What you will learn
- Use discrete and continuous random variables and understand how they interact.
- Deal with conditional probabilities and conditional distributions.
- Obtain understanding into some limiting results, in particular the Central Limit Theorem.
- Make and interpret numerical and graphical summaries of datasets and find the connection to concepts from probability theory.
- Several techniques to find estimators and assess their quality.
- Perform inferential statistics: hypothesis testing, confidence intervals and linear regression, also in non-standard situations.
- Use the R software package to apply all these statistical techniques on real datasets.
Whether you want to make a strong start to a master’s degree, solidify your knowledge in a professional context or simply brush up on fundamentals in probability and statistics, this program will get you up to speed.
Statistics is used quite intensively in many engineering contexts and master’s programs. 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). You will also want to perform some analysis (inferential statistics), build a model that mimics reality, estimate some quantities, or test some hypotheses. Along the way you will learn how to apply these concepts to datasets, using the statistical software R.
This program also provides an introduction to probability theory. You will encounter discrete and continuous random variables and learn in which situations they appear, what their properties are and how they interact. Probability theory can be applied to learn more about real-life problems, and it is useful for building models. Moreover, it provides the basis for statistics and applications in data analysis. Therefore, it is a useful subject for any aspiring engineer.
These courses are self-paced, self-contained and modular, to make it easier to review specific topics and practice as often as you want without having to follow the entire courses.
This program is ideal for:
- Prospective engineering students who want to meet the prerequisites for a MSc program, be better prepared or refresh their mathematics knowledge before starting a master’s degree.
- Engineering or bachelor students who realize that they have a gap in their math knowledge or would like an additional challenge in mathematics not offered by their studies.
- Working professionals who would like to improve their math knowledge.
- Anyone interested in university level mathematics.
This program will refresh your knowledge and review the relevant topics. As review courses, you are expected to have previously studied or be familiar with most of the material.
This program is part of our series ‘Mastering Mathematics for Engineers’, together with ‘Mastering Calculus’ and ‘Mastering Linear Algebra’.
Courses in this program
DelftX's Mastering Probability and Statistics Professional Certificate
- 4–6 hours per week, for 6 weeks
This course provides an introduction to probability theory. You will encounter discrete and continuous random variables and learn in which situations they appear, what their properties are and how they interact.
- 4–6 hours per week, for 3 weeks
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.
- Probability theory can be applied to learn more about real-life problems, and it is a useful subject for any aspiring or practicing engineer. Likewise, in many engineering master’s programs or professions, statistics is used quite intensively.
- A strong foundation in mathematics is critical for any aspiring or practicing engineer.
- Knowledge of probability and statistics is needed to succeed in an engineering master’s or profession in areas such as modeling, finance, signal processing, logistics, machine learning, data science and more.