PROBABILITY AND STATISTICS FOR DATA SCIENCE

Course Description

The objective of this course is to provide the necessary knowledge of probability and statistics theory to solve a variety of data science problems. Fundamental concepts such as random variables, independence, expected values, standard errors and central limit theorem are covered. The tools that are frequently used in data science, like Bayesian inference and maximum likelihood estimation, are highlighted. Upon completion of this course, students will be capable of employing probabilistic and statistical models for data manipulation and using the statistical programming language R to perform statistical data analysis. Students will be able to suggest statistical techniques to estimate the parameters of probabilistic models that represent randomness in life. Students will also be prepared to use the new techniques in later machine learning courses.

PROBABILITY AND STATISTICS FOR DATA SCIENCE

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Course outcomes

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Course Syllabus

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Learning Outcomes

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Program Outcomes

1. Demonstrate a thorough understanding of the theories, frameworks, and models in order to assess and comprehend the state of the art in data science.
2. Review the literature and apply data science theories and methodology in new research and experiments.
3. Analyze datasets using supervised and unsupervised machine learning techniques.
4. Design, develop and test statistics and informatics software systems for data management, analysis and problem solving.
5. Conceptualize and develop efficient visuals for a range of data types and analytical tasks, and carry out independent research on a range of theoretical and applied subjects in visualization and visual analytics.
6. Obtain a high level of proficiency in communication, problem solving, research or project-related activities and function effectively as a team member or a leader to accomplish a common goal in a multidisciplinary team.
7. Develop and implement optimal solutions to overcome challenges associated with managing large datasets by utilizing parallel methods, cloud computing, and non-relational data storage and retrieval (NoSQL).
8. Demonstrate an understanding of the interdisciplinary of data, information, and communications, as well as the ability to evaluate the leading research methods for data collection and analysis.
9. Demonstrate a deep understanding of the ethical issues surrounding the use of data and apply ethical decision making in real-world data-related applications.
10. Demonstrate capability of analyzing, synthesizing, and evaluating knowledge from a wide range of fields and be capable of lifelong self-directed learning.

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