About Me
My career journey has taken me from managing data as a Database Administrator to analyzing it as a Lab Analyst, where I specialized in report generation and statistical summaries.This experience has fueled my passion for data, and I'm now actively pursuing a career in data analytics.I'm eager to apply my unique blend of technical and analytical skills to solve complex business challenges and contribute to data-driven strategies.
Skills
Tableau | Power Bi | AWS | R | Python | Excel
Data Visualization
Medical Data Analytics
Team & Work Management
Report Analytics
Featured Projects
R / Python [Building]
Coursera Bellabeat capstone project
Creating analysis process to deliver a marketing strategy, and Recommendation based on given Analysis parameters.
Certifications
On Progress
Coursera Bellabeat capstone project
By analyzing Bellabeat data to understand the relationship between calorie burn and various factors like activity, distance, time, and weight.
Final Report
GithubThe report explores the factors influencing calorie expenditure using a publicly available Fitbit dataset related to Bellabeat. The analysis focuses on daily and hourly activity data, distance, time,sleep records, and weight logs. The primary goal is to identify the most influential factors in calorie burn and emphasize the importance of calorie tracking for health management. The data is cleaned, transformed, and merged into a final table for analysis. Visualizations are used to explore the relationships between calorie burn and distance, activity duration, steps, sleep, and weight. The report concludes by highlighting the importance of calorie management for health and provides links to external resources for further reading.Potential Questions and Answers (Based on the Report):1. What is the main objective of this study?Answer: To investigate the relationship between calorie burn and various activity, sleep, distance, time,and weight-related parameters collected by a fitness tracker. The goal is to understand which factors are most influential in calorie expenditure and emphasize the importance of calorie tracking for health.2. What data sources were used in this analysis?Answer: The analysis uses a publicly available Fitbit dataset. This dataset includes daily activity summaries, daily and hourly calorie estimates, intensity levels, step counts, sleep records, distance, time,and weight logs.3. What were the key steps in the data cleaning and preprocessing stage?Answer: The data cleaning process involved:
Loading the various CSV files.
Checking column names and data integrity.
Converting date columns to the correct date format.
Merging the datasets into a single finaltable based on Id and date.
Removing unnecessary columns (e.g., redundant calorie columns, BMI, etc.).
Saving the cleaned data to a new CSV file. (Note: While the code includes a View() call, it should be removed from a final report as it doesn't render in the output.)4. What were the main visualizations used in the Exploratory Data Analysis (EDA)?Answer: The EDA primarily used scatter plots with smooth trend lines to visualize the relationship between calorie burn and:
Different levels of active distance (Very Active, Moderately Active, Lightly Active, Sedentary Active).
Time spent in different activity levels (Very Active Minutes, Fairly Active Minutes, Lightly Active Minutes, Sedentary Minutes).
Total steps.
Total distance.
Total minutes asleep.
Total time in bed.
Weight.
The relationship between total time in bed and total minutes asleep.5. What were the apparent relationships observed between calorie burn and other variables?Answer: The visualizations (if they were displayed correctly in the knitted output) would likely show positive correlations between calorie burn and:
Active distance (especially very active distance).
Active minutes.
Total steps.
Total distance.The relationship with weight is also likely to be positive, although the magnitude of the effect would depend on the data. Sleep duration might show a more complex relationship, as very little or very much sleep could both be associated with lower calorie expenditure.6. What is the "Act Phase" of the analysis?Answer: The "Act Phase" emphasizes the importance of calorie management for health and provides links to external resources (a book and an NIH article) for further investigation into the benefits of calorie management and its role in reducing the risk of chronic diseases. It's a call to action for the reader to learn more.7. What is a key improvement that could be made to this report?Answer: The include=FALSE in the setup chunk was preventing the visualizations from being shown. Changing this to TRUE (or removing it altogether) is essential. Also, the code chunks in the EDA section should have include=TRUE (or no include at all) for the plots to render. The use of View() in the code chunks is also problematic as it doesn't render in the output. It's good practice to remove these calls from the final report.8. What further analysis could be done?Answer: Several areas could be explored further:
Statistical modeling (e.g., regression) to quantify the relationships between calorie burn and the other variables.
Analysis of hourly activity data to identify peak activity times and their impact on calorie burn.
Segmentation of users based on activity levels or other characteristics.
Consideration of other potentially relevant variables (e.g., age, gender, heart rate data).
A deeper dive into the sleep data and its correlation to activity and weight.