Info-Graphics: Data Analysis and Reporting Techniques

Info-Graphics: Data Analysis and Reporting Techniques

INTRODUCTION

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.

PROGRAM OBJECTIVES

• 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?

• Project management 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.
• This 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.

PROGRAM OUTLINE

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 

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

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                       

Scenario Analysis, Confidence and Six Sigma

• Modeling Scenario
• Interactive Spreadsheets
• Confidence Intervals
• Control Charts
• An Introduction to Six Sigma
• Error Bars

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
  

Correlation Analysis and Anova                       

• Differences between Data Sets
• Correlation Analysis
• Analysis Of Variance (ANOVA)
• Overall Review of Concepts Learned and How they can be applied in Practice

Permintaan Brosur penawaran Training

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