Tony's Hypertrophy Study
Timeframe
1 Month
Role
Teacher: Guided client in Excel
Data Analyst: Cleaned data with SQL & Python
Presenter: Presented insights using Tableau
Tools
Microsoft Excel, Adobe Illustrator, SQL Workbench, Python, Tableau
Objectives:
-To determine how lifestyle choices, such as pre-workout calories, sleep, soreness, and gym crowd density, affect muscle growth and hypertrophy.
-To provide data-driven insights that help Tony design more effective training programs for his clients.
Overview
Tony, a 26-year-old yoga instructor, developed an interest in weightlifting. Combining yoga with strength training, he aims to start a personal training business. With a background in physical education and yoga, Tony is eager to study muscle growth, particularly hypertrophy.
Problem
Tony's primary question for his new business is: Do lifestyle choices affect muscle growth? Specifically, he wants to understand the impact of pre-workout calories, sleep, soreness, and gym crowd density on muscle growth.
Solution
To analyze how lifestyle factors influence muscle growth, we recorded data in the following columns:
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Date: Workout date
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Time of Day: When the workout took place
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Sleep Hours: Hours slept before the workout
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Pre-workout Calories: Calories consumed before the workout
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Muscle Group: Targeted muscle group
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Muscle: Specific muscles worked
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Set: Set number
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Exercise: Type of exercise
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Weight/Resistance: Amount used
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Reps: Number of repetitions
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Intensity (1-10): Workout intensity (7+ indicates hypertrophy)
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Post-workout Rest: Rest time after the workout (in mins)
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Gym Crowd Density: Measured in bars (1 to 3)
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Sore Muscles: Muscles that felt sore
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Soreness Level: Level of soreness
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Days Since Last Workout: Days since the previous workout
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Previous Workout Muscle Group: Muscle group targeted in the last workout
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Exercise Summary: Summary of exercises performed
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Average Rep Time (secs): Average time per repetition
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Average Rest Time (secs): Average rest time between sets
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Workout Duration (mins): Total workout time
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Total Time (mins): Total time spent including rest
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Notes: Additional notes about the workout
PROCESS
Data Collection
Preparation
Feature Engineering
Dashboard Creation
Data
Collection
Excel
Tony recorded his workout data using Excel. As Tony wasn't familiar with Excel, I guided him through the process, simplifying explanations to match his style. This experience taught me patience and adaptability.
Recognizing his unique communication style, I tailored my approach to suit Tony's preferences. Knowing he was a visual learner, I created a web picture chart using Adobe Illustrator to present the data in a format that resonated with him.
Preparation
SQL
I prepared the data for analysis using SQL.
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Created TonyAmData and TonyPmData tables for workout data storage.
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Combined data from TonyAmData and TonyPmData into AllTonysWorkouts.
What I Learned
Recognizing data types for each column is important for maintaining data consistency and accuracy, which aids in better analysis. Ensuring data quality by maintaining accuracy, completeness, and trustworthiness is crucial. Managing metadata well is key to effective database management and overall data quality.
Removing Duplicates:
Deleted duplicate records based on specific criteria using a Common Table Expression (CTE) named DuplicateColumns.
What I Learned
Using a CTE helped me avoid deleting vital data by mistake. Recognizing that duplicates can stem from human errors or system glitches, I've implemented safeguards to maintain data integrity. This practice is essential for data-driven decision-making, ensuring the analysis is based on reliable data.
Error Detection and Correction for Each Column:
Standardized exercise data, corrected spelling errors, and applied naming conventions.
What I Learned
Correcting spelling errors and applying naming conventions make data consistent, reducing confusion and ensuring uniformity. This highlights the importance of manual data cleaning and documentation, which leads to better data decision-making.
Correcting Muscle Group Entries
Corrected entries where 'Leg / Shoulder' was mistakenly used as the muscle group by assigning 'Shoulder' to the 'Pull' category and 'Leg' to the 'Legs' category for consistency.
Fixing Mislabeled Muscle Groups
This helps classify data accurately, making it easier to analyze and organize data. Properly categorized data improves visualization clarity in platforms like Tableau.
What I Learned
Mistakenly inputting data like 'Leg / Shoulder' instead of specific muscle groups highlights the need for careful data review. Ensuring accuracy and consistency is vital to prevent errors and make data usable in analytical tools like Tableau, Excel, Power BI, RStudio, and Python. Documenting these corrections ensures that the data-cleaning process is transparent and recoverable.
Feature Engineering
SQL
After preparing the data, I performed feature engineering to enhance the dataset.
Extracting and Populating
For the second_muscle_that_is_sore and second_soreness columns, parsed values from the muscle_that_is_sore column to improve accuracy for visualization or analysis purposes.
Prepping for Pie Chart
Combined soreness data from both primary and secondary muscle groups, calculating the maximum soreness level for each group on every date. This was done to visualize soreness distribution, for a pie chart representation in Tableau.
What I Learned
Drafting my visuals and deciding the story I wanted to convey was crucial in solving the business problem. This guided my feature engineering work, helping me execute it effectively. Using mathematical thinking and logical problem-solving, I ensured the data was appropriately prepared for analysis.
Total Weight Calculations for Accurate Analysis
Used a CTE to calculate the total weight lifted per exercise and updated the total_weight_lifted_per_exercise column accordingly. This was crucial in generating accurate visuals for analysis, especially in determining hypertrophy.
Enhancing Hypertrophy Potential Visualization
Updated the stimulate_hypertrophy column using a case statement, assigning "Yes" if the intensity rating is eight or above, and "No" otherwise to aid in the visual representation of hypertrophy potential.
What I Learned
Creating the "stimulate_hypertrophy" and total_weight_lifted_per_exercise columns in response to the need for clearer visualizations demonstrates a problem-solving approach to data analysis.
Whether working with small data or big data, my approach to making data-driven decisions remains consistent. I apply mathematical thinking, logical problem-solving, and statistical analysis to extract insights and inform my decisions. The tools may differ, but the core principles of data analysis remain unchanged.
Feature Engineering
Python
I used Python to find correlations between numerical columns and visualized them with a heatmap. This helped me spot patterns and gain insights that I might have missed initially.
Significant Findings
Calorie Intake: Calorie intake before the workout shows minimal effect on intensity, with only a 0.4 difference.
Soreness: 60% of muscle soreness is in the legs, with the rest affecting the upper torso.
Sleep Duration: Sleep duration shows inconsistent effects on workout intensity.
Main Finding: Later times correlate with increased gym crowding, lower intensity, and a 10% decrease in hypertrophy stimulation.
Analysis:
Gym Crowding: Gym crowding increases later in the day, reducing workout intensity. Adjusting workout time can improve effectiveness.
Exploration with Trainer: With a trainer, we can explore reasons for this impact, such as limited equipment access, wait times, or anxiety.
Dashboard
Creation
Tableau
Created a dashboard to visualize the workout data, providing insights into the impact of various lifestyle factors on muscle growth.
Using elements of art and design principles, I created effective visualizations in Tableau and ensured accessibility through design thinking.
What I Learned
In my data storytelling, I combined visuals and narratives to engage the audience and convey meaning. I tailored dashboards with filters for actionable insights and used clear descriptions to guide the viewer. I applied text hierarchy to emphasize key points and used colors strategically: darker backgrounds for narration and white for tables.
Preparing data story presentations taught me to structure them well and be ready for Q&A sessions. Understanding stakeholder expectations and addressing questions effectively was key to successful communication.
REFLECTION
What I Learned So Far
Solving Data-Driven Problems: A Team Effort
Teaching Tony highlighted the importance of empathy and adapting to individual communication styles. Our collaboration was mutually beneficial: I guided him in data recording and visualization, while he deepened my understanding of hypertrophy, showcasing the value of teamwork.
Commitment to Continuous Learning
This project sharpened my analytical skills. Ensuring data consistency and using Tableau, I turned Tony's workout data into actionable insights, solving his business questions. This experience fuels my excitement for continuous learning and applying these skills in future projects.
"The ability to take data—to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it—that's going to be a hugely important skill in the next decades."
Improving Data Collection Practices
While Excel proved useful for recording workout data, I encountered issues with unused columns and inadequate planning of outcome variables. For instance, while capturing workout timing was beneficial, some columns ended up underutilized. Looking back, prioritizing essential variables such as fatigue levels would have enhanced the accuracy and focus of my data collection efforts.
MOVING FORWARD
Expanding Data Collection and Analysis
We'll investigate how individual preferences (introvert vs. extrovert) influence gym performance, alongside factors such as workout order, social interactions, and environmental conditions like temperature and noise while expanding our data collection efforts.
Exploring Additional Variables
Based on our initial analysis suggesting that gym crowd density correlates with lower hypertrophy, we acknowledge the potential for other factors influencing these outcomes. Exploring variables such as mental state and later-time workout performance could provide deeper insights into the complex relationships between lifestyle choices and muscle growth.
Bias-Free Fitness Insights: Embracing Diversity in Data Collection
Tony plans to record data that considers diverse factors like gender and body types, aiming for a more inclusive dataset. This approach will help analyze how different groups respond to training variables, giving deeper insights into muscle growth and fitness outcomes without bias.