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Data analysis has been seeping into software engineering for some time now thanks to the technological advances being developed worldwide. This discipline is a key player in the digital advances we’re seeing as a human race. Things like computer programs and businesses are taking advantage of data analysis as it helps us see what areas in our lives can be improved. As more data analysis software is developed, more solutions with ease of use will be available to the public in different industries.
Data Analysis Process
In software engineering and other trades, data analysis takes the same shape, although with different topics. For instance, a software engineer may have to analyze data that pertains to human behavior in order to purposefully code their programs. Likewise, a scientist can analyze the data gathered from an experiment to understand which chemical components or molecules work better together. There are infinite possibilities when it comes to data analysis.
Here is a guide to data analysis in software engineering.
Identify a business problem.
At the beginning of the process, a software engineer will most likely already have a problem they’d like to solve, whether that’s world hunger, homelessness, or boredom. The engineer will meet with multiple people to plan out how the problem will be solved. They will also survey the public to see if their solution is viable. In the case the engineer works for another company and not their own, they may have to contact clients and other executive personnel to determine goals, costs, labor efforts, and deadlines.
Collect data to help solve the problem.
Once the problem is identified, the engineer moves on to the next step, which is collecting data to solve said problem. When determining how to improve school lunch budgeting, the engineer and their team will gather the names of the schools in their areas of interest along with the head count of each school and the contact information for the establishment. Their software solution could help schools order bulk food ingredients from entities that want to participate in the initiative.
Clean the data for analysis.
Next, the engineers will clean the data and analyze it to ensure they have the right information that will help them develop their software solution. They will eliminate from the list any school that doesn’t meet their requirements and begin sorting the schools that do qualify into their databases. They can use this clean information to begin planning their budgets and labor as well as their milestones.
Analyze the data by manipulating it.
Once they have collected the data, they will apply certain parameters to it to see how they will develop their solution. For instance, they can sort the data by school names, locations, budgets, and any other way that seems appropriate for their project. They can also visualize the data by creating graphs or charts with it to spot any trends or patterns.
For instance, they can notice trends like certain schools in an area don’t have a budget for lunch by the end of the third quarter of the school year. This can help them make shopping plans within their software so the school administrators can better plan their grocery shopping lists and agendas.
Interpret the results of the analysis.
Lastly, they can draw conclusions about the data they have gathered. The engineers and developers can finally build various features into their analytic software that will provide key insight and help schools order food for their students, depending on their findings. They can create shopping lists and favorites and incorporate messaging APIs and other things that will enhance their overall software product. At this point, they will present their feature ideas and determine if they have everything they need to start coding their solution.
