This week, I am working to get the dynamic time warping functionality into my program. The process of doing so includes re-processing the features to include the time series, putting each series back together when we construct sequences, and then performing the DTW to generate a number that will be used to compute the kNN of each sequence which can then be used for predictions with the models. The processing time of these activities has gone up significantly since we have been using five different metrics with each of the F phase datasets. I am returning to school next week, and once I've completed the DTW processing all that will remain before we put together our second paper (The date for the reach journal we would like to submit it to is October 1), I am hoping I will have time to look again into the Agglomerative Hierarchical Clustering concept, which I did not successfully complete when we explored it earlier in the summer and then changed focus to the paper. We heard back
I will summarize my goal regarding my preparation for our research project with the following objectives:
1. to obtain a broad, working understanding of Python
2. to learn how to best utilize Python libraries and tools specific to data analysis
3. to become a helpful member of the student research team at St. Luke's Rehabilitation Institute, where our data collection will occur
My Python background is very limited. Prior to my current year of school, I had only taken a single class that involved it. In that class, we focused solely on simple math calculations and basic graphing functions. In the two years since then, my knowledge of the language shrank down to almost nothing. My memories of it, however unspecific, are fond ones.
To accomplish my first task, I started working through Learn Python the Hard Way, by Zed Shaw. The book was designed to be intelligible to anyone, regardless of previous coding experience, so there was quite a bit that I was able to move over once I began to get the hang of syntax and structure. Once I felt that I had a grip on the basics, I was able to begin working on a selection of online course materials provided by my mentor, Dr. Gina Sprint. These lectures and activities allowed me to observe and practice object-oriented programming in Python. They also introduced me to Jupyter Notebook, which I will certainly use going forward.
For my second objective, I moved into the provided coursework’s sections that were specific to data analysis. The central topics of the material were the NumPy and Pandas libraries. The biggest takeaway was a general idea of the Pandas DataFrame. Dr. Sprint explained at our check-in meeting that this was the data structure we’d be using for the majority of our analysis. I do not believe that I have reached a point of ‘accomplishment’ in this area, as there is a great deal I do not know about DataFrames. Rather, I think this will be a skillset I can build upon as we progress through the different stages of research.
1. to obtain a broad, working understanding of Python
2. to learn how to best utilize Python libraries and tools specific to data analysis
3. to become a helpful member of the student research team at St. Luke's Rehabilitation Institute, where our data collection will occur
My Python background is very limited. Prior to my current year of school, I had only taken a single class that involved it. In that class, we focused solely on simple math calculations and basic graphing functions. In the two years since then, my knowledge of the language shrank down to almost nothing. My memories of it, however unspecific, are fond ones.
To accomplish my first task, I started working through Learn Python the Hard Way, by Zed Shaw. The book was designed to be intelligible to anyone, regardless of previous coding experience, so there was quite a bit that I was able to move over once I began to get the hang of syntax and structure. Once I felt that I had a grip on the basics, I was able to begin working on a selection of online course materials provided by my mentor, Dr. Gina Sprint. These lectures and activities allowed me to observe and practice object-oriented programming in Python. They also introduced me to Jupyter Notebook, which I will certainly use going forward.
For my second objective, I moved into the provided coursework’s sections that were specific to data analysis. The central topics of the material were the NumPy and Pandas libraries. The biggest takeaway was a general idea of the Pandas DataFrame. Dr. Sprint explained at our check-in meeting that this was the data structure we’d be using for the majority of our analysis. I do not believe that I have reached a point of ‘accomplishment’ in this area, as there is a great deal I do not know about DataFrames. Rather, I think this will be a skillset I can build upon as we progress through the different stages of research.
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