MENTOR’S NOTE - DR. NILAKANTAN N S

Quriosity | December 2019

During December, 2019, I was part ofthe POMS International Conference2019 India at SIMSR, as a conferenceco-chair & program chair.The conference was planned rightfrom Jan 2019 and was conductedon 13-14 Dec, 2019. The initialgroundwork was laid around May2018 when I attended POMS AnnualConference at Houston where Idiscussed the possibility ofconducting the conference at SIMSRwith POMS officials Prof. SushilGupta and Prof. Nagesh Murthy.After a decision by the institute inJanuary, we – Prof. Lamba andmyself- attended the next POMSconference at Washington D.C. andfinalized the arrangements.Two committees – international andadvisory - were formed forpromoting the conference withadvice inputs from the committeemembers. Another committee- withreviewers from around the world -was formed for review of the papersreceived for the conference. A fourthcommittee – organizing committeewas also formed to deal with theground requirements.

A lot of work went into the organisingof the conference by the conferenceand program chairs and the organisingcommittee.Over 100 papers were received andaround 75 papers were presented inthe conference on various topics ofoperations and supply chainmanagement, modeling and research.The conference had key-note speechesby renowned academics andpractitioners. A practitioner’s forumwas also conducted by Prof. DinoPetrarolo of POMS in which hemoderated the discussions by a panelof expert practitioners.Overall, the conference experience wasvery good and we hope to repeat suchconferences in the future.

Index

Quriosity | December 2019

1. Main Article …4

Predictive analytics and its impact on businesses

- Arka Roy Chowdhury (PGDM - FS, 2019-21)

2. Sub- Article …11

Analytics in Sports

- Devendra Panda (PGDM, 2019-21)

3. Quantcept ….17

Predictive analytics for better marketing performance

- Deepa Singh (PGDM-IB, 2019-21)

4. Internship Experience …23

-Himanshu Data

( PGDM, 2018-20)

5. Quant- Guru …24

Vashishtha Narayan Singh

- Anshul Kanodia (PGDM, 2019-21)

6. KSS and Events Update …27

Quant Skilling

- Rushab Jain

(PGDM - FS , 2019-21)

7. News Digest …28

- Kaustubh Karanje

(PGDM , 2018-20)

8. Curiosity Updates …30

- Bradley Fernandes

(PGDM -FS , 2019-21)

9. Quant Fun …34

Sudoku and Suguru

- Akansha Maheshwari

(PGDM , 2019-21)

10. Quant Connect …36

PREDICTIVE ANALYTICS AND IT’S IMPACT ON BUSINESSES

Quriosity | December 2019

Predictive analytics encompassesstatistical techniques used to analysecurrent and historical data in order tomake a reasonable prediction aboutthe future. In a businessenvironment, organisations employpredictive analytics models toidentify market trends, opportunitiesand risks. Using predictive analytics,organisation are able to assesspotential risks and opportunities toachieve competitive marketadvantages. The technique ofpredictive analytics is a part of datamining focusing on extractinginformation from historical data anduse the data to predict behavioralpatterns and trends. Typically,predictive analysis can be applied toany type of unknown events topredict the present and futureevents.Thus enabling businesses to usepredictive models to exploit patternsfound in historical data, to identifypotential risks and opportunitiesbefore they occur.

- Arka Roy ChowdhuryPGDM FS (2019-21)

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Quriosity | December 2019

Classification ModelThe classification model is, insome ways, the simplest of theseveral types of predictiveanalytics models in use. Themodel puts data into categoriesbased on its learning fromhistorical data.Classification models answer yesor no questions, given the broadanalysis that helps guide decisiveaction. These models answerquestions such as:• For a retail shop

manager/owner, “Is thiscustomer about to churn?”

• For a person giving loans, “Willthis loan be approved?” or “Isthis applicant likely to default?”

• For an online banking provider,“Is this a fraudulenttransaction?”

The variety of possibilities withthe classification model—and theease with which it can beretrained with new data—meansit can be applied to many differentindustries.

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Clustering ModelThe clustering model sorts datainto separate, nested smartgroups based on similarattributes. If an e-commercemedia company like Netflix islooking to implement targetedmarketing campaigns for theircustomers, they could gothrough the hundreds ofthousands of records to create atailored strategy for eachindividual. Now the questionarises whether it is the mostefficient use of time, probablynot. Using the clustering model,one can quickly separate thecustomers into similar groupsbased on commoncharacteristics and hence devisestrategies for each group at alarger scale.Other instances of predictive

modeling technique might

include grouping loan applicants

into “smart buckets” based on

loan attributes, identifying areas

Quriosity | December 2019

in a city with a high volume ofcrime, and benchmarking SaaS(software as a service) customerdata into groups to identify globalpatterns of use.Forecast ModelOne of the most widelyused models for predictiveanalytics is the forecast modelwhich deals in metric valueprediction, estimating the numericvalue for new data based on itslearnings from historical data.Historical numerical data is thetype of data this model requires.Scenarios include:• Using this model a SaaS

company can estimate howmany customers they are likelyto convert within a given week.

• A call center using the forecastmodel can predict how manysupport calls they will receiveper hour.

• A shoe store can calculate howmuch inventory they shouldkeep on hand to meet demandduring a particular sales period.

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The forecast model is adaptableand can efficiently handlemultiple input parameters. If arestaurant owner wants topredict the number of customersshe is likely to receive in thefollowing week, the model is builtto take into account factors thatcould impact this, such as: Isthere an event close by? What’sthe weather forecast? Has therebeen an illness going around?Outliers ModelThis model is oriented around theaccumulation of anomalous dataentries within a dataset. Theoutliers model has been designedto identify anomalous figureseither with themselves or inconjunction with other categoriesand numbers.• Recording a sudden spike in

support calls, which couldindicate a product failure thatmight lead to a recall

• Finding unusual or anomalousdata within transactions,

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insurance claims, or to identifyfraud and• Finding unusual information in

your NetOps logs and noticingthe signs of impendingunplanned downtime etc.

The outlier model is most usefulfor predictive analytics in retailand finance.Time Series ModelThe time series model takes asequence of data points captured,where time is the inputparameter. Time series modeluses data collected over a periodof time (years/months asrequired) to develop a numericalmetric and predicts the next threeto six weeks of data using thatmetric. Usage for this modelincludes the number of daily callsreceived in the past three monthssales for the past 20 quarters in anIKEA store, or the number ofpatients who showed up at agiven hospital in the past sixweeks where the parameter

which is the measurablecharacteristic of the hypothesistesting can be chosen as per therequirements of the testing. It alsodoes a trend analysis taking intoaccount seasonal, cyclical andrandom variants to measure theimpact on the metric.If the owner of a sweet shopwishes to predict how manypeople are likely to visit hisbusiness, he might refer to thecrude method of averaging thetotal number of visitors over thepast 90 days. However, growth isnot always static or linear, it isever-changing, and the time seriesmodel can, therefore, better helpus model exponential growth andbetter align the model to acompany’s trend. Time SeriesModel can also forecast formultiple projects or multipleregions at the same time insteadof just one at a time.Therefore this form of analyticsmost commonly uses historical

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Quriosity | November 2019

descriptive and behavioral data,however by adding interactionand attitudinal data one canexpect significant accuracy in theperformance of these predictivemodels. This tool in recent timehave been widely used inidentifying anomalies andpredicting business solutions. Inthis article we shall further discusshow predictive analytics hasimpacted businesses and can beused to boost sales,simultaneously how it can be usedin the finance domain to predictthe future.Big Data has become the definingparameter in sales and marketingfor quite a while now. Analystsand managers often say how BigData can empower teams and cantransform businesses. AnalyzingBig Data to generate predictionsabout future events requires stateof the art software and hugevolumes of data. One of the keybenefits of predictive analytics isparsing specify learnings from

enormous quantities ofinformation.

Here we shall discuss a few ideasfor predictive analytics inbusinesses to boost one’s bottomline. To begin with, we woulddiscuss how predictive analyticsimpact sales.Predictive analytics helpsbusinesses find the hottest leads.Using descriptive, behavioural andother types of data the analyticssystem evaluates and analyses onclosed sales and using thesefindings businesses generatepersonas for their buyers, andthen add prospects to thesepersonas based on demographicsand other information. This canhelp the sales team targetcustomers, who are more likely tobuy from, rather than initiatingcontact and discovering theperspective customers, in which alarge amount of energy andresources are to be invested.

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Predictive analytics helpsbusinesses predict buyer behavior.Analyst uses Big Data to predictfuture behavior. They use pastcustomer purchasing behavior topredict how its customer's futurebuying trends, thus generatingaccurate buyer personas fortargeted marketing. Thetechnique of predictive analytics isalso used to segment and targetemail campaigns.Predictive analytics also helpsproject future finances ofbusinesses. It helps sharpenonline revenue projectionssimultaneously can help shrinkextraordinary expenses. Side byside it can also be used to improvesupply-chain efficiency. Predictiveanalytics takes massive amountsof data from point-of-sale systemsand make real-time forecasts ofwhen and where products arelikely to sell out or not move at all,thus helping in inventorymanagement.

But the technique has fewpossible roadblocks. For predictiveanalytics to work effectively andefficiently, the software requireshigh volumes of quality data. Thisis the reason why companies likeGoogle and Facebook collect somuch data from their users,because when it comes to dataanalytics quantity matters. Thus itbecomes very difficult for smallbusinesses and probably becomesthe single largest hurdle thesebusinesses encounter. Although,initially it can take time andhealthy investment, in the longrun it would be worth it.Thus in this ever-changingbusiness world, staying ahead isstaying in profit. Predictiveanalytics is the most recent toolwhich is being applied in financialservices, retail management,human resource, and otherbusiness domains to givecompanies the edge over theircompetitors.

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This tool has also created a demand for analyst profiles in companiesover varied businesses. Predictive analytics will win over businessesuntil the next innovative tool comes into the market.

References:https://www.adobe.com/in/analytics/predictive-analytics.html?gclid=Cj0KCQiA2vjuBRCqARIsAJL5a-JfTPfwiItPIJaJhPJeQzEDYRrV2gpEkpc84f8JPR9TIoGTybU02xUaAu3REALw_wcB&s_cid=7011O0000028JPOQA2&sdid=FR7NZ8NK&mv=search&edtamo=true&ef_id=Cj0KCQiA2vjuBRCqARIsAJL5a-JfTPfwiItPIJaJhPJeQzEDYRrV2gpEkpc84f8JPR9TIoGTybU02xUaAu3REALw_wcB:G:s&s_kwcid=AL!3085!3!352779551010!e!!g!!predictive%20analyticshttps://www.webopedia.com/TERM/P/predictive_analytics.htmlhttps://www.sas.com/en_in/insights/analytics/predictive-analytics.html

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ANALYTICS IN SPORTS

Data Analytics has found its wayinto each and every industry inthe world today Governments,companies and research institutesare using data analytics to theiradvantage, digging deeper intochunks of data that helps themmake better decisions. HealthCare, Banking, Transportation,Security, Manufacturing,Communication, you name it andData analytics is used in that fieldto improve efficiencies and makecalculated assessments aboutfuture events.Sports is another such industrywhere analytics has made asignificant impact. The sportsindustry is booming and notexpected to stop anytime soon.According to Goldman Sachsanalysts the bond between sportsand science is only strengthening.Goldman Sachs has said the NorthAmerican professional sports

market has already crossed $64billion in revenue per year andthis is number could go up to$70 billion by 2020.With such high potential formoney the competition has alsoincreased. Every sports team andplayer is ready to use any meansnecessary to gain thatcompetitive advantage andthat’s where analytics comes in.Data is being collected to helpteams to victory. Whether it’sthe teams in NBA in the UnitedStates or Premier League teamsin England, data analytics iswidely used to enhanceperformance and changestrategies to change results.“It’s about translating that dataASAP and being very, very intune with the numbers. Youcan’t be a year behind, you can’tbe a month behind,” said AtlantaFalcon’s general manager

- Devendra PandaPGDM (2019-21)

11Quriosity | December 2019 11

Thomas Dimitroff when talkingabout on the use and value ofanalytics today in Americanfootball.Baseball was one of the sportswhere analytics was used first.The famous journey of theOakland Athletics from astruggling team, to a team with awinning streak of twenty gamesin a season was a result ofanalytics. They are one of thefirst and the best examples ofthe power of data analytics.Oakland A’s general managerBilly Beane used numbers torecruit players instead of thetraditional way of trusting hisgut. He used actual facts to picktalent and gain success.This feat inspired the book andthen the movie, both named‘Moneyball.’ Today recruitmentbased on big data is used bymany teams in Major LeagueBaseball and is followed byteams in other sports.

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Nowadays there is a greateremphasis on collecting andanalyzing the data. There are awide range of tools availabledepending on the sport tocollect data for analysis.Wearable’s can help track andmonitor performance of theplayers and athletes. Watchescan track location, heart rate,speed, whereas sensors in theathletes’ uniforms trackhydration and fatigue levels. Allthis information can be collectedand analyzed in real time. Incontact sports like rugby orAmerican football, sensors helpto analyze the impact of a tackleand whether the player is at arisk of getting injured can bedetermined by comparing withhistorical data. Video Analysis ofpractice sessions and previousgames also helps to makestrategies.

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Predictive Analytics is used insports by coaches andmanagement to make educatedassessments and predict thescores for future games. It helpsthe coaches and players makeinformed and effective decisionsthat can alter the result of thegame. They could select thepossible team to play on filed andalso the best tactics to beat theiropponents.Data is also being used to improveon field football performance byanalyzing opposing team strategyand finding ways to counter it.Scottish football team Hearts usedinformation from the InStatdatabase to predict and devise astrategy that would help thembeat Celtic – and it worked. Theyare not alone, as more than 1,500clubs and national teams useInStat, giving the companyinformation on more than400,000 players. InStat is one ofthe few sports analyticscompanies that provides tools and

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data analysis to various individualsand teams.Analytics is also used to enhancethe experience of the fans. TeamManagements utilize the power ofbig data and sports analytics toattract new spectators whileincreasing the loyalty of theirexisting fan base. Fans are nowmore than just spectators theyparticipate, cheer, support andconnect with the players of theteams. The teams in NFL or MLBor even NBA have mobile appshelping visitors to find availableparking, obtain exclusive offersand receive traffic informationtoo. They can also use the apps toorder food which will be deliveredto their seats. Half time shows atsporting events are live streamedand can be viewed by millions ofpeople at the same time. Appsalso give the fans access to instantreplays and alternate views.During the FIFA World Cup inBrazil in order to make the gamesmore interactive Globo TV in

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in Brazil used social media trends to allow fans to share their views inreal time during games and the commentators read the comments andtalk about them on Live TV. They had predicted with analytical toolsthat this would be a good way get fans more engaged and it did.

Rather than just analyzing the past, the application of big data analytics

will prove useful in sports to predict future outcomes. With even larger

chunks of data available, machine learning and AI will help to find

patterns or trends and help devise strategies that could alter the

performance altogether. However, this is just the start and scope of

analytics in sports is much larger. Everyone wants that competitive edge

and currents analytics is the best way to gain it.

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References:

https://www.businessinsider.in/tech/goldman-sachs-theres-a-fortune-to-be-

made-analyzing-sports-

stats/articleshow/59582221.cms

https://www.theringer.com/nfl/2018/12/19/18148153/nfl-analytics-revolution

https://www.intel.co.uk/content/www/uk/en/it-management/cloud-analytic-

hub/data-powered-

football.html

https://www.ibm.com/downloads/cas/ZLEKNQLR

http://tanukamandal.com/2017/12/12/sports-analytics-changed-play/

https://soulpageit.com/when-data-and-analytics-meet-sports/

https://www.techxty.com/columns/big-data-analytics-changing-the-future-of-

sports/Rather

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QUANTCEPT- Marketing Analytics

- Deepa SinghPGDM IB (2019-21)

Due to rise of Big Data and AI,marketers have powerful analyticstools than ever before. Here,we’ll see what predictive analyticsis, why businesses need it, how tomeasure it, and best practices forimplementing it for bettermarketing performance, higherROI and, ultimately, fastersuccess.Predictive marketing analytics isthe use of data, statisticalalgorithms and machine learningtechniques to identify thelikelihood of future outcomesbased on historical data.Steps in predictive analyticsprocess:• Defining outcomes: Determine

the business questions thatyou want the data to answer.

• Data collection: Have a planfor what data you need, howto collect it, and how toorganize it.

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• Data analysis & Statisitcs :Analyze data for usefulinformation and deduceconclusions about thecustomers. Conclusions aretested.

• Modeling: Predictions arecreated about the customer’sbehavior.

• Deployment & Monitoring : Usethe data to find marketingstrategies. Analyze effectivenessof predictive data-drivencampaigns.

Different types of businessanalytics:• Descriptive analytics - historical

data and performance is looked at.

• Predictive analytics - past datais used along with algorithms areused to predict outcome.

• Prescriptive analytics - bestcourse of action is determined.

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The use cases for Predictive Marketing Analytics

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Detailed Lead Scoring: Lead scoring is a methodology used to rank prospects against a scale thatrepresents the perceived value each lead represents to the organization. The resulting score is used todetermine which leads a receiving function (e.g. sales, partners, teleprospecting) will engage, in order ofpriority.

Lead Segmentation for Campaign Nurturing: Predictive analytics help businesses group leads bysegment and create lead nurturing campaigns that are tailored specifically to move the process furtherdown the sales funnel based on the demographic and behavioral data.

Targeted Content Distribution: Predictive analytics can answer which types of content work better forcertain leads. If leads receive higher-quality communication from an organization, the probability of salesconversion increases.

Lifetime Value Prediction: Customer Lifetime Value (CLV) means how much a customer is worththroughout the entire span of relationship with them. With predictive analytics, we can take thehistorical data of each customer and use it to forecast the future lifespan of the relationship with themas well as how much revenue that relationship is likely to generate.

Churn Rate Prediction: Churn rate is the rate of attrition, which is the percentage of subscribers or userswho stop their subscriptions within a certain period. With predictive analytics, you can identify thewarning signs that alert you to the loss of a customer and allow you to provide the necessary follow-upor nurturing before it’s too late.

Upselling and Cross-Selling Readiness: Using the available data about customer buying behavior,businesses can upsell, cross-sell or combine both to increase profit.

Understanding Product Fit: Equipped with historical purchase, behavior and leads data, businesses canbetter understand exactly what customers’ needs and wants are.

Optimization of Marketing Campaigns: By applying predictive analytics in organizations, risks can besignificantly reduced because decisions will be made based on data, not merely unproven assumptionsthat rely on instincts and some educated guesses.

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Predictive Marketing Analytics Measurement

• Metrics: Individual data points related to one specificmeasurement

• Analytics: Combines metrics to achieve a holistic view of thedata and draw conclusions.

Metrics that are used in calculating performance and ROI are:

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CAC or Customer Acquisition Cost: CAC is the average amount of money spent to acquire a new customer. It is calculated based on the total sales and marketing cost divided by the number of new customers within a certain time period. You can create two types of CACs: the 100% online CAC and the combination of online and offline CAC.

Marketing Percentage of CAC: What’s the percentage of the CAC that pertains to marketing cost? To come up with the ratio, the total marketing cost is divided by the sales and marketing costs.

Ratio of LTV and CAC (LTV : CAC): You’ll get the ratio by dividing the Customer Lifetime Value (CLV) – or the Lifetime Customer Value (LCV) – by the Customer Acquisition Cost (CAC).

Marketing Originated Customer Percentage: This metric measures how much of your newbusiness comes from your marketing leads. After dividing the total number of leads in a monthwith the total number of new customers, you’ll get the Marketing Originated CustomerPercentage.

Time to Earn Back CAC: It’s important to know how long it will take you to earn back the moneyyou spent to acquire each customer so that you can set future marketing budgets and realisticrevenue goals. Figure out the total time (weeks, months, quarters or years) needed to earn backthe CAC.

Marketing Influenced Customer Percentage: This metric measures the role that your overallmarketing efforts had in your acquisition of new customers. To find this figure, the newcustomer total is divided by the total number of customers who actually engaged with yourmarketing activities.

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How to Implement Predictive Marketing Analytics for Optimized Business Decisions?

The primary classes of predictive models:

There are three basic scoring categories that marketers use:

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Cluster modeling

• It is a way of segmenting customers into groups based on several variables at once. With it, you can target demographics and personas using behavioral clustering, product-based clustering and brand-based clustering.

Propensity modeling

• It is used to predict customer behaviors based on predictive lifetime value, likelihood of engagement, propensity to unsubscribe, propensity to convert, propensity to buy and propensity to churn.

Collaborative filtering

• It is primarily used for recommending products, services and advertisements based on past variables, including buying behaviors. This filtering is common for upselling, cross-selling and next-selling.

Predictive Scoring• Prospects, leads and accounts are prioritized based on their likelihood of purchasing

action.

Identification Models• Prospects are identified and acquired based on similarities with existing customers’

variables.

Automated Segmentation• Leads are segmented for custom and personalized contents.

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Tools available for predictive marketing analytics:1. EverString2. Infer3. Halo4. BOARD5. SAS Advanced Analytics6. RapidMiner Studio

References:https://www.singlegrain.com/digital-marketing-analytics/how-to-use-predictive-analytics-for-better-marketing-performance/

22Quriosity | December 2019 22

INTERNSHIP EXPERIENCE

- Himanshu Data PGDM (2018-20)

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I worked with the coreManagement Team of DISQ androle was to find the financialavenues for scale and grow phasestartups so that these startups canbecome financially sustainableafter they move out of Incubationperiod of DISQ.My experience was very vivid andquiet different. I have to meet allthe startups in DISQ and listen totheir ideas, growth plans,technology and analytics quotientfor business development,products and services, clientconversion plan, futuresustainability plan.In a nutshell, if you have a knackfor Indian startups and like to workwith them and help in theirgrowth, then DISQ is the place foryou as you will also be able toaccomplish the goal of giving backto the society.

I interned at Digital ImpactSquare in Nasik which is part ofCSR fund of TCS, where theyincubate Social Startups whichare solving society’s problemusing Technology.Process Details:Phase 1: PPA at SIMSR. One ofthe leading members of TCS CSRfund where he introduced aboutDISQ.Phase 2: Registration at DISQwebsite where we have tosubmit a video and solutionregarding our ideas to make thissociety a better place.Phase 3: After shortlisting, wewere called for Businessthonround at TCS office at Thane.Here we were assigned to astartup and we have to make abusiness plan for that startupand have to present that idea infront of TCS dignitaries. After thisround, final students wereshortlisted for the offer letter.

Quriosity | December 2019 23

QUANTGURU – VASHISHTHA NARAYAN SINGH

Quriosity | November 2019

- Anshul KanodiaPGDM (2019-21)

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As this year comes to an end, wethe Quantinuum committeewould like to remember a mathsgenius, India’s own beautifulmind who left us this year forheavenly abode. The person incontext is Vashishtha NarayanSingh (2nd Apr,1946 to 14th

Nov,2019), he was the mathsgenius who, in the ’60s, streakeda path from Bihar to Berkeleyand sometime soon after slippedinto mental illness, leavingbehind a thesis, a few letters,some scribbles on the wall.

Singh was born on 2 April 1946to Lal Bahadur Singh, a policeconstable, and Lahaso Devi inthe Basantpur village of theBhojpur district in Bihar, India.

Singh a child prodigy wasundoubtedly the most giftedmathematics genius afterRamanujan. Unproved sourcesstate he might be the onlyperson to have openlychallenged the widely acceptedscientist himself- Albert Einstein.He received his secondaryeducation from NetarhatResidential School, and hereceived his college educationfrom Patna Science College. Hereceived recognition as astudent when he was allowed byPatna University to appear forexamination in the first year ofits three-year BSc (Hons.)mathematics course and laterMSc examination the next year.

Singh joined the University ofCalifornia, Berkeley in 1965 andreceived a PhD in ReproducingKernels and Operators with aCyclic Vector (Cycle Vector SpaceTheory) in 1969 under doctoraladvisor John L. Kelley.

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After receiving his PhD, Singhjoined the University ofWashington at Seattle as anassistant professor, and hereportedly worked with the USspace agency NASA at that time– although his alleged role in theApollo moon-landing should betaken with a pinch of moondust. One such story from thatperiod is about Singh beingcalled in to do some calculationswhen computers at Nasa hadstopped working for somereason and when the machinesstarted functioning, hiscalculations matched those ofthe machines. But theseunconfirmed stories of a‘legend’ should not diminish inany way Singh’s genuinemathematical genius.

He then returned to India in1974 to teach at Indian Instituteof Technology Kanpur. After 8months, he joined Tata Instituteof Fundamental Research (TIFR),Bombay where he worked on ashort-term position. Later hewas appointed as a faculty atthe Indian Statistical Institute,Kolkata.

Tragically, at the height of hismathematical prowess around1973-74, Singh starteddisplaying traits of mentalinstability. By the mid-1980s, thegenius was a has-been. One ofthose Oh-could-have-beenstories that are often told in theannals of science.

In this new millennium, Singhwas a lost soul. A genius whofelt the entire world conspiredagainst him and a silentmathematician meditating hisnew theorems and lemmas.

It is the culmination ofmisinformed relatives,government apathy and genesthat turned his life upside-down.

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Vashishtha Narayan Singh’sstory, and his four-decades-oldfight with schizophrenia, mayappear to have the samecontours as those of NobelLaureate John Forbes Nash,the troubled mathematicalgenius and inventor of the‘Nash equilibrium’ in gametheory, on whose life theaward-winning biography bySylvia Nasar, ‘A BeautifulMind,’ and Ron Howard-directed Oscar winning film ofthe same, was based on. Thesimilarities - schizophrenia,numbers, and a lifelong questfor beauty in mathematics --cannot be extended to thelives lived by both the men.

Referenceshttps://www.firstpost.com/tech/science/mathematical-genius-vashishtha-narayans-life-is-a-tale-for-mental-health-awareness-in-indiamathematical-genius-vashishtha-narayans-life-is-a-tale-for-mental-health-awareness-in-india-7723741.html

https://en.wikipedia.org/wiki/Vashishtha_Narayan_Singh

https://indianexpress.com/article/india/vashishthas-world-vashishtha-narayan-singh-mathematician-6144681/

http://yourviews.mindstick.com/view/70547/remembering-vashishtha-narayan-singh-the-genius-mathematician-who-died-due-to-system-s-negligence

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KSS & EVENT UPDATES

- Rushab JainPGDM FS (2019-21)

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Team Quantinuum very successfully completed a SIMSR intracollege competition based on data Analytics and Quants, called as“Quantiz” with the tagline “Experience Quants like never before”.Started with the motive of delivering an analytical treat for quantenthusiasts, the competition started with an online round held on4th December 2019 which was followed by 4 offline rounds held on9th December, which included interesting puzzles and mind-boggling excel tactics to work upon. The 2 teams - '3 Guna Lagaan'and 'The Hustlers' emerged as the winners and runners-uprespectively. The winning team won a cash prize of Rs. 12000/-along with a certificate.

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NEWS DIGEST

- Kaustubh KaranjePGDM (2018-20)

Effective data architecture key to supporting analyticsinitiatives: Rahul Mehta, CitiusTech

Companies are realising that they can benefit tremendously fromdata and analytics to drive positive outcomes for themselves as wellas customers. For this, organizations need to have a clear vision thatfocuses on driving efficiency, productivity and profitability. However,there are several challenges associated with data analytics whichmanagers find difficult to handle.

Reference:

https://cio.economictimes.indiatimes.com/news/business-analytics/effective-data-architecture-key-to-support-analytics-initiatives-rahul-mehta-citiustech/72755055

Drones and Data: How Utility Companies May Need toRethink their Approach to Data Management

Ilkka Hiidenheimo, Sharper Shape CEO, provides detail aboutmanaging the robust data sets generated from drone utilityinspections. While leveraging drone technology is an enormousbenefit, it requires capabilities like AI and machine learning andbetter workflow processes to turn complex information into not onlyclear and simple, but also actionable – ultimately revolutionizingprocesses that will lead to long-term, beneficial results.

Reference:

https://insidebigdata.com/2019/12/28/drones-and-data-how-utility-companies-may-need-to-rethink-their-approach-to-data-management/

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Google's New Explainable AI Service, by Tirthajyoti Sarkar

Google has started offering a new service for “explainable AI” or XAI,as it is fashionably called. Presently offered tools are modest, but theintent is in the right direction.

Reference:

https://www.kdnuggets.com/2019/12/googles-new-explainable-ai-service.html

When Is It Ethical to Not Replace Humans with AI?

There are legitimate questions about the ethics of employing AI inplace of human workers. But what about when there's a moralimperative to automate?

Reference:

https://www.informationweek.com/big-data/ai-machine-learning/when-is-it-ethical-to-not-replace-humans-with-ai/a/d-id/1336661?

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CURIOSITY UPDATES

-Bradley Fernandes PGDM FS (2019-21)

CARTOSAT-3, 13 US nano satellites successfullylaunched

ISRO has magnificently launched its third-generation earth-imaging satellite into space from Satish Dhawan Space Centre inSriharikota, Andhra Pradesh.

The launch will improve India's ability in high-resolution imagingand also strengthen India as a global launch destination for smallsatellites using its workhorse rocket Polar Satellite Launch Vehicle.Besides the CARTOSAT 3 satellite, the PSLV C-47 rocket launchedcarried 13 nano satellites from the US, was roped in through thenew commercial arm New Space India.

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The Cartosat 3 follows similar launches in an earlier series deployedfor cartography (map-making applications), infrastructure planning,coastal land use and regulation, road-network monitoring and moreimportantly, change detection in bringing out geographical and man-made features.According to the latest ISRO annual report, the CARTOSAT 3 is a 3-axis agile satellite with a spatial resolution parameter of 0.25 m, animprovement from the earlier series, which had the spatialresolution parameter at less than 1 m.

While the Polar Satellite Launch Vehicle has attracted contracts withforeign firms to accommodate satellites from countries such asFrance, Canada, and the US through its first commercial arm Antrix,the commercial satellites aboard the PSLV C-47 have arrived througharrangements through the second commercial entity New SpaceIndia, set up in March this year.Through Antrix, ISRO completed 239 deals over the last three yearsand earned operational revenue over Rs 6,280 crore.

References:https://economictimes.indiatimes.com/news/science/our-own-orbiter-had-located-vikram-lander-isro-chief/videoshow/72360815.cmshttps://economictimes.indiatimes.com/news/science/isros-tracking-centre-assumes-control-of-cartosat-3/articleshow/72255671.cms

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Nearly 24-hour countdown for launch of RISAT-2BR1, unfurl antenna of RISAT-2BR1 satellite

At Chennai the nearly 24 hour countdown began Tuesday 10th

December for the launch of India's radar imaging earth observation satellite RISAT-2BR1 on board PLSV-C48 from its spaceport of Sriharikota.The workhorse Polar Satellite Launch Vehicle, on its 50th mission, PSLV-C48, was scheduled to lift off from the first launch pad of the Satish Dhawan Space Centre at Sriharikota, about 130 km from chennai at 3.25 PM on Wednesday 11th December, the Indian Space Research Organisation stated.

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Scientists at the Indian Space Research Organisation (ISRO) onThursday 12th December, undertook an operation to unfurl theantenna that was inside the radar imaging earth observationsatellite RISAT-2BR1, a day after it was launched from Sriharikota.This intricate technology involved unfurling and deployment of the3.6 m antenna which was folded and stored during launch.

On Wednesday 11th December, ISRO successfully launched RISAT-2BR1 and nine foreign satellites on-board PSLV-C48 in its 50th flight.RISAT-2BR1 is India's radar imaging earth observation satellite whichwill be used in the field of agriculture, forestry and disastermanagement and also for military purposes.

References:https://economictimes.indiatimes.com/news/science/nearly-24-hour-countdown-begins-for-launch-of-risat-2br1/articleshow/72459710.cms

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QUANT FUN

-Akansha Maheshwari PGDM IB (2019-21)

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SUDOKU

Rules:

The goal of Sudoku is to fill in a9×9 grid with digits so that eachcolumn, row, and 3×3 sectioncontains the numbers between 1to 9. Initially, the 9×9 grid willhave some of the squares filledin. Your challenge is to use logicto fill in the missing digits andcomplete the above grid.

SUGURU

Rules :

The lines marked in bold indicateareas, called cages, from one tofive squares in size. Fill each cagewith unique digits, starting upfrom 1. Say for example a 2-square cage contains thenumbers 1 and 2, and a 5-squarecage contains the numbers from1 to 5. Adjacent (touching)squares, even ones that touchdiagonally, should never containthe same number.

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QUANT FUN - SOLUTIONS

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References: https://krazydad.com/sudoku/https://krazydad.com/suguru/

SUDOKU SUGURU

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QUANT CONNECT

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Quantinuum, the Quants andAnalytics committee of K.J. SomaiyaInstitute of Management Studiesand Research aims to empowerstudents and professionals alike toorganize and understand numbersand, in turn, to make good andrational decisions as futuremanagers. The newsletter publishedmonthly consists of a gamut ofarticles for readers ranging frombeginners to advanced learners so asto further enrich the young mindsunderstand the contributions madeto the field of mathematics alongwith a couple of brain- rackingsections of puzzles to tickle the graycells.

For any further queries and feedback,please contact the following address:

K.J. Somaiya Institute of ManagementStudies and Research, Vidya Nagar,Vidyavihar, Ghatkopar East, Mumbai -400077 or drop us a mail at:

quriosity.quantinuum@gmail.com

Mentor: Prof. N.S.Nilakantan

Email: nilakantan@somaiya.edu

Team Leaders:

Ankit Bhargava ankit.bhargava@somaiya.edu

Santanu Mitra santanu.m@somaiya.edu

Himanshu Data himanshu.data@somaiya.edu

Editor: Kaustubh Karanjequriosity.quantinuum@gmail.com

Editorial Team:1. Akansha Maheshwari2. Anshul Kanodia3. Arka Roy Chowdhury4. Bradley Fernandes5. Deepa Singh6. Devendra Panda7. Gargi Borde8. Roli Dube9. Rushabh Jain

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