ABOUT THIS COURSE:
This is an asynchronous, self-directed course for beginner level learners. This course introduces the language and core concepts of probability theory and provides basic principles of statistical inference. Engages in practical experience of statistical data analysis through python programing/simulation with real data. Students will have the chance to use a variety of real data to solve design problems.
LEARNING GOALS AND OBJECTIVES:
In this class, learners will acquire the skills to utilize their understanding of probabilistic and statistical principles to examine sets of data and draw significant insights from them. We will cover both theoretical and applied facets, initiating each subject with context and a clear grasp before progressing to logical reasoning and demonstrable methods. Every subject will be accompanied by a Python Notebook, allowing students to execute and adapt it to explore the concepts studied, fostering a more profound comprehension.
COURSE STRUCTURE:
This course contains five modules (Units):
1. Fundamental Properties of Probability
2. Distribution of One Variable - Discrete Data
3. Continuous Distribution
4. Hypothesis Tests
5. Tests of Means of numerical data
Every module consists of multiple lessons presented in both lecture notes and practice lab. There are also quizzes that are automatically graded to assess comprehension.
PREREQUISITES:
Data analysis in the Lab sessions will be performed using Python and MS-Excel.
No previous knowledge of these packages is required.
PYTHON NOTEBOOKS:
Each topic will be accompanied by a Python Jupyter Notebook with programs for visualizing, simulating, and exploring the material covered. You will be able to run the programs, modify the algorithms, and experiment with the simulations to get a better feel and understanding of the concepts covered.
EVALUATION:
This is a pass/fail course.