A Leap Forward in Data Science: How My Third Block at UBC is Shaping My Marketing Strategy
As I continue my journey in the Master of Data Science program at UBC, the third block has been a deep dive into more specialized areas of data science that are highly relevant to my role as a marketing manager. This block included four courses that have significantly expanded my skills and understanding: DSCI 513 (Databases and Data Retrieval), DSCI 561 (Regression I), DSCI 573 (Feature and Model Selection), and DSCI 522 (Data Science Workflows).
DSCI 513: Databases and Data Retrieval
This course comprehensively introduced databases, the relational model, and SQL. Key topics included data types, aggregations, joining tables, and query optimization. As a marketing manager, this course has empowered me to better understand and utilize customer data, enabling more targeted and effective marketing strategies.
DSCI 561: Regression I
Regression (I) focused on linear regression models, providing insights into how variables interact and are related. Learning about linear regression coefficients, their estimation, and their interpretation was invaluable. This knowledge is crucial for predictive analytics in marketing, allowing me to forecast customer behavior and market trends.
DSCI 573: Feature and Model Selection
This course covered evaluation metrics for classification and regression, feature engineering, feature selection, and model interpretability. Understanding these concepts is critical for developing sophisticated marketing models that can accurately segment customers and predict their preferences and behaviors.
DSCI 522: Data Science Workflows – Project Experience
In DSCI 522, my team and I worked on a project titled “English Language Learning Ability Prediction.” This innovative project aimed to forecast an individual’s aptitude for learning English based on demographic details and linguistic background. We developed a regression model utilizing Ridge and Lasso models and evaluated these using metrics like R-squared, Root Mean Squared Error (RMSE), and Negative Mean Squared Error (NMSE). Our analysis used a subset of a dataset originally compiled from over 680,000 participants, providing a rich source for analyzing language learning patterns. This project was not only an exercise in practical data science but also a demonstration of how data-driven methods can be applied in real-world scenarios, such as predicting language learning abilities.
For more details about this project, you can visit the GitHub repository: English Score Predictor.
How These Courses Enhance My Marketing Skills
Each of these courses has equipped me with new tools and perspectives. From understanding the intricacies of database management to employing advanced regression models, I now have a richer toolkit to approach marketing challenges. Feature and model selection, in particular, have opened up new avenues for refining our marketing strategies, allowing us to target customers more effectively and personalize our communications.
Data Science as a Catalyst for Marketing Innovation
The third block of my data science journey at UBC has been transformative. It has not only expanded my technical skills but also my strategic thinking as a marketing manager. Integrating data science into marketing is no longer just a concept; it’s a reality that I am actively applying to make more informed, impactful marketing decisions.
