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Applied Data Analysis (ADA) Course Descriptions

ADA 101: Foundations of Data Analytics I.
Goal: In an increasingly data-driven world, everyone should be able to understand the numbers that govern so much of our lives. Students will learn the core concepts of inference, data analysis and computing by working with real economic, social and geographic data. Particular attention will be paid to Bayes’ Theorem - one of the most important concepts in applying statistics to the real world. Lastly, this course will cover the implications and dangers of bias in data.
Content: This course teaches students the fundamentals of Data Analytics and Science. By the end of this course, students will be able to: Use industry-standard tools (Python, Anaconda, Jupyter Notebooks); Analyze large data sets; Test hypotheses on datasets; Present data-driven results in a clear manner; Describe the current landscape of the Data Science industry; Recognize examples (and limitations) of Machine Learning in day-to-day life; Articulate and use Bayes’ Rule; Understand the implications of bias in data.
Taught: Fall, Spring.
Prerequisites: MAT 220 Statistics.
Credit: 3 credits.

ADA 102: Foundations of Data Analytics II.
Goal: In an increasingly data-driven world, everyone should be able to understand the numbers that govern so much of our lives. Students will learn the core concepts of inference, data analysis and computing by working with real economic, social and geographic data. This course will also provide students with an introduction to the applications of Data Analytics in the workforce, with specific attention paid to the role of the Data Scientist or Analyst, and to the application of Big Data.
Content: By the end of this course, students will be able to: Deploy A/B testing to meet business objectives; Understand how to design a range of data-driven experiments; Use basic machine learning - including linear regression and classification; Use data to update predictions; Understand the difference between correlation and causality, and how to identify either relation using data; Define Big Data and understand its importance to Business Analytics; Articulate the role of a data scientist or analyst within the workforce
Taught: Fall, Spring.
Prerequisites: ADA 101.
Credit: 3 credits.

ADA 201: Principles and Techniques of Data Analytics I.
Goal: Data Analytics combines data, computation and inferential thinking to solve challenging problems and understand their intricacies. This class explores key principles and techniques of data science, and teaches students how to create informative data visualizations. It also explores particular concepts of Linear Algebra which are central to Data Science.
Content: By the end of this course, students will be able to: Understand and use linear algebra principles to derive prediction algorithms; Effectively collect, sample, clean and analyze data sets; Understand the fundamental principles of Regression Analysis; Use SQL, RegEx, Pandas, and Pytorch to solve data analysis problems; Build effective data visualizations.
Taught: Fall, Spring.
Prerequisites: ADA 102, MAT 205, MAT 210, CSC216, CSC218.
Credit: 3 credits.

ADA 202: Principles and Techniques of Data Analytics II.
Goal: This course builds on Principles and Techniques of Data Analytics I to provide students with a more robust understanding of the tools of a Data Scientist. Data Analytics combines data, computation and inferential thinking to solve challenging problems to thereby better understand the world. This class explores key principles and techniques of data science, including quantitative critical thinking and algorithms for machine learning methods. It will also introduce students to the ways in which data analytics is deployed in healthcare, marketing, political science, criminal justice, and other fields.
Content: By the end of this course students will be able to: Perform feature engineering; Articulate the risks and pitfalls inherent in feature engineering; Understand the basics of how to apply Data Analytics to a wide range of real-world fields; Learn how and when to use a range of regression analysis techniques; Understand how to deploy decision trees; Understand the concepts of Residuals, Multicollinearity, Inference, and Sampling Variability; Demonstrate improved skills in the principles and techniques of data analytics.
Taught: Fall, Spring.
Prerequisites: ADA 201.
Credit: 3 credits.

ADA 401: Data Analytics Practicum.
Goal: To prepare students for the kind of work they will do on Data Science or Analytics teams including communication of results to stakeholders.
Content: Students will complete a Capstone project including a full data science workflow on a set of real data drawn from sports, politics, business or public health.
Taught: Fall, Spring.
Prerequisites: ADA 201, ADA 202.
Credit: 3 Credits.

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