For today’s blog assignment, we are supposed to write about something that we did not cover on the course. It took me a while to think about what I could possibly add since the data science course materials are truly comprehensive. So I thought it would be useful to provide more Pandas references on accessing data, saving data and the pandas.cut data manipulation example that we may not have covered in the sections in detail.
Eight weeks have passed since I started the self-paced data science program and the sections I have found challenging so far are the ones about pandas. I think I spent more than two weeks on those sections trying to learn the syntax for data analysis and manipulation, until I came across the Data Science Career Student Resource on Pandas Recipe for Success with links to tutorial videos. I am a visual learner and I found it really helpful to build up the muscle memory for python pandas by coding along and spending time practicing DataFrames manipulations and performing operations. Part of the reason I think that I spent more time learning pandas is because I currently have an issue at work where I still need to deal with flat file interfaces, and I wanted to explore time-saving tools to help with data analysis across flat files, table outputs from SQL, and cache databases. After learning pandas DataFrames, here are a few tips on how pandas has helped me to be more organized and to speed up my work.
The choice of visualization tool varies depending on the problem being addressed and the specific needs of each individual project. If the project involves measuring a process that includes a small sample size of continuous data types for the purpose of displaying the frequency of the distribution of data and estimate where most of the values tend to occur, and data patterns (shape), then a histogram would be the visualization tool of choice.
Hi everyone. I am a Registered Nurse working as a Clinical Informatics Specialist at a large Health System in the Northeast. Computers, information science, and their applications in the healthcare setting have always fascinated me. Before there were electronic medical records at my very first job, I remember having yellow post-it notes on the front of patient chart binders to remind the nurses when the patient’s next assessment was due and the other tasks associated for continuity of service. These post-its were taped onto the chart binder and would always fall off the charts. I was frustrated and thought that there must be a better way to track and report this administrative information. I taught myself MS Excel to organize my patient’s data and be able to track patient visits, provider information, immunizations, and other details to speed up administrative work and reporting to give me more time for the more important task, which is direct patient care. Later, my spreadsheets were being imported by the IT Department into the financial system (clinical systems were still not implemented at that time) to speed up billing.