This course will introduce students to the foundations of statistics necessary for data science application. The topics covered are primarily to help students understand the basis of advanced techniques which will be applied in other courses. The course will address key concepts necessary for understanding the results from many data science techniques and provide for proper interpretation of results.
The course will not focus on excessive hand computations, instead, it will be heavily skewed toward employing and relating the concepts to the real-world areas. Concepts are made concrete through numerical computation.
On successful completion of this course, a student should be able to:
Explain basic statistical principles and their limitations
Apply statistical principles using software
Apply and interpret statistical models on different data sets
Explain the basic principles of statistical inference
Apply core concepts of probability theory
3. Statistics I: (perfect knowledge of the uncertainty)
4. Statistics II: (imperfect knowledge of the uncertainty)
The coursework will consist of 1 project and 2 written assignments. The assignments should allow students to demonstrate that the specific learning outcomes have been achieved.
Coursework (60%)
2 Projects (20% each) 40%
2 Written Assignments (10% each) 20%
Final Written Examination (2 hrs). 40%
Students will be required to pass both the coursework and the final examination to pass the course.
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