Data Analysis Techniques

This Data Analysis Techniques training course adopts a problem-based learning approach, whereby delegates are presented with a series of highly realistic data analysis problems, which are drawn from the widest possible range of applications – from engineering to finance and from product sales to six sigma quality control.

Each problem has been carefully chosen to present and exemplify a specific data analysis approach. All of the analytical methods are generic, meaning that they can be applied to data from any industry sector, business type or organization.

The Data Analysis Techniques training course is entirely hands-on, with delegates spending all of their time working in Microsoft Excel to solve the problems posed.

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

It should be noted that the course does not cover the subjects of data acquisition, databases, data management, dashboards or the analysis of text-based information.

This 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.

This Gulf Rowad Data Analysis Techniques training course is suitable for a very wide range of professionals from any industry sector, but will greatly benefit people working in the following fields:

• Performance assessment and monitoring
• Planning
• Data analysis
• Management and leadership
• Finance
• Human resources
• Quality control
• Engineering and technology
• …in fact, any job function which uses data to make decisions.

Organisational Impact

Modern corporate ethos demands continual improvements in workplace efficiency and reductions in operating, maintenance, support service and administration costs.

Managers, administrators and analysts must continue to make better use of data to inform the business decision-making process, to identify improvements and efficiencies, and hence to achieve ever more stretching performance targets.

This Data Analysis Techniques training course will provide employees with the tools and understanding to analyze all forms of numerical business data and the ability to make robust, evidence-based decisions from it.

Personal Impact

Delegates will gain experience and hence confidence in how to approach a data analysis problem in a planned and logical manner. They will learn and practice how to extract information from wide ranging forms of numerical data and how to interpret this to make evidence-based decisions.

Delegates will also become much more proficient in the use of Microsoft Excel, including the use of many of its advanced features like Pivot Tables, interactive spreadsheets using Forms Controls, the Data Analysis Tool pack and the inbuilt solving engine called Solver.