Advanced-Data Analysis Techniques

Introduction

The Statistical Analysis of Numerical Information is demonstrated to be a powerful tool for providing everyday insight into topics such as corporate finance, manufacturing processes, and quality control.

However, with the rise of the Internet of Things, significant growth in Big Data, and ever-increasing demands to model and predict, many of the analytical possibilities and needs of a modern, high-performing company cannot be met using traditional statistical methods alone.

More and more businesses are dealing with complex modeling and simulation challenges, such as analyzing to optimize production systems, maximizing fulfillment performance, reducing operating costs, resisting risk, detecting fraud, and forecasting future performance and outcomes.

Prerequisite

This Advanced Data Analysis Techniques Analytics training course is intended for participants who have previously attended the course (this is a fundamental prerequisite for this training) and thus have a solid understanding of traditional data analysis techniques.

This Analytics training course demonstrates how to build on the method learned in the Data Analysis Techniques training seminar to create a variety of powerful modeling, simulation, and predictive analytical methods by using examples.

Bayesian models, Newtonian and genetic optimization approaches, Monte Carlo simulation, Markov models, advanced what If review, Time Series models, Linear Programming, and other methods are covered.

This Analytics training course on Advanced Data Analysis Techniques utilizes advanced features of Microsoft Excel throughout, and all participants must be fully competent in both Excel and the statistical analysis of data.

Course Objectives of Advanced Data Analysis Techniques

This Analytics training course on purposes to give those required in explaining numerical data with the knowledge and practical capabilities required to convert data into meaningful information via the usage of a range of very powerful modeling, simulation, and predictive analytical techniques. 

The specific objectives are as follows:

• To instruct delegates how to explain a wide range of business difficulties which demand modeling, simulation, and predictive analytical approaches
• To explain delegates how to perform a wide range of the more common modeling, simulation and predictive analytical methods using Microsoft Excel 2010 (or higher) and in particular the Solver tool
• To provide delegates with both a conceptual understanding and practical experience of a range of the more common modeling, simulation and predictive analytical techniques, including Bayesian models, conventional and genetic optimization methods, Monte Carlo models, Markov models, What If analysis, Time Series models, Linear Programming, and more
• To give delegates the ability to recognize which modeling, simulation, and imminent analysis arrangements are best satisfied to which sorts of problems
• To give participants adequate background and professional experience to be capable to judge when a utilized technique will likely lead to incorrect conclusions
• To implement a clear knowledge of why the best companies in the world see modeling, simulation, and predictive analytics as being imperative to delivering the right quality products and optimized services at the lowest probable costs

Course Methodology of Advanced Data Analysis Techniques

This Analytics training course on Adopts a problem‐based training program, in which participants are performed with a group of real problems described from the widest possible range of applications – they vary from insurance to supply chain logistics, from chemistry to engineering, and from production optimization to financial risk assessment. Each problem presents and exemplifies the need for a different modeling or analytical method.

This training course is entirely applications‐oriented, reducing the time used on the theory and mathematics of analysis and maximizing the time wasted on the use of practical methods from within Excel, along with the understanding of how and why such practices work.

participants will consume almost all of their time investigating the use of modeling and simulation techniques using Microsoft Excel, to promote solutions to the true problems that are conferred.

Organizational Impact of Advanced Data Analysis Techniques

Businesses that can make excellent arrangements, and can certainly predict future trends and behaviors, can enhance considerably their capability to compete on the global stage.

As a result of sending their employees on this Analytics training course, businesses can expect to profit from:

• A team from intuition-based to information-based judgment making
• The preparation of accurate solutions to difficult problems
• Enhanced forecasting and future role prediction
• Superior modeling and simulation of business manners
• More intelligent risk estimation and risk-informed decision making
• Enhanced capitalization on the wealth of information comprised in Big Data

Personal Impact of Advanced Data Analysis Techniques

Participants will each increase inclusive understanding and many of user experiences of a wide range of the more common modeling, simulation, and sinister analytic techniques.

 All of these will have direct importance to a wide range of business matters, specifically, participants will acquire:

• New penetrations into the use of optimization, modeling, and foresight using Microsoft Excel
• Knowledge of Linear Programming
• A judgment of how and when to use Newtonian and Genetic Optimization Approaches
•  Awareness of Scenario Analysis, Markov Modeling, and Monte Carlo Simulation
• The capability to identify which types of analysis are relevant to particular kinds of problems
• Adequate situational awareness to assess when a technique will manage to incorrect results