Data Analysis in Engineering
Data analysis in engineering involves the systematic process of inspecting, cleaning, transforming, and modeling data to extract useful information and make informed decisions. It encompasses various statistical and computational techniques to interpret and analyze complex engineering data sets, enabling engineers to identify patterns, trends, and insights that drive improvements in design, performance, and decision-making processes.
3 Key excerpts on "Data Analysis in Engineering"
eBook - ePubThe Handbook for Market Research for Life Sciences Companies
Finding the Answers You Need to Understand Your Market
- Jean-Francois Denault(Author)
- 2017(Publication Date)
- Productivity Press(Publisher)
...Chapter 4 Analyzing Data Data analysis is the process of evaluating, transforming, classifying, and modeling data. The current chapter will specifically deal with the evaluation to classification process, while presentation models will be discussed in depth in Chapter 5. Before diving into analysis, it’s important to note that the market research process can be iterative. It is possible to “jump” back and forth between the collection and analysis of data. For example, during initial data analysis, it is possible to find out that some new research areas or some of your data is insufficient, and a new data collection effort is necessary. The researcher should account for this possibility. Case in point, a while back, a client had done a web survey to identify some trends related to salary and incentives in healthcare. He had collected an impressive sample (over 3000 entries), and I was brought in to analyze the data. Nonetheless, during the initial analysis of the data sample, the demographic data revealed that the overall weight of U.S. East Coast respondents was too large compared with the other U.S. geographic regions. As such, my client had to proceed with a second data collection effort, this time focusing on the other regions, if he wanted to obtain a representative sample. In this chapter, we will provide an overview of data analysis. Our objective is not to turn the reader into a statistical analyst guru, but rather to give him a better sense of how to read the data he has collected, and some tools to better understand it. As such, we will be going over the basic elements of data clean up, followed by some words on quantitative and qualitative data analysis, and closing with some barriers to effective data analysis. On the importance of your own data analysis … We’re impressed with what some entrepreneurs are able to find using a limited amount of resources...
eBook - ePub- David Crowther, Geoff Lancaster(Authors)
- 2012(Publication Date)
- Routledge(Publisher)
...Information, remember, is data which is in a form which can be used for explanation, or more specifically in the context of this book, for decision making. Four key roles for analysis in this respect involve the processes of ‘distillation,’ ‘classification,’‘identification,’ and ‘communication.’ Distillation Most research/consultancy exercises often result in huge amounts of data. Neither the researcher nor the client wants to be faced with a mass of data with the ensuing need to sift through it and try and establish what it all means. A key purpose of analysis, therefore, is to distil potentially large amounts of data into forms that are more readily managed and absorbed, and also discard data that is not appropriate in the context of the research project. At its simplest, this distillation will take the form of summarizing data using, for example, tables, diagrams, or may, alternatively, and in addition, summarize and distil data numerically through measures such as average dispersion, standard deviation, and so on. Failure to distil data effectively is one of the most frequent reasons for failures to understand and implement research findings. Classification Related to the above, data analysis should also help to classify data. This involves the grouping of data into categories that allow the researcher and manager to quickly see what factors are involved and potentially what the data means. Classifying data helps to encourage the development of order from chaos. Identification Much data analysis is concerned with establishing causes and/or relationships between factors. Data analysis enables these relationships, and particularly causal relationships to be identified (Krzanowski 1988). Communication The final purpose for analysis involves the important aspect of communicating research findings. It is very difficult to communicate raw data, either to managers in an organization, or to other researchers in a field of inquiry...
eBook - ePubEngineering Analytics
Advances in Research and Applications
- Luis Rabelo, Edgar Gutierrez-Franco, Alfonso Sarmiento, Christopher Mejía-Argueta(Authors)
- 2021(Publication Date)
- CRC Press(Publisher)
...Analytics is a series of processes, tools, and techniques that help to understand the business. Businesses can analyze historical information and current trends and make decisions with greater strategic agility. With this process, valuable business knowledge is obtained for decision makers. At the University of La Sabana in Colombia (www.unisabana.edu.co/), these data analysis techniques and processes are used to analyze millions of records. For example, data is taken from state tests to know how this institution is performing in each test. The results are compared over time to see their evolution, and they are contrasted between academic programs and between accredited universities that are references for this institution. They also analyze at a granular level, with students being the smallest grain available. This granular level allows other types of analysis to be carried out, combining statistical concepts that establish relationships with variables collected when entering the university and each student’s academic development. Some academic success factors can be determined, understood as a good result in the state test that evaluates professional skills. Analytical reports relying on these data analysis tools are built. The reports are shared with the different stakeholders such as government officials, deans, unit directors, program directors, professors, and students. This process helps to know precisely the strengths and weaknesses, and of course, to establish plans of action. The analytical tools developed by the project team make it easier to identify patterns, trends, and relationships between the variables analyzed. All this helps to make informed decisions to validate the curriculum, identifying possible opportunities for improvement. The results, in turn, contribute to the academic reputation of higher education institutions...
