Bootcamp graduate Linda Ju, now a Data Science Consultant at Slalom in Seattle, recently took to Medium to write a blog series about her career transition from finance to data science and how her experience in the Data Science Bootcamp helped her get there. Linda was a recipient of our Advancing Women in Data Science Scholarship.
In the opening, expansive post, How I Became a Data Scientist, she writes: "From experienced professional to bootcamp student. Finance to Data Science. Brooklyn to Seattle. Anyone can take something meaningful away from this series of posts. For those who don’t know me, you’ll read about making a career change and my experience with the Metis bootcamp. For those who do, you’ll get the full story from my decision to leave NYC to where I am now."
That post is a wonderful read as well as the project-specific blog posts, which summarize her work completed during the bootcamp in great detail. She tackled projects on a range of topics, tapping into her financial industry expertise, as well as her lifelong passion for basketball and more. Check out the posts below, and thanks to Linda for sharing her experience and allowing us to further share it here!
How I Became a Data Scientist
"What made my experience in the program extra special were the people who were on this journey with me, including my instructors, Career Advisor, and last but not least, my classmates. Given the rigor and thoroughness of the application process, my peers were bright, motivated, and incredibly supportive. It was comforting to see people drop their work to help others. I was often the beneficiary of their kindness while working on projects and during my job search. Additionally, the bootcamp was intense, with lots of material to cover on tight deadlines. It was a relief to have a group of people going through the same experience to hang out and bond with."
Project 1: Optimizing Street Team Placement at Subway Stations
"Project One was a group project focused on Exploratory Data Analysis (“EDA”), which is completed in order to become more familiar with data by summarizing and visualizing it. EDA is almost always the first step in the data analysis process and is critical in preparing for more advanced modeling. With our newly acquired knowledge of python, pandas, numpy, and data visualization tools, we set out to form recommendations for an advocacy organization."
Project 2: Predicting Daily Fantasy Basketball
"Project Two was focused on Regression, an analysis that examines the relationship between one or more independent variables and a dependent variable. Regression is one of the fundamental tools in predictive analysis and can be used in many different business applications."
Project 3: Predicting Credit-Worthy Consumer Loans
"Project Three was focused on Classification, popular data science concept which predicts the class/target/category of data. A classic example is detecting whether an e-mail is spam or not spam. A model is trained on e-mails which are labeled as spam and not spam, then it makes predictions on new e-mails which it has not seen before. With our newly acquired knowledge of various classification techniques and metrics, we trained classifiers to make predictions."
Project 4: Analyzing President Trump's Economic & Financial Tweets
"Project Four was focused on unsupervised learning techniques and Natural Language Processing (“NLP”). As opposed to supervised learning techniques such as Regression and Classification in which models are trained on known outcomes, unsupervised learning involves inferring patterns and structures from unlabeled data. NLP is a branch of Artificial Intelligence in which machines understand, interpret, and respond to human language. Because language data is often unstructured, unsupervised learning plays an important role in solving NLP problems."
Project 5: Predicting Stock Performance from Quarterly Earnings Conference Calls
"Project Five is the passion project, the final project where all choices are made by the student. For my final passion project, I did my own NLP analysis on earnings calls, as detailed in this blog. I presented my work and findings from this project to potential employers at Metis’s Career Day."