1da-intro
Go to Introduction | Go to Workshops | Go to MatLab primer
First Year Data Analysis and Presentation 2012-2013
A series of lectures and demonstrations by Andrew McKinley [email a.mckinley]
Room 841, Chemistry Department, Imperial College London
Go to Introduction | Go to Workshops | Go to MatLab primer
Course Overview
Sets of data are everywhere; data are collected on almost every aspect of our lives and in everything we do. Anything which is measured must be subject to stringent data analysis if the results are to have any meaning; from measuring journey times to the sizes of the shoes on our feet.
Chemistry is a physical science; many of our results are borne of measurements, and to extricate meaning we must analyse the data generated from those measurements.
This course will provide an introduction to the basic principles of data analysis (including errors) and data presentation, and will give you an idea of what to expect when analysing data in the laboratories.
Intended Learning Outcomes
By the end of the course, students should:
- be familiar with the use of the College Virtual Learning Environment (VLE)
- understand the requirement for data analysis techniques in the physical sciences
- Recognise the importance of analysing data correctly.
- be able to use significant figures, decimal places and scientific notation, and know when each is appropriate.
- understand averages, and recognise when each is appropriate.
- be able to determine sources of error in an experiment, identify systematic and random errors, and apply statistical techniques to calculate the standard error.
- employ software in an advanced manner to better understand the process of data handling.
Use these learning outcomes as a framework to measure your progress through the course - as you conduct the assignment, reflect upon these outcomes and ask yourself if you have achieved them!
Structure of the course
- Lecture classes (Pippard LT, Level 5, Sherfield Building)
- The perils of bad data analysis and presentation
- Errors and Scientific notation
- Averages: The Mean, the Mode and the Median
- The Gaussian, or 'normal' distribution of data
- Standard deviations
- Relevance to Chemistry
- Workshop
- Exploring the Gaussian function
- Methods of fitting straight line graphs
- Using Solver to iterate solutions of complex data sets
Workshop Slots
You will be allocated one of four groups (A1, A2, B1 and B2, listings can be found here). Workshops take place during Weeks 7 and 8 (12th-13rd November 2012)
- Group A1: Thursday 15th November 2012, 14:00-17:00
- Group A2: Thursday 22nd November 2012, 14:00-17:00
- Group B1: Monday 12th November 2012, 13:00-16:00
- Group B2: Monday 19th November 2012, 13:00-16:00
During these hours the computer suite is reserved for your group and demonstrators will be available to answer your questions in the class. Please do feel free to work on this course outside of these hours - students doing course and study work always have priority over students using the computers for non-chemistry related uses.
Coursework
It is expected that the contents of this course will contribute towards your analysis of data generated in laboratories, as a result there is no solely assessed work on "Data Analysis", however the content of this course strongly supports the laboratory classes. Completion of the workshop exercises however is a condition for the award of First Year Honours, and you must make sure you complete the BlackBoard assessments in time.
You are encouraged to familiarise yourself further with data analysis through the use of appropriate tutorial texts and to use such methods as appropriate to analyse the data generated throughout your experimental career at Imperial College.
What this course is not...
This course is not intended to be a comprehensive guide to data analysis - there are many textbooks available (see Recommended Texts) which go into far more depth about data analysis. The course will however show you what levels of analysis are appropriate for undergraduate chemistry laboratories and how to present your data in an appropriate manner.
Lectures
The four lectures are intended to give you insight into the area of data and error analysis and to detail what is expected from you when analysing data for reports and papers in Chemistry It is not possible in the time available to teach everything about this subject, nor is it necessary to do so for successful pursuit of a degree in Chemistry. You should however keep up to date with your error analysis through use of the recommended texts, and further information can be found in these easily accessible texts.
Lecture notes have been placed on Blackboard in the "1st Year Chemistry Foundation" module, in the Data Analysis folder. These have been adapted from the recommended text (MATU), pulling out key areas of interest and relevance, however other areas explored in this book will be useful to you in later years of your degree course.
Recommended textbooks
Measurements and their Uncertainties - I.G. Hughes, T.P.A. Hase, Oxford University Press, 2010, ISBN: 9780199566334
How to use Excel in Analytical Chemistry - R. de Levie, Cambridge University Press, 2001, ISBN: 0521642825.
CARE - This is an older textbook, so refers to older versions of Excel; all the principles of analysis are sound and are applicable to newer versions of Excel as well as other data analysis packages.