Competency F

Nearly 2.5 quintillion bytes of data are produced by Internet users every day, making big data an extremely valuable commodity for many different industries (Petrov, 2020). Analyzing data can provide insights into user behavior and trends, and informatics professionals will often conduct informatics analyses on big data sources to extract these insights. Data analysis is useful in a wide variety of fields and creating data visualizations is an excellent way to make large data sets easier to understand (Heitzman, 2019).

I’ve had the opportunity to learn about different data analytics techniques in this program and apply these analyses to real-world data sets in the health science field. In INFM 213, I learned how to process epidemiology data sets, write analytical code, and apply statistical operations to the results using an analytics software called SAS. During this course, I created a project that used data from the National Health and Nutrition Examination Survey (NHANES) to determine the relationship between diabetes and depression. I processed the data in SAS and wrote my own code in the SAS programming language to analyze the dataset. In INFM 203, I learned how to wrangle and process large datasets using both Python and Hadoop. My project for this course used datasets from the California Health and Human Services Open Data Portal (CHHS) to determine possible patterns in kindergarten immunizations throughout the state. I used Python and Jupyter notebook to process the data, record my findings, and created a presentation to discuss my results with my classmates. Both of these projects helped me better understand how to conduct informatics analyses on datasets and create visualizations to summarize the results.

Big data is growing quickly due to advances in technology and has become a valuable commodity for many different industries. In order to make data useful, information professionals must understand how to analyze large data sets and summarize their findings through visualizations. Many different fields use information analysis to better understand their users’ behaviors and capture patterns and trends in the data. The health science field has harnessed data analysis for a wide variety of uses, including public health research, early disease detection, and population health trends. In INFM 213, I created an epidemiology project that analyzed the NHANES dataset with statistical methods to find the relationship between diabetes and depression. Additionally, I summarized my findings using data visualizations, which made the large dataset easier to understand. I also learned about the information analysis process in INFM 203 and used a dataset in the CHHS Open Data Portal to find patterns in kindergarten vaccination in the state of California. Both of these projects taught me how to analyze data in different ways, and I also learned how to use two different programming languages to process the data. These experiences have helped me expand my knowledge about the process and benefits of data analysis and will help me create better solutions for users in the future.