Quriosity november 2016 by Quantinuum@simsr

Topics

Page No

How Predictive Analytics Are Transforming Health 4 Care? By Bharat Gupta What is predictive Analytics By Milcah George

News Digest By Maheshwaran

Quant Guru By Rupal Doshi

Quriosity Updates By Arunava Dey

Quant Fun By Amod Kulkarni

Quant Connect

Editor’s Note November 2016 Heartiest greetings to all! With great excitement and fervour, team Quantinuum presents “Quriosity” - the Quant magazine of SIMSR. In this issue, we have the main story by the editorial team on “How Predictive Analytics Are Transforming Health Care”, which describes how Rapid growth of data analytics enables the health sector to spot trends, from avoidable readmissions to staffing needs. The sub article by Milcah George on “What is Predictive Analytics” talks about use of data, statistical algorithms and machine-learning techniques to identify the likelihood of future outcomes based on previous data. In our Quant guru section, Rupal Doshi writes about Jay Forrester, who was professor emeritus in the MIT Sloan School of Management, the founder of the field of system dynamics, and a pioneer of digital computing. The News digest features news related to machine learning in ecommerce, a chat box feature that makes your cocktail, supply chain in finance, and application of artificial intelligence in eye care which has been covered by Maheshwaran Kumar. Curiosity updates by Arunava Dey cover new ventures and investigations by NASA scientists in earth science fieldwork and space missions. In our Quants Fun section we have an article by Amod Kulkarni which talks about 10 interesting and quirky facts about mathematics. So, look around and enjoy reading about the quantitative aspects around the world.

Happy Learning, Editorial Team!

Cover Story November 2016 How Predictive Analytics Are Transforming Health Care? Rapid growth of data analytics enables hospitals to spot trends, from avoidable readmissions to staffing needs. The health care field is on the cusp of entering the era of “Moneyball” medicine. Just ask Eric Topol, M.D., director of San Diego-based Scripps Translational Science Institute. He recently hired Paul DePodesta, a data analytics guru who transformed the Oakland Athletics baseball team and now has his algorithms set on doing the same for STSI and health care. And he is not alone. More than 200 data analytics companies are vying for the attention of health care organizations, which are sitting on an untapped trove of data. With the near universal adoption of electronic health records, large hospitals and health systems have begun to recognize something that consumer retailers have relied on for more than a decade: With the right analytics, data can predict the future and help organizations get out in front of consumer trends. In the context of health care, predictive analytics systems are being used, for instance, to understand which patients are at higher risk for hospital readmission, to reduce hospital stays after joint replacement and to anticipate staffing needs while reducing overtime. Some health care organizations have opted to build their own data analysis systems to suit their needs, while others have found industry partners offering predictive analytics systems tailored to the health care industry. Analytics options range from huge general use vendors such as SAS, IBM and Oracle to niche players developing specialized tools for the health care market. For organizations looking to implement analytics, those that have already taken the plunge suggest starting by taking stock of your organization’s current state. “The first thing you need to know is what is happening in your population,” says Rishi Sikka, M.D., senior vice president of clinical transformation for Advocate Health Care in Illinois. “Do you know who is an attributed patient in your population? Do you know who is being readmitted today in your population? Do you know who is visiting the ER?”

Operating as a value-based care organization for several years, Advocate has been moving from fee-for-service revenue models to value-based reimbursement, which is driving health care organizations toward sophisticated analytics systems that can quantify quality measures and track process improvements. Sikka says that five years ago advocate had a big problem with siloed data spread across many EHR systems that did not play well together. With motivation to improve patient care and control costs, Advocate’s leadership chose to invest in a partnership with Cerner, a cloud-based analytics platform that could integrate data from all of the EHRs within Advocate’s existing information technology infrastructure. Cerner’s EHR-agnostic approach was attractive. “Being able to have a data platform that can take in all that data regardless of source and then push it out into a variety of EMRs was important,” says Sikka.

Capturing clinicians’ hearts and minds Cerner worked directly with physician-led teams to pick a case study in which an intervention could truly make a difference in patient care. To get physicians on board, the Advocate clinical innovation team held focus groups and explained how analytics models that integrate data from many sources can have more predictive power than models that rely solely on retrospective administrative data. “As an industry, when we talk about population health, we tend to talk a lot about cost and utilization, and those are extremely important … but that’s not something that gets clinicians excited,” Sikka says. “If you start with the clinical scenario and why it is important to patients … that’s where you really start to capture the hearts and minds of physicians.” Source: http://www.hhnmag.com/articles/7011-predictive-analytics-is-transforming-health-care By Bharat Gupta PGDM FS (2015-17)

What is Predictive Analytics Predictive analytics is the use of data, statistical algorithms and machine-learning techniques to identify the likelihood of future outcomes based on historical data. The goal is to go beyond descriptive statistics and reporting on what has happened to providing a best assessment on what will happen in the future. The end result is to streamline decision making and produce new insights that lead to better actions. Predictive models use known results to develop (or train) a model that can be used to predict values for different or new data. The modeling results in predictions that represent a probability of the target variable (for example, revenue) based on estimated significance from a set of input variables. This is different from descriptive models that help you understand what happened or diagnostic models that help you understand key relationships and determine why something happened. More and more organizations are turning to predictive analytics to increase their bottom line and competitive advantage using predictive analytics. Some of the most common applications of predictive analytics include: 

Fraud detection and security – Predictive analytics can help stop losses due to fraudulent activity before they occur. By combining multiple detection methods – business rules, anomaly detection, link analytics, etc. – you get greater accuracy and better predictive performance. And in today’s world, cybersecurity is a growing concern. High-performance behavioral analytics examine all actions on a network in real time to spot abnormalities that may indicate occupational fraud, zero-day vulnerabilities and advanced persistent threats.



Marketing – Most modern organizations use predictive analytics to determine customer responses or purchases, as well as promote cross-sell opportunities. Predictive models help businesses attract, retain and grow the most profitable customers and maximize their marketing spending.



Operations – Many companies use predictive models to forecast inventory and manage factory resources. Airlines use predictive analytics to decide how many tickets to sell at each price for a flight. Hotels try to predict the number of guests they can expect on any given night to adjust prices to maximize occupancy and increase revenue. Predictive analytics enables organizations to function more efficiently.



Risk – One of the most well-known examples of predictive analytics is credit scoring. Credit scores are used ubiquitously to assess a buyer’s likelihood of default for purchases ranging from homes to cars to insurance. A credit score is a number generated by a predictive model that incorporates

all of the data relevant to a person’s creditworthiness. Other riskrelated uses include insurance claims and collections.

What do you need to get started? 1.

The first thing you need to get started using predictive analytics is a problem to solve. What do you want to know about the future based on the past? What do you want to understand and predict? You’ll also want to consider what will be done with the predictions. What decisions will be driven by the insights? What actions will be taken?

Second, you’ll need data. In today’s world, that means data from a lot of places. Transactional systems, data collected by sensors, third-party information, call center notes, web logs, etc. You’ll need a data wrangler, or someone with data management experience, to help you cleanse and prep the data for analysis. To prepare the data for a predictive modeling exercise also requires someone who understands both the data and the business problem. How you define your target is essential to how you can interpret the outcome. (Data preparation is considered one of the most time-consuming aspects of the analysis process. So be prepared for that).

After that, the predictive model building begins. With increasingly easy-to-use software becoming more available, a wider array of people can build analytical models. But you’ll still likely need some sort of data analyst who can help you refine your models and come up with the best performer. And then you might need someone in IT who can help deploy your models. That means putting the models to work on your chosen data - and that’s where you get your results.

Predictive modeling requires a team approach. You need people who understand the business problem to be solved. Someone who knows how to prepare data for analysis. Someone who can build and refine the models. Someone in IT to ensure that you have the right analytics infrastructure for model building and deployment. And an executive sponsor can help make your analytic hopes a reality.

Source: http://www.predictiveanalyticstoday.com/what-is-predictive-analytics/

By Milcah George PGDM Core (2016-18)

News Digest November 2016 Myntra shines the spotlight on AI to drive growth: Bangalore based fashion portal Myntra which was recently acquired by Ecommerce Company Fliptkart is aiming on Artificial Intelligence and Machine Learning to deliver personalized customer experiences. Myntra is using customer data to curate lines based on current fashion trends and make a one-of-a-kind personalized store experience. Myntra is using customer data to curate lines based on current fashion trends and make a one-of-a-kind personalized store experience. CEO of Myntra and Jabong, Ananth Narayanan, cited in a recent interview that fashion and lifestyle is the most exciting segment in ecommerce right now. â&#x20AC;&#x153;And undoubtedly, Myntra will continue to create more excitement for its consumers through its selection, service and engagement,â&#x20AC;? said Ananth. Source: http://analyticsindiamag.com/myntra-shines-spotlight-ai-drive-growth/

Say hello to Simi - a chatbot that makes your cocktail There is a new chatbot in town and this one does more than just booking cabs, hotels, movie tickets or even compiling your shopping list. Seemingly created for the Indian gimlet-eyed crowd who will definitely love it, Simi â&#x20AC;&#x201C; Your Personal Bartender is a bartender chatbot that suggests Do-It-Yourself cocktail recipes with ingredients that can be easily locally sourced. It has a sold 300 number of recipes and as per reports, over a period of time the bot will stock 2000 recipes. The recipes have been crafted in consultation with certified bartenders. The recipes can be created from everyday ingredients found in an Indian household such as fruits like lemons, oranges to juices in combination with offerings from liquor brand.

Source: http://analyticsindiamag.com/say-hello-simi-chatbot-makes-cocktail/

https://www.facebook.com/simiBartender/ Mahindra and IBM join hands to develop Blockchain Solution for supply chain Finance This Blockchain Solution by Mahindra, one of the largest diversified multinational group of companies based in India and IBM, has the potential to reinvent supply chain finances across India. It would do so by enhancing security, transparency and operational processes. Invoice discounting, the process of bundling and selling invoices at a discount, is a major source of working capital finance for many suppliers. This new solution aims to enable more suppliers to access credit, with the goal of driving more financial inclusion throughout the supply chain. Blockchain technology can help enable Mahindra Finance access transactions recorded on a shared ledger in near real-time, enabling it to develop and offer new products to small and mid-sized enterprises. This is a radical process and technology shift which has the potential to drive business growth into the future. Speed-up invoice discounting. The Blockchain solution app will avoid human errors hence ensuring consistent records, do away with delayed payments, would enable parties involved in the transactions to act on same shared ledger, and updating only their part of the process, infuse trust and transparency into the supply chain financing process and overall an efficient supply chain solution for Mahindra Financeâ&#x20AC;&#x2122;s small and mid-sized enterprises loans business. Source: http://analyticsindiamag.com/mahindra-ibm-join-hands-developing-blockchain-solution-supplychain-finance/ By Maheshwaran PGDM Core (2016-18)

QuantGuru November 2016 JAY WRIGHT FORRESTER Forrester was born on July 14, 1918. Born and raised on a cattle ranch in the Nebraska Sandhills, Jay Wright Forrester developed an early interest in designing electrical systems. As a senior in high school, he built a twelve volt, wind-driven electric plant that provided electricity to his family’s homestead. Forrester received a scholarship to attend agricultural college but instead followed his passion and enrolled in engineering at the University of Nebraska. He was introduced to the behaviour of systems through the study of theoretical dynamics in electrical engineering. Forrester went on to the Massachusetts Institute of Technology after receiving a Bachelor’s degree. He accepted a paid research assistantship with Gordon S. Brown at the Servomechanism Laboratory. During the Second World War, Brown and Forrester developed servomechanisms for controlling radar antennas and gun mounts. In this experience, Forrester saw how research and theory can relate to practical uses. After the War, Forrester was the director of the MIT Digital Computer Laboratory until 1951. He was responsible for the design and construction of Whirlwind I, one of the first high speed computers. In his computing work, Forrester invented random-access, coincident-current magnetic memory that served as the standard memory device for digital computers for many years. He headed the Digital Computer Division of MIT’s Lincoln Laboratory. By 1956, however, Forrester felt as if the pioneering days in digital computers were over and moved to management studies. MIT’s Sloan School of Management was founded upon the expectation that a management school in a technical environment would develop a more distinct culture than one in a liberal arts setting such as Harvard or Chicago. It was in this environment where Forrester launched the idea of system dynamics. Unlike traditional management research and other social sciences, system dynamics developed through intimate contact with real world application. Forrester’s early work was inspired by a series of discussions with General Electric personnel. He first published the philosophy and methodology of the subject in Industrial Dynamics (1961). Eight years later his third book, Urban Dynamics (1969), made waves as it presented a theory of urban interactions and identified reasons for the failures of past policies. In 1970, Forrester began work with the Club of Rome, a global think tank designed as a “global catalyst for change through the identification and analysis of crucial problems facing humanity.” At the Club of Rome, Forrester developed a model capturing feedbacks among population, natural resources, pollution, agricultural and industrial production, capital investment, and quality of life.

His book on the subject, World Dynamics (1971), posed important questions about the relationship between growth and the quality of life. As systems dynamics began to solidify in the late 1970s, Forrester was on the forefront of organizing the field. He served as first president of the System Dynamics Society in 1983 and focused on enhancing the education of the subject. He retired in from MIT 1989 but has since received a number of accolades for his lifeâ&#x20AC;&#x2122;s work. That year, President George H. W. Bush awarded him the National Medal of Technology. In 2006, Forrester was elected into the International Federation of Operational Research Societiesâ&#x20AC;&#x2122; Operational Research Hall of Fame. Source: http://web.mit.edu/sysdyn/sd-intro/D-4165-1.pdf

By Rupal Doshi PGDM FS (2016-18)

Quriosity Updates November 2016 NASA Plans another Busy Year for Earth Science Fieldwork NASA scientists are crisscrossing the globe in 2017 – from a Hawaiian volcano to Colorado mountain tops and west Pacific islands – to investigate critical scientific questions about how our planet is changing and what impacts humans are having on it. Field experiments are an important part of NASA’s Earth science research. Scientists worldwide use the agency’s field data, together with satellite observations and computer models, to tackle environmental challenges and advance our knowledge of how the Earth works as a complex, integrated system. New Investigations Three new field campaigns kick off this month. Scientists preparing for a future Hyperspectral Infrared Imager (HyspIRI) mission will take to the skies above Hawaii to collect airborne data on coral reef health and volcanic emissions and eruptions. This airborne experiment supports a potential HyspIRI satellite mission to study the world’s ecosystems and provide information on natural disasters.

NASA Awards Engineering Contract for Earth, Space Science Missions NASA has awarded a contract to KBRwyle Technology Solutions, LLC of Columbia, Maryland, for engineering services to support more than 20 NASA exploration missions. The Ground Systems and Missions Operations-2 contract is cost-plus-award fee, indefinite delivery/indefinite quantity, with a total maximum ordering value of $442 million. The effective ordering period begins March 1 and runs through Feb. 28, 2022. KBRwyle will support a wide range of mission operations, including concept studies, formulation development, implementation, operations, sustaining engineering and decommissioning. They also will support operations studies, systems engineering, design, implementation, integration and testing of ground systems and operations products, mission operations and sustaining engineering. The contract supports various NASA missions managed by Space Science Mission Operations and Earth Science Mission Operations at NASA’s Goddard Space Flight Center in Greenbelt, Maryland. These include, but are not limited to, the Polar Operational Environmental Satellite, Magnetospheric Multi-Scale, Global Precipitation Measurement etc Source: www.nasa.gov By Arunava Dey PGDM FS (2016-18)

QUANTS FUN November 2016 10 Interesting facts about Mathematics!

Numbers are one of the most important parts of our lives. From studies, career, daily activities to relationships, everything is related to numbers. Thus, Mathematics proves to be an essential subject for students. Even though numbers can be scary sometimes, but if learned properly and with fun, they can be real fun. Let us see few interesting facts about mathematics: 1. Hundred in reality means 120 Confusing as it may sound after the heading, the weirdly interesting thing is that the word “hundred” is derived from another word “hundrath” which actually means 120. Not very logical thing for this logic subject! 2. The popular number of all If math would be a high school, number 7 would be the most popular number of all. It is because of many reasons like this is “arithmetically unique”. It is the only number you can’t really multiply or divide and still keep it in that group.

3. Google is all about maths The lifeline of today’s time, Google search engine is a term which is derived from word “googol” which is a mathematical term for the number 1 followed by 100 zeros which reflect infinite amount of search on the internet. 4. The crazy multiplication in maths

Few interesting things about math is how crazy it gets with its function. For instance if you multiply 111,111,111 by 111,111,111 , this becomes equal to 12,345,678,987,654,321. The numbers seem like going in the same way over and over again. 5. Most important equation of maths If you have to come up with the most exquisite piece in maths, then use this equation which has five most important numbers of Maths in it. The equation is  + 1 = 0. Seems like love to mathematicians. 6. The dreadfully long division Another crazy application of Math comes in when number 1 is divided by 998001. The answer would give you a complete sequence from 000 to 999 in order. Don’t agree with this? Go ahead and try it and be ready to waste a one whole…notebook! 7. Zero is not there in roman numerals Did you know that one of the most important numbers, Zero is not represented in the Roman numerals? Derived from Arabic word, ‘sifr’, it is known from a variety of other names like naught, zip, nil and zilch.

8. Story of Pi

It was mathematician William Shanks who calculated the value of Pi (π) which was to 707 places but he made a mistake on the 528th digit and henceforth incorrectly calculating every digit after that. Pi is therefore not a fraction and this makes it irrational number which neither repeats nor does it end when written as a decimal. 9. Calculus means pebbles in Greek Pythagoras, used to have small rocks to signify numbers while working on mathematical equations. This led to origin of word Calculus which means pebbles in Greek. 10. Pizza and Pi are related You will be amazed to know that the Pizza has a radius of “z” and height “a”, and therefore its volume is Pi x z x z x a which makes up Pizza. Interesting enough to enjoy little math with Pizza!! Source: http://www.careerindia.com/news/20-interesting-amazing-facts-about-maths015921.html By Amod Kulkarni MMS (2016-18)

Quant Connect November 2016 Quantinuum, the Quant forum of KJ Somaiya Institute of Management Studies and Research aims to empower students and professionals alike to organize and understand numbers and, in turn, to make good and rational decisions as future managers. The newsletter published monthly consists of a gamut of articles for readers ranging from beginners to advanced learners so as to further enrich the young minds understand the contributions made to the field of mathematics along with a couple of brain- racking sections of Sudoku to tickle the gray cells. For any further queries and feedback, please contact the following address: KJ Somaiya Institute of Management Studies and Research, Vidya Nagar, VidyaVihar, Ghatkopar East, Mumbai -400077 Mentor Prof. N.S.Nilakantan (+919820680741) â&#x20AC;&#x201C; Email â&#x20AC;&#x201C; nilakantan@somaiya.edu

Team Leaders:

Bharat Bhushnam (+91 70457 81029) Vatsal Mehta (+91 98219 87294) Bharat Gupta (+91 98116 13264)

Editorial Team:

Rupal Doshi (+919831427640) Amod Kulkarni (+919833701015) Maheshwaran Kumar (+919566173411) Aditi Mehta (+91 97570 99599) Arunava Dey (+917720093053) Anish Sagar (+919981944441) Milcah George (+917406186087)

Designing Team:

Sudeep Kumar Sahoo (+919833056863) Shreyas Kulkarni (+918600106378) Ashish Mahadik (+919819741018)