MATH 2530 - Introductory Data Science

• 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 n-tile 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 one-sample 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.