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.
The Data Analysis Techniques training course shows delegates how to use Microsoft Excel to perform a wide range of powerful analysis and interpretations, using real data. This is possible because, although most people are familiar with Microsoft Excel, they generally only use a small fraction of its true capability. This training course will show delegates how to make the most of Excel by explaining and demonstrating many of the very powerful analytical, visualization and interpretation capabilities that it is capable of.
This Energy Training Centre training course will highlight how to:
- Approach a data analysis problem in a logical manner
- Maximize the accuracy of analysis and minimize the chance of errors
- Extract and represent information from complex numerical data
- Interpret features from within data and make evidence-based decisions
- Perform a ‘what if’ scenario analysis
- Predict the future behavior of a system or a process using regression analysis
- Predict the future behavior of a system or a process, using data science methods
- Use analysis of variance (ANOVA) to minimize the risk of false interpretation
- Make the most of Microsoft Excel by using many of the advanced features that it contains
This 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 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 training course is entirely hands-on, with delegates spending all of their time working in Microsoft Excel to solve the problems posed.
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.
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.
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 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.
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
