Provide a list of the instructional strategies that will be used to achieve course learning outcomes, such as lecture or non-traditional methods such as online classes or the use of experiential instruction.Frequent Patterns (Shopping Basket Analysis).Converting data into actionable information and the role of data in decision making at various levels of society.Communication of the Data Science Findings and What It Means.Data visualization - (including graphs, charts, and histograms - univariate qualitative, univariate quantitative, bivariate).Empirical, Categorical, and Numerical DistributionsĬommunicate data-driven insights in multiple media modes.Interpreting results of the data analysis/Data Interpretation, possibly including, but not limited to the following: Using Computational Tools and Statistical Techniques for basic data manipulation Distributions (including measures of central tendency and spread).Methods of Data Analysis, including, but not limited to: The role of data in decision making at various levels of society.Data errors and appropriateness/Cleaning Data.Sources of data, data collection and types of data.Please be as comprehensive as possible within the limits of an outline. Provide a topical outline demonstrating the breadth and depth of the course. Mine data to develop predictive models and evaluation.Identify legal issues surrounding the use of data.Identify goals and methods of testing hypotheses.(should account for 20 – 30% of course content) Differentiate between ethical and unethical uses of data science.Effectively communicate methods and findings in a variety of modes.Be able to draw accurate and useful conclusions from a data analysis.Use appropriate tools and technology to collect, process, transform, summarize, and visualize data.Apply appropriate descriptive and inferential methods to summarize data and identify associations and relationships.Be able to identify the categorical and/or numerical data types in a given data set.Be able to apply basic data cleaning techniques to prepare data for analysis.Identify and appropriately acknowledge sources of data.Explain the importance of and be able to formulate a data analysis problem statement that is clear, concise, and measurable.Course Learning Outcomes Required Outcomes for all Sections of the Course (should account for 70 – 80% of course content) Students will develop skills in appropriate technology and basic statistical methods by completing hands-on projects focused on real-world data and addresses the social consequences of data analysis and application. This course is intended to provide an introduction into the field of Data Science. DATA 1501: Introduction to Data Science Course Description
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