Info-Graphics: Data Analysis and Reporting Technique


  • 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

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.


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



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

DAY 2                                         

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

DAY 3                            

Scenario Analysis, Confidence and Six Sigma

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


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 

DAY 5                                  

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


A Certificate of Completion will be issued to those who attend & successfully complete the programme.


  08:30 – 10:15 First Session

 10:15 – 10:30 Coffee Break

10:30 – 12:15 Second Session

 12:15 – 12:30 Coffee Break

12:30 – 14:00 Third Session

 14:00 – 15:00 Lunch

Training Methodology:

This interactive training course includes the following training methodologies as a percentage of the total tuition hours:

  • 30% Lectures, Concepts, Role Play
  • 20% Workshops & Work Presentations, Techniques
  • 20% Based on Case Studies & Practical Exercises
  • 10% Videos, Software & General Discussions
  • 20% Application
  • Pre and Post Test


 The Fee for the seminar, including instruction materials, documentation, lunch, coffee/tea breaks & snack is: