# Data Analysis Techniques

### Introduction:

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 that result in improved business performance it is vital to base decision making on appropriate analysis and interpretation of numerical data.

### Course Objectives:

At the end of this conference the participants will be able 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
• Give delegates the ability to recognize which types of analysis are best suited to particular types of problems
• Give delegates sufficient background and theoretical knowledge to be able to judge when an applied technique will likely lead to incorrect conclusions
• 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
• Introduce some basic statistical methods and concepts
• Explore the use of Excel 2010 or 2013 for data analysis and the capabilities of the Data Analysis Tool Pack

### Targeted Audience:

This Data Analysis Techniques training course has been designed for professionals whose jobs involved 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.

### 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 visualization, 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)