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ENGIN178

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ENGIN 178 - Statistics and Data Science for Engineers

Engineering Undergraduate COE - College of Engineering

Subject

ENGIN

Course Number

178

Department

Engineering

Course Level

Undergraduate

Course Title

Statistics and Data Science for Engineers

Course Description

This course provides a foundation in data science with emphasis on the application of statistics and machine learning to engineering problems. The course combines theoretical topics in probability and statistical inference with practical methods for solving problems in code. Each topic is demonstrated with examples from engineering. These include hypothesis testing, principal component analysis, clustering, linear regression, time series analysis, classification, and deep learning. Math 53 and 54 are recommended before Engin 178, Math 53 and 54 are allowed concurrently.

Minimum Units

4

Maximum Units

4

Grading Basis

Default Letter Grade; P/NP Option

Method of Assessment

Written Exam

Instructors

Papadopoulos

Prerequisites

ENGIN 7; MATH 51; MATH 51; MATH 53; and MATH 54 (may be taken concurrently)

Repeat Rules

Course is not repeatable for credit.

Credit Restriction Courses. Students will receive no credit for this course if following the course(s) have already been completed.

-

Credit Restrictions. Upon passing, students can use the following course(s) to replace a deficient grade for this course.

Students will receive no credit for ENGIN 178 after completing ENGIN 78.

Credit Replacement Courses

-

Course Objectives

To provide a theoretical and conceptual basis for students to understand the role of data in engineering. To introduce the concepts of quantitative statistics and probability. To enable students to import, clean, visualize, and interpret data sets using modern computer languages. To familiarize students with a range of techniques for building models from data. To teach students how to build and train machine learning models. To demonstrate the use of data science in engineering tasks.

Student Learning Outcomes

An ability to apply knowledge of mathematics, science, and engineering. An ability to design and conduct experiments, as well as to analyze and interpret data. An ability to design a system, component, or process to meet desired needs within realistic constraints such as economic, environmental, social, political, ethical, health and safety, manufacturability, and sustainability. An ability to identify, formulate, and solve engineering problems. The broad education necessary to understand the impact of engineering solutions in a global, economic, environmental, and societal context. A knowledge of contemporary issues. An ability to use the techniques, skills, and modern engineering tools necessary for engineering practice.

Formats

Lecture, Laboratory

Term

Fall and Spring

Weeks

15 weeks

Weeks

15

Lecture Hours

3

Lecture Hours Min

3

Lecture Hours Max

3

Lecture Mode of Instruction

In Person

Laboratory Hours

2

Laboratory Hours Min

2

Laboratory Hours Max

2

Laboratory Mode of Instruction

In Person

Outside Work Hours

9

Outside Work Hours Min

9

Outside Work Hours Max

9