# Certificate in Applied Data Science

## Introduction

Like the **Data Analysis Minor**, the **Applied Data Science Certificate** provides a basic introduction to data analysis, including the theory and practical skills needed to collect and prepare data for analysis, explore and visualize data, build models and test hypotheses, discover insights, and communicate results in meaningful ways.

The **Certificate in Applied Data Science** adds to these skills by extending the necessary computational background and exposure to "Big Data" topics (deep learning, high-performance computing, etc.). It incorporates additional courses in machine learning, and the development of strong statistical computing and programming skills. Through the Practicum experience, students have an opportunity to work closely with a faculty member, with real world data applying these skills to their own areas of interest.

## Certificate Requirements

To earn the Applied Data Science Certificate, students must complete seven graded courses and the capstone Data Analysis Practicum. Before admitted to the program students are asked to submit a tentative plan of study.

Code | Title | Hours |
---|---|---|

Select one of the following basic knowledge courses: | 1 | |

Quantitative Methods for the Biological and Environmental Sciences | ||

Elementary Statistics | ||

Statistics: An Activity-Based Approach | ||

Applied Data Analysis | ||

Digging the Digital Era: A Data Science Primer | ||

An Introduction to Data Journalism | ||

Select two courses from the following mathematical, statistical and computing foundation courses, each from a different group: | 2 | |

Mathematical Foundations | ||

Vectors and Matrices | ||

Linear Algebra | ||

Discrete Mathematics | ||

Graph Theory | ||

Applied Vectors and Matrices | ||

Statistical Foundations | ||

Introductory Econometrics | ||

An Introduction to Probability | ||

Mathematical Statistics | ||

Computing Foundations | ||

Bioinformatics Programming | ||

Introduction to Programming | ||

How to Design Programs | ||

Computer Science I | ||

Computer Science II | ||

Select two of the following applied data science courses: | 2 | |

Exploratory Data Analysis and Pattern Discovery | ||

Applications of Machine Learning in Data Analysis | ||

Quantitative Textual Analysis: Introduction to Text Mining | ||

Select two credits from the following applied electives: | 2 | |

Introduction to GIS | ||

Advanced Econometrics | ||

Introduction to Forecasting in Economics and Finance | ||

Empirical Methods for Political Science | ||

Advanced Topics in Media Analysis | ||

Computational Physics (0.5 credits) | ||

Introduction to Survey Design and Analysis | ||

Introduction to (Geo)Spatial Data Analysis and Visualization | ||

Proseminar: Machine Learning Methods for Audio and Video Analysis | ||

Introduction to Network Analysis | ||

Data Visualization: An Introduction | ||

Data Visualization: An Introduction | ||

Experimental Design and Causal Inference | ||

Longitudinal Data Analysis (0.5 credits) | ||

Hierarchical Linear Models (0.5 credits) | ||

Latent Variable Analysis (0.5 credits) | ||

Survival Analysis (0.5 credits) | ||

Applied Time Series Analysis | ||

Bayesian Data Analysis: A Primer (0.5 credits) | ||

Advanced R: Building Open-Source Tools for Data Science | ||

can count QAC 380 or 381, not both | ||

Introduction to Statistical Consulting | ||

QAC Praxis Service Learning Lab | ||

NOTE: at least one of the electives should be a 300 level course | ||

The capstone Data Analysis Practicum that includes an ethics and epistemology seminar discussion as well as completing an independent data science project. | 1 |

## Additional Information

- Some of the courses that count toward the certificate may have a prerequisite, such as calculus. These prerequisites do not count toward the certificate, and students attempting to complete the certificate are not recused from these prerequisites.
- Mathematics majors cannot count courses in the foundations groups already covered by their major toward the certificate. They must instead complete one course from the statistical foundations group and complete three applied elective courses. Alternatively to completing three applied elective courses, they can take either MATH232 or COMP212 and complete two applied elective courses.
- Computer science majors cannot count courses in the foundations groups already covered by their major toward the certificate. They must instead complete one course from the statistical foundations group and complete three applied elective courses. Alternatively, they can complete both MATH231 and MATH232 and complete two applied elective courses.
- It is strongly recommended that students who are not mathematics or computer science majors take courses in the computing foundations group to satisfy the certificate requirements. They can also substitute either MATH232 or COMP212 for one of their applied elective courses.
- Economics majors and minors cannot count ECON300 toward the certificate and must instead complete one course from each of the other two foundation groups.
- Students cannot count more than one course towards this certificate that also counts toward completion of any of their majors or minors.
- One course taken elsewhere may substitute as appropriate for any of the above courses and count toward the certificate, subject to the QAC Advisory Committee’s approval (where routine approval may be delegated to the QAC director).
- Students can substitute a course from among the applied data science and applied elective courses for the basic knowledge course, subject to approval.
- Only graded courses can satisfy the requirements for the data analysis minor and the applied data science certificate. Courses completed with a CR/U grading mode will not satisfy the requirements of the two programs.
- Students cannot receive both the data analysis minor and the applied data science certificate.

### contact

Director of the QAC