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Application of Small-Scale Data Analytics to a Pre-Existing Accounting Process

Description

The concept of data analytics has become a primary focus for companies of all types, and from within all industries. Leveraging data to enhance the decision making power of management is now vital for companies to remain competitive. Beginning as

The concept of data analytics has become a primary focus for companies of all types, and from within all industries. Leveraging data to enhance the decision making power of management is now vital for companies to remain competitive. Beginning as a movement pioneered by tech-startups and teams of university researchers, data analytics is reshaping every industry that it touches, and the field of accounting has been no exception.
Corporate buzzword terms like “big data” and “data analytics” are vague in meaning, and are thrown around by media sources often enough to obfuscate their actual meanings. These concepts are then associated with company-wide initiatives beyond the reach of the individual, in a nebulous world where people know that analytics happens, but don’t understand what it is.
The power of data analytics is not reserved for company-wide initiatives, or only employed by Silicon Valley tech start-ups. Its impacts are visible down at the team or department level, and can be conducted by the individual employees. The field of data analytics is evolving, and within it exists a rapid transition in which the individual employee is becoming a source for insight and value creation through the adoption of analytics based approaches.
The purpose of this thesis is to showcase an example of this claim, and demonstrate how an analytics based approach was applied to an existing accounting process to create new insights and information. To do this, I will discuss my development of an Excel based Dashboard Analytics tool, which I completed during my internship with Bechtel Corporation throughout the summer of 2018, and I will use this analytics tool to demonstrate the improvements that small-scale analytics had on a pre-existing process. During this discussion, I will address conceptual aspects of database design that related to my project, and will show how I applied this classroom learning to a working environment. The paper will begin with an overview of the desired goals of the group in which I was based, and will then analyze how the needs of the group led to the creation and implementation of this new analytics-based reporting tool. I will conclude with a discussion of the potential future use of this tool, and how the inclusion of these analytical approaches will continue to shape the working environment.

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Agent

Created

Date Created
2019-05

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Predictive Modeling of 4th Down Selection in Power 5 Conference: Data Analytics

Description

Predictive analytics have been used in a wide variety of settings, including healthcare,
sports, banking, and other disciplines. We use predictive analytics and modeling to
determine the impact of certain factors that increase the probability of a successful

Predictive analytics have been used in a wide variety of settings, including healthcare,
sports, banking, and other disciplines. We use predictive analytics and modeling to
determine the impact of certain factors that increase the probability of a successful
fourth down conversion in the Power 5 conferences. The logistic regression models
predict the likelihood of going for fourth down with a 64% or more probability based on
2015-17 data obtained from ESPN’s college football API. Offense type though important
but non-measurable was incorporated as a random effect. We found that distance to go,
play type, field position, and week of the season were key leading covariates in
predictability. On average, our model performed as much as 14% better than coaches
in 2018.

Contributors

Created

Date Created
2019-05

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The Capabilities and Obstacles of Integrating Machine Learning into a Supply Chain

Description

Only an Executive Summary of the project is included.
The goal of this project is to develop a deeper understanding of how machine learning pertains to the business world and how business professionals can capitalize on its capabilities. It

Only an Executive Summary of the project is included.
The goal of this project is to develop a deeper understanding of how machine learning pertains to the business world and how business professionals can capitalize on its capabilities. It explores the end-to-end process of integrating a machine and the tradeoffs and obstacles to consider. This topic is extremely pertinent today as the advent of big data increases and the use of machine learning and artificial intelligence is expanding across industries and functional roles. The approach I took was to expand on a project I championed as a Microsoft intern where I facilitated the integration of a forecasting machine learning model firsthand into the business. I supplement my findings from the experience with research on machine learning as a disruptive technology. This paper will not delve into the technical aspects of coding a machine model, but rather provide a holistic overview of developing the model from a business perspective. My findings show that, while the advantages of machine learning are large and widespread, a lack of visibility and transparency into the algorithms behind machine learning, the necessity for large amounts of data, and the overall complexity of creating accurate models are all tradeoffs to consider when deciding whether or not machine learning is suitable for a certain objective. The results of this paper are important in order to increase the understanding of any business professional on the capabilities and obstacles of integrating machine learning into their business operations.

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Agent

Created

Date Created
2019-05

Data Analytics to Identify the Genetic Basis for Resilience to Temperature Stress in Soybeans

Description

This paper explores the ability to predict yields of soybeans based on genetics and environmental factors. Based on the biology of soybeans, it has been shown that yields are best when soybeans grow within a certain temperature range. The event

This paper explores the ability to predict yields of soybeans based on genetics and environmental factors. Based on the biology of soybeans, it has been shown that yields are best when soybeans grow within a certain temperature range. The event a soybean is exposed to temperature outside their accepted range is labeled as an instance of stress. Currently, there are few models that use genetic information to predict how crops may respond to stress. Using data provided by an agricultural business, a model was developed that can categorically label soybean varieties by their yield response to stress using genetic data. The model clusters varieties based on their yield production in response to stress. The clustering criteria is based on variance distribution and correlation. A logistic regression is then fitted to identify significant gene markers in varieties with minimal yield variance. Such characteristics provide a probabilistic outlook of how certain varieties will perform when planted in different regions. Given changing global climate conditions, this model demonstrates the potential of using data to efficiently develop and grow crops adjusted to climate changes.

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Agent

Created

Date Created
2018-05

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Twitter Analytics

Description

Twitter is one of the most powerful communication tools ever created. There are over 1.3 billion registered Twitter users (Smith, 2016). 100 million daily people actively use Twitter every day. 6,000 tweets are tweeted every second. Communication has never been

Twitter is one of the most powerful communication tools ever created. There are over 1.3 billion registered Twitter users (Smith, 2016). 100 million daily people actively use Twitter every day. 6,000 tweets are tweeted every second. Communication has never been so abundant, public, and chronicled. Not only is there a gigantic population to market to, but also a wealth of information about that population to record and draw insights from. However, many companies' Twitter accounts fail to generate popular posts on a regular basis. The content that they produce is ineffective and uninteresting. In my opinion, these companies are failing to take advantage of a huge opportunity. I decided to dive into the Twitter accounts of some of my favorite companies to see what they were doing wrong and how they could improve. My thesis investigates 18 different company Twitter accounts from four different industries: Athletic Apparel, Technology, Online Entertainment, and Car Manufacturing. I pulled 200 tweets from each company and cleaned and organized the data into an Excel spreadsheet. I investigated how certain variables impacted tweet popularity across the four industries. First, I looked at tweet format to determine whether posts, retweets, or replies were the best format. Then, I analyzed how different elements of a tweet's content could impact the tweet's popularity. Specifically, I looked at the effects of including links, hashtags, and questions into the tweet. Next, I tried to determine the optimal tweet length for each industry. And finally, I compared each industry's tweet sentiment preferences. I then summarized my findings into a series of recommendations for companies to improve their tweet popularity.

Contributors

Agent

Created

Date Created
2016-05

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Marketing to Millennials Within the Airline and Finance Industries Across Cultures

Description

Millennials are the group of people that make up the newer generation of the world's population and they are constantly surrounded by technology, as well as known for having different values than the previous generations. Marketers have to adapt to

Millennials are the group of people that make up the newer generation of the world's population and they are constantly surrounded by technology, as well as known for having different values than the previous generations. Marketers have to adapt to newer ways to appeal to millennials and secure their loyalty since millennials are always on the lookout for the next best thing and will "trade up for brands that matter, but trade down when brand value is weak", it poses a challenge for the marketing departments of companies (Fromm, J. & Parks, J.). The airline industry is one of the fastest growing sectors as "the total number of people flying on U.S. airlines will increase from 745.5 million in 2014 and grow to 1.15 billion in 2034," which shows that airlines have a wider population to market to, and will need to improve their marketing strategies to differentiate from competitors (Power). The financial sector also has a difficult time reaching out to millennials because "millennials are hesitant to take financial risks," as well as downing in college debt, while not making as much money as previous generations (Fromm, J. & Parks, J.). By looking into the marketing strategies, specifically using social media platforms, of the two industries, an understanding can be gathered of what millennials are attracted to. Along with looking at the marketing strategies of financial and airline industries, I looked at the perspectives of these industries in different countries, which is important to look at because then we can see if the values of millennials vary across different cultures. Countries chosen for research to further examine their cultural differences in terms of marketing practices are the United States and England. The main form of marketing that was used for this research were social media accounts of the companies, and seeing how they used the social networking platforms to reach and engage with their consumers, especially with those of the millennial generation. The companies chosen for further research for the airline industry from England were British Airways, EasyJet, and Virgin Atlantic, while for the U.S. Delta Airlines, Inc., Southwest Airlines, and United were chosen. The companies chosen to further examine within the finance industry from England include Barclay's, HSBC, and Lloyd's Bank, while for the U.S. the banks selected were Bank of America, JPMorgan Chase, and Wells Fargo. The companies for this study were chosen because they are among the top five in their industry, as well as all companies that I have had previous interactions with. It was meant to see what the companies at the top of the industry were doing that set them apart from their competitors in terms of social media marketing content and see if there were features they lacked that could be changed or improvements they could make. A survey was also conducted to get a better idea of the attitudes and behaviors of millennials when it comes to the airline and finance industries, as well as towards social media marketing practices.

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Created

Date Created
2016-05

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Energy Internet of Things Collaborative Thesis

Description

For our collaborative thesis we explored the US electric utility market and how the Internet of Things technology movement could capture a possible advancement of the current existing grid. Our objective of this project was to successfully understand the market

For our collaborative thesis we explored the US electric utility market and how the Internet of Things technology movement could capture a possible advancement of the current existing grid. Our objective of this project was to successfully understand the market trends in the utility space and identify where a semiconductor manufacturing company, with a focus on IoT technology, could penetrate the market using their products. The methodology used for our research was to conduct industry interviews to formulate common trends in the utility and industrial hardware manufacturer industries. From there, we composed various strategies that The Company should explore. These strategies were backed up using qualitative reasoning and forecasted discounted cash flow and net present value analysis. We confirmed that The Company should use specific silicon microprocessors and microcontrollers that pertained to each of the four devices analytics demand. Along with a silicon strategy, our group believes that there is a strong argument for a data analytics software package by forming strategic partnerships in this space.

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Agent

Created

Date Created
2016-05

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Data Analysis of Jungle Pattern in League of Legends with Implications for Players and Game Developers

Description

League of Legends is a Multiplayer Online Battle Arena (MOBA) game. MOBA games are generally formatted where two teams of five, each player controlling a character (champion), will try to take each other's base as quickly as possible. Currently, with

League of Legends is a Multiplayer Online Battle Arena (MOBA) game. MOBA games are generally formatted where two teams of five, each player controlling a character (champion), will try to take each other's base as quickly as possible. Currently, with about 70 million, League of Legends is number one in the digital entertainment industry with $1.63 billion dollars of revenue in year 2015. This research analysis scopes in on the niche of the "Jungler" role between different tiers of player in League of Legends. I uncovered differences in player strategy that may explain the achievement of high rank using data aggregation through Riot Games' API, data slicing with time-sensitive data, random sampling, clustering by tiers, graphical techniques to display the cluster, distribution analysis and finally, a comprehensive factor analysis on the data's implications.

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Agent

Created

Date Created
2016-05

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A Predictive Statistical Analysis on Loan Data

Description

Created predictive models using R to determine significant variables that help determine whether someone will default on their loans using a data set of almost 900,000 loan applicants.

Contributors

Agent

Created

Date Created
2021-05

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Predictive Modeling of 4th Down Selection in Power 5 Conference: Data Analytics

Description

Predictive analytics have been used in a wide variety of settings, including healthcare, sports, banking, and other disciplines. We use predictive analytics and modeling to determine the impact of certain factors that increase the probability of a successful fourth down

Predictive analytics have been used in a wide variety of settings, including healthcare, sports, banking, and other disciplines. We use predictive analytics and modeling to determine the impact of certain factors that increase the probability of a successful fourth down conversion in the Power 5 conferences. The logistic regression models predict the likelihood of going for fourth down with a 64% or more probability based on 2015-17 data obtained from ESPN’s college football API. Offense type though important but non-measurable was incorporated as a random effect. We found that distance to go, play type, field position, and week of the season were key leading covariates in predictability. On average, our model performed as much as 14% better than coaches in 2018.

Contributors

Agent

Created

Date Created
2019-05