Professional and short courses
Making Sense of Data
|Location||Cavendish, Central London|
|Faculty||Science and Technology|
The world is evolving under the flow of enormous information. The modern scientific breakthrough of capturing, storing and computing is generating an unprecedented amount of data. Every examination and treatment under the health care system, every sales transaction of retail and financial companies, each click made on the website of any organisations' log or biologist in the lab are continuously creating data. The volume of data being generated is leading to information overload and the ability to make sense of all this data is becoming increasingly important. The full potential of data is often not realised as the raw data is commonly inconsistent, inaccurate and imperfect. This is particularly acute in the health and social care sectors. Understanding the uncertainty associated with data use in modelling is key.
The aim of this course is to explore step-by-step methods to discover various properties of data and thus making sense of it. This will eventually act as a building block for advanced modelling and analytical purpose.
This course will also provide you with an intuitive introduction to applied statistical tools and fundamental statistical skills for data preparations and handling raw data as well as dimensions of uncertainty and how to deal with it.
Who is this course for?
The course is relevant to all those with an interest in the rigorous evaluation of data analysis in health care, particularly in local authorities and clinical commissioning groups and primary care trusts. Nonetheless, this will also applicable to other disciplines such as business, financial and social sectors.
Participants are expected to have a basic level of numeracy.
|We run this course on request for a number of people. Please register your interest.||1 day||
|Register your interest|
- What data is
- Importance of data
- Types and sources of data
- Primary and secondary data
- IT systems and databases in health service
Data preparation and preliminary analysis
- Steps of data preparation
- Data input, edit and modify
- Editing and pivoting table/chart
- Dictionary information
- Data cleaning: outliers and missing values
- Data uncertainty
Hands-on session using SPSS
- Data manipulation and preparation using SPSS
Exploratory data analysis (EDA)
- Rationale for EDA
- Different tools and methods for EDA
Hands-on session using SPSS
- EDA and reporting findings using SPSS
At the end of this module you will be able to:
- evaluate the importance of data and when and how to collect data for modelling exercises
- locate key sources of data in the Health Service
- manage when data are imperfect
- store and analyse data
- distinguish between sources of variability: underlying statistical uncertainty (e.g. death, illness, travel time), uncertainty in the quality of data, about the future and variability that can be understood and managed (e.g. variation by time of day, season of year)
- determine what techniques are appropriate for various situations
- prepare data efficiently and effectively
- undertake basic exploratory data analysis, interpret and effectively display the results of the analyses using tables and charts
- undertake exploratory data analysis using a suitable statistical package such as SPSS
- practical experience of using the latest version of SPSS (IBM version 20)
Muhammad Saiful Islam
Muhammad Saiful Islam is a Lecturer and PhD Candidate at the University of Westminster. He has obtained a BSc and an MSc in Applied Statistics both from the Institute of Statistical Research and Training, University of Dhaka, Bangladesh. After finishing his MSc, he has worked at the International Centre for Diarrhoeal Disease Research Bangladesh as a Senior Research Assistant. Now he is working at Health and Social Care Modelling Group at the University of Westminster and his research concentrates on the modelling impact of climate change on health for the elderly population in England. Mr Islam’s research interest surrounds hospital episode statistics, health care, environmental and occupational health, climate change, predictive modelling, data mining, databases, statistical modelling, biostatistics, survival analysis and longitudinal data analysis.
Philip Worrall obtained a BA (Econ) in Economics from the University of Manchester and an MSc in Operational Research and Management Science from Lancaster University. His MSc project was conducted at the NHS Derbyshire County Primary Care Trust, where he investigated the challenges of building COPD strategic planning models to model long-term future demand. At present he studies for a PhD at the University of Westminster on strategic health care planning and forecasting long-term care demand in particular, and works part-time for the NHS London Procurement Programme. He has extensive experience in applying problem structuring and forecasting methods to develop health care modelling tools.