Data Analysis Techniques - Oxford Management
data analysis techniques what is data analysis data analysis refers
Seismic Data Analysis Techniques in Hydrocarbon Exploration explains the fundamental concepts and skills used to acquire seismic data in the oil industry and the step-by-step techniques necessary to extract the sections that trap hydrocarbons as well as seismic data interpretation skills.
Quantitative Data Analysis Techniques for Data-Driven Marketing
This module is designed to provide knowledge of the basic data analysis techniques necessary for understanding and analysing transportation related datasets. This includes identification of suitable analysis methods, applications/calculations of appropriate techniques and models (including with the use of statistical analysis software), interpretation of model and statistical test results and presentation of conclusions.
When you understand data analysis techniques, you take a big step towards making product and process improvements. How do you know when you implement a change, that the change was effective?
You can learn these tools and start using them within one hour. The introduction into data analysis video provides basic data analysis techniques. Learn how data links with improvement. Learn the key statistics that drives improvement. Learn how to present data in easy to review formats. Prevent undesired arguments. Help yourself and management to focus on the important measurements. To make you effective, this training video shows you to use Microsoft Excel to quickly conduct data analysis. Now you realized things did not improve. How can you prove to your manager and others that things are not improved? A discussion without data is a wasted conversation. In fact approaching others without data could lead to unwanted arguments. Understanding data analysis techniques avoids he said - she said arguments. Quantitative data analysis attempts to remove researcher bias from gathered data. Heavy use of statistics, probabilities or other mathematical techniques allows individuals to use standard methods for interpreting data. When researchers attempt to use qualitative data analysis techniques — often based on the individual’s personal background, preferences or basic research and reasoning principles — the gathered data may be misread or incorrectly interpreted. Therefore, mathematical techniques are less susceptible to these errors and often more accepted by other individuals or researchers.