Difference between revisions of "User:Hdaguinsin"
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• Data science bootcamp | • Data science bootcamp | ||
| − | • Pandas, data cleaning , machine learning techniques | + | • Pandas, data cleaning, machine learning techniques |
• Data visualization with iris and employment data | • Data visualization with iris and employment data | ||
| Line 56: | Line 56: | ||
'''Week 4''' | '''Week 4''' | ||
| − | • | + | • Data exploration |
| − | • | + | • Received the data sets |
| + | |||
| + | • described samples, data exploration | ||
• investigate patterns of correlation | • investigate patterns of correlation | ||
| − | • | + | • Fill null spaces with averages (ended up not using) |
| − | • | + | • Compare the cohort’s data to national avg |
| − | • | + | • Different bc cohort has 85th percentile of body fat |
| − | • | + | • proposed making a health/fitness score |
| − | • | + | '''Week 5''' |
| + | |||
| + | • Creation of the composite file, necessary so all information for each subject is in one location. | ||
| + | |||
| + | • Compiled data across 20 files | ||
| + | |||
| + | • Worked to compile data in R | ||
| + | |||
| + | • Proved difficult as data was organized differently | ||
| + | |||
| + | • Created a G file which contained all of the children's fitness test data | ||
| + | |||
| + | '''Week 6''' | ||
| + | |||
| + | • Switched to compile data manually in Excel. | ||
| + | |||
| + | • Learned about data management and organization. | ||
| + | |||
| + | • I used a combination of R and Excel to clean the data and merge files. | ||
| + | |||
| + | • I created a step by step instruction manual detailing the steps I took so that future students could replicate my process. | ||
| + | |||
| + | |||
| + | '''Week 7''' | ||
| + | |||
| + | • Created a consort flow diagram for the program | ||
| + | |||
| + | • Created table 1 with baseline measurements of subjects in study | ||
| + | |||
| + | • Wrote out a data dictionary for the composite file for swift application in future data collection and analysis. | ||
| + | |||
| + | • Performed hypothesis generating exercises. | ||
| + | |||
| + | • Began to prepare a presentation for the team. | ||
| + | |||
| + | '''Week 8''' | ||
| + | |||
| + | • Presented my progress to the team. | ||
| + | |||
| + | • Received valuable feedback and centering for final analyses. | ||
| + | |||
| + | • Wrote out a data dictionary for the composite file for swift application in future data collection and analysis. | ||
| + | |||
| + | • Manipulated the composite file for future use. | ||
| + | |||
| + | • Mapped out analysis for the fitabase fitbit data. | ||
| + | |||
| + | '''Week 9''' | ||
| + | |||
| + | • Converted child BMI to BMI percentile using CDC standards, as regular BMI does not apply to children. | ||
| + | |||
| + | • Compared fit children BMI and change to control. | ||
| + | |||
| + | • Created various graphs to ascertain the best method of data visualization and communication. | ||
| + | |||
| + | • Cleaning and pre-processing of the fitabase file. | ||
| + | |||
| + | '''Week 10''' | ||
| + | |||
| + | • Finished the pre-processing the fitabase file so it only includes subjects with sufficient data organized into pre and post program. | ||
| + | |||
| + | • Created graphs quantifying fitabase data. | ||
| + | |||
| + | • Created a poster. | ||
| − | • | + | • Presented poster and powerpoint to the REU group as well as other REU programs. |
| − | • | + | • Wrote the scientific paper |
Latest revision as of 16:05, 5 August 2020
About Me
My name is Hannah Daguinsin. I am a rising junior at Xavier University where I am majoring in Computer Science and Biology and minoring in Chemistry.
Work Log
Week 1
• Orientation
• Data science bootcamp
• Pandas, data cleaning, machine learning techniques
• Data visualization with iris and employment data
• Read research papers
Week 2
• Completed citi training
• Ethics and research workshop with Dr. Brylow
• Attended technical writing seminar
• Read research papers
Week 3
• Meet with team
• State primary research hypotheses
• Ho: No change in cohort sedentary activity after the program
• HA: Decrease in cohort sedentary activity due to program
• Ho: No change in cohort MPA ( moderate-intensity physical activity) after the program
• HA: Increase in cohort mpa due to program
• Data elements: cohort sedentary activity data from T1 and T6, cohort mpa data from T1 and T6.
• Treatment- cohort
• Control- the control group, height weight BMI, no fitbit data
• Statistical tests: paired T-test, regression analysis
Week 4
• Data exploration
• Received the data sets
• described samples, data exploration
• investigate patterns of correlation
• Fill null spaces with averages (ended up not using)
• Compare the cohort’s data to national avg
• Different bc cohort has 85th percentile of body fat
• proposed making a health/fitness score
Week 5
• Creation of the composite file, necessary so all information for each subject is in one location.
• Compiled data across 20 files
• Worked to compile data in R
• Proved difficult as data was organized differently
• Created a G file which contained all of the children's fitness test data
Week 6
• Switched to compile data manually in Excel.
• Learned about data management and organization.
• I used a combination of R and Excel to clean the data and merge files.
• I created a step by step instruction manual detailing the steps I took so that future students could replicate my process.
Week 7
• Created a consort flow diagram for the program
• Created table 1 with baseline measurements of subjects in study
• Wrote out a data dictionary for the composite file for swift application in future data collection and analysis.
• Performed hypothesis generating exercises.
• Began to prepare a presentation for the team.
Week 8
• Presented my progress to the team.
• Received valuable feedback and centering for final analyses.
• Wrote out a data dictionary for the composite file for swift application in future data collection and analysis.
• Manipulated the composite file for future use.
• Mapped out analysis for the fitabase fitbit data.
Week 9
• Converted child BMI to BMI percentile using CDC standards, as regular BMI does not apply to children.
• Compared fit children BMI and change to control.
• Created various graphs to ascertain the best method of data visualization and communication.
• Cleaning and pre-processing of the fitabase file.
Week 10
• Finished the pre-processing the fitabase file so it only includes subjects with sufficient data organized into pre and post program.
• Created graphs quantifying fitabase data.
• Created a poster.
• Presented poster and powerpoint to the REU group as well as other REU programs.
• Wrote the scientific paper
