DATANOMIQ MeetUp: Interactive Data Exploration and GUI’s in JupyterNotebooks

After our first successful collaboration Meetup with Mister Spex, we straightly continue with our next partner: VW Digital Labs!

Join us on Wednesday, October 9 for our DATANOMIQ Data Science Meetup at VW Digital Labs and get inspired.

When:
Wednesday, October 9, time TBA

Where:
VW Digital Labs
Stralauer Allee 7, 10245 Berlin

 

AGENDA
18:30 doors open
19:00 Interactive Data Exploration and GUI’s in JupyterNotebooks – Christopher Kipp.
– using ipywidgets to get basic UI components and connet them
– qgrid to make Dataframes interactive (sortable, filterable, …)
– building interactive visualisations with bqplot

19:20 Q&A

10 minute break

19:40 second presentation
20:00 Q&A

20:15 networking

 

FREE ENTRY, snacks and drinks sponsored by VW digital labs.

Make sure to get your ticket: https://www.eventbrite.de/e/datanomiq-meetup-interactive-data-exploration-and-guis-in-jupyternotebook-tickets-72931655545

Entrance only with registration.

 

Join our MeetUp group: https://www.meetup.com/de-DE/DATANOMIQ-Data-Science-Berlin/

6 Important Reasons for the Java Experts to learn Hadoop Skills

You must be well aware of the fact that Java and Hadoop Skills are in high demand these days. Gone are the days when advancement work moved around Java and social database. Today organizations are managing big information. It is genuinely big. From gigabytes to petabytes in size and social databases are exceptionally restricted to store it. Additionally, organizations are progressively outsourcing the Java development jobs to different groups who are as of now having big data experts.

Ever wondered what your future would have in store for you if you possess Hadoop as well as Java skills? No? Let us take a look. Today we shall discuss the point that why is it preferable for Java Developers to learn Hadoop.

Hadoop is the Future Java-based Framework that Leads the Industry

Data analysis is the current marketing strategy that the companies are adopting these days. What’s more, Hadoop is to process and comprehend all the Big Data that is generated all the time. As a rule, Hadoop is broadly utilized by practically all organizations from big and small and in practically all business spaces. It is an open-source stage where Java owes a noteworthy segment of its success

The processing channel of Hadoop, which is MapReduce, is written in Java. Thus, a Hadoop engineer needs to compose MapReduce contents in Java for Big data analysis. Notwithstanding that, HDFS, which is the record arrangement of Hadoop, is additionally Java-based programming language at its core. Along these lines, a Hadoop developer needs to compose documents from local framework to HDFS through deployment, which likewise includes Java programming.

Learn Hadoop: It is More Comfortable for a Java Developer

Hadoop is more of an environment than a standalone innovation. Also, Hadoop is a Java-based innovation. Regardless of whether it is Hadoop 1 which was about HDFS and MapReduce or Hadoop2 biological system that spreads HDFS, Spark, Yarn, MapReduce, Tez, Flink, Giraph, Storm, JVM is the base for all. Indeed, even a portion of the broadly utilized programming languages utilized in a portion of the Hadoop biological system segments like Spark is JVM based. The run of the mill models is Scala and Clojure.

Consequently, if you have a Java foundation, understanding Hadoop is progressively easier for you. Also, here, a Hadoop engineer needs Java programming information to work in MapReduce or Spark structure. Thus, if you are as of now a Java designer with a logical twist of the brain, you are one stage ahead to turn into a Hadoop developer.

IT Industry is looking for Professionals with Java and Hadoop Skills

If you pursue the expected set of responsibilities and range of abilities required for a Hadoop designer in places of work, wherever you will watch the reference of Java. As Hadoop needs solid Java foundation, from this time forward associations are searching for Java designers as the best substitution for Hadoop engineers. It is savvy asset usage for organizations as they don’t have to prepare Java for new recruits to learn Hadoop for tasks.

Nonetheless, the accessible market asset for Hadoop is less. Therefore, there is a noteworthy possibility for Java designers in the Hadoop occupation field. Henceforth, as a Java designer, on the off chance that you are not yet arrived up in your fantasy organization, learning Hadoop, will without a doubt help you to discover the chance to one of your top picks.

Combined Java and Hadoop Skills Means Better Pay Packages

You will be progressively keen on learning Hadoop on the off chance that you investigate Gartner report on big information industry. According to the report, the Big Data industry has just come to the $50 billion points. Additionally, over 64% of the main 720 organizations worldwide are prepared to put resources into big information innovation. Notwithstanding that when you are a mix of a Java and Hadoop engineer, you can appreciate 250% pay climb with a normal yearly compensation of $150,000.It is about the yearly pay of a senior Hadoop developer.

Besides, when you change to Big Data Hadoop, it very well may be useful to improve the nature of work. You will manage unpredictable and greater tasks. It does not just give you a better extension to demonstrate your expertise yet, in addition, to set up yourself as a profitable asset who can have any kind of effect.

Adapting Big Data Hadoop can be exceptionally advantageous because it will assist you in dealing with greater, complex activities a lot simpler and convey preferable yield over your associates. To be considered for examinations, you should be somebody who can have any kind of effect in the group, and that is the thing that Hadoop lets you be.

Learning Hadoop will open New Opportunities to Other Lucrative Fields

Big data is only not going to learn Hadoop. When you are in Big information space, you have sufficient chance to jump other Java and Hadoop engineer. There are different exceedingly requesting zones in big information like Artificial Intelligence, Machine Learning, Data Science. You can utilize your Java and Hadoop engineer expertise as a springboard to take your vocation to the following level. In any case, the move will give you the best outcome once you move from Java to Hadoop and increase fundamental working knowledge.

Java with Hadoop opens new skylines of occupation jobs, for example, data scientist, data analyst business intelligence analyst, DBA, etc.

Premier organizations prefer Hadoop Developers with Java skills

Throughout the years the Internet has been the greatest driver of information, and the new data produced in 2012 remained at 2500 Exabyte. The computerized world developed by 62% a year ago to 800K petabytes and will keep on developing to the tune of 1.2 zeta bytes during the present year. Gartner gauges the market of Hadoop Ecosystem to $77 million and predicts it will come to the $813 million marks by 2016.

A review of LinkedIn profiles referencing Hadoop as their abilities uncovered that just about 17000 individuals are working in Companies like Cisco, HP, TCS, Oracle, Amazon, Yahoo, and Facebook, and so on. Aside from this Java proficient who learn Hadoop can begin their vocations with numerous new businesses like Platfora, Alpine information labs, Trifacta, Datatorrent, and so forth.

Conclusion

You can see that combining your Java skills with Hadoop skills can open the doors of several new opportunities for you. You can get better remuneration for your efforts, and you will always be in high demand. It is high time to learn Hadoop online now if you are a java developer.

How can AI and Machine learning impact healthcare industry?

Healthcare industry is a recession-proof one. Even in times of economic meltdown and financial distress, the healthcare industry can hold its own because mankind will always need healthcare. In fact, during the Great Depression in the US, when the economy was facing a severe slowdown, the healthcare industry expanded, adding 852,000 jobs.

Healthcare AI in the US is slated to reach $6.6 billion in value by 2021.

From clinical trials to new drug research & development, and from innovative medical devices to technology like nanoparticles, AI, and ML has touched every point and has the power to transform them completely.

In fact, according to a study by Accenture, AI applications in healthcare can result in global savings to the tune of $150 billion by 2026.

The possibilities are endless, and the results unthinkable if AI can be properly used.

Here are some of the ways AI and ML can impact the healthcare industry:

1. Solving the Iron Triangle

A problem that has plagued the world for many years the triangle aims to solve a fundamental healthcare problem: that of good quality, accessible treatment at low cost.

Providing all three at the same time is a major challenge in healthcare, as the cost of healthcare is usually high. Here, trying to improve one factor harms another.

But AI can solve this problem in the near future without breaking the triangle, by improving the current healthcare cost-structure. The key to it is AI, and smart machines, that the patient can use for self-treatment for the majority of times, cutting down treatment costs drastically, by reducing human contact and improving quality of life.

2. Diagnostics and Imaging

The US FDA has drastically increased investment on AI in radiology and diagnostics. And it’s not without reason.

The IDx-DR became the first AI system cleared by the US FDA to provide diagnostic decisions. It was a breakthrough discovery to detect early mild diabetic retinopathy. The device was accurate 87.5% of the times, and also detected patients who didn’t have the condition, correctly up to 89.5% of times.

The US FDA also permitted marketing of the Viz.AI a type of clinical decision support system designed to analyze CT scan results to identify possibilities of a stroke in the patients and send the results to a specialist to identify any block.

In fact, diagnostics is fast becoming one of the significant drivers of AI investment in healthcare.

These advances can impact the healthcare industry in a novel way. As more and more devices become AI-enabled, the landscape of healthcare delivery will change.

3. Early screening 

Early screening in case of most diseases can drastically improve the mortality rates of patients and cut down treatment costs by over 50%.

Let’s take the example of colorectal cancer.

The 5-year survival for Stage 1 CRC is around 90%, as compared to only 10% for Stage 4.

Early detection of CRC can be ideally treated with a minimally invasive endoscopy at a low cost of less than $5,000 per year. However, in the case of late-stage CRC, it requires multidisciplinary treatment with multiple surgeries, chemotherapy, and radiation, skyrocketing the costs.

And that is why early detection is essential, and that’s exactly what AI can do. There are already apps on the market that are doing this. For example, Autism & Beyond is a revolutionary app that leveraged the power of Apple’s ResearchKit to gather videos of children and detect their preference for the development of autism, using AI software.

AI used for early screening can save billions of taxpayer dollars of taxpayer money every year, and reduce out of pocket expenditure in the US drastically.

4. Drug research & development

According to the California Biomedical Research Association, it takes around 12 years for a drug to be conceived in the laboratory and go to the patient.

Only 1 out of 5000 drugs that are selected for pre-clinical testing are then used for human testing, and only 20% of them make it to the market for human use.

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The cost to develop a new drug now is more than $2.5 billion.

It is only recently that AI is being used in drug research and discovery. The power of AI can be leveraged to streamline the drug discovery and drug repurposing processes. It can identify patients best suited to the trial, can identify patients in the most need for new medications and can predict any side-effects and idiosyncrasies beforehand.

All of these, for a start, can lead to much safer clinical trials with no unwanted drug reactions.

And then, there is the question of lowering costs. In fact, a study by Carnegie Mellon and a German university estimated that AI could lower drug discovery costs by as much as 70%.

This, in turn, will be transferred to patients in the form of lower drug prices, which will increase accessibility to better medications for patients and improve population health in general.

5. Surgery 

AI-enabled robotic-assisted surgeries are taking over the US. They are increasingly being used to reduce surgeon variations and improve quality.

‘Artificial intelligence can help surgeons perform better’ quotes Dr. John Birkmeyer, a chief clinical officer at Sound Physicians.

Advanced analytics and machine learning techniques are being used concomitantly used to unleash critical insights from the billions of data elements associated with robotic-assisted surgery. If used properly, this can help overcome attendant inefficiencies and improve patient health outcomes.

Artificial intelligence helps surgeons make better clinical decisions in real-time during surgery, and helps them understand the dynamics of the patient, especially during complex operations. It also reduces the length of stay of patients by 21%.

This is ultimately reflected in the patient’s post-operative care and long-term health. It also prevents patient readmissions, saving millions of dollars annually.

A study involving 379 orthopedic patients found out that AI-assisted robotic surgery resulted in five times fewer complications as compared to surgeons working alone.

According to Accenture, AI-assisted robotic surgery could save the US healthcare industry $40 billion annually, by 2026.

6. AI-assisted virtual nurses

AI-assisted virtual nurses could well end up saving the US healthcare industry $20 billion annually, by 2026.

They are available 24/7 to answer any patient queries, monitor patients, and guide them in any way they might want.

Currently, they act as a bridge for information exchange between care providers (doctors) and care receivers (patients), to decide what medications to start, the current health status, the most recent test results, and many other things.

It can save the patient many physical appointments with doctors, and also prevent high hospital readmission rates through simple, engaging, and intelligent care.

Care Angel is one of the finest virtual nurses around. Apart from all of the above, it can also provide wellness checks through voice and AI.

Wrap-Up 

AI and ML in healthcare are still at its infancy. Adoption at a large-scale is missing as of yet. To be successful in the healthcare domain, AI and ML need the endorsement of healthcare providers like physicians and nurses.

However, considerable investment is being made in AI in healthcare, and its increasing at a good rate.

AI in healthcare is currently aimed at improving patient outcomes, taking care of the interests of various stakeholders involved, increasing accessibility, and reducing healthcare costs.

In the near future, however, AI and ML, along with technologies like Data Science will take up a much more holistic role to drive healthcare forward.