Statement of intent reflecting the overview of my career path and goals.

My interest in pursuing Data Science stems from my strong desire to expand my expertise in applying computer systems in manipulating, processing, and analyzing data to solve complex problems. I aim to learn mathematical and statistical representations and models, advanced computational methods, and big data analytics to optimize processes to secure a competitive position in a science or business-related field. I am specifically interested in Data Science and Machine Learning. My goal is to gain advanced knowledge in these aspects and enhance my analytical, critical thinking, and problem-solving capabilities.

After completing my Bachelor’s in Computer Science from Pennsylvania State University, USA, I have sought and taken numerous job assignments that have significantly contributed to my growth and shaped my career interests. For the past three years, I have been privileged to work as a software developer, enhancing my expertise in PySpark ML and Hadoop. As a Hadoop Engineer in Blue Cross Blue Shield, I improved efficiency in SQL-related queries and streamlining data flow from our clients to achieve a cohesive and high-impact operation. During this period, my technical skills improved tenfold due to my intensive involvement in developing a framework to help our clients maintain their data. Primarily, my roles aimed at building pipelines that would pull data from different sources, followed by the integration, consolidation, and structuring to ensure it was more applicable to the client's needs.

My responsibilities when I joined Kaiser Permanente as a PySpark ML Developer further increased my interest in understanding the links between big data and data science. My roles here required a combination of data analytics and optimization, which was integrated into machine learning models to categorize doctors' notes to ensure care delivered was targeted and uniquely tailored to the patient's needs. Using my own AI and mathematical models and representations, such as logistic regression for text classification, I analyzed and optimized the hospital data and developed strong baseline machine learning models that achieved 91% accuracy. Kaiser Permanente was facing the problem of multiple missing data entries in data sets. My second objective was to determine if we could limit deletions to create a larger dataset to make more accurate predictions. I explored various imputation strategies and made sure that there was no bias in the estimate. Ultimately, I was able to minimize deletion by 50%. This, combined with my previous experience as a Hadoop Engineer, expanded my technical skills by exposing and enhancing my understanding of ingestion, batch processing, stream processing, and machine learning.

At the center of my interests has been my fascination with the role of data science and artificial intelligence in optimizing processes and formulating robust strategies to ensure innovative decision-making through leveraging the developing opportunities in computing, data science, and machine learning. I aim to develop the technical skills to combine big data and data sciences to unlock the knowledge contained in large, complex, and challenging data sets generated in the areas of both sciences and businesses in real-time. Despite the benefits gained from my experiences with big data analytics methods, such as Hadoop and Spark, there is still more to learn regarding complex modeling and simulations. I am eager to know how to bring some of the once-incompatible systems together to accelerate insights that contribute to better decisions.

Following my experiences as a Hadoop Engineer and PySpark ML Developer, I began working as a Java Developer for Fannie Mae and helped them implement a new and innovative strategy to improve their web-based reporting system. and came up an innovative method of improving their web reporting system, reducing their financial tracking analysis time by 20%. While I enjoyed my experiences with this client, I still desired to gain some specialized knowledge in data science.

Pushed by these interests and knowledge gaps, I decided to join MIT, where I took a certificate course in Data Science and Machine Learning and achieved 3.7 GPA. Although this was only a 12-week certificate program, it has significantly advanced my data science skills with respect to solving business problems. It has provided foundational knowledge on machine learning, deep learning, prediction, and NLP and expanded my skills in statistics and python. Whilst enrolled in the course at MIT, I undertook academic projects which dealt primarily with utilizing data sets for practical purposes. For example, I built a recommendation system for Netflix users based on the ratings scores of others users, as well as performing database analytics to analyze different manifestations of diabetes in the Pima tribe of Native Americans. I am also currently taking a certificate course in Data Science at IBM, where I intend to develop the hands-on skills required in using technical tools, libraries, and languages used in big data, analyzing, and visualizing data, building pipelines and machine learning models, and expanding my analytical and critical thinking skills.

As mentioned, I aim to delve deeper into these areas of focus while also working on my technical skills to be able to work in competitive positions within organizations and develop strategies capable of solving problems optimally. On a personal note, as well as its intellectual appeal, I am attracted to the personal and even moral aspects of life as a Data Engineer. I admire the way in which our society has organized itself around rationally categorizing information, and find data to be an immensely powerful tool. The prospect of technological advancements in data science excites me greatly, as I hope to achieve a new insight towards working on the current cutting edge of these technologies. In the long run, I aim to get corporate world experience in data science, analytics, and bigdata and grow my skills and expertise in the same fields.