smallIvy
Dec 19, 2023
Graduate / Data Science Masters - University of California - Berkley Statement of Person [2]
Over the last four years, my professional journey as a Data Scientist and Analyst has been marked by intensive work in data analytics, data mining, and data visualization. My experience spans across two technologically demanding fields: AI-level 4 autonomous driving and precision machining and manufacturing technology.
In the autonomous driving sector, characterized by its unique challenges, I have engaged with complex operational data and logs. Addressing the significant bottleneck of achieving a data-driven autonomous driving system, I have actively contributed to developing advanced analytics and data amining techniques that span from data collection to visualization and machine learning. This experience has not only fueled my passion for data science but also directed my focus towards a Machine Learning (ML) centric approach.
However, it is through my exposure to the field's complexities that I recognized knowledge gaps, spurring my pursuit of an advanced degree to bridge these and stand out in the data science landscape. After evaluating various programs aligned with my professional aspirations, I am convinced that UC Berkeley's MIDS degree will offer me the opportunity to transition from a domain-specific Data Science practitioner to a well-rounded ML modeling Data Scientist.
My academic foundation in business marketing is complemented by a solid grasp of data science prerequisites such as linear algebra, calculus, and statistics. This is further enhanced by my graduate experience in Loyola University's Master of Business Data Analytics program, where I delved into advanced statistical coursework (Managerial Statistics), database management, data warehousing, and specialized courses like Analysis of Big Data and data mining, establishing my modeling expertise.
Currently, as a Data Scientist in a Level-4 autonomous driving company, my primary responsibilities encompass data mining, visualization, and big data analytics. I have played a pivotal role in the development of an autonomous driving data loop platform through projects like Automated Scenario Mining (ASM), which bolsters our analysis and utilization of road test data. My contributions underpin the key technologies for ML algorithms in autonomous driving. My work has involved the automatic extraction and categorization of complex driving scenarios, instrumental in building our scenario library and refining our perception prediction ML models.
Furthermore, I am responsible for the development and management of a comprehensive data warehouse for autonomous driving road test data. I established an independent data pipeline for our autonomous driving assistance system, facilitating the visualization of the assistance features' contribution to the core algorithm performance.
My tenure also includes establishing a multidimensional performance assessment system for autonomous driving, creating a continually improving closed-loop evaluation framework that meets the evolving business and technological demands of driverless vehicles. My role has deepened my proficiency in Python, SQL, and Tableau.
In the past two years, I enriched my data science experience by engaging with the Bay Area's vibrant community through meetups and Machine Learning nights. This exposure has allowed me to collaborate closely with algorithm teams, understanding how they empower autonomous vehicles with ML algorithms. For instance, techniques like SIFT, which facilitates object recognition through feature matching, and YOLO, used for object identification and classification, were part of my learning curve, extending beyond autonomous driving to various domains where ML is applied.
The prospect of joining UC Berkeley's MIDS program excites me, as it presents an avenue to systematically enhance my data science skills and deepen my understanding in the field, establishing myself as a data science leader. Courses like Machine Learning, Deep Learning, and Natural Language Processing will broaden my academic horizons and enable precise utilization of existing data. Post-graduation, my goal is to continue as a Data Scientist with a stronger inclination towards ML within the AI domain. Rather than focusing on a singular skillset, I aspire to evolve into a more comprehensive Data Scientist.
Over the last four years, my professional journey as a Data Scientist and Analyst has been marked by intensive work in data analytics, data mining, and data visualization. My experience spans across two technologically demanding fields: AI-level 4 autonomous driving and precision machining and manufacturing technology.
In the autonomous driving sector, characterized by its unique challenges, I have engaged with complex operational data and logs. Addressing the significant bottleneck of achieving a data-driven autonomous driving system, I have actively contributed to developing advanced analytics and data amining techniques that span from data collection to visualization and machine learning. This experience has not only fueled my passion for data science but also directed my focus towards a Machine Learning (ML) centric approach.
However, it is through my exposure to the field's complexities that I recognized knowledge gaps, spurring my pursuit of an advanced degree to bridge these and stand out in the data science landscape. After evaluating various programs aligned with my professional aspirations, I am convinced that UC Berkeley's MIDS degree will offer me the opportunity to transition from a domain-specific Data Science practitioner to a well-rounded ML modeling Data Scientist.
My academic foundation in business marketing is complemented by a solid grasp of data science prerequisites such as linear algebra, calculus, and statistics. This is further enhanced by my graduate experience in Loyola University's Master of Business Data Analytics program, where I delved into advanced statistical coursework (Managerial Statistics), database management, data warehousing, and specialized courses like Analysis of Big Data and data mining, establishing my modeling expertise.
Currently, as a Data Scientist in a Level-4 autonomous driving company, my primary responsibilities encompass data mining, visualization, and big data analytics. I have played a pivotal role in the development of an autonomous driving data loop platform through projects like Automated Scenario Mining (ASM), which bolsters our analysis and utilization of road test data. My contributions underpin the key technologies for ML algorithms in autonomous driving. My work has involved the automatic extraction and categorization of complex driving scenarios, instrumental in building our scenario library and refining our perception prediction ML models.
Furthermore, I am responsible for the development and management of a comprehensive data warehouse for autonomous driving road test data. I established an independent data pipeline for our autonomous driving assistance system, facilitating the visualization of the assistance features' contribution to the core algorithm performance.
My tenure also includes establishing a multidimensional performance assessment system for autonomous driving, creating a continually improving closed-loop evaluation framework that meets the evolving business and technological demands of driverless vehicles. My role has deepened my proficiency in Python, SQL, and Tableau.
In the past two years, I enriched my data science experience by engaging with the Bay Area's vibrant community through meetups and Machine Learning nights. This exposure has allowed me to collaborate closely with algorithm teams, understanding how they empower autonomous vehicles with ML algorithms. For instance, techniques like SIFT, which facilitates object recognition through feature matching, and YOLO, used for object identification and classification, were part of my learning curve, extending beyond autonomous driving to various domains where ML is applied.
The prospect of joining UC Berkeley's MIDS program excites me, as it presents an avenue to systematically enhance my data science skills and deepen my understanding in the field, establishing myself as a data science leader. Courses like Machine Learning, Deep Learning, and Natural Language Processing will broaden my academic horizons and enable precise utilization of existing data. Post-graduation, my goal is to continue as a Data Scientist with a stronger inclination towards ML within the AI domain. Rather than focusing on a singular skillset, I aspire to evolve into a more comprehensive Data Scientist.
