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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.
After a course session ends, it will be archived.
A strong foundation in mathematics is critical for success in all science and engineering disciplines. Whether you want to make a strong start to a master’s degree, prepare for more advanced courses, solidify your knowledge in a professional context or simply brush up on fundamentals, this course will get you up to speed.
In many engineering master’s programs, statistics is used quite intensively. As soon as you are dealing with real-life data, you will need to get an idea of what these data tell you and how you can visualize this (descriptive statistics). But you will also want to perform some analysis (inferential statistics): you may want to build a model that mimics reality, estimate some quantities, or test some hypotheses.
The statistics course in this series will help you refresh your knowledge on these topics. Along the way you will learn how to apply these concepts to datasets, using the statistical software R.
This course offers enough depth to cover the statistics you need to succeed in your engineering master’s or profession in areas such as machine learning, data science and more.
This is a review course
This self-contained course is modular, so you do not need to follow the entire course if you wish to focus on a particular aspect. As a review course you are expected to have previously studied or be familiar with most of the material. Hence the pace will be higher than in an introductory course.
This format is ideal for refreshing your bachelor level mathematics and letting you practice as much as you want. You will get many exercises, to be solved using Grasple or R, for which you will receive intelligent, personal and immediate feedback.
Prior knowledge of all the material covered.
Some basic calculus will be used, along with some aspects of probability theory: computation of expectation and variance of a random variable with known PDF, the central limit theorem, Bayes’ theorem ... We expect you to be familiar with these topics.
This course is a review course. As such we expect that you are already familiar with some basic topics in statistics.
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
Q: I am going to start a master’s in engineering. Is this MOOC mandatory?
A: It is not mandatory, but we believe it could be useful to revisit some of these topics.
Q: Can I use the certificate I get from this MOOC to get an exemption from a certain course?
A: No, you cannot. That is not the goal of these courses.
Q: Can I use this MOOC as a substitute for the Bridging Programme (Schakelprogramma)?
A: No, you cannot.
Q: What level is required to follow this course?
A: It is a review course. Hence the pace will be a bit higher than when you learn about these topics for the first time.
Q: What is the duration of this course?
A: You will probably need 4 to 6 hours per week, for 6 weeks.
Q: I am going to follow this MOOC. Will I have to hand in homework?
A: No. The exercises will be checked automatically, and you will get immediate feedback.
