My Way Toward Data Science — Why I Want to Be a Data Scientist?
When I was looking for a job before graduating from a PhD program I had a hard time to land a job because of two major reasons.
Firstly, I am married and my husband has a decent job at Seattle, so that Seattle was my only target city when I was searching for jobs.
Secondly, my whole educational background and working background is education, which does not have many job opportunities in Seattle. In the second year of my PhD, my academic advisor asked me what I wanted to do after graduate, I said research. So he involved me in a few research projects granted by the Department of Offense and the National Science Foundation that assessed the effectiveness of new instructional model and curriculum model. This resulted in that it was rarely possible for me to find a teaching job. The only jobs that I felt more comfortable and confident to apply is educational research and data analysis.
Though I was passionate about doing research, not many jobs that allow you to do real research(such as experimental studies, advanced statistic modeling or fine qualitative study). After some struggling, I decided to leave academia and focus on industry and more practical job positions. It was a painful process to figure out what I really wanted to do and what I was able to do. It was also hard to balance personal interest and the reality.
I reflected my graduate school experiences and asked myself two critical questions: 1. what I am really good at? and 2. what is the most enjoyable and exciting part through my graduate education? My answer is data analysis and statistical modeling. I vividly remember these days that I ran the Structural Equation Model to find causal correlation between dependent variables and independent variables without conducting an experimental study. It was really exciting and rewarding when the model fit was increased after enormous adjustments. It was even more exciting to see the positive significant coefficients between the independent variables and dependent variables. Whereas, the most exciting part was when the findings are presented in nationwide and international conferences. Audiences showed interest and confidence in the innovative instructional model based on the scientific evidences drawn from the data analysis. It was the time I realized the power of data in driving reformation and making the change.
The following are few models I ran using AMOS when trying to investigate the impact of an innovative undergraduate science curriculum.
That’s why I ended being a senior research and data analyst at Seattle Colleges after I finished my graduate school.
If my work experience in these research grants at the graduate school provided me with the taste of how cute the data analysis and statistic modeling could be based on the data of hundreds students, the work at Seattle Colleges taught me what a monster, but also a treasure the data can be. Our database stores over 20 years students and employees’ data in terms of demographic information, academic progress, financial aid, completion, job placement, and so forth. Before working on the data, the first important thing is to understand the data system and the coding methods via reading coding dictionaries and digging into the tables. I learned to write sql codes to pull data for requestors and projects. SQL becomes my best friend in the daily work to navigate and manipulate data from a complex and messy data system.
I did enjoy the work of diving deep in the database to provide insightful information to answer a variety of operational and research questions. I also enjoyed using Tableau to create intuitive and interactive data visualization so that the stakeholders could assess the performance and issues quickly. I am always proud of how the data I provided drives changes in the organization and how people appreciate that information. Nevertheless, most of my work stops at making pivot tables, calculating means, totals, conducing t test, and creating dashboard showing trends.
I missed my favorite part of conducting working with data that is building sophisticated models to discover the underlying relationship among variables to find causal factors and make predictions. I love data, and I understand how data could empower our work and life. I want to do more about. I want to move forward and go further.
I started self learning toward data science. I took a couple online courses of python, and tasted a little bit of machine learning. However, the progress is super slow and always on and off. I tried to find a new job that could allow me to maximize my knowledge and skills, but I failed all the interviews. It is saying that, there is no such thing as failure, but only learning experiences. Through all these failed interviews, the most important thing I learned is what the job market looks like, what direction I am really interested in, and what I can and cannot offer. I realized that, for doing what I am really interested in, the predictive analysis and machine learning, I need to equip myself better.
That is the time, I know that the data science is the direction I have been looking for and now I am on the way by enrolling in this immersive Data Science boot camp at Flatiron School at Seattle. It is the first time I clearly know what I want to learn and what I want to do after graduate.
It is the second week and I have learned a lot about Python, ProstregSQL, Mongo, and so forth. It is an exciting but also challenging journey. As a new mom, I have to take care of my daughter after school. I am exhausted everyday, but also envigorated because I am learning new knowledge and skills everyday, and these knowledge and skills are very useful and popular in the workplace.
My friend who is a data science once warned me, choosing data science means never-end learning. I told her, that is exactly why I choose this way. Learning is the best way to keep us sharp and young. I look forward to more challenges and fun in the following weeks, and I will keep sharing my experience. Please stay tuned.
