Table of Contents

2021-2022 Catalogue

Cover Page
Wesleyan College: Mission and History
Undergraduate Academic Calendar
Undergraduate Admission
Policies, Procedures and Regulations
Credit Options
Academic Enrichment
Undergraduate Academic Programs

Graduate Programs: Admission, Policies, Procedures and Regulations

The Curriculum

Tuition and Fees
Financial Aid
Errata

2021-2022 Catalogue

Applied Data Analysis

The Applied Data Analysis program is designed to give students with wide and varied interests the tools to further explore computational techniques for data retrieval, analysis, and reporting.

A student who completes the ADA major will have been exposed to programming principles and paradigms, industry-favored programming languages, contemporary data analysis software, and key insights on how to data can inform their understanding. 

Program Goals:

1. Students will be able to evaluate and apply fundamental statistical concepts in the context of a broad range of data problems, including Bayes Theorem, common statistical tests and biases, inference and causal inference and hypothesis testing.
2. Students will be able to perform in-depth exploratory analysis to form hypotheses and use visualization techniques to communicate insights.
3. Students will be able to apply and evaluate machine learning algorithms in a business problem context, with an emphasis on selecting predictive modelling only when appropriate.
4. Students will be able to perform feature engineering and data preprocessing in order to improve the accuracy and efficacy of predictive models.

Requirements – 42 Credit Hours

Data Analytics Core – 15 hours

●    ADA 101 – Foundations of Data Analytics I
●    ADA 102 – Foundations of Data Analytics II
●    ADA 201 – Principles and Techniques of Data Analytics I
●    ADA 202 – Principles and Techniques of Data Analytics II
●     ADA 401 – Data Analytics Practicum or  MAT450 Data Analysis Seminar

Mathematics Requirements – 12 hours

●    MAT 205 Calculus I
●    MAT 220 Statistics
●    MAT 206 Calculus II
●    MAT 210 Linear Algebra

Programming Requirements – 9 hours

●    CSC 120 Web Programming
●    CSC 216 Programming I
●    CSC 218 Programming II

Electives:  6 total hours.  Select two

●    ENG 302 Digital Culture
●    POL 332 Political Science Research Methods
●    PHI 223 Ethics
●    ECO 330 Econometrics
●    PSY 235 Nature and Manifestation of Prejudice
●    MAT 350  Algorithms
●    Math Courses at or above the 200 Level.

Professional Development: Throughout her Wesleyan education each student is given opportunities to explore professional and career choices, and to develop and demonstrate the knowledge and skills essential for professional success. Each student will complete PDE 400 Professional Development Experience and PDE 401 Professional Practice Seminar.

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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