SOP for Data Science Admission; What exactly is a hacker?

Hi all, it's great to find this place. I'm a computer science student with research experiences mainly in machine learning and I am applying for data science graduate programs at a few US universities. Below is my SOP for one program, I will appreciate it if you can point out any grammar errors or provide any comments. Thank you all in advance!

=====================================================================================

big data - opportunities and challenges for hackers

What, exactly, is a hacker? For me, the word "hacker" has more connotations of people who constantly improve themselves to generate good solutions with intellectual curiosity, and less about people who break into others' computers. In the age of data explosion, it's a thrill for hackers to dig into massive data, discovering secrets and solving problems. Therefore, Master Degree in Data science is a logical culmination of my passion, tackling real-world challenges through data science.

The data science and management course I attended at XXX University has triggered my strong interest to solve problems with big data. The program introduced me to fundamental data science knowledge, including database system design, social network analysis as well as statistic tools. I used R to analyze pair panels graphics, generated linear regression models to predict students' GPA scores, based on their GRE scores. One project that I found particularly interesting was looking at data from social media users to find the relationships between followers and the people they follow. By identifying an influencer in a health care forum with PageRank and centralities, companies could deliver healthcare information and related promotions to certain social circles more efficiently.

After returning to college after my visit to XXX, I kept on thinking of ways to analyze social network data and make the most of them. In addition to the field of healthcare, I believe that this technology could benefit many other disciplines, such as business and political elections. So, I started another research project at the XXX lab, where I used CUDA parallel programming to accelerate network centrality calculations. I boosted the efficiency by paralyzing the centrality calculations and using a directed graph with more than 1,000 nodes and edges, the total computation time was reduced from 192 to 2 ms. The power of GPU calculation enabled massive data processing, which can be used to achieve real-time network centrality calculations. In this way, a politician could identify opinion leaders, as well as their attitudes to politics, and adjust her election strategies accordingly.

If computational hardware and data are papers and pigments, then machine learning techniques are the fine brushes to create beautiful artworks. As a research intern in XXX lab in 2016, I trained a classifier to identify obstacles on a road with deep learning framework DIGITS - a useful technique for unmanned vehicles. My team labeled the pictures to be tested and then trained AlexNet to identify them. However, due to a number of mistaken configurations, the system's performance was not ideal. After reading papers discussing deep learning network structures, I enlarged the dataset and adjusted the learning rate and batch size, and with these adjustments, I boosted the accuracy of the model to over 80%. The good understanding of machine learning can promote the better utilization of data.

Currently, I'm working on a project about professional basketball, with the new availability of player tracking data. Traditional methods of tactical analysis largely rely on the knowledge and manual labor of domain experts; Being a huge basketball fan, I have wondered why I am unable to identify frequently appearing patterns in professional basketball matches that describe teams' tactics and behaviors without manually label all offensive tactics. I plan to generate features that differentiate plays from one another, then use clustering techniques to extract frequently appeared player trajectories, and thereby help teams to better understand how both they and their opponents play. Even I'm an amateur basketball fan, by utilizing the power of machine learning, I believe I can discover the tactics of professional plays as domain experts, but even faster and more creative.

The advent of big data presents both opportunities and challenges for hackers, and only those willing to educate themselves and to use the cutting-edge technology will benefit from it. My experiences of machine learning and high-performance computation will allow me to approach these developments with creative and rigorous thinking. Your program would be valuable to me in several ways. First, I can deepen my understanding of statistical models, hone up skills of selecting right strategies for different problems through statistical modules. Further, the project-focused courses can provide chances using computational techniques to work through real-world challenges. Ultimately, ***'s core value to promote the betterment of society, and further our understanding of the nature of the universe will guide me throughout my life-long career. I believe all these experience will be hugely beneficial for me to be a real hacker, solving problems skillfully with real-world implications, as I always want to be.

Shulan, you are taking too much of a casual tone in the writing of this SOP. You should try to be more academic in presentation because you are formally applying for admission to a course that requires a degree of seriousness in its approach. Therefore, you must revise the essay to create a more formal and respectful tone for the reviewer to read. You should start by changing the opening paragraph. Try to avoid using the term "hacker" in the essay because, even though there are some professional, paid hackers, the connotation of the word is still on the negative side in most of the computer world. It is still an underground reference for all intents and purposes. Paragraphs 2 and 3 can be used as is. Lose the first sentence in paragraph 4 because the focus of this paper should immediately be on your professional experience. There is no need to flowery language. Just go direct to the point at all times because the reviewer doesn't have the time to waste in reading your paper and trying to get to the point you are trying to make. Just make the point as soon as you can. Paragraph 5 can also be used intact. Paragraph 6 requires a more university centered discussion that focuses on your 5 year career plan and how the university you have chosen can help you kick start that plan.

@Holt
Thank you so much for taking the time to read my SOP and giving insightful advice. The suggestion of more specific career plan is great. Regarding the tone of this essay, I will revise it and try to be more formal and professional, thanks for pointing out it. About the term "hacker", I read this explanation in a book written by Paul Graham, and this book profoundly affected my choice to study in computer science. Also, in Wikipedia, the definition of hacker is any skilled computer expert that uses their technical knowledge to overcome a problem. However, I understand your concern since it widely refers to security hackers. Do you really think that's not fitting for this essay, even if I added my explanation?

I will post the updated version later. Thank you again for your advice.

Hi all, I've taken your advice and modified my essay accordingly, could you all please check the 2nd version and give some more advice, thanks a lot!

My strong interest to solve problems with big data was triggered by one data science lecture I attended at XXX University. The professor described how he used trajectory data to analyze the behavior of stowaways from Mexica to the U.S. Such first insight of how data scientists approach real-world problems incited me to delve further into the field.

The course also introduced me to fundamental data science knowledge, including database design, social network analysis as well as statistical tools. I programmed in R to analyze data correlations, generated linear regression models to predict students' GPA scores, based on their GRE scores. One project during the course that I found particularly interesting was looking at data from social media users to find the relationships between followers and the people they follow. By identifying an influencer in a healthcare forum with PageRank and centralities, companies could deliver healthcare information and related promotions to certain social circles more efficiently.

After returning to college after my visit to Korea, I kept on thinking of ways to analyze social network data and make the most of them. In addition to the field of healthcare, I believe that this technology could benefit many other disciplines, such as business and political elections. So, I started another research project at the Nvidia-Joint lab, where I used CUDA parallel programming to accelerate network centrality calculations. I boosted the efficiency by paralyzing the centrality calculations and using a directed graph with more than 1,000 nodes and edges, the total computation time was reduced from 192 to 2 ms. The power of GPU calculation enabled massive data processing, which can be used to achieve real-time network centrality calculations. In this way, a politician could identify opinion leaders, as well as their attitudes to politics, and adjust his election strategies accordingly.

Being majored in Computer Science, my machine learning experiences enable me to analyze data efficiently and creatively. In the course of a research project I completed in the summer of 2016, I trained a classifier to identify obstacles on roads - a useful technique for unmanned vehicles, using the Nvidia deep learning framework DIGITS. My team labeled the pictures to be tested and then trained AlexNet to identify them. However, due to a number of mistaken configurations, the system's performance was not ideal. After reading papers discussing deep learning networks, I enlarged the dataset and adjusted the learning rate and batch size, and, with these adjustments, I boosted the accuracy of the model to over 80%.

My resolve to specialize in data science is strengthened by my current work of final year dissertation. My project examines how the analysis of the patterns in player trajectories can be improved using machine learning techniques. Namely, while the new availability of player-tracking data facilitates detailed analyses of the patterns in player movement, traditional tactical analysis largely relies on the knowledge and manual labor of domain experts, which is a very expensive and unscalable approach. Being a huge basketball fan, I have wondered why I am unable to identify frequently appearing patterns in professional basketball matches, which describe teams' tactics and behaviors without manually labeling all offensive tactics used. I plan to generate features that differentiate plays from one another, then use unsupervised machine learning techniques to extract frequently appearing player trajectories.

In this way, I hope to help teams to better understand how both they and their opponents play. Even though I am only an amateur basketball fan, by utilizing the power of machine learning, I believe we can discover the tactics used by professional plays as domain experts, making them even faster and more creative. The excitement of mining basketball plays motivates me to launch a career in sports data analytics, using the knowledge of data science to promotes the development of professional sports, particularly professional basketball.

I believe your program is the perfect choice to help me to achieve this goal for several reasons. First, I can deepen my understanding of statistical models, hone up skills of selecting right strategies for different problems. Further, the project-focused courses can provide chances using computational techniques to work through real-world challenges. I can also hone communication skills through group projects, which are essential when working in the industry. Ultimately, XXX's core value to promote the betterment of society, and further our understanding of the nature of the universe will guide me throughout my career as an ethical and creative data scientist. My creative and rigorous thinking showed in courses and research, my passion for sports, along with my strengthened understanding of data science through your program, will well-prepared me to provide unique insights into this industry.