Analytics, Data Science & Decision Making Short Courses
The Institute for Analytics and Data Science is introducing a series of short online courses across the fields of data science, analytics and decision making. These will range from 1 – 3 days (6 hours a day).
Our interactive online courses are taught live and in small groups. In person study is not available for these courses.
Upcoming Courses
Deep Learning (2 days)
9-10 May 2023
Please note: registration closes on 20 April 2023 at 5PM GMT.
Course abstract:
This is a two-day course on deep learning, which starts from beginning with introductory concepts, and reaches some advanced concepts in the second day.
Prerequisites/knowledge: Moderate programming experience in Python, or strong programming experience in another language are a pre-requisite for joining this course.
Day 1 will act as an introduction to deep-learning with Tensorflow and Keras. This day introduces Tensorflow as a programming language from scratch and shows how to use it to build simple neural networks and perform backpropagation. Students are encouraged to program along with the tutor. The basic underlying workings of TensorFlow and neural networks are taught without resorting to higher-level black box packages, so that students can gain a fundamental understanding of how deep learning works.
Day 2 will focus on Recurrent Neural Networks with Keras. This day will teach a deep understanding of how recurrent neural networks work, what they are used for, and how to implement them efficiently using Keras and Tensorflow. The course culminates with unique advanced recurrent neural network examples applied to control problems. Note that natural-language processing examples will not be covered.
Machine Learning for Causal Inference From Observational Data (1 day)
24 April 2023
Registration is now closed for this course.
Course abstract:
This one-day workshop will:
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Introduce the basic principles of causal modelling (potential outcomes, graphs, causal effects) while emphasising the key role of design and assumptions in obtaining robust estimates.
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Introduce the basic principles of machine learning and the use of machine learning methods to do causal inference (e.g. methods stemming from domain adaptation and propensity scores).
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Show how to implement these techniques for causal analysis and interpret the results in illustrative examples.
The course covers:
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Causal modelling
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Basic machine learning techniques
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Running causal analysis on real data sets
By the end of the course participants will:
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Understand the distinction between causal effects and associations and appreciate the key role of design and possibly untestable assumptions in the estimation of causal effects
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Understand the role of training and testing models on data and the use of regularization to avoid overfitting
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Be able to position machine learning within the causal tool chain.
Practical elements:
The course is roughly 30% theory and 70% practice. Theory: basics of machine learning; fundamentals of causal modelling and analysis. Practice: estimation of causal effects using established methods, popular packages (EconML, scikit-learn) and real data sets.
Prerequisites/knowledge:
Basic knowledge of programming, ideally Python.
Will I need to prepare anything in advance?
Have a Google account to access Google Colab (required for the practical part of the course). An optional, depending on personal needs, introductory Python module. List of example tutorials: https://docs.python-guide.org/intro/learning/
Course Fees
Refund Policy
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Cancellations made more than 14 days before the course date: an administrative charge of £50.00.
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Cancellations made more less than 14 days before the course date: no refund to be made.
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Registered participants on courses cancelled by the short course organisers will be entitled to claim a full refund.
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Extenuating circumstances will also be considered at the organiser’s discretion.
