Data Science

Embark on an exhilarating journey into the world of data science! This engaging course explores the vast realm of mathematics, computing, and data science, revealing the wide range of professionals involved in this field. Through hands-on activities, students will confront messy questions using data, explore sample datasets, and grasp the fundamental relationship between probability and statistics. Essential programming and statistical concepts will be mastered, enabling effective visualization of data using various charts, plots, and graphs, setting the foundation for a comprehensive data science adventure.

Throughout the course, four dynamic units progressively build upon one another. The first unit uncovers the role of data scientists and their significance in various industries. Students will be introduced to the Pyret programming platform and learn about the data Analysis Cycle. The second unit involves gathering, analyzing, and visualizing authentic datasets, empowering students to derive valuable insights and interpretations using pie charts, bar charts, and histograms. In the third unit, programming abilities will be enhanced, allowing custom visualization and analysis of the distribution of data. Students will model data using scatter plots and measure the relationship strength between two variables. Finally, in the fourth unit, students will create filtering functions to focus on specific subsets of data. The ethical dimensions of data science will be explored, with students addressing an original research question, identifying and analyzing data to address that question, and discussing the responsible use of data and analytics. Students are invited to join this journey of learning and discovery, unlocking the true power of data.

Acknowledgements

Completion of Algebra 1.
This course contains an end-of-course proctored exam. Instructions for students to identify an appropriate adult proctor are included in the course lessons.

Course Objectives

Course Essential Questions:

How do we collect and evaluate data to ensure validity?
How do we apply programming and mathematical concepts to describe, analyze and model relationships within data?
How do we accurately visualize data to best represent a given dataset?
What ethical considerations and standards should we adhere to when conducting research, and how can we ensure the integrity of data analysis methods and findings?

Course Learning Objectives:

Create valid datasets by gathering unbiased data using surveys.
Write computer code to visualize data, choosing the appropriate type of graph and calculations, to accurately demonstrate the relationship among variables.
Construct linear and non-linear models to describe and predict relationships within a dataset, demonstrating proficiency in translating real-world problems into mathematical models.
Conduct a thorough examination of the social and ethical implications of data science work, critically evaluating data integrity, method validity, and ethical considerations, and apply this knowledge to several data analysis case studies.

Additional Requirements

This course requires regular access to a computer running Microsoft Windows, Apple Mac OSX, or Chromebook OS.
Students must have a Google account to store programs created in the Pyret programming environment.

This course includes lessons that were developed with materials adapted from Bootstrap: Data Science. Bootstrap: Data Science was authored by Bootstrap Community and is licensed under the Creative Commons 4.0 Unported License.