The Data Science Workflow
Useful analysis runs in a loop: state the question, find and check the data, build something, look at what it says, then report it in a form someone can act on. Most of the work is in the first two stages, and skipping them is what produces confident nonsense.
What a learner can do afterwards
- Turn a business question into one that the available data could answer
- Say what could make a dataset unable to answer a question at all
- Keep an analysis reproducible so a second person gets the same numbers
- Pick a summary the data can honestly support, such as the median when a single far-off value pulls the mean
1 · Read
Pipelines move data to where it can be analysed. Useful analysis then runs in a loop: pose the question, find and check the data, build something, study what it says, then write it up so someone can act. Most of the work sits in the first two stages, and the life cycle names them acquisition and exploration.
A vague ask is why customers leave. You turn it into how many new customers never placed a second order, using the order records your nightly pipeline loads with dates and repeat counts. Now the available data can answer it.
Some datasets cannot answer the question at all: daily money totals cannot say which pastry sold best. Skipping the check is what produces confident nonsense. And you keep every step written so a second person gets the same numbers.
Before you build, write the question and the data check side by side. If the data never captured what the question asks about, no calculation can bring it back.
Pick a summary the data can honestly support. The mean adds the values and divides by the count, so one far-off value drags it. The median, the middle value once sorted, moves little. For waits of 3, 4, 4, 6 and 33 minutes, the mean is 50 / 5 = 10 but the median is 4, the honest summary here.
Ask a question the data can answer, check the data first, and record each step so the numbers reproduce.
The rewritten OpenStax-derived readings were written for this dataset. Practice items adapted from WeBWorK Open Problem Library problems by R Cruz, T Deroin (The College of Idaho); Michael Ratliff (NAU); University of Rochester contributors (CC BY-NC-SA 3.0). Reading summaries adapted from OER Commons records (Statistical Inference via Data Science: A ModernDive into R and the Tidyverse); license not recorded.
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15 questions wait behind this lesson, each with its answer explained. Every answer feeds the sky: stars light as they are learned, and dim when it is time to come back.