Online Courses

Below is a list of our upcoming online training courses.

You can register for our courses online. To discuss any of our courses or specific training requirements, please call +44 (0) 20 8697 3377 .

Machine Learning using Stata: Introduction & Advanced - in collaboration with Lancaster University (online)

26 - 27 October & 9 - 10 November 2020 Online 4 days (26th October 2020 - 10th November 2020) Stata

Course Overview: Part one

Recent years have witnessed an unprecedented availability of information on social, economic, and health-related phenomena. Researchers, practitioners, and policymakers have nowadays access to huge datasets (the so-called “Big Data”). This data is collected on people, companies and institutions, web and mobile devices and satellites, at an increasing speed and detail.

Machine learning is a relatively new approach to data analytics, which places itself in the intersection between statistics, computer science, and artificial intelligence. It's primary objective is that of turning information into knowledge and value by “letting the data speak”. Machine learning limits prior assumptions about data structure, and relies on a model-free philosophy that supports algorithm development, computational procedures, and graphical inspection more than tight assumptions, algebraic development, and analytical solutions. Machine learning was computationally unfeasible up until a few years ago. It is only possible on the machines of today, with their increased computing power and ability to learn, their hardware development, and with continuous software upgrading.

This course is a primer to machine learning techniques using Stata. Stata owns various packages to perform machine learning which are however poorly known to many Stata users. This course fills this gap by making participants familiar with Stata's potential to draw knowledge and value form row, large, and possibly noisy data. The teaching approach will be mainly based on the graphical language and intuition more than on algebra. The training will make use of instructional as well as real-world examples, and will evenly balance theory and practical sessions.

After the course, participants are expected to have an improved understanding of Stata's potential to perform some of the most used machine learning techniques, thus becoming able to master research tasks including, among others:

  • (i) factor-importance detection
  • (ii) signal-from-noise extraction
  • (iii) correct model specification
  • (iv) model-free classification, both from a data-mining and a causal perspective.

Course Overview: part 2

No prior knowledge of machine learning techniques are required to attend this course, as the first session will start from scratch with a fresh introduction to the subject. This course will focus on three specific techniques not covered in the first-part of the course, that is: regression and classification trees (including bagging, random forests, and boosting), kernel-based regression, and global methods (step-wise, polynomial, spline, and series regressions).

The teaching approach will be mainly based on the graphical language and intuition more so than on algebra. The training will make use of instructional as well as real-world examples, and will evenly balance theory and practical sessions.

After the course, participants are expected to have an improved understanding of Stata's potential to perform some of the most used machine learning techniques, thus becoming able to master research tasks including:

  • (i) factor-importance detection,
  • (ii) signal-from-noise extraction,
  • (iii) model-free regression and classification, both from a data-mining and a causal perspective.

The course is open to people coming from all scientific fields, but it is particularly targeted to researchers working in the medical, epidemiological and socio-economic sciences.

Regression Modelling using Stata

30 - 31 October 2020 Online 2 days (30th October 2020 - 31st October 2020) Stata

Presented By: Dr. Malvina Marchese

Regression modelling is a fundamental tool in the research box of every economist, econometrician or applied researcher in a variety of fields. Join Dr Malvina Marchese and learn the statistical theory behind linear and non-linear regression methods. These methods are taught with specially chosen datasets, using real examples from macro economic and finance research.

This course is for researchers from all academic disciplines who are new to Stata. The course assumes only limited statistical knowledge and experience of using statistical software.

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