SQL for Data Scientists
We are excited to announce that HelioCampus’ own Director of Data Science, Renee Teate, has written a book: SQL for Data Scientists: A Beginner's Guide for Building Datasets for Analysis. At HelioCampus, Renee uses her SQL skills to build datasets for predictive models, helping our higher education institutions to predict enrollment trends, package financial aid and more.
In this webinar, she’ll share more about the role of SQL in her work, specifically how it helps transform the data from higher ed institutions into our standard data extracts and then is used to prepare the data for our predictive models. If you are interested in learning more about SQL, Renee’s journey to becoming a data scientist, why she wrote the book, an open Q&A and more, you won’t want to miss this webinar. We hope to see you online!
For higher education institutions, forecasting future enrollments is key for budget and resource planning. Naïve methods relying on historical conversion metrics and upstream indicators can provide estimates of expected outcomes. However, such methods are not responsive to early signals and do not naturally account for year to year variation in patterns.
Time series forecasting can provide actionable insights based on emerging trends and, when correctly calibrated, generate strong predictions even as conditions on the ground change. This webinar will discuss the requirements for generating a valid forecast, including ways to overcome some common challenges, and walk through two recent case studies demonstrating how well calibrated forecasts are able to accurately predict enrollments despite significant changes in procedures and processes over the last two years at two different institutions.
Data Science solutions using various predictive modeling techniques have been available for several years but many higher education institutions are just dipping their toes into the field for the first time. Some common use cases have drawn strong interest from campus leaders due to ongoing challenges (retention, enrollment goals) and other emerging situations, like the COVID pandemic. Are you ready to use predictive modeling at your institution? What use case would best serve your needs?
In this webinar, we’ll explore use cases in financial aid, admissions, student success, forecasting, and others with a focus on how they are applied in practice. Based on success stories and lessons learned from our work with our clients, we will share advice and considerations on how to thoughtfully and successfully embark on a data science modeling initiative.
At HelioCampus, we go to extra lengths to ensure that our predictive models are as transparent and explainable as possible. We train multiple models using different input fields, algorithms, and settings to ensure we’re producing predictions that make sense and are useful for answering the question at hand. We’ve recently implemented a new set of tools that allow us to more easily and effectively compare model performance and assess which input features have the most influence on each student’s score. Used in the aggregate, these tools can also help identify areas of concern that may be addressed at the policy level.
In this webinar, we’ll highlight the tools our Data Science team uses for model evaluation and explainability, how we use them internally to check our model results, and what we provide to our clients to help increase understanding and adoption of our predictions.