This course has been designed for professionals whose jobs involve in the manipulation, representation, interpretation and/or analysis of data. Familiarity with a PC and in particular with Microsoft Excel (2003, 2007, 2010 or 2013) is assumed.
Programme Overview
The corporate ethos which demands continual improvement in workplace efficiencies and reduced operating, maintenance, support service and administration costs means that managers, analysts and their advisors are faced with ever-challenging analytical problems and performance targets. To make decisions which result in improved business performance it is vital to base decision making on appropriate analysis and interpretation of numerical data.
This training course aims to provide those involved in analysing numerical data with the understanding and practical capabilities needed to convert data into information via appropriate analysis, and then to represent these results in ways that can be readily communicated to others in the organisation.
Objectives
Objectives include:
To provide delegates with both an understanding and practical experience of a range of the more common analytical techniques and representation methods for numerical data
To give delegates the ability to recognize which types of analysis are best suited to particular types of problems
To give delegates sufficient background and theoretical knowledge to be able to judge when an applied technique will likely lead to incorrect conclusions
To provide delegates with a working vocabulary of analytical terms to enable them to converse with people who are experts in the areas of data analysis, statistics and probability, and to be able to read and comprehend common textbooks and journal articles in this field
To introduce some basic statistical methods and concepts
To explore the use of Excel 2010 or 2013 for data analysis and the capabilities of the Data Analysis Tool Pack
Methodology
Each problem presents and exemplifies the need for a different data analysis approach. For reasons of time constraint, it will not be possible to develop solutions during the course to all of the problems posed. Nevertheless, all delegates will be given comprehensive solutions to all of the problems, to take away with them at the end of the course, as future learning resources.
Course Outline
The Basics
Sources of data, data sampling, data accuracy, data completeness, simple representations, dealing with practical issues
Fundamental Statistics
Mean, average, median, mode, rank, variance, covariance, standard deviation, “lies, more lies and statistics”, compensations for small sample sizes, descriptive statistics, insensitive measures
Basics of Data Mining and Representation
Single, two and multi-dimensional data visualisation, trend analysis, how to decide what it is that you want to see, box and whisker charts, common pitfalls and problems
Data Comparison
Correlation analysis, the auto-correlation function, practical considerations of data set dimensionality, multivariate and non-linear correlation
Histograms and Frequency of Occurrence
Histograms, Pareto analysis (sorted histogram), cumulative percentage analysis, the law of diminishing return, percentile analysis
Frequency Analysis
The Fourier transform, periodic and a-periodic data, inverse transformation, practical implications of sample rate, dynamic range and amplitude resolution
Regression Analysis and Curve Fitting
Linear and non-linear regression, order; best fit; minimum variance, maximum likelihood, least squares fits, curve fitting theory, linear, exponential and polynomial curve fits, predictive methods
Probability and Confidence
Probability theory, properties of distributions, expected values, setting confidence limits, risk and uncertainty, ANOVA (Analysis of Variance)
Some More Advanced Ideas
Pivot tables, the Data Analysis Tool Pack, internet-based analysis tools, macros, dynamic spreadsheets, sensitivity analysis
