arjunc137-blog

[arjunc137-blog]

Week 10

Do not cry because it is over, smile because it happened

 This is my final post on this blog. As I reflect on the semester so far, I am proud of what I have achieved and how this semester has changed me and my journey in Northeastern so far. This particular course has played an integral role in this transformation and I thank everyone part of this course for it.

 I originally came into Northeastern clueless as to what I wanted to study. I choose business since I had an underlying interest in the corporate world and finance in general. After finishing the business core classes in my first 3 semesters, I realized that a business degree in itself has very little value and that you can easily be outperformed by someone without prior knowledge. That is when I decided to try and gain some technical skills that will help me gain an edge over the competition.

 I chose information science since it is very application based and not necessarily coding heavy and is line with the macro-trend of data being a key driver in every industry. This course has helped me understand that and realize that. Information science and data are deeply integrated within every part of a business and helps improve your overall efficiency and work output.

 I also got an opportunity to explore multiple languages including XML, SQL and R. I was able to see how these tools can be used together to perform powerful analysis of data and present it in an artful manner. This has driven me to learn more about these languages and become proficient in them.

 After taking this class and my overall experiences so far, I have chosen to do a combine major in Information Science and Finance. I hope to integrate my learning of data and programming with my financial knowledge and become a better analyst.

Sources:

http://is2000.weebly.com/

[arjunc137-blog]

Week 9

I came across a really interesting article in the news the other day regarding ethics and the current state of the technology economy. This article comes right after the recent New York Times posting that confirmed that the Chinese government is using a “vast, secret system” of artificial intelligence and facial recognition to identify and track a Muslim minority section of the society, a majority of which are in detention camps in China. This concept and implementation of technology has shocked the society and its creators. The burning question right now is how we define ethics in the technology industry.

 Information is more accessible than ever, and governments are increasingly finding ways to use that for political agenda. There have been numerous scandals regarding data collections and its target usage across the globe that many people condemn as “lack of trust and privacy”. However, the sad truth is that every piece of information is digitally available somewhere. It is only a matter of when someone will use it to do bad than if. Ironically, to protect us against threats, we once again rely on technology.

 The US government in collaboration with many of the technology giants has started implementing a AI tool that monitors citizens and their information and analyzes for threats. This brings several questions as to how a piece of software can “judge” a person and if truly is ethical on the governments part. A more common and aggressive intrusion is the AI systems in place for all your applications and websites used. Every step you make on the internet is monitored to some degree and shared across platforms. Searching for a movie online? – immediately see an advertisement on Facebook for it. Visited a website to buy new shoes? – boom, an Instagram post with an influencer flaunting them. These synergies and transfer of information isn’t just a coincidence. This is the result of systems talking to systems. You maybe asking yourself, “Wait! That’s not fair! What about my freedom?!”

 Do not fret. Policymakers are being pressurized to come down on these technology firms and force them to follow ethical norms. But, this system is greatly flawed. Activists and researchers claim that these so-called “Code of Ethics” is a fancy get-out-of-jail card for all the companies. The laws being framed are very vague and loosely knit Adjectives such as “are advised to” and “should consider” do not create any change but rather merely act as a warning.

 I am all for the advancement of technology and for our quality of living to improve. But at what cost? Where do we draw the line between ethical practices and mining of information? The more complex the solutions they provide, the greater the penetrate the society and its beings. I look forward to seeing the legal side of technology gaining more importance and practicality that will enable a better outlook.

Sources

https://slate.com/technology/2019/04/ethics-board-google-ai.html

[arjunc137-blog]

Week 8

A picture is worth a thousand words. This saying is very true and even more prevalent in the field of information science. With the size of the data sets increasing exponentially and the number of data parameters increasing with that, it is key to represent the information in a clear and concise manner such that the recipient can fully understand and absorb the information that is being transmitted.

 The process of visualization requires understanding of the cognitive behavior and prejudices that the human brain has regarding the information it perceives. When projecting information we must choose which part of the brain we need to target to convey what type of information. Having a thorough understanding of these technicalities is the first step towards efficient visualization of information.

 The second biggest challenge is the discrepancy between ideals of the presenter and the receiver. Most of the time there is a disconnect in the goals that are trying to be achieved and the content may not accurately represent the required end result.

 A solution to this problem is combining psychology, information and appropriate feedback systems

 Using cognitive biases and using them to your advantage can be key for people to focus on the right information and for it hit the right spot. This could be in the form of colors represented (primary colors tend to be more soothing to the eye) or font sizes and shapes (bold, underline or italics can convey information in a different tone) or even the placement of the objects can tend to sway peoples mind and help in clear communication.

 I previously touched upon the need to collect the right information for the task. Curating a list of information needed and carefully approaching the collection of information. It is always best to be prudent and attract as much information as you can since there is always something new that can come up and change the manner in which the assignment can be headed. You must now have tunnel-vision when it comes to this part of the task.

The final piece of the puzzle is the feedback system. Information is always a two-way street. Being in constant touch with both parties and understanding the needs of the situation can help provide a better product. It is much cheaper to recreate the visualization and report rather than noticing the error after a decision has gone wrong. The devil lies in the details and we must make sure that these are ironed out and do not conflict with the needs.

Sources:

https://www.interaction-design.org/literature/book/the-encyclopedia-of-human-computer-interaction-2nd-ed/visual-representation

[arjunc137-blog]

Week 7

This was probably the most important week so far in my college. I decided to switch majors and accept a co-op – 2 things that have stressed me out the whole semester. I am no officially a Finance and Computer Dual Major and will be cooping at L.E.K. Consulting in Chicago. Finance, Computer Science and Consulting – that is my dream synergy as of now.

 While going through a few articles regarding the 21st century state of finance I was able to identify one common theme across them – innovation in the field off technology. Technology has started to penetrate the field of finance and remove the age-old traditions that have existed I the sector. Old firms are being forced to become more agile and stay ahead of the competition through the use of technology and innovation.

 One such innovation that can be seen across the different platforms in Finance is the use of technical tools to automate tasks and streamline processes. One such tool is Python. Python has slowly become the most common language used with around 8.2 million developers around the world. Over 69% of machine-learning and data scientists use Python to carry out their work. This included traders and analysts. The applications of Python are endless

 In large scale transactions and M&A activities, there are hundreds of activities that are to be carried out using large data sets while simultaneously generating more. Python helps integrate various workflow management tools and implement structures such as waterfall techniques to automate the process and model out the data. You can automate the process of information collection, converting it into database files, sorting the data and forming tables and reports form the data using Python. This greatly reduces the time taken and decreases any data integrity issues since they all follow the same structure

 Constant price tracking and drawing statistical inferences from them is a daunting but important part in making large scale financial decisions. By creating a Python framework and integrating it with a real-estate API and Google can help track and update the condition of each real-estate listing. You can then pull this information to either R or Excel to run statistical analysis to determine the weightage and value of property that can be invested into. Certain qualitative notes such as location, surrounding transactions can also be built into the model.

 To conclude Python is a great language for me or any other Finance professional to better excel (pun intended) at their work performance. It is a very high-level programming that can fully take advantage of computing power and integrate with various other tools. It is also a very concise platform that can be manipulated according to the users’ needs and follows logic and doesn’t require a lot of tedious procedures such as curly brackets, loops or semicolons for functions thereby increasing the readability.

Sources:

https://www.toptal.com/finance/financial-modeling/python-and-finance

[arjunc137-blog]

Week 6

This week in class we spoke about the concept of probability and more specifically Bayesian Analysis. Bayesian analysis is the method of calculating probability of a distribution given an observed distribution. The applications of this unique method have historically been used in testing cases and research. Some examples are cases of crash tests, effectiveness of a drug and insurance. They have always been used in cases of concrete structures with documented values for occurrence of any event. However, with a few clever observations and tricks Bayesian analysis has found its way to one of probabilities oldest customer – gambling.

 Probability was first invented in the 16th century by Gerolamo Cardano who formed a theory that could game the system and help him win gambling bets on simple games. He identified patterns and likelihood of certain events happening and used them to reach a 60% chance of winning the game. At the end of the day he always walked out with a profit. In the modern era, poker is one card game that is relies heavily on probability, with every player sitting on the table quickly calculating their odds. Although the probability of your hand winning is important, the probability of what the other person on the table does is even more important – in poker you always play the person, not the cards.

 Bluffing is an art. The ability to win a round of poker without a good hand or extracting more money when you have a good hand is a skill that all the top players possess. This is obtained through intuition and the ability to understand the opponents play style. Once you are able to get a grasp on how the players in the table function, it is a matter of time before you can calculate your moves to increase your chance of winning.

 An excellent article on Red Chip Poker tells you exactly how this is done. If we know the probabilities of a person pushing for a big play, the probability of them bluffing against the given probability of the pot winnings and the probability of them pushing when they bluff, we can notice a pattern that allows us to calculate if they are bluffing given that they have pushed for a play.

 Although what I said before might seem very straight forward and makes you want to quit your day job to go to the casino, it sadly isn’t always true. The key to probability has always been the accuracy of the number. The outcomes need to be clearly defined and restricted with no outliers. In the case of a poker game the players are free to change their tactics and throw a curve ball. This is a classic example of the quality of information being key in decision making. To counteract this, many online websites are building out AI powered bots that are able to adapt to playing conditions and play against humans and win big money.

Sources:

https://redchippoker.com/bayes-theorem-poker/

[arjunc137-blog]

Week 5

Data analytics is one of the biggest buzzwords of this decade (Blockchain, Machine Learning and Artificial Intelligence are some of the other terms thrown around) that is starting to realize its potential and remove the “buzzword” tag. There have been multiple implementations of this concept through various technologies that have yielded fantastic results.

 We have all heard the term “Data Analytics” before. We have all seen that it could “potentially” do this or do that. But today, companies are actually using them. One area that has seen a major rise in analytics is the music industry. The music industry is a legacy environment that has seen little to no innovation of automation. It is a highly competitive space that provides very little room for growth for artists. Although the revenue generated by music is increasing, the share the artist gets keeps going down. This is due to the low margins they operate on after high channel distribution costs and middlemen. However, advanced analytics are giving artists a chance to better utilize their money and target their audience in a more attractive manner.

 Promotion and media campaign in the music industry have always been mundane. You have your big billboards, TV, radio and print advertisements to showcase your performances. This is a costly affair that doesn’t guarantee any customer penetration or tangible return. With the influx of analytics, this has changed. Artists can now target and identify the group of people they need to listen to their music or buy concert tickets. This is possible after analyzing trends and user behavior and generating sets if people in the same pool. This costs a fraction of mass media, is more tangible and traceable and more importantly has shown proven results. The artists bottom-line will significantly increase allowing for more music and growth.

 Following this trend and realizing the value of this content, major players such as Apple, Spotify and Amazon have heavily invested into analytics for their whole product suite. This has seen an increase in listening times due to the platform suggesting them similar content and revenue for artist as their cut is larger now.

 It is interesting to see an industry as old and big as music entertainment to completely shift its core functioning to better suit the latest technology trends. This is what excited me about the potential for technology and the impact it can have on the grand scheme of things.

  Sources:

https://www.thewrap.com/beatchain-ben-mendoza-data-analytics-musicians/

[arjunc137-blog]

Week 4

Databases have always fascinated me. Storing large sets of data in an organized form and able to retrieve and sort the data seems very interesting but challenging at the same time. I used a bit of MS Access during my internship in the summer of 2018 and found it to be tedious to segregate and arrange data the way we want it to. I had to write multiple queries and create relationships to calculate one field. This is where SQL shines.

 SQL is such a simple yet powerful language that can be integrated across platforms and designed to run on large data sets. I love the format of the language since it resembles my thought process and uses basic English words to form the structure. A complex query that would take me 10-15 mins to run and check can be done in 3 lines of code using SQL. This is revolutionary.

 SQL exists because of the concept of relational databases. Relational databases are databases that are made of multiple tables that each have a unique key and are connected by forming relationships and using secondary keys. This allows for less data to be stored and more malleability.

 The recent push in SQL is for it to be built in the cloud. Google has partnered with Microsoft to announce Cloud SQL that will support Microsoft SQL Server. This ties in with Google’s cloud platform and allows corporations to run their data completely on the cloud and even the processing of large SQL sets. This increases speed and reliability with no need for backups, replications or patches. Google is also pushing for other formats such as NoSQL, PostgreSQL and ties in with Amazon’s framework at the same time.

 Google has also announced plans to integrate data mining and big-data fusions into the cloud. This will enable cross-platform usage of all these tools running off one server with Google’s speed and reliability. Since lot of companies host terabytes of data in the cloud, it is easier for them to do the processing in the, and integrate it with Salesforce or Workday and also transfer it across 100 SaaS applications for increased productivity and cohesiveness

 Sources:

https://techcrunch.com/2019/04/10/google-improves-data-migration-and-integration-for-bigquery/

https://techcrunch.com/2019/04/10/googles-managed-database-service-to-support-microsoft-sql-server/

[arjunc137-blog]

Week 2

In this week’s episode of my journey through Information Science, we take a look at the concept of ontology and how they are the backbone of information science.

 Last week we learnt about the various methods of information collection and their importance. We also looked at the biases present in them and ways to overcome them. After the successful competition of this crucial step we are faced with another obstacle. Information overload. We are now drowned with large volumes of data containing various data points, data types and formats. We are faced with an uphill task of converting all this to maintain uniformity and analyzing this data to reach meaningful conclusions.

 Creating ontologies is a logical step to tackle this problem. An ontology is the design of formal conceptual structures for some domain of inquiry based on a specific contextual perspective. What this means is defining terms and creating relationships that are unique and exist only to this solution. This eliminates any discrepancies in the information that was collected.

An important element in creating ontologies is the ability to share data with others. Labeling different items and giving them definitions allows a 3rd party to come into the picture and understand the information. This data can now also be manipulated easily to try different models and scenarios. The pre-existing definitions of the relationship changed in case of shift of status-quo.

 An ontology consists of 4 basic elements. Entities – Any physical object, place, role, event, etc that is permanent. Attributes – Properties of the above entities, measurements, quantities, etc that are quantifiable. Relationships – relationships are the key aspect that brings together the whole ontology. Relationships can be of 3 types – is a kind of (Taxonomy), is a part of hierarchy (Partonomy) or is associated with (Association). Using these 3 kinds of relationships we can breakdown and easily portray even the most complicated relationships with multiple stakeholders. Constraints – these are the barriers that restrict the movement in a system

 Now that we know how ontologies work, we need a universal language for them to work. This is where UML – Unified Modeling Language. The UML is a bridge that connects the software developers with the information analysts. It has swiftly become the standard language in object oriented design. It helps to reason system behavior, present design to communicate with stakeholders and help drive implementation.

The different diagrams built using UML can depict complex structures and combine problem solving components into them. It helps break down the complexity into steps that can be used to easily code the architecture. The flexible nature of this allows real-time transformation of structures from multiple stakeholders wherein you can perfect the model over time.

 To conclude, UML is here to stay. The abundant programs and software programmers available allow for specialized work and efficient depiction of the data in various architectures. It is very malleable to be used in a variety of industry to solve multiple problems while maintaining clear communication across parties.

 References

Henderson-Sellers, Brian. (2005). UML ? the Good, the Bad or the Ugly? Perspectives from a panel of experts. Software and System Modeling. 4. 4-13. 10.1007/s10270-004-0076-8.

(n.d.). Retrieved January 20, 2019, from https://www.edrawsoft.com/uml-introduction.php

[arjunc137-blog]

Week 1

Welcome! This is the journey of a so-called “Business Major” trying to add a new skill to his LinkedIn page. I found that the business classes that I have been taking so far in college are focused more on the decisions and actions taken post data collection or information processing. This has created a one-directional thought process that is in cognizant to the process and mindset that goes behind the scene to achieve these said results. I am determined to change this and learn the steps that taken to create, store, collect, organize and structure various sources of data and information.

The first couple of lectures of IS2000 have been thought-provoking to say the least. Although I have done surveys and interviews as part of my projects, they were mostly doctored. They were only done to work with the end results and prove theorems. There wasn’t any thought behind framing them, implementing them or tracking them. I considered them as mundane tasks that were as simple as they sounded- ask a few questions to a few random people to determine a collective consensus on a certain topic. However, I have quickly come to realize that there is a much bigger impact and thought that goes into making a “simple” survey.

The questions that are formed and the structure of a survey is more important than we thought. While discussing this topic in class some very interesting points were brought up. The type of response being controlled, which assumes you know all the outcomes and is easily monitored or controlled, or being free-range, which improves the quality of answers but is tough to analyze, or the length of a survey that changes the mood of the participant, or even the time they are sent restricts few sections of the population, all of these above psychological or demographic have a profound impact on the survey that I have never thought about. Personally, I too have these biases while participating in such activities and did not think about these before.

This shows that the biggest mistake you can make while performing a survey isn’t articulating your questions incorrectly or segmenting your audience, it would be to not take the psychological information into account. An effective survey is one that is thought through from the participants mindset. The success of a survey isn’t receiving the most number of entries or getting a clear-cut result. It would be to receive honest and accurate feedback from the participant that would improve the quality of information procured and yield better results over time. (Murphy, 2016)

I am looking forward to exploring more about such intricacies of data collection and its extraction into useful information that includes the analytics of the process.

  References

Murphy, L. (2016, October 26). Customer Psychology and the Unexpected Power of Surveys. Retrieved January 13, 2019, from https://sixteenventures.com/psychology-of-surveys

[arjunc137-blog]

Week 3

This week was very interesting from an information science perspective. It was a trip down memory lane for me that took me back to my childhood days of using HTML to create goofy websites with funky art about my favorite super hero or animal. I went back in time because of a new language that was introduced in class – XML or eXtensible Markup Language.

 Tags such as <title> and <head> were replaced with tags that contain information. This was a simple yet confusing concept. XML doesn’t really “do” anything. It doesn’t run any program or perform any calculations. It is just information that is wrapped in tags. The beauty of the language comes from this key difference. This allows XML to be a fluid language that can easily be integrated into other powerful languages and manipulate the data given.

 The most interesting thing about XML however is its application. Developed in 1998, XML has been tried and tested in multiple environment and has made it as a versatile format that can be modified for a particular task. Although XML in itself has very little application, combining it with other languages or structures gives it infinite possibilities. This is because of its simple yet comprehensive structure that can be extended and modified endlessly. In today's world XML is used in almost every industry. Every Word, Excel or PowerPoint is stored in Open Office XML (OOXML). Every time you file your taxes (PSA- Tax season is upon us! File those returns!) or a company reports its financials, the IRS and SEC transfer it in XBRL. Even things such as asking your Alexa device to switch off the lights uses SSML (Speech Synthesis Markup Language) to transfer information. The possibilities are truly endless, with next-gen technology such as Internet of Things and self-driving automobiles extending their usage.

 One thing about XML is certain – it has matured. With millions of people having removed bugs and streamlined the processing, there is very little if no room for error. The language has reached a point where its at its maximum applications and is widely deployed. There are also tools and systems being built that take advantage of packages that are built on top of XML without even noticing it. In a few years down the line people might forget that such a language exists. However, the underlying code and core XML structure is here to stay

Sources:

https://sdtimes.com/webdev/w3c-xml-is-everywhere/

https://www.w3schools.com/xml/xml_whatis.asp