This course covers both the theoretical and applied aspects of regression Math 644: Regression Analysis Methods – An in-depth study of linear regression, the most commonly used data analysis method.Math 661: Applied Statistics – An introduction to basic foundational ideas in probability, statistical inference and data exploration and analysis methods.Although not required, Math 226 (Discrete Analysis) or equivalent is highly recommended. A year of calculus, equivalent to Math 111 and 112. Some experience with programming, equivalent to CS100. The certificate program is designed to be flexible, requiring only one core course, allowing students to select three elective courses in order to tailor the certificate to their needs. Examples include actuaries who are seeking to add data science and statistical learning tools to the other quantitative methods they are already employing in their work computer scientists who want to complement their computing skills with statistical training people who are exploring transitioning into careers in data science. This program is suitable for people with a quantitative background and/or some experience with dealing with data, and who would like to obtain some grounding in statistical methods and concepts in the context of data science. Who would be suited to take this program? The Statistics for Data Science Graduate Certificate from NJIT will provide an introduction to randomness, sampling, data generation, statistical modeling, data analysis and statistical computing, which participants can leverage to enter the fields of machine learning, data analytics and data science. That information may be used for prediction, decision-making or understanding underlying phenomena, whether it is public health, finance, environmental studies, or technology. Statistics for Data Science is about using statistical concepts and methods to extract and make sense of information contained in data.
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