Rutgers University 3 credits 291
This course introduces essential skills of working with data in applied settings (education, health, public policy) using statistical programming and other tools. Primary topics include: data cleaning, management, analysis, visualization, collaboration, and reporting. Best practices for workflows and reproducible research, ethical considerations for working with human subjects data, core issues in statistical programming, and specialized approaches for complex data are introduced. Although this subject matter sits at the intersection of quantitative social science and data science, this is a fully introductory course for which no prior programming experience is necessary. Students will engage in active, problem-based learning and work with real datasets in group and individual projects. Students will leave the course with the confidence to tackle analytic problems using the tools of statistical programming and with practical, marketable skills that allow them to leverage real-world data in professional and research contexts.
Our subject experts help with essays, research papers, case studies, discussion posts, and capstone projects for these programs β written from scratch to your course requirements.
Program and course information is reproduced from each university's published academic catalog for informational purposes and may change. Always confirm current requirements with your university. We are an independent academic support service and are not affiliated with or endorsed by the universities listed.
Expert guides on essay writing, citation styles, study skills, and academic success.
View All Articles
Learn how to write a good research question for a paper. Narrow your topic, identify a research problem, and create a focused question with practical examples.
Learn how to choose a research topic you can actually research. Discover how to narrow broad ideas, find reliable sources, and develop a focused research question.
Learn how to cite sources in a research paper using APA, MLA, and Chicago styles, with in-text citation examples and reference-list tips.