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Oxford Management Centre

Info-graphics: Data Analysis and Reporting Techniques

Oct 20—24
5 days
Dubai, United Arab Emirates
USD 4950
USD 990 per day

How it works



Corporate ethos which demands continual improvement in work place 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 seminar 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 include:

  • To provide delegates with both understanding and practical experience of a range more common to 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 that will 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
  • To explore the use of Excel 2010 or 2013 for Data Analysis and the capabilities of the Data Analysis Tool
  • It should be noted that the training course does not cover the subjects of data acquisition, databases, data management, data warehousing or the analysis of text-based information.


The training course adopts a problem-based learning approach, in which delegates are presented with a series of real numerical data analysis problems drawn from the widest possible range of applications – from engineering to finance and from logistics to quality control.

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 training 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 training course, as future learning resources.

The training course is entirely applications-oriented, minimizing the time spent on the mathematics of analysis and maximizing the time spent on the use of practical methods in Excel, along with the understanding why such methods work.

Delegates will spend almost all of the time exploring Excel’s data analysis and representation functionality, including the Data Analysis Tool Pack, to investigate the totally realistic data analysis problems.


Organisations that are able to make optimum decisions will enhance their ability to compete on the global stage. The participants on this training course, and therefore the teams that they work within will, as a result of their training, be better positioned to influence the organisation with recommendations based on objective data analysis that in turn produce a higher performing business.

Individuals exposed to this training will develop new insights to the usefulness of Excel and the field of data analysis, and they will learn why the best companies in the world see data analysis as being essential to delivering the right quality products and services at the lowest costs.


Participants will gain an understanding and practical experience of a range of the more common analytical techniques and data representation methods, which have direct relevance to a wide range of issues. The ability to recognize which types of analysis are best suited to particular types of issue will be addressed, and delegates will be given sufficient background and theoretical knowledge to be able to judge when an applied technique will likely lead to incorrect conclusions.



The Basics

Sources of data, data sampling, data accuracy, data completeness, simple representations, dealing with practical

Fundamental Statistics

Mean, average, median, mode, rank, variance, covariance, standard deviation, “lies, more lies and statistics”, compensations for small sample sizes, descriptive statistics, insensitive

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

Data Comparison

Correlation analysis, the autocorrelation function, practical considerations of data set dimensionality, multivariate and non-linear

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

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

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

Day1 -

Introduction and Descriptive Statistics

  • What is data analysis
  • A reminder of elementary statistics
  • A quick-start tutorial for Excel
  • Describing data sets using statistics
  • Representing data sets graphically
  • How to create infographic in Excel
  • The normal distribution
  • Mini-Case studies

Day2 -

Frequency and Time Series Analysis

  • Frequency of occurrence
  • Histograms
  • Pareto analysis
  • Pivot tables and pivot charts
  • Creating Excel dashboard
  • Time series analysis
  • Trending data
  • Estimation theory
  • Mini-Case studies

Day3 -

Scenario Analysis, Confidence and Six Sigma

  • Modeling scenario
  • Interactive spreadsheets
  • Confidence intervals
  • Control charts
  • An Introduction to Six Sigma
  • Error bars
  • Mini-Case studies

Day4 -

Regression Analysis Equations and System Modeling

  • Simple regression analysis
  • Curve fitting
  • Describing data using equations
  • Prediction
  • Modeling single input single output systems
  • Modeling multiple input single output systems
  • Constraint optimization using Solver
  • Mini-Case studies

Day5 -

Correlation Analysis and Anova

  • Differences between data sets
  • Correlation analysis
  • Analysis of variance (ANOVA)
  • Mini-Case studies
  • Overall review of concepts learned and how they can be applied in practice

Who should attend

The training course has been designed for professionals whose jobs involve 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 training course involves extensive computer-based data analysis using Excel 2010 and therefore delegates will be expected to be numerate and to enjoy working with numerical data on a computer.

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