per 1 million)
– 6 standard deviation from the process mean

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]

The goal is to turn data into information, and information into insight. – Carly Fiorina

[ PODCAST OF THE WEEK]

#BigData @AnalyticsWeek #FutureOfData #Podcast with @Beena_Ammanath, @GE

 #BigData @AnalyticsWeek #FutureOfData #Podcast with @Beena_Ammanath, @GE

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iTunes  GooglePlay

[ FACT OF THE WEEK]

A quarter of decision-makers surveyed predict that data volumes in their companies will rise by more than 60 per cent by the end of 2014, with the average of all respondents anticipating a growth of no less than 42 per cent.

Sourced from: Analytics.CLUB #WEB Newsletter