Matching Items (20)

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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

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.

Contributors

Agent

Created

Date Created
  • 2019-05

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Project Resolve: Reducing Recidivism using Advanced Analytics

Description

The United States has an institutional prison system built on the principle of retributive justice combined with racial prejudice that despite countless efforts for reform currently holds 2.3 million individuals,

The United States has an institutional prison system built on the principle of retributive justice combined with racial prejudice that despite countless efforts for reform currently holds 2.3 million individuals, primarily minorities, behind bars. This institution has remained largely unchanged, meanwhile 83.4% of those who enter the system will return within one decade and it currently costs nearly $39 billion each year (Alper 4). Because the prison institution consistently fails to address the core root of crime, there is a great need to reconsider the approach taken towards those who break our nation’s laws with the dual purpose of enhancing freedom and reducing crime. This paper outlines an original theoretical framework being implemented by Project Resolve that can help to identify and implement solutions for our prison system without reliance on political, institutional, or societal approval. The method focuses on three core goals, the first is to connect as much of the data surrounding prisoners and the formerly incarcerated as possible, the second is to use modern analytic approaches to analyze and propose superior solutions for rehabilitation, the third is shifting focus to public interest technology both inside prisons and the parole process. The combination of these objectives has the potential to reduce recidivism to significantly, deter criminals before initial offense, and to implement a truly equitable prison institution.

Contributors

Agent

Created

Date Created
  • 2020-05

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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

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.

Contributors

Agent

Created

Date Created
  • 2016-05

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

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.

Contributors

Agent

Created

Date Created
  • 2018-05

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Marketing Implications of Big Data: An Examination of the Retail Industry

Description

As the use of Big Data gains momentum and transitions into mainstream adoption, marketers are racing to generate valuable insights that can create well-informed strategic business decisions. The retail market

As the use of Big Data gains momentum and transitions into mainstream adoption, marketers are racing to generate valuable insights that can create well-informed strategic business decisions. The retail market is a fiercely competitive industry, and the rapid adoption of smartphones and tablets have led e-commerce rivals to grow at an unbelievable rate. Retailers are able to collect and analyze data from both their physical stores and e-commerce platforms, placing them in a unique position to be able to fully capitalize on the power of Big Data. This thesis is an examination of Big Data and how marketers can use it to create better experiences for consumers. Insights generated from the use of Big Data can result in increased customer engagement, loyalty, and retention for an organization. Businesses of all sizes, whether it be enterprise, small-to-midsize, and even solely e-commerce organizations have successfully implemented Big Data technology. However, there are issues regarding challenges and the ethical and legal concerns that need to be addressed as the world continues to adopt the use of Big Data analytics and insights. With the abundance of data collected in today's digital world, marketers must take advantage of available resources to improve the overall customer experience.

Contributors

Agent

Created

Date Created
  • 2014-05

Quantitative Analysis of Creative Factors Driving TV Streaming Success

Description

This thesis includes three separate documents: a) a comprehensive document detailing the methods and analysis of the creative factors tied to series success, b) an hour long pilot script based

This thesis includes three separate documents: a) a comprehensive document detailing the methods and analysis of the creative factors tied to series success, b) an hour long pilot script based on this data, and c) an industry-standard pitch deck for a TV show created with data insights. In a larger sense, the aim of this study is to take the first steps in remedying information asymmetry between streaming services and content creators. If streaming services were more transparent with their data and communicated to their creators what has been proven to work in the past, showrunners and staff writers could have a new tool to increase the competitiveness of their series and aid in show renewal each year.

Contributors

Agent

Created

Date Created
  • 2021-05

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Data Analysis of Effects of Officer Briefing Synergy in Combat Flight Simulation Game Dreadnought (2017)

Description

Dreadnought is a free-to-play multiplayer flight simulation in which two teams of 8 players each compete against one another to complete an objective. Each player controls a large-scale spaceship, various

Dreadnought is a free-to-play multiplayer flight simulation in which two teams of 8 players each compete against one another to complete an objective. Each player controls a large-scale spaceship, various aspects of which can be customized to improve a player’s performance in a game. One such aspect is Officer Briefings, which are passive abilities that grant ships additional capabilities. Two of these Briefings, known as Retaliator and Get My Good Side, have strong synergy when used together, which has led to the Dreadnought community’s claiming that the Briefings are too powerful and should be rebalanced to be more in line with the power levels of other Briefings. This study collected gameplay data with and without the use of these specific Officer Briefings to determine the precise impact on gameplay. Linear correlation matrices and inference on two means were used to determine performance impact. It was found that, although these Officer Briefings do improve an individual player’s performance in a game, they do not have a consistent impact on the player’s team performance, and that these Officer Briefings are therefore not in need of rebalancing.

Contributors

Created

Date Created
  • 2021-05

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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

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.

Contributors

Agent

Created

Date Created
  • 2016-05

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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.

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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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