Oct 15, 20: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

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Productivity  Source

[ FEATURED COURSE]

Learning from data: Machine learning course

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

[ FEATURED READ]

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

image

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

[ TIPS & TRICKS OF THE WEEK]

Finding a success in your data science ? Find a mentor
Yes, most of us dont feel a need but most of us really could use one. As most of data science professionals work in their own isolations, getting an unbiased perspective is not easy. Many times, it is also not easy to understand how the data science progression is going to be. Getting a network of mentors address these issues easily, it gives data professionals an outside perspective and unbiased ally. It’s extremely important for successful data science professionals to build a mentor network and use it through their success.

[ DATA SCIENCE Q&A]

Q:What is an outlier? Explain how you might screen for outliers and what would you do if you found them in your dataset. Also, explain what an inlier is and how you might screen for them and what would you do if you found them in your dataset
A: Outliers:
– An observation point that is distant from other observations
– Can occur by chance in any distribution
– Often, they indicate measurement error or a heavy-tailed distribution
– Measurement error: discard them or use robust statistics
– Heavy-tailed distribution: high skewness, can’t use tools assuming a normal distribution
– Three-sigma rules (normally distributed data): 1 in 22 observations will differ by twice the standard deviation from the mean
– Three-sigma rules: 1 in 370 observations will differ by three times the standard deviation from the mean

Three-sigma rules example: in a sample of 1000 observations, the presence of up to 5 observations deviating from the mean by more than three times the standard deviation is within the range of what can be expected, being less than twice the expected number and hence within 1 standard deviation of the expected number (Poisson distribution).

If the nature of the distribution is known a priori, it is possible to see if the number of outliers deviate significantly from what can be expected. For a given cutoff (samples fall beyond the cutoff with probability p), the number of outliers can be approximated with a Poisson distribution with lambda=pn. Example: if one takes a normal distribution with a cutoff 3 standard deviations from the mean, p=0.3% and thus we can approximate the number of samples whose deviation exceed 3 sigmas by a Poisson with lambda=3

Identifying outliers:
– No rigid mathematical method
– Subjective exercise: be careful
– Boxplots
– QQ plots (sample quantiles Vs theoretical quantiles)

Handling outliers:
– Depends on the cause
– Retention: when the underlying model is confidently known
– Regression problems: only exclude points which exhibit a large degree of influence on the estimated coefficients (Cook’s distance)

Inlier:
– Observation lying within the general distribution of other observed values
– Doesn’t perturb the results but are non-conforming and unusual
– Simple example: observation recorded in the wrong unit (°F instead of °C)

Identifying inliers:
– Mahalanobi’s distance
– Used to calculate the distance between two random vectors
– Difference with Euclidean distance: accounts for correlations
– Discard them

Source

[ VIDEO OF THE WEEK]

@AnalyticsWeek: Big Data Health Informatics for the 21st Century: Gil Alterovitz

 @AnalyticsWeek: Big Data Health Informatics for the 21st Century: Gil Alterovitz

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[ QUOTE OF THE WEEK]

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

[ PODCAST OF THE WEEK]

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

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

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[ FACT OF THE WEEK]

We are seeing a massive growth in video and photo data, where every minute up to 300 hours of video are uploaded to YouTube alone.

Sourced from: Analytics.CLUB #WEB Newsletter

Oct 08, 20: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

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Data Mining  Source

[ AnalyticsWeek BYTES]

>> BOB in The Netherlands by bobehayes

>> Creative ways to use analytics to address social issue by analyticsweekpick

>> The 5GC Network Revolution: MEC and SBA Testing by administrator

Wanna write? Click Here

[ FEATURED COURSE]

Introduction to Apache Spark

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Learn the fundamentals and architecture of Apache Spark, the leading cluster-computing framework among professionals…. more

[ FEATURED READ]

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

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What are the jobs of the future? How many will there be? And who will have them? As technology continues to accelerate and machines begin taking care of themselves, fewer people will be necessary. Artificial intelligence… more

[ TIPS & TRICKS OF THE WEEK]

Save yourself from zombie apocalypse from unscalable models
One living and breathing zombie in today’s analytical models is the pulsating absence of error bars. Not every model is scalable or holds ground with increasing data. Error bars that is tagged to almost every models should be duly calibrated. As business models rake in more data the error bars keep it sensible and in check. If error bars are not accounted for, we will make our models susceptible to failure leading us to halloween that we never wants to see.

[ DATA SCIENCE Q&A]

Q:What is cross-validation? How to do it right?
A: It’s a model validation technique for assessing how the results of a statistical analysis will generalize to an independent data set. Mainly used in settings where the goal is prediction and one wants to estimate how accurately a model will perform in practice. The goal of cross-validation is to define a data set to test the model in the training phase (i.e. validation data set) in order to limit problems like overfitting, and get an insight on how the model will generalize to an independent data set.

Examples: leave-one-out cross validation, K-fold cross validation

How to do it right?

the training and validation data sets have to be drawn from the same population
predicting stock prices: trained for a certain 5-year period, it’s unrealistic to treat the subsequent 5-year a draw from the same population
common mistake: for instance the step of choosing the kernel parameters of a SVM should be cross-validated as well
Bias-variance trade-off for k-fold cross validation:

Leave-one-out cross-validation: gives approximately unbiased estimates of the test error since each training set contains almost the entire data set (n?1n?1 observations).

But: we average the outputs of n fitted models, each of which is trained on an almost identical set of observations hence the outputs are highly correlated. Since the variance of a mean of quantities increases when correlation of these quantities increase, the test error estimate from a LOOCV has higher variance than the one obtained with k-fold cross validation

Typically, we choose k=5 or k=10, as these values have been shown empirically to yield test error estimates that suffer neither from excessively high bias nor high variance.
Source

[ VIDEO OF THE WEEK]

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

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

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[ QUOTE OF THE WEEK]

The data fabric is the next middleware. – Todd Papaioannou

[ PODCAST OF THE WEEK]

#BigData @AnalyticsWeek #FutureOfData #Podcast with Dr. Nipa Basu, @DnBUS

 #BigData @AnalyticsWeek #FutureOfData #Podcast with Dr. Nipa Basu, @DnBUS

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[ FACT OF THE WEEK]

Every person in the US tweeting three tweets per minute for 26,976 years.

Sourced from: Analytics.CLUB #WEB Newsletter

The Reasons Why Data Analytics is Important in the Financial Sector

As of
late, there is increasing use of data analytics in various industries and
organizations across the spectrum. Considering the changing market needs and
customer expectations, businesses of this age need timely information and data
from the right sources in order to help the business decision makers to make
the best moves. Finances always play a crucial role in enhancing the value of a
business, while financial analytics is slowly taking over the conventional
approaches to finance management.

The concept of Financial Analytics

Financial
analytics is the specialty which takes a new perspective to the financial data
of an organization. Analytics will help gain a wider and in-depth insight into
the finances and take the most appropriate action in order to enhance overall
business performance. When done right, financial analytics has a highly
positive impact on all parts of a business.

Data
analytics in financial sector plays a crucial role in not only calculating the
profit of a business but also to answer many important business questions
alongside forecasting the future of any business. The roles of financial
analytics in modern-day business include, but not limited to:

  • Providing
    timely information which helps any organizational decision makers to take
    crucial decisions at the right point in time.

  • Businesses
    of all types and scales need to have a very sound financial planning as
    well forecasting to leverage the best marketing conditions, and data
    analytics helps to get it done at best.

  • Emerging
    technology-driven business models and the fast-changing needs of the
    conventional financial departments make financial analytics a necessity.

  • Financial
    analytics also helps to shape future business goals. Decision-making
    strategies are also getting fine-tuned with an analytical approach.

  • Financial
    analytics largely focus on measuring, managing, and maintaining tangible
    assets of business like machinery, cash, human resources, and more.

  • Proper
    financial analytics gives a better insight into the current financial
    status of any business and help businesses to improve cash flow and
    profitability.

  • Financial
    analytics helps to make smart financial decisions in order to increase the
    revenue by minimizing the wastages.

All
finance-related tasks or business like accounting, taxation, budgeting, and
other areas have a data warehouse which combines with analytics in order to run
the business more effectively and achieve the goals as planned. There are
various business models now benefitted from financial analytics as:

Major uses of financial analytics

When
done the right way, financial analytics will help any business to:

  • Understand
    the performance status of a business
  • What
    works and what not for the organization
  • Measure
    and properly manage the value of all tangible and intangible assets
  • Better
    plan and manage company investments
  • Forecast
    the market fluctuations and take timely decisions.
  • Increase
    information systems functionalities
  • Increase
    revenue and profits

In a typical financial analytics environment, most of the functions are automated, and thereby cutting a lot in terms of resources to manage it. This further enabled the finance professionals and administrators to focus more on the developmental business goals than focusing on processing transactions and reconciling the financial documents.Organizations like NationaldebtRelief.com deploy financial analytics in terms of debt management, too, which is another aspect of effective financial management.

Answering business questions

Modern-day
businesses, even the smallest-scale businesses, are becoming more complex
nowadays due to the advancement and adoption of technology. There are plenty of
questions that arise for the business people to answer in order to make an
appropriate administration plan effectively. Analytics helps in providing
answers to all the questions and enable the executives and managers in an
organization to gain access to more detailed and accurate financial
information. This can further strengthen the employee relations inside the
organization, which also will positively contribute towards the growth.

If you
are new to the concept of financial analytics, then it is advisable to contact
a skilled data analytics consulting for your financial analytics, which may be
to give you an answer to these critical questions like:

  • What
    kind of risks your business are exposed to?
  • What
    measures to be taken to enhance the business process and make it more
    effective?
  • Are
    you making the investments at the right path?
  • How
    good your profit generation is against various sales channels?
  • Which
    market segment is bringing more profit to your business?
  • What
    factors may affect your business in the future?

Integrated data analytics

Once a
specific thing to note while thinking of financial analytics is that nowadays,
companies tend to use ‘integrated financial analytics’ in order to excel in the
increasing market competition. With the use of integrated financial analytics,
organizations may be able to analyze the data better and share information with
the various sources in and out of the organization. Thus, organizations may be
able to better survive by instantly adapting to changing economic conditions.

Financial analytics software

Considering
the need for high-end analytics in mind, financial analytics tools are there in
plenty, which you need to choose diligently. Good software will help improve
the financial performance of your organization by providing proper information
and help increase the cash flow through proper management of receivables,
payables, and inventory. Good analytics software will be able to offer timely
financial reports and also helps n financial forecasting and budget planning.

Some
of the major features to look for in good financial analytics software are:

  • Analytics
    of fixed assets
  • Analytics
    of general ledger
  • Analytics
    of budgetary control
  • Tools
    to analyze financial performance
  • Profitability
    analytics
  • Payables
    and receivables analytics
  • Proactive
    intelligence
  • Pre-built
    and customizable metrics and data models
  • Instant
    integration with other existing ERP systems

In
fact, as a business administrator of being at any position responsible for
business decision making, you need to understand that finance is the language
of business. All the organizational goals of a given business are in-line with
the defined terms of finance, and the output and success of any administrative
move are measured in terms of finances. In the modern world of technology,
financial analytics is the game of analyzing the comprehensive data (big data)
involved in not only a financial statement but also from various other data
generating resources of an organization. The objective of any good financial
analytics system you adopt is to provide useful information to business
decision-makers and let them drive a business into more prosper.

Source: The Reasons Why Data Analytics is Important in the Financial Sector

Oct 01, 20: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

image
Data security  Source

[ FEATURED COURSE]

Tackle Real Data Challenges

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Learn scalable data management, evaluate big data technologies, and design effective visualizations…. more

[ FEATURED READ]

Big Data: A Revolution That Will Transform How We Live, Work, and Think

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“Illuminating and very timely . . . a fascinating — and sometimes alarming — survey of big data’s growing effect on just about everything: business, government, science and medicine, privacy, and even on the way we think… more

[ TIPS & TRICKS OF THE WEEK]

Strong business case could save your project
Like anything in corporate culture, the project is oftentimes about the business, not the technology. With data analysis, the same type of thinking goes. It’s not always about the technicality but about the business implications. Data science project success criteria should include project management success criteria as well. This will ensure smooth adoption, easy buy-ins, room for wins and co-operating stakeholders. So, a good data scientist should also possess some qualities of a good project manager.

[ DATA SCIENCE Q&A]

Q:What is the 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]

Making sense of unstructured data by turning strings into things

 Making sense of unstructured data by turning strings into things

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

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

[ 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]

As recently as 2009 there were only a handful of big data projects and total industry revenues were under $100 million. By the end of 2012 more than 90 percent of the Fortune 500 will likely have at least some big data initiatives under way.

Sourced from: Analytics.CLUB #WEB Newsletter

Benefits and Risks of Artificial Intelligence

We might still be decades away from the superhuman artificial intelligence (AI), like sentient HAL 9000 from 2001: A Space Odyssey, but our fear of robots having a mind of their own and acting at their own (free) will and using it against humankind is nonetheless present. Even some of the greatest minds of our […]

The post Benefits and Risks of Artificial Intelligence appeared first on TechSpective.

Originally Posted at: Benefits and Risks of Artificial Intelligence by administrator

How the Insurtech Is Disrupting the Insurance Industry (and AXA XL Is an Example)

The Insurtech boom is well known: insurance companies are integrating digital technologies into their traditional processes and everyday workflows in order to reduce manual efforts, time and costs. Process automation and new digital experience impact all sectors but the insurance is perhaps the most affected sector of all.

The World Insurtech Report 2018, released last October from Capgemini, states that the Insurtech sector saw “investment increase at a compound annual growth rate of 36.5 percent between 2014 and 2017.” In its Global Insurance trends analysis 2018, EY said that “Insurtech continues to be a hot area within the overall Fintech investment space having seen deal values rise 32% YoY and 45% CAGR since 2012.” According to Technavio, who published a new research report on the global Insurtech market, “the global insurtech market will grow by almost USD 15.63 billion during 2019-2023,” at a CAGR of more than 41%.

Therefore, it seems that insurtech attracts attention and a lot of investments, and in the future it will also continue to play a key role in using new technologies to provide a strategic advantage in the insurance sector.

Driving value for insurance: Insurtech and AI

Transform the value chain, improve  the customer experience, enable  human capabilities, increase efficiencies, create new products and redefine business models: new technologies can impact the insurance industry in several ways.  Among the different emerging technologies impacting insurance companies, Artificial Intelligence implementation is effective because it can be applied to the most data-intensive processes, providing gains and extracting insight from textual information. AI can read documents in the same way people do, recognizing the useful information they contain automatically. This reduces the time spent on insurance tasks from hours, down to seconds. Using AI, insurance companies can :

Accelerate claims management

Source

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

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[  COVER OF THE WEEK ]

image
Insights  Source

[ FEATURED COURSE]

Introduction to Apache Spark

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Learn the fundamentals and architecture of Apache Spark, the leading cluster-computing framework among professionals…. more

[ FEATURED READ]

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

image

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

[ TIPS & TRICKS OF THE WEEK]

Finding a success in your data science ? Find a mentor
Yes, most of us dont feel a need but most of us really could use one. As most of data science professionals work in their own isolations, getting an unbiased perspective is not easy. Many times, it is also not easy to understand how the data science progression is going to be. Getting a network of mentors address these issues easily, it gives data professionals an outside perspective and unbiased ally. It’s extremely important for successful data science professionals to build a mentor network and use it through their success.

[ DATA SCIENCE Q&A]

Q: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]

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

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

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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]

Solving #FutureOfOrgs with #Detonate mindset (by @steven_goldbach & @geofftuff) #FutureOfData #Podcast

 Solving #FutureOfOrgs with #Detonate mindset (by @steven_goldbach & @geofftuff) #FutureOfData #Podcast

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[ FACT OF THE WEEK]

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