Course Features

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Duration

3 weeks

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Delivery Method

Online

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Available on

Limited Access

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Accessibility

Mobile, Desktop, Laptop

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Language

English

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Subtitles

English

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Level

Intermediate

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Effort

6 hours per week

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Teaching Type

Self Paced

Course Description

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.

Course Overview

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International Faculty

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Post Course Interactions

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Instructor-Moderated Discussions

Skills You Will Gain

Prerequisites/Requirements

As such we expect that you are already familiar with some basic topics in statistics.

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

Prior knowledge of all the material covered

What You Will Learn

Apply certain procedures (resampling, bootstrapping, non-parametric approach) when confronted with non-standard situations.

Construct and interpret confidence intervals, learn how to perform hypothesis testing in various settings, and know how these two concepts are related.

Make and interpret numerical and graphical summaries of datasets.

Perform simple and multiple linear regression on quantitative and categorical variables.

Use the R software package to perform all these tasks.

Use various techniques to find estimators for unknown parameters and how to compare them.

Course Instructors

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Annoesjka Cabo

Prof. at Delft University of Technology

Annoesjka Cabo is professor in Statistics for Innovation in Education. She has a passion for teaching and anything that can improve students’ learning. Her research is on statistical modeling for mea...
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Christophe Smet

Dr. at Delft University of Technology

Christophe Smet is a lecturer at TU Delft, mainly responsible for bachelor courses in probability theory and statistics. He enjoys teaching a lot and he has previous teaching experience at the univer...
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Rik Lopuhaä

Dr. at Delft University of Technology

Rik Lopuhaä studied Mathematics at the University of Amsterdam and received a PhD from TU Delft on robust multivariate statistical methods. His research interest is asymptotic theory for statistical ...
Course Cover