Catalog Information
MATH 2530 Introductory Data Science, Ohio University, Fall 202223
 Description
 Students learn the basics of data acquisition, organization, and analysis; acquire handson experience with statistical estimation and inference, data modelling, and visualization; and explore machine learning applications, data privacy, and ethics.
 Requisites
 (Math placement 2 or higher) or MATH 1200 or 1500 or PSY 1110
 Credit Hours
 4
 Repeat/Retake Information
 May be retaken two times excluding withdrawals, but only last course taken counts.
 Lecture/Lab Hours
 3.0 lecture; 1.5 recitation
 Grades
 Eligible Grades: AF,WP,WF,WN,FN,AU,I
 Learning Outcomes

 Students will be able to distinguish between types and sources of data.
 Students will be able to acquire raw data from a variety of sources.
 Students will be able to ensure the clarity, completeness, and stability of the data through the organization of that data.
 Students will be able to identify incorrect, incomplete, inaccurate, irrelevant, or missing data and then modify, replace, or delete that information as needed.
 Students will be able to classify and summarize data using traditional plots.
 Students will be able to select appropriate charting techniques based on the type of data and the number of variables they intend to present.
 Students will be able to compare traditional and dynamic graphing techniques and give reasons for and justify why a dynamic plot may be the appropriate choice (or is the appropriate choice for a specific data set).
 Students will be able to identify common distribution models and discern what types of data fit certain models.
 Students will be able to develop an analytic model and trendline for a time series, and then predict the last ntile of data in order to evaluate the effectiveness of their model.
 Students will be able to locate data visualizations and deconstruct the graph in order to evaluate the effectiveness of the visualization.
 Students will be able to write and implement generative models for situations ranging from simple onesample problems to more complex settings
 Students will be able to estimate the parameters of a model, use simulation methods to evaluate different estimators, and describe their bias and variance.
 Students will be able to use simulation methods to understand the implications of statistical models.
 Students will be able to define machine learning and statistical learning, as well as differentiate between supervised and unsupervised learning.
 Students will be able to classify data using supervised machine learning techniques, search for and define a function that describes how different measured variables are related to one another and utilize predictive techniques such as linear regression.
 Students will be able to use algorithms to draw inferences from datasets consisting of input data without labeled responses.
 Students will be able to consider the local legislation, and identify the relevant laws, rules, and regulations pertaining to protection of personal data.
 Students will be able to discern bias from fairness in finance, medicine, and society in order to prevent incorrect or distorted conclusions.
 Students will be able to identify clarity in methods of analysis of data and demonstrate how conclusions can be misleading.
 Students will be able to cite sampling bias in its various forms.
 Quantitative Reasoning Learning Outcomes

 Interpretation. Students will be able to explain information presented in mathematical forms (e.g., equations, graphs, diagrams, tables, words).
 Representation. Students will be able to convert relevant information into various mathematical forms (e.g., equations, graphs, diagrams, tables, words).
 Calculation. Students will be able to calculate relevant information using various mathematical formulas.
 Application / Analysis. Students will be able to make judgments and draw appropriate conclusions based on the quantitative analysis of data while recognizing the limits of this analysis.
 Assumptions. Students will be able to make and evaluate important assumptions in estimation, modeling, and data analysis.
 Communications. Students will be able to express quantitative evidence in support of the argument or purpose of the work (in terms of what evidence is used and how it is formatted, presented, and contextualized).