INTRODUCTION

Everyone agrees that evidence-based decision making is the key to corporate high performance. In a world where everything is now measured and recorded, the data that companies hold can be a very rich and powerful source of evidence, but only if it can be analyzed and interpreted accurately and reliably. This Data Analysis Techniques training course shows delegates, by example, how to analyze and interpret data and hence how to make robust and defensible evidence-based business decisions.

Objectives

This Data Analysis Techniques training course aims to provide those involved in analyzing numerical data with the understanding and practical capabilities needed to convert data into information via appropriate analysis, how to represent these results in ways that can be readily communicated to others in the organization, and how to use the information to make evidence

based business decisions.

At the end of this Data Analysis Techniques training course, you will have:

A good understanding and extensive practical experience of a range of common analytical techniques and interpretation methods for numerical data

The ability to recognize which types of analysis are best suited to particular types of problems

The ability to judge when an applied technique will likely lead to incorrect conclusions

A good understanding of a wide range of common statistical methods and approaches

Course Outline

Day 1

Logical and Reliable Data Analysis, Descriptive Statistics, and Pivot Tables

Importing data into Excel

Best practice when analyzing data

Analyzing and representing coded data

Descriptive statistics and their real meanings

Performing a frequency analysis

The use of pivot tables and pivot charts

Noisy and incomplete data, statistical significance and dealing with outliers

Day 2

Data Mode Shape Analysis

Plotting data against time

Generating data mode shapes

Fitting curves to data

Correlating mode shape to time-based events

Interpreting time series analyses

Moving average calculations

Day 3

Scenario Analysis and Interactive Spreadsheets

Deterministic systems analysis

What if and visual scenario analysis

Dynamic / interactive spreadsheets and the use of forms control

Moving window, conditional and adaptive calculations

Measuring the sensitivity of calculated variables

Day 4

Regression Analysis and Correlation

Equations of curves

The prediction of future behavior using data shape – regression analysis

Linear, polynomial, exponential and power curve fits

The dangers of over-fitting

Data end effects

Correlation and causality

Day 5

Data Driven Methods and Analysis of Variance

Non-deterministic system

Data driven methods

One step ahead future prediction using data science (multivariate correlation)

Two factor analysis of variance