This course emphasizes developing analytical skills through hands-on projects and interactive in-class activities. While no programming is required, students will engage with data to draw insights, interpret relationships, and make informed predictions.
Structure & Workflow
The coursework follows a structured learning process:
Building Skills – Learn key concepts through lectures and in-class activities.
Applying Knowledge – Complete projects that reinforce these concepts.
Demonstrating Understanding – Participate in 1-1 oral exams with a TA to articulate your reasoning and conclusions from your projects to earn full credit.
Assignments & Activities
Projects (4 Total) – These major assignments guide you through essential data analysis techniques:
Drawing Conclusions from a Table – Analyze a dataset in Excel to summarize key insights.
Relating Variables – Explore relationships between different factors in the data using Tableau.
Making Inferences – Use data to support conclusions and justify claims using Tableau.
Numerical or Categorical Predictions – Predict outcomes based on given patterns using BigML.
For all projects, students must choose their own datasets ensuring the data meets all the requirements of project's objectives.
In-Class Activities (~8 Total) – These short exercises help reinforce core concepts needed for project success. They serve as checkpoints for understanding before moving on to larger assignments.
Coursework Expectations
All assignments are self-paced, allowing flexibility in completion. However, there will be an individual deadline to complete each project including the oral evaluation.
In any extraordinary event an individual project's deadline is not met, the student is expected to finish the full set of projects and activities by the end of the quarter to receive credit.