Data Science: Systems Analysis

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There have been many useful topics that were covered in Learning design and technology. As a major in Data Science three topics that stood out the most were systems thinking, digital divide, and E-learning in the future. I enjoyed the entire concept of system theory since it allows individuals to analyze the overall picture in order to find the most optimal, innovative, and efficient methods. As a Data scientist, using systems thinking is absolutely crucial, since analyst must be able to work with large datasets and make interpretations. There are many characteristics of an effective system thinking approach, it should consist of a plan that analyzes the efficiency measurements of inputs and outputs of the overall process. The system approach …show more content…

A major component of Data science is working with statistical models, such as machine learning algorithms in order to transform the data into useful information. The systems thinking the approach will improve machine learning models, and expand applications for big data. A primary example of how this is being implemented in a real-world situation is the deer population in Yellowstone. Park rangers collect were able to collect detailed data about the development of the ecosystem by analyzing the history of the park. Park rangers noticed that the grassland in the park was being destroyed, due to a growing deer population. Therefore by using the data collected and applying a systems thinking approach, they were able to come up with a strategy of introducing wolves into the park to tackle to solve this problem. Ultimately, the introduction of the wolves was able to save the grassland in the park. Systems thinking can be used in various industries, such as having engineers use systems thinking to make improvements, such as analyzing and finding the most efficient ratio between fuel input and output motion. System thinking can be one of the essential tools for various …show more content…

This can be done through the use of multiple variable linear regressions in Minitab. The age and gender would be the independent variable, while the height would be the dependent variable. Then through Minitab, the user can perform an analysis to determine if the two independent variables are effective predictors, by checking the p-value. I enjoyed working with data sets in statistics and I plan to get as much exposure to data mining software as possible in my career path. Some statistical analysis software like Tableau allows users to transform raw data into actionable insights that can make an impact on society, through the use of creating interactive visual analytics. The industry for Data Science is highly competitive, most students tend to have masters or even Ph.D. degrees, as opposed to a bachelor's degree. As mentioned previously there have been many beneficial topics covered in LDT, that have positive impact my method of thinking in my personal and future career path. Some those topics include systems thinking, digital divide, and E-learning in the future. Not only did the topics covered in LDT provide value, they made me become aware of how I can implement them in my career path and impact humanity in a positive

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