Final Project

Due by Augyst 9th, 11:59 pm



Tests course objectives and expected learning outcomes.

Course Objective

  • Understand the fundamental concepts and principles of data science, including data collection, preprocessing, analysis, and interpretation.

  • Apply data analysis and visualization techniques to derive insights from diverse datasets on Week 6

  • Gain familiarity with machine learning algorithms and their practical applications.

  • Develop proficiency in using data science tools and programming languages.

  • Engage in critical thinking and problem-solving through project-based assignments.

  • Explore the ethical considerations associated with data-driven decision-making.

  • Stay informed about current trends and developments in data science and artificial intelligence.

Expected Learning Outcomes

A student who successfully completes this course will be able to:

  • Explain the difference between different measures of centrality and variability (means vs. medians, variance vs. interquartile range, etc.)

  • Convert a raw data source into a version appropriate for downstream analysis using Python. | Week 4 & 5

  • Write appropriate visualizations for different sources and types of data | Week 6

  • Explain why we seek to build machine learning models that generalize rather than memorize their input. | Week 6

  • Explain the different uses for training, validation, and testing datasets | Week 6

  • Select the appropriate evaluation measure for the dataset and task being solved

  • Articulate the difference between supervised and unsupervised machine learning, as well as select the appropriate methodology for a given problem | Week 6

  • Demonstrate awareness of bias and ethics in data science.