Sep 26, 19: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

Warning: file_get_contents(http://events.analytics.club/tw/eventpull.php?cat=WEB): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

Warning: file_get_contents(http://events.analytics.club/tw/eventpull.php?cat=WEB): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

Warning: file_get_contents(http://events.analytics.club/tw/eventpull.php?cat=WEB): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

Warning: file_get_contents(http://events.analytics.club/tw/eventpull.php?cat=WEB): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

Warning: file_get_contents(http://events.analytics.club/tw/eventpull.php?cat=WEB): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

Warning: file_get_contents(http://events.analytics.club/tw/eventpull.php?cat=WEB): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

Warning: file_get_contents(http://news.analyticsweek.com/tw/newspull.php): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

Warning: file_get_contents(http://news.analyticsweek.com/tw/newspull.php): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

Warning: file_get_contents(http://news.analyticsweek.com/tw/newspull.php): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

[  COVER OF THE WEEK ]

image
Data analyst  Source

[ AnalyticsWeek BYTES]

>> The Big List: 80 Of The Hottest SEO, Social Media & Digital Analytics Tools For Marketers by analyticsweekpick

>> 100 Greatest Quotes On Leadership by v1shal

>> Clear Off the Table by analyticsweek

Wanna write? Click Here

[ FEATURED COURSE]

The Analytics Edge

image

This is an Archived Course
EdX keeps courses open for enrollment after they end to allow learners to explore content and continue learning. All features and materials may not be available, and course content will not be… more

[ FEATURED READ]

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

image

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

[ TIPS & TRICKS OF THE WEEK]

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:Explain what resampling methods are and why they are useful?
A: * repeatedly drawing samples from a training set and refitting a model of interest on each sample in order to obtain additional information about the fitted model
* example: repeatedly draw different samples from training data, fit a linear regression to each new sample, and then examine the extent to which the resulting fit differ
* most common are: cross-validation and the bootstrap
* cross-validation: random sampling with no replacement
* bootstrap: random sampling with replacement
* cross-validation: evaluating model performance, model selection (select the appropriate level of flexibility)
* bootstrap: mostly used to quantify the uncertainty associated with a given estimator or statistical learning method

Source

[ VIDEO OF THE WEEK]

#FutureOfData with @theClaymethod, @TiVo discussing running analytics in media industry

 #FutureOfData with @theClaymethod, @TiVo discussing running analytics in media industry

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

For every two degrees the temperature goes up, check-ins at ice cream shops go up by 2%. – Andrew Hogue, Foursquare

[ PODCAST OF THE WEEK]

@AngelaZutavern & @JoshDSullivan @BoozAllen discussed Mathematical Corporation #FutureOfData

 @AngelaZutavern & @JoshDSullivan @BoozAllen discussed Mathematical Corporation #FutureOfData

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

How Bayer Pharmaceuticals Found the Right Prescription for Clinical Data Access

Like other pharmaceutical companies, Bayer Pharmaceuticals conducts research to discover new drugs, test and validate their effectiveness and safety, and introduce them to the market. That process requires accumulating, analyzing and storing vast amounts of clinical data coming from patients and healthy volunteers, which is recorded on an electronic case report form (eCRF). Data is also collected from laboratories and electronic devices and all data is automatically anonymized at the point of collection.

Bayer wanted to gather more data about its drugs to comply with documentation requirements such as those in GxP, a collection of quality guidelines and regulations created by the U.S. Food and Drug Administration (FDA).

Building a microservices-based architecture

To make it faster and easier for researchers to analyze drug development data, the company deployed a microservices-based architecture for its data platform.

dGerman software developer QuinScape GmbH helped Bayer deploy the Talend-powered Karapit framework, which the company is using to integrate several clinical databases and support the pharmacokinetics dataflow and biosampling parts of the drug development process.

“Through Talend microservices, we can obtain clinical pharmacokinetic data more rapidly in order to determine drug doses and better characterize compounds and adhere to quality processes” – Dr. Ivana Adams, Project Manager, Translational Science Systems

Understanding pharmacokinetics data to optimize drug doses

The role of pharmacokinetics (PK) in drug discovery can be described simply as the study of “what a body does to a drug,” and includes the rate and extent to which drugs are absorbed into the body and distributed to the body tissues. It also includes the rate and pathways by which drugs are eliminated from the body by metabolism and excretion. Understanding these processes is extremely important for prescribers because they form the basis for the optimal dose regimen and explain the inter-individual variations in the response to drug therapy

Having clean, verified traceable clinical data saves development time, accelerates the process of determining proper dosage and drug interactions, and leaves a clear audit trail as per FDA GxP guidelines.

Read the full case study here. 

The post How Bayer Pharmaceuticals Found the Right Prescription for Clinical Data Access appeared first on Talend Real-Time Open Source Data Integration Software.

Source: How Bayer Pharmaceuticals Found the Right Prescription for Clinical Data Access

Talend and Red Hat OpenShift Integration: A Primer

One of the aspects I am always fascinated about Talend is its ability to run programs according to multiple job execution methodologies. Today I wanted to write an overview of a new way of executing data integration jobs using Talend and Red Hat OpenShift Platform.

First and foremost, let us do a quick recap of the standard ways of running Talend jobs. Users usually run Talend jobs using Talend schedulers which can be either in the Cloud or On-premise. Other methods include creating standalone jobs, building web services from Talend Jobs, building OSGI Bundle for ESB and the latest entry to this list from Talend 7.1 onwards is building the job as Docker image. For this blog, we are going to focus on the Docker route and show you how Talend Data Integration jobs can be used with Red Hat OpenShift Platform. 

I would also highly recommend reading two other interesting Talend blogs related to the interaction between Talend and Docker, which are:

  1. Going Serverless with Talend through CI/CD and Containers by Thibaut Gourdel
  2. Overview: Talend Server Applications with Docker by Michaël Gainhao 

Before going to other details, let’s get into the basics of containers, Docker and Red Hat OpenShift Platform. For all those are already proficient in container technology, I would recommend skipping ahead to the next section of the blog.

Containers, Docker, Kubernetes and Red Hat OpenShift

What is a container? A container is a standardized unit of software which is quite lightweight and can be executed without environment related constraints. Docker is the most popular container platform and it has helped the Information technology industry in two major fronts i.e. reduction in the infrastructure and maintenance cost and reduction in turnaround time to bring applications to market. 

The diagram above shows how the various levels Docker container platform and Talend jobs are stacked in application containers. The Docker platform interacts with underlying infrastructure and host operating system and it helps the application containers to run in a seamless manner without knowing the complexities of the underlying layers.

Kubernetes

Next, let us quickly talk about Kubernetes and how it has helped in the growth of container technology. When we are building more and more containers, we will need an orchestrator who can control the management, automatic deployment and scaling of the containers and Kubernetes is the software platform which does this orchestration in a magical way.

Kubernetes helps to coordinate a cluster of computers as a single unit and we can deploy containerized applications on top of the cluster. It consists of Pods which acts as logical host for the containers and these pods are running on top of worker machines in Kubernetes called Nodes. There are a lot of other concepts in Kubernetes but let us limit ourselves to the context of the blog since Talend Job containers are executed on top of these Pods.

Red Hat OpenShift

OpenShift is the open source container application platform from Red Hat which is built on top of Docker containers and Kubernetes container cluster manager. I am republishing the official OpenShift block diagram from Red Hat website for your quick reference.

OpenShift comes in a few flavors apart from the free (Red Hat OpenShift Online Starter) version.

  1. Red Hat OpenShift Online Pro
  2. Red Hat OpenShift Dedicated
  3. Red Hat OpenShift Container Platform

OpenShift Online Pro and Dedicated will be running on top of Red Hat hosted infrastructure and OpenShift Container Platform can be set up on top of customer’s own infrastructure.

Now let’s move to our familiar territory where we are planning to convert the Talend job to Docker container.

Talend Job Conversion to Container and Image Registry Storage

Considering the customers who are using older versions of Talend, we will first create a Docker image from a sample Talend job. Those who are already using Talend 7.1 version, you have the capability to export the Talend jobs to Docker as mentioned in the introduction section. So, you can safely move to the next section where the Docker image is already available and we will meet you there. People who are still with me, let us quickly build a Docker image for a sample job 😊.

I have created a simple job where I am generating random first and last names and then printing them on the console.  We are going to build a standalone job zip file from the Talend job and the zip will be placed in the target directory of the server, where Docker is available.

The next step will be to create a Docker file which will store the instructions to perform while building a Docker container from the Talend standalone zip file. The steps in the Docker file is as shown below.

FROM anapsix/alpine-java:8u121b13_jdk



ARG talend_job

ARG talend_version



LABEL maintainer="nthampi@talend.com" 

    talend.job=${talend_job} 

    talend.version=${talend_version}



ENV TALEND_JOB ${talend_job}

ENV TALEND_VERSION ${talend_version}

ENV ARGS ""



WORKDIR /opt/talend



COPY ${TALEND_JOB}_${talend_version}.zip .


RUN unzip ${TALEND_JOB}_${TALEND_VERSION}.zip && 

    rm -rf ${TALEND_JOB}_${TALEND_VERSION}.zip && 

    chmod +x ${TALEND_JOB}/${TALEND_JOB}_run.sh


CMD ["/bin/sh","-c","${TALEND_JOB}/${TALEND_JOB}_run.sh ${ARGS} "]

If you notice the various commands specified in the Docker file, we could identify that we are creating a base Alpine java image. On top of that we are adding additional instructions in a layered format. The instructions specify to unzip the file that contains the Talend job and execute the right shell script file. Now, we have created the Docker file which will be used for the container build.

The statement to create the Docker build for the Talend job is below. 

docker build /home/centos/talend/ -f /home/centos/talend/dockerfile.txt -t nikhilthampi/helloworld:0.1 --build-arg talend_job=helloworld --build-arg talend_version=0.1

The docker images command will list the newly created container with the container name and container tag already present such as “nikhilthampi/helloworld” and “0.1” respectively.

If you are interested in moving the Docker image to a Docker repository, you can login to Docker using the command below and push the container to Docker Hub.

The image will be now available in the Docker hub repository as shown below.

Similarly, you can load the container to a Red Hat OpenShift image repository. The first step is to configure the OpenShift client in the server and follow the steps below for installing in CentOS.

wget https://mirror.openshift.com/pub/openshift-v3/clients/3.9.31/linux/oc.tar.gz
tar -xvf oc.tar.gz
cd /opt
mkdir oc
mv /home/centos/oc /opt/oc/oc
export PATH=$PATH:/opt/oc

The next step is to go OpenShift Console and get the login credentials from the site as shown below. You will be provided with login credentials with a token.

Using the token, you will be able to login to OpenShift and the details of successful login are shown below. I have already created a project called “docker” inside OpenShift and OpenShift will be start using this project.

We can now tag the container we have created and push the container to OpenShift image registry and the sample pattern is as shown below.

docker tag  //

docker push //

The screenshot below is sample output we will be getting from OpenShift after executing the commands.

The container image can be viewed through OpenShift Console also and it will be available under Image Streams section of the project.

Alright! We have completed the tasks involved in transferring the Talend job docker image to OpenShift image registry.  Don’t you think it is easy? Instead of doing the container image migration manually, CI/CD can be also used to do the deployment to docker registries. It is not in the scope of the current blog and I would recommend going through CI/CD blogs of Talend to automate the above steps.

Talend Job execution in OpenShift

Now, let’s get to Talend job execution in OpenShift. The first step to create a job in Openshift is to configure the corresponding YAML file. Below is the sample YAML file which I have created for the “helloworld” job.

apiVersion: batch/v1

kind: Job

metadata:

  name: helloworld

spec:

  template:

    spec:

      activeDeadlineSeconds: 180

      containers:

      - name: helloworld

        image: docker-registry.default.svc:5000/docker/helloworld

      restartPolicy: Never

  backoffLimit: 4

Instead of Pod, Route or Service, we have created a Job kind and we have also added the source image registry details to the YAML file. Once the YAML file is ready, below command must be executed in the command line to generate the job in OpenShift.

Once the success message is generated by the command, we will be able to see the entry got created under Other Resources -> Job section of OpenShift.

If you go the Pods section of OpenShift, you will be able to see that the Talend job has been executed successfully and the logs have been captured as shown below.

I hope your journey through the blog to execute Talend and Red Hat Openshift job was quite easy and interesting. There are a lot of other interesting blogs on various subject areas of Talend. I would highly recommend checking them also to increase your knowledge of Talend and how Talend is interacting with many interesting technologies in IT.

Till I come back with a new blog, enjoy your time using Talend! 

References

https://www.docker.com/resources/what-container

https://kubernetes.io/docs/tutorials/kubernetes-basics/

https://www.openshift.com/learn/what-is-openshift/

https://docs.openshift.com/container-platform/3.5/dev_guide/jobs.html#creating-a-job

The post Talend and Red Hat OpenShift Integration: A Primer appeared first on Talend Real-Time Open Source Data Integration Software.

Source by analyticsweekpick

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

Warning: file_get_contents(http://events.analytics.club/tw/eventpull.php?cat=WEB): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

Warning: file_get_contents(http://events.analytics.club/tw/eventpull.php?cat=WEB): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

Warning: file_get_contents(http://events.analytics.club/tw/eventpull.php?cat=WEB): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

Warning: file_get_contents(http://events.analytics.club/tw/eventpull.php?cat=WEB): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

Warning: file_get_contents(http://events.analytics.club/tw/eventpull.php?cat=WEB): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

Warning: file_get_contents(http://events.analytics.club/tw/eventpull.php?cat=WEB): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

Warning: file_get_contents(http://news.analyticsweek.com/tw/newspull.php): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

Warning: file_get_contents(http://news.analyticsweek.com/tw/newspull.php): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

Warning: file_get_contents(http://news.analyticsweek.com/tw/newspull.php): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

[  COVER OF THE WEEK ]

image
SQL Database  Source

[ AnalyticsWeek BYTES]

>> 22 tips for better data science by analyticsweekpick

>> 6 Questions to Ask When Preparing Data for Analysis by analyticsweek

>> The UX of Brokerage Websites by analyticsweek

Wanna write? Click Here

[ FEATURED COURSE]

Tackle Real Data Challenges

image

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

[ FEATURED READ]

Machine Learning With Random Forests And Decision Trees: A Visual Guide For Beginners

image

If you are looking for a book to help you understand how the machine learning algorithms “Random Forest” and “Decision Trees” work behind the scenes, then this is a good book for you. Those two algorithms are commonly u… 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: collaborative filtering, n-grams, cosine distance?
A: Collaborative filtering:
– Technique used by some recommender systems
– Filtering for information or patterns using techniques involving collaboration of multiple agents: viewpoints, data sources.
1. A user expresses his/her preferences by rating items (movies, CDs.)
2. The system matches this user’s ratings against other users’ and finds people with most similar tastes
3. With similar users, the system recommends items that the similar users have rated highly but not yet being rated by this user

n-grams:
– Contiguous sequence of n items from a given sequence of text or speech
– ‘Andrew is a talented data scientist”
– Bi-gram: ‘Andrew is”, ‘is a”, ‘a talented”.
– Tri-grams: ‘Andrew is a”, ‘is a talented”, ‘a talented data”.
– An n-gram model models sequences using statistical properties of n-grams; see: Shannon Game
– More concisely, n-gram model: P(Xi|Xi?(n?1)…Xi?1): Markov model
– N-gram model: each word depends only on the n?1 last words

Issues:
– when facing infrequent n-grams
– solution: smooth the probability distributions by assigning non-zero probabilities to unseen words or n-grams
– Methods: Good-Turing, Backoff, Kneser-Kney smoothing

Cosine distance:
– How similar are two documents?
– Perfect similarity/agreement: 1
– No agreement : 0 (orthogonality)
– Measures the orientation, not magnitude

Given two vectors A and B representing word frequencies:
cosine-similarity(A,B)=?A,B?/||A||?||B||

Source

[ VIDEO OF THE WEEK]

Jeff Palmucci @TripAdvisor discusses managing a #MachineLearning #AI Team

 Jeff Palmucci @TripAdvisor discusses managing a #MachineLearning #AI Team

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

With data collection, ‘the sooner the better’ is always the best answer. – Marissa Mayer

[ PODCAST OF THE WEEK]

Solving #FutureOfWork with #Detonate mindset (by @steven_goldbach & @geofftuff) #JobsOfFuture #Podcast

 Solving #FutureOfWork with #Detonate mindset (by @steven_goldbach & @geofftuff) #JobsOfFuture #Podcast

Subscribe 

iTunes  GooglePlay

[ FACT OF THE WEEK]

Distributed computing (performing computing tasks using a network of computers in the cloud) is very real. Google GOOGL -0.53% uses it every day to involve about 1,000 computers in answering a single search query, which takes no more than 0.2 seconds to complete.

Sourced from: Analytics.CLUB #WEB Newsletter

Big Data Provides Big Insights for U.S. Hospitals

The U.S. government provides a variety of publicly available databases that include metrics on the performance of US hospitals, including patient experience (PX) database, health outcome database, process of care database and medical spending database. Applying Big Data principles on these disparate data sources, I integrated different metrics from their respective databases to better understand the quality of US hospitals and determine ways they can improve the patient experience and the overall healthcare delivery system. I spent the summer analyzing this data, and wrote many posts about it.

Why the Patient Experience (PX) has Become an Important Topic for U.S. Hospitals

The Centers for Medicare & Medicaid Services (CMS) will be using patient feedback about their care as part of their reimbursement plan for acute care hospitals (see Hospital Value-Based Purchasing (VBP) program). The purpose of the VBP program is to promote better clinical outcomes for patients and improve their experience of care during hospital stays. Not surprisingly, hospitals are focusing on improving the patient experience to ensure they receive the maximum of their incentive payments.

Key Findings from Analyses of Big Data of US Hospitals

Hospitals, like all big businesses, struggle with knowing “if you do this, then you will succeed with this.” While hospital administrators can rely on gut feelings, intuition and anecdotal evidence to guide their decisions on how to improve their hospitals, data-driven decision-making provides better, more reliable, insights about real things hospital administrators can do to improve their hospitals. While interpretation of my analyses of these Big Data are debatable, the data are what they are.

I have highlighted some key findings below (with accompanying blog posts) that provide value for different constituencies: 1) healthcare consumers can find the best hospitals, 2) healthcare providers can focus on areas that improve how they deliver healthcare, and 3) healthcare researchers can uncover deeper insights about factors that impact the patient experience and health outcomes.

  1. Healthcare Consumers Can Use Interactive Maps of US Hospital Ratings to Select the Best Provider. Healthcare consumers can use interactive maps to understand the quality of their hospitals with respect to three metrics: 1) Map of US hospitals on patient satisfaction, 2) Map of US hospitals on health outcomes, and 3) Map of US hospitals on process of care. Take a look at each to know how your hospital performs.
  2. Hospitals Can Use Patient Surveys to Improve Patient Loyalty. Hospitals might be focusing on the wrong areas to improve patient loyalty. While researchers found that hospitals’ top 3 priorities to improve the patient experience are focused on 1) reducing noise, 2) improving patient rounding and 3) the improving the discharge process and instructions, analysis of HCAHPS survey results show that hospitals will likely receive greater return on their improvement investment (ROI) if they focus on improving the patient experience along these dimensions: 1) pain management, 2) staff responsiveness and 3) staff explaining meds.
  3. There are Differences in the Patient Experience across Acute Care and Critical Access Hospitals. Acute care hospitals receive lower patient satisfaction ratings compared to critical access hospitals. Differences across these two types of hospitals also extends to ways to improve the patient experience. The key areas for improving patient loyalty/advocacy differ across hospital types. ACHs need to focus on 1) Staff explains meds, 2) Responsiveness and 3) Pain management. CAHs need to focus on 1) Pain management and 2) Responsiveness.
  4. Patient Satisfaction is Related to Health Outcomes and Process of Care Measures. The patient experience that had the highest correlation with readmission rates and process of care measures was “Given Information about my Recovery upon discharge“.  Hospitals who received good patient ratings on this dimension also experienced lower readmission rates and higher process of care scores compared to hospitals with poor patient ratings in this area.
  5. Medical Spending is Not Related to Patient Satisfaction. I found that hospitals with lower medical spend per patient are able to deliver a comparable patient experience to hospitals with greater medical spend per patient.

The value of insights gained from combining, integrating disparate databases (especially ones including both attitudinal and operational/objective metrics) provide much greater value than any single database can provide by itself.  That is one of the major values of using Big Data principles. The integrated health care Big Data set provided rich in insights and allowed us to answer bigger questions about how to best improve the patient experience and health outcomes.

Source: Big Data Provides Big Insights for U.S. Hospitals by bobehayes

Logi Tutorial: How to Integrate Logi with Microsoft Active Directory for Enhanced User Authentication

This post originally appeared on dbSeer, a business analytics consulting firm and Logi Analytics partner.

As an increasing number of companies are moving their infrastructure to Microsoft’s Azure, it seems natural to rely on its Active Directory for user authentication. Logi application users can also reap the benefits of this enterprise level security infrastructure without having to duplicate anything. Additionally, even smaller companies who use Office365 without any other infrastructure on the cloud, excluding email of course, can take advantage of this authentication.

Integrating Logi applications with Microsoft’s Active Directory produces two main benefits: attaining world class security for your Logi applications, and simplifying matters by having a single source of authentication. The following post describes how this integration is done.

1. Register Your Application with Microsoft

First, register your application with Azure Active Directory v. 2.0. This will allow us to request an access token from Microsoft for the user. To do this navigate to “https://apps.dev.microsoft.com/#/appList,” and click the “Add an app” button. After entering your application name, on the following page, click the “Add Platform” button and select “Web”. Under Redirect URLs, enter the URL of your website logon page (sample format: https:////.jsp). Microsoft does not support redirects to http sites, so your page must either use https or localhost. Make note of the redirect URL and application ID for the next step.

2. Create Custom Log-on Page for Logi Application

Microsoft allows users to give permissions to an application using their OAuth2 sign-in page. This process returns an access token, which has a name, email address, and several other pieces of information embedded within which we use to identify the user.

These next steps show you how to create a log-in page that redirects users to the Microsoft sign-in, retrieves the access token, and passes whichever value you want to use to identify the employee to Logi.

1) Download the rdLogonCustom.jsp file or copy paste the contents into a file. Place it in the base folder of your application.
2) Configure the following settings within the rdLogonCustom.jsp file to match your Logi application:

Change the ‘action’ element in the HTML body to the address of your main Logi application page:


Change the “redirectUri” and “appId” in the buildAuthUrl() function to match the information from your application registration with Azure AD v2.0:

The sample log-on page redirects the user to Microsoft’s page, allows the user to sign in before redirecting back to the log-on page. At the log-on page, it parses the token for the email address, passes the value to the authentication element using the hidden input to pass as a request parameter.

If you want to use a different value from the access token to identify the user, adjust the key in the “document.getElementById(‘email’).value = payload.” in the bottom of the custom logon file to match your desired value.

3. Configure Logi App

In your _Settings.lgx file, add a security element with the following settings:

*If your log-on page and failure page have different names, adjust accordingly.
Under the security element, add an authentication element with a data layer that uses the value found in @Request.email~ to identify the user. Optionally, you can add rights and roles elements to the security element as well.

In conclusion, utilizing this integration for your Logi applicatons can not only make your process more efficient by eliminating a duplicate authentication, but it can also provide for an added level of security because of Microsoft’s robust infrastructure.

Source: Logi Tutorial: How to Integrate Logi with Microsoft Active Directory for Enhanced User Authentication

Sep 12, 19: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

Warning: file_get_contents(http://events.analytics.club/tw/eventpull.php?cat=WEB): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

Warning: file_get_contents(http://events.analytics.club/tw/eventpull.php?cat=WEB): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

Warning: file_get_contents(http://events.analytics.club/tw/eventpull.php?cat=WEB): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

Warning: file_get_contents(http://events.analytics.club/tw/eventpull.php?cat=WEB): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

Warning: file_get_contents(http://events.analytics.club/tw/eventpull.php?cat=WEB): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

Warning: file_get_contents(http://events.analytics.club/tw/eventpull.php?cat=WEB): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

Warning: file_get_contents(http://news.analyticsweek.com/tw/newspull.php): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

Warning: file_get_contents(http://news.analyticsweek.com/tw/newspull.php): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

Warning: file_get_contents(http://news.analyticsweek.com/tw/newspull.php): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

[  COVER OF THE WEEK ]

image
Ethics  Source

[ AnalyticsWeek BYTES]

>> All the Ways to Connect Your Azure SQL Data Warehouse with Talend by analyticsweekpick

>> How oil and gas firms are failing to grasp the necessity of Big Data analytics by analyticsweekpick

>> 5 Ways Manufacturing Analytics Will Change Your Business by analyticsweek

Wanna write? Click Here

[ FEATURED COURSE]

Statistical Thinking and Data Analysis

image

This course is an introduction to statistical data analysis. Topics are chosen from applied probability, sampling, estimation, hypothesis testing, linear regression, analysis of variance, categorical data analysis, and n… more

[ FEATURED READ]

Antifragile: Things That Gain from Disorder

image

Antifragile is a standalone book in Nassim Nicholas Taleb’s landmark Incerto series, an investigation of opacity, luck, uncertainty, probability, human error, risk, and decision-making in a world we don’t understand. The… more

[ TIPS & TRICKS OF THE WEEK]

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

[ DATA SCIENCE Q&A]

Q:Provide examples of machine-to-machine communications?
A: Telemedicine
– Heart patients wear specialized monitor which gather information regarding heart state
– The collected data is sent to an electronic implanted device which sends back electric shocks to the patient for correcting incorrect rhythms

Product restocking
– Vending machines are capable of messaging the distributor whenever an item is running out of stock

Source

[ VIDEO OF THE WEEK]

Pascal Marmier (@pmarmier) @SwissRe discusses running data driven innovation catalyst

 Pascal Marmier (@pmarmier) @SwissRe discusses running data driven innovation catalyst

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

He uses statistics as a drunken man uses lamp posts—for support rather than for illumination. – Andrew Lang

[ PODCAST OF THE WEEK]

George (@RedPointCTO / @RedPointGlobal) on becoming an unbiased #Technologist in #DataDriven World #FutureOfData #Podcast

 George (@RedPointCTO / @RedPointGlobal) on becoming an unbiased #Technologist in #DataDriven World #FutureOfData #Podcast

Subscribe 

iTunes  GooglePlay

[ FACT OF THE WEEK]

Big data is a top business priority and drives enormous opportunity for business improvement. Wikibon’s own study projects that big data will be a $50 billion business by 2017.

Sourced from: Analytics.CLUB #WEB Newsletter

3 Big Data Stocks Worth Considering

Big data is a trend that I’ve followed for some time now, and even though it’s still in its early stages, I expect it to continue to be a game changer as we move further into the future.

smartphone tech sector news 3 Big Data Stocks Worth ConsideringAs our Internet footprint has grown, all the data we create — from credit cards to passwords and pictures uploaded on Instagram — has to be managed somehow.

This data is too vast to be entered into traditional relational databases, so more powerful tools are needed for companies to utilize the information to analyze customers’ behavior and predict what they may do in the future.

Big data makes it all possible, and as a result is one of the dominant themes for technology growth investing. We’ve invested in several of these types of companies in my GameChangers service over the years, one of which we’ll talk more about in just a moment.

First, let’s start with two of the biggest and best big data names out there. They’re among the best pure plays, and while I’m not sure the time is quite right to invest in either right now, they are both garnering some buzz in the tech world.

Big Data Stocks: Splunk (SPLK)

Splunk185 3 Big Data Stocks Worth ConsideringThe first is Splunk (SPLK). Splunk’s flagship product is Splunk Enterprise, which at its core is a proprietary machine data engine that enables dynamic creation on the fly. Users can then run queries on data without having to understand the structure of the information prior to collection and indexing.

Faster, streamlined processes mean more efficient (and more profitable) businesses.

While Splunk is very small in terms of revenues, with January 2015 fiscal year sales of just $451 million, it is growing rapidly, and I’m keeping an eye on the name as it may present a strong opportunity down the road.

However, I do not want to overpay for it. Splunk brings effective technology to the table that is gaining market acceptance, and has strong security software partners with its recent entry into security analytics. At the right price, the stock could also be a takeover candidate for a larger IT company looking to enhance its Big Data presence.

Big Data Stocks: Tableau Software (DATA)

TableauSoftware185 3 Big Data Stocks Worth ConsideringAnother name on my radar is Tableau Software (DATA), which performs similar functions as Splunk’s. Its primary product, VizQL, translates drag-and-drop actions into data queries. In this way, the company puts data directly in the hands of decision makers, without first having to go through technical specialists.

In fact, the company believes all employees, no matter what their rank in the company, can use their product, leading to the democratization of data.

DATA is also growing rapidly, even faster than Splunk. Revenues were up 78% in 2014, and 75% in the first quarter of 2015, including license revenue growth of more than 70%. That rate is expected to slow somewhat, with revenues for all of 2015 estimated to increase to a still strong 50%.

Tableau stock is also very expensive, trading at 12X expected 2015 revenues of $618 million and close to 300X projected EPS of 40 cents for the year. DATA is a little risky to buy at current levels, but it is a name to keep an eye on in any pullback.

Big Data Stocks: Red Hat (RHT)

red hat rht stock logo 185 3 Big Data Stocks Worth ConsideringThe company we made money on earlier this year in my GameChangers service isRed Hat (RHT). We booked a 15% profit in just a few months after it popped 11% on fourth-quarter earnings.

Red Hat is the world’s largest leading provider of open-source solutions, providing software to 90% of Fortune 500 companies. Some of RHT’s customers include well-known names like Sprint (S), Adobe Systems (ADBE) and Cigna Corporation (CI).

Management’s goal is to become the undisputed leader of enterprise cloud computing, and it sees its popular Linux operating system as a way to the top. If RHT is successful — as I expect it will be — Red Hat should have a lengthy period of expanded growth as corporations increasingly move into the cloud.

Red Hat’s operating results had always clearly demonstrated that its solutions are gaining greater acceptance in IT departments, as revenues had more doubled in the five years between 2009 and 2014 from $748 million to $1.53 billion. I had expected to see the strong sales growth continue throughout 2015, and it did. As I mentioned, impressive fiscal fourth-quarter results sent the shares 11% higher.

I recommended my subscribers sell their stake in the company at the end of March because I believed any further near-term upside was limited. Since then, shares have traded mostly between $75 and $80. It is now at the very top of that range and may be on the verge of breaking above it after the company reported fiscal first-quarter results last night.

Although orders were a little slow, RHT beat estimates on both the top and bottom lines in the first quarter. Earnings of 44 cents per share were up 29% quarter-over-quarter, besting estimates on the Street for earnings of 41 cents. Revenue climbed 14% to $481 million, while analysts had been expecting $472.6 million.

At this point, RHT is now back in uncharted territory, climbing to a new 52-week high earlier today. This is a company with plenty of growth opportunities ahead, and while growth may slow a bit in the near term following the stock’s impressive climb so far this year, RHT stands to gain as corporation continue to adopt additional cloud technologies.

To read the original article on InvestorPlace, click here.

Source

Sep 05, 19: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

Warning: file_get_contents(http://events.analytics.club/tw/eventpull.php?cat=WEB): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

Warning: file_get_contents(http://events.analytics.club/tw/eventpull.php?cat=WEB): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

Warning: file_get_contents(http://events.analytics.club/tw/eventpull.php?cat=WEB): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

Warning: file_get_contents(http://events.analytics.club/tw/eventpull.php?cat=WEB): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

Warning: file_get_contents(http://events.analytics.club/tw/eventpull.php?cat=WEB): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

Warning: file_get_contents(http://events.analytics.club/tw/eventpull.php?cat=WEB): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

Warning: file_get_contents(http://news.analyticsweek.com/tw/newspull.php): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

Warning: file_get_contents(http://news.analyticsweek.com/tw/newspull.php): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

Warning: file_get_contents(http://news.analyticsweek.com/tw/newspull.php): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

[  COVER OF THE WEEK ]

image
Data analyst  Source

[ AnalyticsWeek BYTES]

>> Out of the Loop on the Internet of Things? Here’s a Brief Guide. by analyticsweekpick

>> Three final talent tips: how to hire data scientists by analyticsweekpick

>> Measuring The Customer Experience Requires Fewer Questions Than You Think by bobehayes

Wanna write? Click Here

[ FEATURED COURSE]

Machine Learning

image

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

[ FEATURED READ]

Introduction to Graph Theory (Dover Books on Mathematics)

image

A stimulating excursion into pure mathematics aimed at “the mathematically traumatized,” but great fun for mathematical hobbyists and serious mathematicians as well. Requiring only high school algebra as mathematical bac… 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 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]

#GlobalBusiness at the speed of The #BigAnalytics

 #GlobalBusiness at the speed of The #BigAnalytics

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

Data is the new science. Big Data holds the answers. – Pat Gelsinger

[ PODCAST OF THE WEEK]

#FutureOfData with @theClaymethod, @TiVo discussing running analytics in media industry

 #FutureOfData with @theClaymethod, @TiVo discussing running analytics in media industry

Subscribe 

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