INTRODUCTION TO PROBABILITY AND STATISTICS

Course Description

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INTRODUCTION TO PROBABILITY AND STATISTICS

Evaluation Tools (Active Term)

Course outcomes

1. 01 Apply(4) statistical methods in the engineering problem-solving approach
2. 02 Compute(3) and interpret(3) descriptive statistics using numerical and graphical techniques
3. 03 Identify(2) and apply(4) the basic concepts of probability
4. 04 Apply(4) probability theory to set up tree diagrams
5. 05 Apply(4) probability theory via Bayes’ Rule.
6. 06 Describe(3) the properties of random variables, discrete and continuous distribution functions.
7. 07 Identify(2) the basic concepts of joint probability distribution.

Course Syllabus

Reference Books & Course Materials

1. 01 Jay L. Devore, Probability and Statistics for Engineering and Sciences, 8th ed., Brooks/Cole Cengage Learning
2. 02 R.E.Walpole, R.H.Myers, S.L.Myers, K.Ye, Probability and Statistics for Engineers and Scientists, 7th ed., Prentice Hall, 2002.

Learning Outcomes

1. L01 Apply(4) statistical methods in the engineering problem-solving approach
2. L02 Compute(3) and interpret(3) descriptive statistics using numerical and graphical techniques
3. L03 Identify(2) and apply(4) the basic concepts of probability
4. L04 Apply(4) probability theory to set up tree diagrams
5. L05 Apply(4) probability theory via Bayes’ Rule.
6. L06 Describe(3) the properties of random variables, discrete and continuous distribution functions.
7. L07 Identify(2) the basic concepts of joint probability distribution.

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.

Po-Lo Matrix