Diving into data

Case Study: Analyzing Fitness Data in Alteryx

Giniya Gupta

Sr. Analyst

Recap Refresh

Recap Refresh

Imported datasets
Scanned and cleaned data
Removed duplicate rows and counted unique Ids
Calculated average daily steps

Journey Highlights

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All fields: V_string data type
NULL values: 65 NULL values found in the Fat column
Unique Ids: DailySteps (24), WeightLogInfo (8), SleepDay (33)
SleepDay: 3 duplicate rows
Average step count exceeded 8,000 for only 11 days

Data

Combine Datasets

Join datasets into a single table:
- Id
- Date
Ids help to match corresponding records across datasets
Dates allow us to align events chronologically

Table

Calculate Body Mass Index (BMI)

BMI categorizes:

Underweight, Normal weight, Overweight, Obese
BMI formula

$BMI = Weight / Height^2$

- Weight in Kgs and height in Meters

Gain insights into the health and fitness profiles of the customer base

BMI

¹ https://www.cdc.gov/healthyweight/assessing/bmi/adult_bmi/index.html

Fitness Insights

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Refer to guidelines provided by CDC and NIH
Use sitting thresholds to evaluate the health risks associated with prolonged sitting
Calculate lifestyle index to assess user's lifestyle based on average daily steps

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Aggregate and Compare

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Compare our aggregated numbers against standardized norms from CDC and NIH
Provide insights into our customers' health and fitness habits

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Flexibility in Alteryx

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Alteryx is a flexible tool.
There are numerous ways to solve a problem in Alteryx.
We encourage you to experiment with different methods throughout the course.

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Methods

Let's practice!

Case Study: Analyzing Fitness Data in Alteryx

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