VOLUME 9 ISSUE 12
DECEMBER 2018
QURIOSITY MONTHLY NEWSLETTER OF QUANTINUUM – THE QUANT & ANALYTICS COMMITTEE @SIMSR
ALGORITHM TRADING!! Making automated trading possible | p. 03
QUANT GURU
QUANT FUN
Dr. Kannan Soundararajan known for contributions to automorphic L-functions and multiplicative number theory | p. 12
Surprising puzzles and questions to rack your brain | p. 23
EDITOR’S NOTE Welcome to the latest issue of Quriosity, the monthly newsletter of Quantinuum! Quantinuum - the Quant and Analytics committee of K.J. Somaiya Institute of Management Studies and Research aims to empower students and professionals alike to organize and analyze numbers and in turn, to make good and rational decisions as future managers. The newsletter published monthly consists of articles which will enrich the young minds by informing about the contributions made in the field of quant, analytics, and mathematics. The objective of Quriosity is to publish up-to-date articles on data analytics, alongside relevant and insightful news. This way the magazine aspires to be vibrant, engaging and accessible, and at the same time integrative. In this issue we have added some exciting new sections for our dear readers. The first is “Book Review” section where we review a book “Big Data Now” by O’Reilly Media related to analytics and data science. The second new section is “Quant Tutorial” in which we have short tutorials on different functions of any statistical software or computer language related to analytics. This week’s quant tutorial is on Tableau. Our main story is on Algorithmic Trading which has become a new norm amongst the high-profile traders and investors. We have also included a technical topic on CHAID analysis and an event report on Machine Learning workshop which was conducted by Quantinnum. We sincerely hope that our upgradations will help our readers gain insights on the latest developments in field of analytics and data science. If you wish to submit articles or news items, either individually or collaboratively, you are welcome to write to us at – quriosity.quantinuum@gmail.com Thank you and Happy Reading! Quriosity Editorial Team Quantinuum@SIMSR Editorial Team: VVNS Anudeep (+91 9441201685) Khushbu Mehta (+91 9930158610) Tanmay Nikam (+91 9699288587) Kaustubh Karanje (+91 7738219050) Dhyan Baby K (+91 9809245308) Shubham Thakur Saumya Joshi Akshay Nandan
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IN THIS ISSUE
Main story Algorithm Trading by Khushbu Mehta … pg.03 Sub article CHAID analysis by Tejal Jadhav … pg. 07 Sub article Hyper-Personalization by Arun Vijay … pg.10 Quant Guru Dr. Kannan Soundararajan by Dhyan Baby K … pg.12 Curiosity Update by Saumya Joshi … pg. 13 News Digest by Saumya Joshi … pg. 15 Event Report by Karthik Phani …pg. 17 Book Review “Big Data Now”- author by Akshay Nandan R … pg. 19 Quant Tutorial Tutorial on Tableau by Akshay Nandan R … pg.21 Quant Fun by Abhishek Bawa … pg.23 Quant Connect … pg. 26
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QUANT COVER STORY
Algorithm Trading by Khushbu Mehta MMS Finance 2017-19
Algorithmic trading, a technological advancement to the securities market, is catching up quick among Indian traders and investors. Market regulators, SEBI have recently established a strong framework for algorithmic trading so that is standard, transparent and ethical and gets accepted by various traders and investors. To stay up with dynamic times, it has become essential for skilled traders and arbitrageurs to increase speed of execution by using new technology tools. This technique of trading first entered stock markets in mid-1980s, and nowadays it constitutes nearly seventy per cent of total trading volumes in developed markets. Algo trade is nothing but orders placed on the exchange platform by computers through a programme designed by the user. It was introduced in India in 2009 and is already quite the craze among institutional investors and accounts for 35-40 per cent of turnover on the Indian exchanges. Algo trades will involve completely different degrees of manual intervention. In zero-touch algos, programs determine the trading chance and execute it with no manual intervention. Here, the trades are also initiated by pre-set technical levels or quantitative indicators or arbitrage opportunities within the market, based on the client’s preference. However ordinarily used algos in India use Application Programming Interfaces (API) that enable investors to pick out their strategy, programme their needs and then execute it through the broker.
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Difference between Algorithmic Trading, Automated Trading, Quantitative Trading, and High-Frequency Trading Algorithmic Trading – Algorithmic trading means turning a trading plan into an algorithmic trading strategy via an algorithm/logic. The strategy can then be back-tested with historical data to check if it gives positive returns and less scope of error when implemented in real markets. The algorithmic trading strategy can be executed either manually or can be automated. Quantitative Trading – It involves using advanced mathematical and statistical models for formulating and executing a strategy. Automated Trading – It means complete automation i.e. the order generation, submission, and execution process. HFT (High-Frequency) Trading – Trading strategies can be categorized as low-frequency, medium-frequency and high-frequency strategies depending on the holding time of the trade. High-frequency strategies are strategies which get executed in an automated way in short time in a sub-second time scale. It holds the trade positions for a very short span and try to make small profits per trade and execute millions of trades every day. Since there are various algo trading approaches for trading and investing, few are discussed below: Momentum investing It is one of the most basic and common algorithmic trading systems followed by investors. This approach waits for the market trend to move significantly in one direction and with high volume. With this great momentum the investor might invest in the five best performing shares in an index based on a 12- month performance of the stock. A more difficult version of this could be when momentum is blended over time. The investor will then have to interpret both relative and absolute momentum. Furthermore, using this approach investors are able to rebalance their portfolio weekly, monthly, quarterly, or even yearly. Mean revision There is a tendency of many asset prices to revert to the mean after sometime i.e. after being oversold or overbought. Mean reversion strategy uses this tendency to make the algorithm. Investors who follow this strategy make an assumption that the price of the share will revert back to its long-period average price. So they purchase the assets when it is trading at the lower end of a trading range and decide to sell when the assets price approach the centre of the trading range or a moving average. Factor-based investing It is a strategy used by investors to select stocks on attributes that are related to higher returns, based on past data. Few factors that are included - market capitalization, momentum, earnings momentum, beta, dividends, debt/equity ratio and free cash flow. Financial investors will combine these factors using a static weighing system, or a dynamic allocation and select their securities.
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ETF rotation strategies Some investors with certain amount of risk capacity choose to use exchange traded fund to optimize the return for that amount of risk. The strategy can be to rotate into ETFs with strong momentum and maximize return. While selecting ETFs, investor must consider the correlation between the two. By selecting the uncorrelated ETFs, investors can reduce and control its risks. Investors use these strategies to explore the patterns and trends and get the advantage of the low fees charged by ETFs.
Smart beta It is a strategy used by investors in an attempt to reduce the gap between active and passive investing. The objective of the strategy is to minimize risk or maximize diversification at a lower cost as compared to a traditional active investing. This strategy emphasizes on capturing various investment factors or market inefficiencies by making rules and formulating a strategy. Market capitalization based on the index can be used as a fundamental metric for the rule. Many investors use smart beta systems for portfolio risk management and diversification. The smart beta strategy applies to various asset classes other than equities such as fixed income, multi-asset classes, and commodities. Trend following This is one of the oldest strategies used by investors. It involves algorithms monitoring the market for technical indicators to initiate the trade. Generally, these trades use technical analysis (use moving averages, Fibonacci series), market patterns (like W pattern, U pattern etc.) and indicators (like relative strength index, stochastic index etc.) to make decisions. The purpose of this strategy is to buy assets when prices breaks the resistance levels and sell short assets which fall below support levels. This strategy is popular among investors because of its easy to use and understand. Sentiment analysis This trading strategy is determined by crowd sentiments, as investors purchase stocks to predict the crowd’s reactions due to recent and relevant news. The objective of this strategy is to analyse the unstructured data from newspaper articles, social posts, reports, blog posts, videos. Many advisors and investors utilize this strategy to capture short-term change in price and obtain quick benefits. Statistical arbitrage strategy Arbitrage is buying a listed stock from one market at lower price and selling the same in other market at slightly higher price. Implementing an algorithm to identify the price differentials and allow profitable opportunities in an efficient manner is a Statistical arbitrage system. It consists of a set of quantitatively driven trading strategies. These strategies exploit the relative price movements across thousands of financial instruments by analysing the differences and patterns of the prices.
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Seasonality strategies Investors must create strategies depending on the time of the year. Many investors are aware that markets generally have better returns during the warm, summer months and at the end of the year. In order to avoid capital loss, financial investors and advisors may choose to sell their positions with losses towards the end of December to get tax benefits. Some of these strategies focus on creating long-term returns, while some on short-term returns. Many algorithmic trading strategies, like the ones above, are great for investors and advisors who are looking to optimize their portfolio risk-return trade-off. SEBI has been quite frugal with the regulations in this domain. SEBI guidelines on algorithmic trading have assisted in adoption of the technique and the Indian market have not seen many flash crashes as compared to similar instances in the developed markets. “Algo trading can be beneficial for small-time investors, as it increases liquidity in the market and thereby simplifies the entry and exit process. Increasing depth of algo trading would be good for capital markets as it will remove price inefficiencies in traded securities,� says Ajay Kejriwal, President, Choice Broking.
References: https://blog.quantopian.com/common-types-of-trading-algorithms/ https://www.investopedia.com/articles/active-trading/101014/basics-algorithmic-tradingconcepts-and-examples.asp https://learn.alphadroid.com/blog/algorithmic-trading-strategies/common-types-ofalgorithmic-trading-strategies/ https://economictimes.indiatimes.com/markets/stocks/news/slow-steady-algo-trading-takesover-decent-share-on-dalal-street/articleshow/66214063.cms https://www.thehindubusinessline.com/opinion/columns/slate/all-you-wanted-to-know-aboutalgo-trading/article23417138.ece https://www.quantinsti.com/blog/learn-algorithmic-trading
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SUB ARTICLE
CHAID analysis by Tejal Jadhav PGDM Finance 2017-19
What is CHAID analysis? CHAID Stands for, Chi-square Automatic Interaction Detector (CHAID) is a technique created by Gordon V. Kass in 1980. This tool helps in understanding relationship between variables. CHAID analysis builds a predictive model, or tree, to help determine how variables best merge to explain the outcome in the given dependent variable. The chi-square independence test is used to determine if there is a significant relationship/ difference between two nominal (categorical) variables. What are the requisites of this analysis? Using CHAID analysis, ordinal, nominal and continuous data can be analysed, where continuous predictors are split into categories with approximately equal number of observations. How does it work? CHAID produces all possible data table representing results of the entire group for each categorical predictor until the best outcome is achieved and no further splitting can be obtained. CHAID analysis splits the target (dependent variable) into two or more categories that are called the initial, or parent nodes, and then the nodes are split using statistical algorithms into child nodes. Preparing predictors: The first step is to convert continuous data into categorical data by dividing the respective continuous distributions into a number of categories so that approximately equal numbers of observations are obtained. Merging categories: The next step is to cycle through the predictors to determine for each predictor the pair of categories that is least significantly different with respect to the dependent variable; for classification problems, it will compute a Chi-square test; for regression problems (where the dependent variable is continuous), F tests is performed. If the respective test for a given pair of independent variable is not statistically significant as defined by an alpha-to-merge value, then it will merge the respective predictor categories and repeat this step. Split variable selection: The next step is to choose the split the predictor variable with the smallest adjusted p-value, i.e., the independent variable that will give the most significant split; if the smallest adjusted p-value for any predictor is greater than calculated alpha-to-split value 7
(which is obtained from confidence level), then no further splits will be performed and the respective node is a terminal node. Continue this process until no further splits can be performed (given the alpha-to-merge and alpha-to-split values).
What are uses of CHAID analysis? Tree helps us to understand the relationships between the split variables and the associated related factor through visual presentation. The building up of the decision tree starts with identifying the target variable or dependent variable; which would be considered the root.
Decision tree components In CHAID analysis, following are the levels of the decision tree:
Root node: Root node contains the dependent, or target, variable. Parent’s node: The algorithm splits the target variable into two or more categories. These categories are called parent node or initial node
Child node: Independent variable categories which come below the parent’s categories in the CHAID analysis tree are called the child node 1. Terminal node: In the CHAID analysis tree, the category that is a major influence on the dependent variable comes first and the less important category comes last. Thus, it is called the terminal node.
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Advantages of CHAID 1. Gives quick results 2. Decision trees built by CHAID are wider as it is not constrained to make binary splits, making it very popular in market research. 3. CHAID will result in many terminal nodes connected to a single branch, which can be summarized in a simple two-way contingency table, with multiple categories for each variable. Disadvantages of CHAID 1. Because of splits the variable’s range into smaller subranges, the algorithm requires larger quantities of data to get dependable results. In this case, The CHAID tree may be short and uninteresting because the multiple splits are hard to relate to real business conditions. 2. Real variables are forced into categorical bins before analysis, which may not be helpful, particularly if the order in the values should be preserved. For example, CHAID can group “low” and “high” versus “middle,” which may not be desired. What are the applications of CHAID analysis? CHAID analysis is mainly used in market research for example; CHAID can be used in Tourism to understand market segmentation based on various factors like age, gender, geography etc. Also, it can be used in predictive modelling example, Credit card risk prediction using details of account holders like, age, income, number of credit cards, etc. References: “Popular Decision Tree: CHAID Analysis, Automatic Interaction Detection” by Statistica software “CHAID Analysis” Complete Dissertation by statistics solutions “Using Chi-Square Statistic in Research” Complete Dissertation by statistics solutions
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SUB ARTICLE
Hyper-Personalization by Arun Vijay PGDM 2018-20
Hyper-Personalization We live in a world that’s heavily penetrated by digital platforms, which has given prevalence to data. As our online activity has grown, so has the ability of sellers to market their products through more platforms than ever before. But this overload of information and online activity has led marketers to ask the question “How to stand out among the clutter?”. It is said that a user is either gripped on or loses interest in the first 8 seconds of reading a message. So the trick is to grip the attention of the user in these 8 seconds by making the message clutter free and highly targeted. And this is achieved through hyper-personalization. Personalization vs. Hyper-Personalization: Personalization tricks such as adding the name and designation to a communication mail have existed for some time now. What makes Hyper-Personalization different is the use of real time and behavioural data to create highly contextual messages that is highly targeted or highly personalized. This real time data is collected from multiple channels and various touch-points and is used to tailor the message or the product or the services. This can be illustrated using an example where a person spends time browsing for a pair of running shoes but leaves without buying anything. Here, let’s say the data collected are
He has a history with brand X and prefers that brand He has done shopping mainly on Sunday evenings between 5-10PM He notices and indulges mostly on app notifications He mostly ends up buying an item only if there is discount shown for it
Now these data are used to design a Hyper-Personalized message to person which will be sent to him on a Sunday evening between 5 to 10pm and the message will be an app notification saying that there is a discount sale of brand X running shoes. Here his search history and real time data like time spent are used to come to a more personalized message. Need for Hyper-Personalization: As the people get more and more dependent on the internet for information, there is a need for marketers to get smart and improve their approach. According to Google search frequency of the phrase “best” has increased by 80% in the last 2 years. This points to the fact that users are more informed and opinionated these days and therefore user engagement becomes more crucial. Statistics show user engagement with content has decreased by 60% and this means that information overload is making the user to lose interest and tune out all the noise. This is where Hyper-Personalization comes into play as it makes the user engagement more targeted and customized and can thus grip the attention of the user more often. Accenture backs this up by its finding that 75% of the consumers are more likely to buy from someone who offers a more customized product which is tailored to their requirements and preferences. 10
Relevance of Hyper-Personalization: We live in a world where data is readily available. And with the advent of AI and machine learning, wielding this data has never been more effective. We know that studies show customers tend to prefer a more customized e-commerce and retail experience and this in turn leads to higher revenue and a more loyal customer base. Add that with the fact that over 40% of consumers say they are comfortable having a retailer monitor their shopping patterns and purchases, you get the feeling that customers in the new age demand a great shopping experience along with the actual product. And this has given more relevance to the concept of Hyper-Personalization. Use of context: We have talked about how Hyper-Personalization is highly contextualized. Let us further elaborate the idea. Hyper-Personalization has gone beyond the traditional personalization by going beyond just looking at the customer data but by also looking at the customer context. For example the data of whether someone uses an iOS or Android can be contextualized to mean different things like demography, income, willingness to spend and so on. Now this information can be used to market to these people with items in the same context for example a luxury watch for an iOS user as he is more likely to spend money on luxury items. Similarly by taking into context the various data, we can personalize the message or campaign more in order to suit the target. We have seen how marketers have began customizing their messages in order to capture the fancy of the customers and how the easy availability of data has given prominence to this movement and hence Hyper-Personalization, but there is no strict definition as such of HyperPersonalization. There is no point of customization where you say this is now HyperPersonalization. The level of customization to a message or a campaign or an item is done depending on the industry and its financial power and thus can vary from organization to organization. What we can say is that customers want customized product, and this makes Hyper-Personalization a necessary thing to invest in for marketers.
References: https://dotcms.com/blog/post/what-is-hyper-personalizationhttps://webengage.com/blog/hyper-personalization-marketing-future/ https://www.smartfocus.com/en/blog/hyper-personalized-marketing-how-exactly-it-done http://www.quantmarketing.com/news/hyper-personalisation/ https://www.oracle.com/uk/applications/customer-experience/banking.html
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QUANTGURU
Kannan Soundararajan (b. 1973) by Dhyan Baby K PG FS 2018-20 Kannan Soundararajan is a renowned Indian mathematician who is currently working as a professor of mathematics at Stanford University. Soundararajan is known for his work in the field of analytic number theory especially in the subfields of automorphic L-functions and multiplicative number theory. He currently lives in Palo Alto with his wife and son. Soundararajan was born on 27th December 1973 in Chennai and did his schooling from Padma Seshadri High School in Nungambakkam in Chennai. He showed great affinity for mathematics since his childhood and attended the reputed Research Science Institute in 1989. In 1991, he represented India at the International Mathematical Olympiad and went on to win a silver medal. Soundararajan moved to the United Stated and completed his undergraduate studies with highest honors from University of Michigan where he later went on to serve as a professor. During his undergrad days, he won the inaugural Morgan Prize for his work in the field of analytic number theory. He also completed his Ph.D from Princeton University. After completing his Ph.D, Soundararajan received his first five year fellowship from American Institute of Mathematics. He has also held positions at Princeton University and Institute for Advanced Study. He is famously known for proving a conjecture of Ron Graham in combinational number theory for which he worked with Ramachandran Balasubramanian and has made extensive contributions in settling the arithmetic Quantum Unique Ergodicity conjecture for Mass wave forms and modular forms. He won the Salem Prize in 2003 for his contribution to the field of Dirichlet L-functions and related character sums. He won the SASTRA Ramanujan Prize in 2005 for his work number theory. He has also won the Infosys Science Foundation Prize 2011 and Ostrowski Prize in 2011. He was elected to the 2018 class of fellows of the American Mathematics Society.
References: https://www.revolvy.com/page/Kannan-Soundararajan
https://www.mansworldindia.com/people/the-biggest-indian-eggheads/ https://en.wikipedia.org/wiki/Kannan_Soundararajan
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CURIOSITY UPDATE by Saumya Joshi PGDM 2018-20 NASA’s InSight (Interior Exploration using Seismic Investigations, Geodesy and Heat Transport) Nasa’s InSight lander successfully touched down on the surface of Mars on 26th November after an uneventful six-month journey followed by a harrowing seven-hour descent watched by science enthusiasts across the globe. Its true mission is to take the ‘vital signs’ of Mars and allow us to learn more about the Red Planet. The InSight lander recently sent its selfie and audio recordings of Martian winds. Its primary two-year mission includes investigating Mar’s deep interiors, which includes the seismic disturbances through the planet’s crust, mantle and core which have been termed as ‘Marquakes’.
https://medium.com/predict/after-the-mars-landingwhat-now-for-insight-58839c2d60c6 ISRO working on new technology to bring back dead rockets back to life in space Indian Space Research Organization is working on a new technology where it will use the last stage of the PSLV rocket for space experiments. It will perform a technology demonstration of this new system when it launches the PSLV C44 rocket in January. ISRO chairman K Sivan said, “Normally, the last stage of a PSLV rocket after releasing the primary satellite in space becomes dead and categorised as debris. Now, we are working on a new technology where we will give life to this “dead” last stage of PSLV for six months. This will be the most cost-effective way to perform experiments in space as we don’t have to launch a separate rocket for the purpose.” He said that “India is the only country in the world that is working on this new technology. https://economictimes.indiatimes.com/news/science/in-a-1st-isro-will-make-dead-rocketcome-alive-in-space/articleshow/67112594.cms
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Next-Generation of GPS Satellites Are Headed to Space US Air Force is about to launch the first of a new generation of GPS satellites, designed to be more accurate, secure and versatile. Compared with their predecessors, GPS III satellites will have a stronger military signal that's harder to jam - an improvement that became more urgent after Norway accused Russia of disrupting GPS signals during a NATO military exercise this fall. GPS III also will provide a new civilian signal compatible with other countries' navigation satellites, such as the European Union's Galileo system. That means civilian receivers capable of receiving the new signal will have more satellites to lock in on, improving accuracy. https://gadgets.ndtv.com/telecom/news/nextgeneration-of-gps-satellites-are-headed-to-space1963632
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NEWS DIGEST by Saumya Joshi PGDM 2018-20 Indian Railways to Use Emotional Intelligence to Serve Passengers Better After adopting artificial intelligence to serve better food, handle queries or even prevent mishaps, the Indian Railways are now on the path of incorporating emotional intelligence to improve customer service. This move will help senior officials improve delivery systems in a faster and more decisive way. General Managers have also been empowered to organize Emotional Intelligence training for junior officers working in the divisions.
https://www.analyticsindiamag.com/indian-railways-to-use-emotional-intelligence-to-servepassengers-better/ Five Trends That Will Drive the Rise of Robots In 2019 Industrial automation has entered into a golden era of adoption and technological advancement, with consumer robotics taking the next level of growth indicators. The coming year promises to hold greater rewards for the Robotics sector with disruptive technologies and upgrades. Here are the five trends that will drive the Rise of Robots in 2019.
https://www.analyticsinsight.net/five-trends-that-will-drive-the-rise-of-robots-in-2019/ Increasing demand for connected cars Among Mainstream Customers We are at the cusp of the fourth industrial revolution. Known as Industry 4.0, it is proving to be a turning point for the automotive industry. Most automotive facilities are in a phase of evolving seamless human–machine connectedness. Simultaneously, consumer preferences have been edging towards greater connectivity with their automobiles, further encouraging the industry to evolve. Yielding to these rising demands, vehicle manufacturers are working hard to achieve digital maturity across their broader enterprises, by offering their best tech features in even their smallest cars.
https://www.livemint.com/Opinion/8JgKNroSEo7rhA2V0GOI7N/Opinion--Industry-40-andits-adoption-in-connected-cars.html 15
What are conversational AIs and why are they becoming popular? Conversational AIs are essentially an evolved version of chatbots and aim to conduct humanlike interactions with humans. The AI uses natural language processing (NLP) to understand how a normal person will communicate, instead of trying to serve a pre-defined set of options. Contrary to popular belief, users are increasingly getting accustomed to these conversational AIs. While traditional channels for interacting with businesses continue to exist, consumers are also aligning themselves with the new-age tools.
https://www.livemint.com/Home-Page/rz68cl3Mwx27YzqHnC1g4J/What-areconversational-AIs-and-why-are-they-becoming-popula.html
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EVENT REPORT
Machine Learning by Karthik Phani PGDM IB 2018-20 Quantinuum- The Quant and Analytical committee of KJSIMSR had organised a 4-hour workshop on “Introduction to Classical Machine learning” on 8th December 2018. Many students were interested and we received about 80 registrations. But due to limited seats, 35 students we selected on first-cum-first basis. Machine learning is one of the buzz words when it comes to industry 4.0. It has wider applications from social media platform i.e. Facebook’s face recognition to manufacturing units. Machine learning can also be used commercially, where many companies increase their customer satisfaction and reduce costs with the help of various machine learning techniques. Jeff Bezos, Chairman, CEO of Amazon has stated that “Machine learning and AI is a horizontal enabling layer. It will empower and improve every business, every government organization, philanthropy — basically there’s no institution in the world that cannot be improved with machine learning. At Amazon, some of the things we’re doing are superficially obvious, and they’re interesting, and they’re cool. I’m thinking of things like Alexa and Echo, our voice assistant, I’m thinking about our autonomous Prime Air delivery drones. Those things use a tremendous amount of machine learning, machine vision systems, natural language understanding and a bunch of other techniques”.
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This workshop was conducted by Mr. Raj Darshan Dhyani, Chief technology Officer, ImarkServe and Mr. Prasant K. Sharma, Research Assistant, IIT Bombay. The workshop was a huge success and students were fully engaged by the way the workshop was conducted by Mr. Raj Darshan and Mr. Prasant. Many attendees requested for a 2 day workshop on advance machine learning. Other students who could not attend requested for arranging the same workshop again. On the basis of these overwhelming feedbacks, we will try to conduct more and more such enriching workshops which would help the students to grow professionally.
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BOOK REVIEW
Big Data Now by Akshay Nandan R PGDM 2018-20
Title: Big Data Now Author: O’Reilly Media, Inc.
Big Data Now by O’Reilly Media, Inc., presents different areas of big data, its applications, and its role across industries. In the very beginning, it gives essential information about what big data looks like, and various structures and definitions of the same. In this segment, it lays emphasis on three Vs: volume, velocity and variety, which are primarily used to characterize big data. It explains how Hadoop uses its distributed filesystem (HDFS) to handle volume and distribute datasets among multiple servers, and about the pattern that is followed in this process, which is also utilized by Facebook to build its models. In a similar fashion, concepts are being discussed in detail to handle the other two characteristics of big data as well. From here, it moves on to big data tools, techniques and strategies and provides guidance for turning big data theories into big data products. It introduces a four step approach called the drivetrain approach, inspired by the model used in self-driving cars. It further elaborates this 19
method with the help of a case study on Optimal Decisions Group, which applied this four-step approach to solve a wide range of problems.
The part that forms the crux of the book is where it explains the applications of big data with examples of big data in action. One such application is NASA’s Astrophysics Data System (ADS), hosted by the Smithsonian Astrophysical Observatory. It provides access to abstracts and about half a million journals that are present in the astronomical literature. The highlight of the chapter is that it has captured the complete interview with the man who was behind this project, where he explains what he and his team had done in order to build such a system. Besides, it explains other applications like how big data could be used in cities to solve infrastructure problems, how it is applied in marketing to optimize customer relationship, and also covers the recent trends in big data such as automation, deep data and data visualization. The last section of this book is all about exploring the possibilities that arise when big data and healthcare come together. It explains how big data has the potential to revolutionize and transform health care if used in an effective manner. It emphasises the importance of building efficient healthcare systems with effective administration, where patients pay for the results that they get rather than the procedures that they go through.
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QUANT TUTORIAL
Tutorial on Tableau by Akshay Nandan R PGDM 2018-20
How to do data extraction on Tableau? Here is a tutorial on how to create data extracts on Tableau. Data extracts are nothing but saved subsets of a data source. Data can be extracted in a file by creating filters and limits to the data that contains the file.
Right click on the left corner of the worksheet Left click on extract data Select specifications
Add new data to Tableau Data Extract: To add more information to the current extract, Data
Extract
Append Data from a file
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For further reading open the link given below: https://data-flair.training/blogs/tableau-data-extract/
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QUANTFUN 1. When a number is divided by 13, the remainder is 11. When the same number is divided by 17, then remainder is 9. What is the number?
2. This week’s Sudoku Challenge
3. Which letter replaces the question mark?
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Answer 1 349 Explanation x = 13p + 11 and x = 17q + 9 13p + 11 = 17q + 9 17q - 13p = 2 2 + 13p q= 17 The least value of p for which q =
2 + 13p is a whole number is p = 26 17
x = (13 x 26 + 11) = (338 + 11) = 349
Answer 2
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Answer 3 M (Working in rows, add together the numerical values of the left and right hand letters to give the numerical value of the central letter).
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QUANT CONNECT Quantinuum, the Quant and Analytics committee of K.J. 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: K.J. Somaiya Institute of Management Studies and Research, Vidya Nagar, Vidyavihar, Ghatkopar East, Mumbai -400077 or drop us a mail at quriosity.quantinuum@gmail.com Mentor: Prof. N.S.Nilakantan (+919820680741) Email – nilakantan@somaiya.edu Team Leaders: Purav Shah (+918511929416) VVNS Anudeep (+919441201685) Yatharth Jaiswal (+919969698361)
Editorial Team: VVNS Anudeep (+91 9441201685) Khushbu Mehta (+91 9930158610) Tanmay Nikam (+91 9699288587) Kaustubh Karanje (+91 7738219050) Dhyan Baby K (+91 9809245308)
Shubham Thakur Saumya Joshi Akshay Nandan
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