Data Analysis Techniques

What are the Goals?

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
• The ability to use Microsoft Excel 2016, 2019 or 365 to analyze and interpret a wide range of real data types
• Experience of how to transform numerical data into evidence and hence how to make informed business decisions

Who is this Training Course for?

This Data Analysis Techniques training course has been designed for any professional whose job involves the manipulation, analysis, representation and interpretation of data, including the subsequent evidence-based business decision making process.

Familiarity with a PC and in particular with Microsoft Excel (2013 or higher) is assumed, and delegates will be expected to be numerate and to enjoy working with numerical data on a computer.

Daily Agenda

Day One – 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 Two – 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 Three – Scenario Analysis and Interactive Spreadsheets

• Representing analytical problems as multi-input, single-output (MISO) systems
• 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 Four – 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
• Goodness of fit (sum of square error – SSE) and R2
• Evaluating equations, solving equations, and using Solver
• Correlation and causality

Day Five – Data Driven Methods and Analysis of Variance

• Non-deterministic system
• Data driven methods
• One step ahead future prediction using data science (multivariate correlation)
• Single factor analysis of variance (ANOVA)
• Two factor analysis of variance
• A demonstration of artificial intelligence – the travelling salesman problem