Advanced Data Analysis Techniques

About the Course

We live through the Age of Data Analysis, Information and Statistics and the challenging times of tectonic shifts within geopolitical realms. Today, data is no longer scarce – it’s overpowering and can help avoid mistakes and pitfalls. The main focus is going through the overwhelming volume of data available to organisations and businesses and correctly interpreting its implications. However, for organisations and employees to sort through all this information, data and its relations, there is a great need for the right statistical data analysis without falling into the chasm of buzzwords and bad data.

As the industry is in its current obsession with Big Data and Data Analytics, the software companies and statisticians have produced a lot of valuable tools and techniques available to large organisations. This Advanced Data Analytics training course shows by example how to build on the methods learned and to create a variety of powerful modelling, simulation and predictive analytical methods.

The methods introduced include Bayesian models, Newtonian and genetic optimisation methods, Monte Carlo simulation, Markov models, advanced What If analysis, Time Series models, Linear Programming, and more, as well as the introduction and use of several available software for data analytics, from the basic ones to the very complex and advanced ones.

Core Objectives

Delegates will achieve the following objectives:

• Use measurement systems for data gathering
• Adequately prepare and optimise the process designs
• Conduct accuracy tests for error resolution within the data gathered
• Develop data-gathering plan
• Apply statistical methods for statistical experiment designs

Training Approach

This training course will utilise various proven adult learning techniques to ensure maximum understanding, comprehension and retention of the information presented. This includes theoretical presentation of the concepts, actual implementation of statistical tools and techniques, setting up and conducting the experiment and verifying and interpreting results. The delegates will use software for data visualisation, analysis and simulation.

The Attendees

This training course is dedicated to the professionals working within data analytics in every industry using the data for process optimisation, systems improvement and experiments design. It also describes the sensors used and the methods to test and improve the accuracy of statistical measurement.

This training course will be valuable to professionals, including (but not limited to) the following:

• Process Engineers
• Data Scientists
• Project Managers
• Anyone involved in the digitalisation of operations

Daily Discussion

DAY ONE: MEASUREMENT SYSTEMS FOR DATA COLLECTION

• Signal Gathering, Processing and Control
• Measurement System Calibration
• Uncertainty Analysis in Measurement Systems
• Signal Statistical Parameters
• Digital Signal Measurement and Analysis
• Removing the Noise and Bias from the Data

DAY TWO: BASIC STATISTICS AND PROBABILITY CONCEPTS

• Sets, Union, and Intersection
• Probability Density Functions
• Data rejection, single and paired variable outlier determination
• Chi-Squared Distribution
• Hypothesis Testing

DAY THREE: CORRELATION AND REGRESSION

• Correlation and Causation
• Least Squares Regression Analysis
• Linear Regression
• Regression Confidence Analysis
• Determining Autocorrelation
• Determining Cross-correlation

DAY FOUR: STATISTICS FOR PROCESS CONTROL

• Statistical Process Control (SPC) Charts
• Statistics for Process Capability Analysis
• Identifying Sources of Error
  • Systematic Errors
  • Random Errors
• The calculation for the estimate of the combined uncertainties
• Experiment Design

DAY FIVE: DATA ANALYSIS FOR MANAGERIAL DECISION MAKING

• Capacity, Utilisation, and Bottlenecks
• The Dangers of Process Variability
• Data Analysis Use
• Clustering
• Predictive and Prescriptive Analytics