tangc2
Feb 14, 2020
Graduate / Review My Personal Statement For a Master Degree In Data Science at Columbia University [3]
Describe how your professional and academic background has prepared you to pursue an advanced degree in the field of engineering or applied science at Columbia University. If there are any special circumstances that need to be brought to the attention of the Admission Committee, please include that information.
The term 'Data Science' appeared frequently during the final year of my undergraduate studies. It initially did not spark much interest in me however, because I neglected to see its role within the healthcare industry, where I was determined to pursue my career. Ironically, my work introduced me to the field of neuro-marketing. Despite its loose relation to healthcare, I was immediately captivated by the idea of utilizing advanced analytics to distill consequential insights from a person's physiological responses. Coincidentally, my side-hustle at the time also exposed me to Facebook Ads, which is powered by big data. These two data-driven technologies made me recognize the ubiquitous impacts of data science and its ever-evolving application to the healthcare industry. Meanwhile, I found myself unsatisfied with the lengthy development life cycle of conventional biomedical devices. These factors together motivated me to shift my focus to the digital aspects of healthcare and pursue an advanced degree in Data Science. I believe the Master of Science in Data Science (MSDS) at Columbia University will equip me with the pragmatic skills to achieve this goal. Upon completion of MSDS, I envision myself working as a data scientist in a data-oriented biomedical company. Eventually, I hope to oversee a startup where I will be able to apply my knowledge and experience towards the digital development of the healthcare industry.
My background in biomedical engineering has equipped me with the essential knowledge to succeed in a math and computation-heavy program. During my undergraduate studies, I took courses on calculus, linear algebra, and statistical inference, and achieved A+ in most of them. I also gained substantial exposure to computer programming. Despite achieving less superior grades in introductory programming courses, I corroborated core competences in more advanced subjects such as Image Processing and Computer Aided Engineering, which demanded a robust understanding of principles such as data structures, algorithms, and object-oriented paradigm. Moreover, I constantly sought resources to expand my programming knowledge, one of which was a SQL Bootcamp hosted by Kaggle, where I became adept with sophisticated querying techniques and Google MySQL. My work also offered me the opportunities to practice Python, VBA, and C in an industrial setting, which reinforced my coding skills. Throughout the years, I have acquired technical requisites to excel in the field of Data Science.
With a master's degree in biomedical engineering and working experience in a biomedical startup company, I have developed professional knowledge on both the regulatory and the product development aspects of the healthcare industry. These crucial attributes give me a competitive edge that would significantly complement my goal of working in a data-driven healthcare company. In addition, the collaborative nature of healthcare often required me to consult with practitioners and engineers with various expertise to ensure the design of a reliable solution. This has strengthened my communication and project management skills, which I believe are critical in an interdisciplinary field like data science, where I need to convey information effectively to software developers, managements and stakeholders from different backgrounds. Finally, there are always inevitable ethical issues to consider in engineering and healthcare. My background has provided me with a professional mentality that would be beneficial when dealing with emerging problems like privacy in the age of big data.
In the past year, my aspiration to become a data scientist motivated me to undertake several certified courses on Coursera. These included IBM Data Science Professional Certificate, Machine Learning by Stanford University, and Deep Learning by deeplearning.ai. This self-education process instilled in me a solid understanding of classic ML/DL algorithms, data science methodologies, as well as various real-world applications including facial and speech recognition through hands-on coursework. On top of that, I actively keep abreast of relevant topics by subscribing to Medium and reading books such as Data Science for Business by Foster Provost. This learning experience fostered my confidence to participate in a Kaggle competition, where I ranked in the top 4%. To further fortify my proficiency with analytical tools like NumPy, scikit-learn, Keras, TensorFlow and Matplotlib, I am currently volunteering at a data mining lab at XXXXX University, where I contributed to the design of a Sentiment Classifier with a CNN. However, despite these accomplishments, I realized I have some knowledge gaps that are difficult to address through my self-directed learning. For example, what metrics could be used to evaluate the importance of a categorical feature? And how to determine the optimal number of layers in a NN? These challenges made me see the value of a systematic approach that a school curriculum could offer and affirmed my desire for a master's degree in Data Science.
I am excited about the opportunities presented by the Data Science Institute at Columbia University. Since I am interested in the development of digital solutions to enhance healthcare outcome, it is imperative to have a robust understanding of sophisticated feature selection and model building techniques. Fortunately, the Data Science Institute offers a well-structured curriculum. On the one hand, core industry preparation courses such as Algorithms for Data Science and Statistical Inference & Modelling could effectively close my existing knowledge gaps and impart advanced data mining concepts. On the other hand, I have the flexibility to learn state-of-the-art ML/DP algorithms through electives like Modern Statistics: Applied Machine Learning for Image Analysis and Computer Science: Applied Deep Learning, as well as to participate in Health Analytics research. Also, its capstone project and internship component allow me to practice my skills in an industrial setting while gaining a realistic exposure to the full life cycle of a real business problem. Moreover, Columbia University is one of the most prestigious institutes in North America that purveys renowned faculty and talented peers for me to network with. Its advantageous location in a cosmopolitan city provides endless possibilities for career advancements. I value the academic and professional opportunities that Master of Science in Data Science (MSDS) at Columbia University offers, and I am confident that I have every ability to succeed in this competitive field. Please consider my admission.
application essay for columbia
Describe how your professional and academic background has prepared you to pursue an advanced degree in the field of engineering or applied science at Columbia University. If there are any special circumstances that need to be brought to the attention of the Admission Committee, please include that information.
The term 'Data Science' appeared frequently during the final year of my undergraduate studies. It initially did not spark much interest in me however, because I neglected to see its role within the healthcare industry, where I was determined to pursue my career. Ironically, my work introduced me to the field of neuro-marketing. Despite its loose relation to healthcare, I was immediately captivated by the idea of utilizing advanced analytics to distill consequential insights from a person's physiological responses. Coincidentally, my side-hustle at the time also exposed me to Facebook Ads, which is powered by big data. These two data-driven technologies made me recognize the ubiquitous impacts of data science and its ever-evolving application to the healthcare industry. Meanwhile, I found myself unsatisfied with the lengthy development life cycle of conventional biomedical devices. These factors together motivated me to shift my focus to the digital aspects of healthcare and pursue an advanced degree in Data Science. I believe the Master of Science in Data Science (MSDS) at Columbia University will equip me with the pragmatic skills to achieve this goal. Upon completion of MSDS, I envision myself working as a data scientist in a data-oriented biomedical company. Eventually, I hope to oversee a startup where I will be able to apply my knowledge and experience towards the digital development of the healthcare industry.
My background in biomedical engineering has equipped me with the essential knowledge to succeed in a math and computation-heavy program. During my undergraduate studies, I took courses on calculus, linear algebra, and statistical inference, and achieved A+ in most of them. I also gained substantial exposure to computer programming. Despite achieving less superior grades in introductory programming courses, I corroborated core competences in more advanced subjects such as Image Processing and Computer Aided Engineering, which demanded a robust understanding of principles such as data structures, algorithms, and object-oriented paradigm. Moreover, I constantly sought resources to expand my programming knowledge, one of which was a SQL Bootcamp hosted by Kaggle, where I became adept with sophisticated querying techniques and Google MySQL. My work also offered me the opportunities to practice Python, VBA, and C in an industrial setting, which reinforced my coding skills. Throughout the years, I have acquired technical requisites to excel in the field of Data Science.
With a master's degree in biomedical engineering and working experience in a biomedical startup company, I have developed professional knowledge on both the regulatory and the product development aspects of the healthcare industry. These crucial attributes give me a competitive edge that would significantly complement my goal of working in a data-driven healthcare company. In addition, the collaborative nature of healthcare often required me to consult with practitioners and engineers with various expertise to ensure the design of a reliable solution. This has strengthened my communication and project management skills, which I believe are critical in an interdisciplinary field like data science, where I need to convey information effectively to software developers, managements and stakeholders from different backgrounds. Finally, there are always inevitable ethical issues to consider in engineering and healthcare. My background has provided me with a professional mentality that would be beneficial when dealing with emerging problems like privacy in the age of big data.
In the past year, my aspiration to become a data scientist motivated me to undertake several certified courses on Coursera. These included IBM Data Science Professional Certificate, Machine Learning by Stanford University, and Deep Learning by deeplearning.ai. This self-education process instilled in me a solid understanding of classic ML/DL algorithms, data science methodologies, as well as various real-world applications including facial and speech recognition through hands-on coursework. On top of that, I actively keep abreast of relevant topics by subscribing to Medium and reading books such as Data Science for Business by Foster Provost. This learning experience fostered my confidence to participate in a Kaggle competition, where I ranked in the top 4%. To further fortify my proficiency with analytical tools like NumPy, scikit-learn, Keras, TensorFlow and Matplotlib, I am currently volunteering at a data mining lab at XXXXX University, where I contributed to the design of a Sentiment Classifier with a CNN. However, despite these accomplishments, I realized I have some knowledge gaps that are difficult to address through my self-directed learning. For example, what metrics could be used to evaluate the importance of a categorical feature? And how to determine the optimal number of layers in a NN? These challenges made me see the value of a systematic approach that a school curriculum could offer and affirmed my desire for a master's degree in Data Science.
I am excited about the opportunities presented by the Data Science Institute at Columbia University. Since I am interested in the development of digital solutions to enhance healthcare outcome, it is imperative to have a robust understanding of sophisticated feature selection and model building techniques. Fortunately, the Data Science Institute offers a well-structured curriculum. On the one hand, core industry preparation courses such as Algorithms for Data Science and Statistical Inference & Modelling could effectively close my existing knowledge gaps and impart advanced data mining concepts. On the other hand, I have the flexibility to learn state-of-the-art ML/DP algorithms through electives like Modern Statistics: Applied Machine Learning for Image Analysis and Computer Science: Applied Deep Learning, as well as to participate in Health Analytics research. Also, its capstone project and internship component allow me to practice my skills in an industrial setting while gaining a realistic exposure to the full life cycle of a real business problem. Moreover, Columbia University is one of the most prestigious institutes in North America that purveys renowned faculty and talented peers for me to network with. Its advantageous location in a cosmopolitan city provides endless possibilities for career advancements. I value the academic and professional opportunities that Master of Science in Data Science (MSDS) at Columbia University offers, and I am confident that I have every ability to succeed in this competitive field. Please consider my admission.
