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

Department

• Mathematics

Semesters Course Will Be Offered

• Fall

For more detailed course information view the 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.

Topic Outline

1. This is an introductory course on data science. Data science is a multidisciplinary field incorporating elements of mathematics, statistics, computer programming, and data skills. In data science, statistical, mathematical, and data skills are applied to solve disciplinary problems in disciplinary data. Statistical concepts are taught while students simultaneously learn the R programming language and environment to simulate results.
2. 1. Introduction to data science: four pillars of inference
3.     a. Strength of evidence
4.     b. Breadth of results
5.     c. Size of effect
6.     d. Cause of effect
7. 2. Statistical inference using one group
8.     a. Random processes and chance-based models
9.     b. Data visualization
10.     c. Exploratory data analysis
11.     d. Parameter estimation using bootstrapped distributions
12.     e. Distribution of outcomes under the null hypothesis
13.     f. Hypothesis tests for proportion and mean
14.     g. Errors and significance
15.     h. Confidence intervals for proportion and mean
16.     i. Factors affecting the width and precision of intervals
17. 3. Statistical inference using two groups
18.     a. Contingency tables
19.     b. Hypothesis tests for proportion and mean
20.     c. Confidence intervals for proportion and mean
21.     d. Effect size statistics
22. 4. Modeling
23.     a. Scatterplots and correlation
24.     b. Ordinary least squares regression
25.     c. Inference with correlation and regression coefficients
26.     d. Prediction with the least squares regression equation
27.     e. Logistic regression coefficients: log-odds and odds ratio
28.     f. Logistic regression using maximum likelihood
29.     g. Prediction with logistic regression