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A batch of the best highlights from what Kevin's read, .
this is how you would use it with a Python script. We first define the match:
```yaml
- trigger: ":pyscript"
replace: "{{output}}"
vars:
- name: myvar
type: echo
params:
echo: "my variable"
- name: output
type: script
params:
args:
- python
- /path/to/your/script.py
```
And then, inside the script:
```python
/path/to/your/script.py
import osmyvar = os.environ['ESPANSO_MYVAR']# Do whatever you want with the myvar variable
```
• Finding and Understanding the Data
• Cleaning the Data and Feature Engineering
• Tuning and Evaluating
• Using the Model and Presenting Results
The Machine Learning Process
Codecademy
When we unpack the common threads of how various people define data engineering, an obvious pattern emerges:
a **data engineer**
*gets data, stores it, and prepares it for consumption*
by **data scientists**, **analysts**, and others.
We define data engineering and data engineer as follows:
**Data engineering** is
the *development*, *implementation*, and *maintenance*
of **systems** and **processes** that take in raw data
and produce high-quality, consistent information
that supports downstream use cases,
such as analysis and machine learning.
**Data engineering** is
the intersection of
*security*,
*data management*,
*DataOps*,
*data architecture*,
*orchestration*, and
*software engineering*.
A **data engineer**
*manages the data engineering lifecycle*,
beginning with getting data from source systems and
ending with serving data for use cases,
such as analysis or machine learning.
Fundamentals of Data Engineering
Reis, Joe;Housley, Matt;
...catch up on these, and many more highlights
Readwise