Course Introduction

Course Introduction#

This course is aimed at experienced Health and Social Care Data Professionals who would like to explore a career or better understand concepts in Data Science.

Prerequisites

Whilst our course is designed as an entry level course, we recommend the following experience

  • Experience in using programming languages (preferably R or Python) or SQL

  • Maths at GCSE level 4+ or a C grade and above

  • Approximately two hours per week for revision in addition to the course (three weeks)

Students will need access to Python 3.8+ and Jupyter running on an environment that has the libraries listed in the requirements.txt file located here installed:

We recommend that if you do not have Python currently installed that you use the Miniconda instance of Python.

Program Timetable

The course is delivered in person over a three week time period. We recommend, where possible to deliver the training as a block so that students are able to retain information between modules.

Week

Time Slot

Monday

Tuesday

Wednesday

Thursday

Friday

1

AM

Python for Analysis

Python for Analysis

Decision Science

Pandas & Data Wrangling

Data Pre-processing

1

PM

Python for Analysis

Python for Analysis

Numpy

Pandas & Data Wrangling

Data Pre-processing

2

AM

Plotly & Seaborn

Maths & Statistics

Optional Module 1

Optional Module 2

Version Control & Structuring Project

2

PM

Plotly & Seaborn

Maths & Statistics

Optional Module 1

Optional Module 2

Ethics & Project Planning

3

AM

Project Work

Project Work

Project Work

Project Work

Project Work

3

PM

Project Work

Project Work

Project Work

Project Work

Presentations

Optional Modules

Candidates can choose two optional modules which cover specific concepts of Data Science in week two. This is aimed to allow students to focus on areas that they are able to take back to their places of work.

  • Classification & Regression

  • Network Analysis

  • Time Series Modelling

  • Simulation Modelling

  • Unsupervised Learning (Anomaly Detection & Clustering)

  • Causal Inference (Measuring Change)

  • Object Orientated Design

  • Geospatial Analysis

Course Writers

Andy Mayne

Dr Chris Sampson

Ben Holdsworth

Sam Vautier

Chief Analytical Scientist,
PenCHORD Associate Research Fellow &
Chief Technology Officer for GW-SDE

Senior Data Scientist

Healthcare Statistician &
PenCHORD Associate Research Fellow

Data Scientist