Courses

Currently, all of our training courses are being held online.

All of our courses are hosted by expert certified trainers and research professionals who teach through a mix of demonstrative and practical sessions to provide high-class, practical training.

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

An Introduction to Machine Learning using Stata - In collaboration with Lancaster University

7th - 8th April 2021 Online 2 days (7th April 2021 - 8th April 2021) Stata

Presented by Dr. Giovanni Cerulli

Course Timetable: 10am - 12pm & 2pm - 4pm (London time)

This course is a primer to machine learning techniques using Stata. Stata owns today 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 (and knowledgeable of) Stata potential to draw knowledge and value from rows of large, and possibly noisy data. The teaching approach will be 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 balance evenly theory and practical sessions.

Building Your Own Models In EViews

9th - 10th April 2021 Online 2 days (9th April 2021 - 10th April 2021) EViews

Presented by Dr. Malvina Marchese

This course is for economists, econometricians and applied researchers who want to go even further in EViews by coding something that is not implemented in the existing routines such as long memory GARCH models or Value at Risk. In addition, one may wish to make life easier and to ask EViews to perform some repetitive tasks. This is where EViews Programming starts. The goal of this training course is to make life easier, namely to do things with only a small investment instead of learning a completely new language.

This course will be running online, as a Zoom webinar.

Data Science For Health Researchers: An Introduction to Stata

15th - 16th April 2021 Online 2 days (15th April 2021 - 16th April 2021) Stata

Overview

Presented by: Dr. Vincent O'Sullivan

Course Timetable: 10am - 12pm & 2pm - 4pm

This course is for professionals and researchers who are new to Stata. The course assumes only limited statistical knowledge and experience of using statistical software. The participants will be introduced to Stata’s interface. They will be shown how manage and prepare datasets for analysis. The fundamentals of data analysis and visualization will also be taught. Then, the participants will be introduced to two of the main data analysis tools: linear regression and logistic regression. Participants will be taught the statistical theory behind these methods, and they will apply these methods to specially chosen datasets using examples from health research.

Advanced Panel Data Analysis with Stata

23rd - 24th April 2021 Online 2 days (23rd April 2021 - 24th April 2021) Stata

Presented by Dr. Malvina Marchese (Cass Business School, City, University of London)

Course Timetable: 10am - 12pm & 2pm - 4pm

The course follows the Panel data Analysis with Stata and aims at provide participants with a theoretical and practical understanding of advanced panel methods, i.e. non-linear panel models.

Each session briefly introduces the different methodologies, discussing strengths and weaknesses with a focus on the interpretation of the results. Hands-on sessions with many practical examples and exercises to discuss the different methodologies on panel data analysis.

Advances in Causal Inference using Stata (Online)

14th - 15th May 2021 Online 2 days (14th May 2021 - 15th May 2021) Stata

Presented by: Dr. Giovanni Cerulli

Course Timetable: 10am - 12pm & 2pm - 4pm

Econometric modelling for causal inference and program evaluation have witnessed a tremendous development in the last decade, with new approaches and methods addressing an expanding set of challenging problems, both in medical and the social sciences. This course covers some recent developments in causal inference and program evaluation using Stata.

It will provide participants with the essential tools, both theoretical and applied, for a proper use of recent micro-econometric methods for policy evaluation and causal modelling in situations where the standard treatment setting poses limitations.

More specifically, the course will focus on these approaches: (i) Difference-in-differences (DID) with time-varying and time-fixed binary treatment; (ii) the Synthetic Control Method (SCM) for program evaluation, suitable when datasets on many times and locations are available; (iii) models for multivalued and quantile treatment effect estimation.

After attending the course, the participant will be able to setting up and managing a correct evaluation design using Stata, by identifying the policy framework, the appropriate econometric method to use interpreting correctly the results. The course will provide various instructional examples on real datasets.

Instrumental Variables and Structural Equation Modelling using Stata - Online

7th - 8th June 2021 Online 2 days (7th June 2021 - 8th June 2021) Stata

Course Overview

Presented by Dr. Giovanni Cerulli

Course Timetable: 10am - 12pm & 2pm - 4pm

This course provides participants with the essential tools, both theoretical and applied, for a proper use of instrumental variables (IV) and structural equation models (SEM) for statistical causal modelling using Stata.

Introduction to Linear Mixed Models using Stata (online)

23 September - 24 September 2021 (10am-1pm 2-5pm, London Time) Online 2 days (23rd September 2021 - 24th September 2021) Stata

Presented by Sandro Leidi & James Gallagher

This course is running online, via Zoom.

Mixed models are a modern powerful data analysis tool to analyse clustered data, typically arising in studies where the levels of a factor are a random selection from a wider pool, or in the presence of a multi-level nested structure with different levels of variability.

Potential benefits of mixed models are greater generalisability of results and accommodation of missing values. In particular, mixed models have been used in clinical trials to analyse repeated measures, where measurements taken over time naturally cluster according to patient.

The course will illustrate medical and health related applications of mixed modelling, such as multi-centre trials, cross-over trials, and the analysis of repeated measures. The course focuses on the linear mixed model, assuming normally distributed data, and on how to fit it and interpret its results.

Only essential theoretical aspects of mixed models will be summarised.

Advanced Machine Learning using Stata - In collaboration with Lancaster University

25th - 26th October 2021 Online 2 days (25th October 2021 - 26th October 2021) Stata

Course Timetable: 10am - 12pm & 2pm - 4pm

Presented by Dr. Giovanni Cerulli

This course will focus on three specific techniques: 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.

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.

Stata Winter School Online

14th - 18th December, 2020 Online 5 days (14th December 2020 - 18th December 2020) Stata

Course Overview

The Stata Winter School consists of a series of one and two-day courses which can be taken individually or as a whole as required. The School is aimed at students, academics and professionals who want to develop and strengthen their data processing, programming, graphics and statistical skills using Stata. All of the courses are taught interactively using a blend of theory, follow-along demonstrations and exercises.

For the first time, we will be running our winter school entirely online, so you can join from the comfort of your home, anywhere in the world.

The course timetable: 5.5 hours of live teaching over 3 sessions: 10.00-12.00; 13.00-15.00; 15.30-17.00 (GMT). Each session will include time for Q&A.

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