This course provides an overview of bachelor-level statistics. You will review the concepts of descriptive and inferential statistics. You will use the statistical software package R on real data to gain insight in these topics.
This comprehensive Statistics course, offered by DelftX, is designed to provide a solid foundation in mathematics and statistics, crucial for success in science and engineering disciplines. It's an intermediate-level course that serves as an excellent refresher for those preparing for a master's degree, advancing their professional skills, or looking to solidify their knowledge in statistics and data analysis.
The course covers a wide range of topics, from descriptive statistics to advanced concepts like linear regression and bootstrapping. It uniquely combines theoretical knowledge with practical application using the R programming language, making it ideal for those interested in machine learning, data science, and other data-driven fields.
This is a review course, so students are expected to have prior knowledge of the material covered. Basic calculus and familiarity with probability theory concepts such as expectation, variance, central limit theorem, and Bayes' theorem are required. Some experience with basic statistical concepts is also expected.
Week 1: Descriptive statistics
Week 2: Estimator theory
Week 3: Hypothesis testing
Week 4: Confidence intervals (CI)
Week 5: Linear regression
Week 6: Bootstrap and resampling
This course offers a unique opportunity to master statistics and probability, providing a strong mathematics foundation essential for success in engineering, data science, and machine learning. By combining theoretical knowledge with practical application using R, students will develop valuable data analysis skills applicable in various professional contexts. Whether you're aiming to excel in your engineering master's program or enhance your career prospects in data-driven fields, this statistics course will equip you with the tools and knowledge needed to succeed in today's data-centric world.
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