MAT-165 Introduction to Data Science

This course covers techniques for working with data, including getting and cleaning data, exploratory data analysis, data visualization, and statistical modeling and prediction. Students will learn how to ask good questions, apply data to practical problems, and communicate data analytic results. Statistical computing is integrated into the course. This course carries SUNY General Education Mathematics (and Quantitative Reasoning) credit

Credits

3

Prerequisite

MAT-145 or Placement into Math Level 3 or Higher

Lecture Contact Hours

4

Lab Contact Hours

0

Other Contact Hours

0

Department

• Mathematics

Grading Scheme

• Letter

SUNY Gen Ed Credit

• Yes

Semesters Course Will Be Offered

• Fall

Course Learning Outcomes

1. Import, organize and manipulate real-world data to support disciplinary inquiry.
2. Analyze and interpret descriptive statistics and graphical summaries of data regarding center, variation, and shape.
3. Apply the principles of inferential statistics to develop, evaluate, and interpret models based on data.
4. Communicate statistical results to technical and non-technical audiences.

View Course Outline

MAT 165: Introduction to Data Science

Department

Mathematics

Course Description

This course covers techniques for working with data, including getting and cleaning data, exploratory data analysis, data visualization, and statistical modeling and prediction. Students will learn how to ask good questions, apply data to practical problems, and communicate data analytic results. Statistical computing is integrated into the course. This course carries SUNY General Education Mathematics (and Quantitative Reasoning) credit

Credit Hours

3

Contact Hours

Lecture	4
Lab	0
Other	0

Grading Scheme

Letter

Semester(s) Course Will Be Offered

Fall

Prerequisites

MAT-145 or Placement into Math Level 3 or Higher

SUNY General Education Course

• Mathematics and Quantitative Reasoning

Course Learning Outcomes

1. Import, organize and manipulate real-world data to support disciplinary inquiry.
2. Analyze and interpret descriptive statistics and graphical summaries of data regarding center, variation, and shape.
3. Apply the principles of inferential statistics to develop, evaluate, and interpret models based on data.
4. Communicate statistical results to technical and non-technical audiences.