Course Description
This math course is designed for students who are interested in applying statistical models with real datasets. The course will include applied examples of data collection, processing, representation, interpretation, analysis, and evaluation to provide students with hands-on experience and introduction to data science. Students will use a popular open source data science tool, the “R” open source statistical analysis and visualization system, to aid in data management. At the end of the course, students will have had the opportunity to gain insights on data through examples, discussions, and individual projects based on student interests.
In Data Analytics class, students will gain (1) technical skills around Google spreadsheet, Tableau software, and R programming language, (2) mathematical skills around data representation, interpretation, analysis, and evaluation (including descriptive statistics, linear regression, and hypothesis testing), and (3) non-technical transferable skills, such as communication, collaboration, and critical thinking, as they work on various projects involving real-world datasets.
Unit Topics
Semester 1
Unit 1 :: Data Visualization & Spreadsheets
Unit 2 :: Descriptive Statistics
Unit 3 :: Linear Regression
Midterm Exam :: Coding, Analysis, Evaluation
Semester 2
Unit 5 :: Worldviews & Property Project
Unit 6 :: Hypothesis Testing
Unit 7 :: Individual Project
Final Exam :: Coding, Analysis, and Evaluation
Learning Targets, Standards, and Grade Breakdown
Each standard (rubric adapted from The Association of American Colleges and Universities) will be assessed at least three times per semester. Please note that the standards will be assessed through various modes including, but not limited to, online-based assessments, presentations, papers, and programming-based exams. Each assignment will carry heavier weight with time. As time progresses, students are expected to show deeper understanding of the learning targets.
DA1: Data Representation (10%)
DA2: Data Interpretation (10%)
DA3: Calculation (10%)
DA4: Quantitative Analysis (15%)
DA5: Evaluation of Data (15%)
DA6: Coding Language (10%)
DA7: Programming Process (10%)
DA8: Feedback & Reflection (10%)
DA9: Worldview (10%)
Student Voice: Data and Worldviews
"... being able to understand worldviews is an integral part of data analytics, as no one has all the same worldviews, and this is evident when analyzing data. People’s worldviews affect how they choose to present their data, which data they choose to report, and which pieces of data they choose to prioritize in their analysis. Worldviews are based off of personal experiences and connections, which makes it valuable to be able to comprehend others' worldviews to be able to understand why someone chose how and what data to present, or when working with clients you can gain a better understanding of what data your client will find most interesting and important. Understanding various worldviews can lead to understanding certain systems and models that have been in place and why they aren’t just or fair, such as the “Teacher Assessment Tool” and the U.S justice system in regards to African American convicts from [Weapons of Math Destruction by Cathy O'Neil], as well as the organization of house and families in segregated neighborhoods [as presented by Kontinentalist]."