These Massive Open Online Courses (MOOCs) are available on external MOOC platforms, such as Coursera and FutureLearn.

Understanding statistics is essential to understand research in the social and behavioral sciences. This course introduces the basics of statistics; not just how to calculate them, but also how to evaluate them. In the first part of the course, methods of descriptive statistics are discussed, e.g. what cases and variables are and how measures of central tendency can be computed (mean, median and mode) and dispersion (standard deviation and variance). Next, the assessment of relationships between variables is discussed, and the concepts correlation and regression are introduced. The second part of the course is concerned with the basics of probability: calculating probabilities, probability distributions and sampling distributions. The third part of the course consists of an introduction to methods of inferential statistics. Confidence intervals and significance tests are discussed and learners are trained to calculate and generate these statistics using freely available statistical software.
This course is designed to teach learners the basic math they will need in order to be successful in almost any data science math course and was created for learners who have basic math skills but may not have taken algebra or pre-calculus. Data Science Math Skills introduces the core math that data science is built upon, with no extra complexity, introducing unfamiliar ideas and math symbols one-at-a-time.
This introductory to intermediate course teaches enterprise data storage and management. It covers basics of enterprise IT infrastructure, virtualization, storage devices, and insights in infrastructure management.

On this course you will learn the basics of Linked Data and the Semantic Web - exploring how this new Web of Data isn’t about creating a big collection of standalone datasets, but is instead about using a common format to ensure data is interrelated.

This course introduces learners to sampling and exploring data, as well as basic probability theory and Bayes' rule. Learners examine various types of sampling methods, and discuss how such methods can impact the scope of inference. A variety of exploratory data analysis techniques are covered, including numeric summary statistics and basic data visualization.
This hands-on course will teach you how to write your own computer programs, one line of code at a time. You’ll learn how to access open data, clean it and analyse it and to produce visualisations. You will also learn how to write up and share your analyses, privately or publicly.
Within healthcare there are thousands of complex and variable processes that generate data including treatment of patients, lab results and internal logistic processes. Analysing this data is vital for improving these processes and ending bottlenecks. On this course you will explore how process mining can help turn this data into valuable insights by looking at different areas of process mining and seeing how it has been applied. You will even get the chance to apply process mining on real life healthcare data.

Process mining is a new and exciting field which combines business process management with data science. Using process mining techniques you can analyse and visualise business processes based on event data recorded in event logs.

Process mining is the missing link between model-based process analysis and data-oriented analysis techniques. Through concrete data sets and easy to use software the course provides data science knowledge that can be applied directly to analyze and improve processes in a variety of domains.
This free online course will help you to navigate your own path through the complex landscape of smart cities. You’ll hear from smart city innovators and entrepreneurs, city leaders, communities and business, connecting with learners from around the world to reflect on issues facing smart cities of different sizes and situations.

You can find more data science courses on the EDSA dashboard.