Course Details
Course Outline
5 days course
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 autocorrelation 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 spread sheets, sensitivity analysis.
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