The third step in the data lifecycle is data
Once we’ve stored our data, we need to process
them in order to prepare them for analysis.
This involves several steps:
First, we may need to transform the data.
For example, we need to reshape tables, rename
columns, convert data types, and encode or
Next, we may need to clean the data.
For example, we need to ensure internal data
consistency, deal with missing values, and
handle errors and anomalies.
Finally, may we need to query the data to
extract just the subset of data we need for our analysis.
For example, we need to select, filter, sort,
group, and aggregate data from the persistent
Essentially, we need to prepare our data so
that our analysis will produce reliable results.
We can perform these data-processing tasks
one of three ways:
First, we can perform these steps manually
using tools like Microsoft Excel.
This option is only recommended for a quick,
one-time, low-risk data analysis, that doesn’t
require any automation, auditing, or reproducibility.
Second, we can create scripts with programming
languages like SQL, Python, or R.
Using scripts allows us to repeat the entire
process automatically, iterate on the design
over time, and document each step in the process.
Finally, we can create an automated data-processing
pipeline, also known as a “Data ETL”, which
stands for Extract, Transform, and Load.
An automated pipeline provides a much more
robust solution for data processing, but comes
at a much higher cost to create and maintain.
Ultimately, we generally spend a lot of time
and energy processing data in data science,
so we want to choose the option that minimizes
this effort in the long run.