Data Science and Decision Making

Spring Term (2021): Postgraduate (MSc), University of Essex, 2021

The aim of this module is to familiarise students with the whole pipeline of processing, analysing, presenting and making decisions using data. This is a research-led, MSc level course, where students are expected to develop a complete end-to-end data science application. The course blends data analysis, decision making and visualisation with practical python programming. The module assumes a reasonable programming background and is not suitable for students without prior programming experience. We are going to use Python programming in this module.

The aim of this module is to equip students with the theoretical tools and practical understanding necessary to create end-to-end data science applications, all the way from the initial concept to final deliverable.

Key Python libraries for this modules are: NumPy, Pandas, scikit-learn, Tensorflow, Keras, Matplotlib, and Seaborn.

For running the codes, I would highly recommend using Google Colab. Colaboratory is a Google research project created to help disseminate machine learning education and research. It’s a Jupyter notebook environment that requires no setup to use and runs entirely in the cloud. This means that as long as you have a Google account, you can freely train your models on a K80 GPU.

Learning Outcomes

After completing this module, students will be expected to:

  1. Understand the basics of the python data science and decision making (Dr Haider Raza)

  2. Summary and re-sampling statistics (cross-validation, permutation tests, bootstrapping) (Ana Matrán-Fernández)

  3. Data Exploration: Clustering methods, Dimensionality Reduction, Data Transformation

  4. Predictive Modelling and related methods (Ana Matrán-Fernández)

  5. Recommender Systems (Ana Matrán-Fernández)

  6. Bandits (Ana Matrán-Fernández)

  7. Deep Learning for Images and Text (Convolution Neural Networks and Recurrent Neural Networks) (Dr Haider Raza)

  8. Dataset Shift and Transfer Learning (Dr Haider Raza)

  9. Generative Models: Variational Auto-Encoders (VAEs) and Generative Adversarial Networks (GANs) (Dr Haider Raza)

  10. ML model deployment in cloud (Expert Lecture: Mr Ajith Varjala, Data Scientist)


I am sharing this module with Dr Ana Matran-Fernandez.

Lab work is available GitHub

Teaching Materials Available via Moodle