Section 1.1: Initial Data Verification

A common pitfall is wasting hours waiting for data to download, only to discover it’s incorrect or irrelevant. If you're dealing with large datasets, it's crucial to verify the record count and test your queries on a smaller data subset.

Here’s my typical approach when I begin working with a new data source:

1. Execute a query to retrieve the first 10 records to ensure the data aligns with my needs.
2. Run a query to count the total records in the table, comparing this with the source system, if accessible.
3. For complex queries—such as selecting records with the most recent load date or transactions dated on the first of the month—I test the logic by limiting results to the first 10 records. This may include grouping certain values to confirm the query's structure is correct.

Additionally, embedding business logic into your database queries can enhance analysis efficiency.

Subsection 1.1.1: Utilizing Parallel Processing

When I mention "parallel processing," I’m referring to a broader context beyond just machine learning models. My method involves running workflows both on the cloud (I utilize AWS) and my local machine. This allows me to set up lengthy tasks on the cloud while I continue working on other assignments on my computer.

Automating processes to run on a server, even when you’re not logged in, can be advantageous, although this may not always be practical during the development of your data cleaning and analysis code.

Photo by Max Duzij on Unsplash

Having multiple screens can be a lifesaver when navigating between local and cloud environments.

Section 1.2: Organizing and Optimizing Your Work

I continually strive for better organization in my work. Occasionally, I revisit previous projects and wonder what my thought process was at that time. To counter this, I’ve started implementing a documentation system for smaller projects, while larger ones may have pre-existing organizational standards within my team.

Here are some practices that help me maintain organization:

• Assign descriptive names to files.
• Create an archive folder for older files that may still be relevant; don’t hesitate to delete those you won’t use.
• Use folders for emails related to specific projects or topics to simplify future reference.
• Document within the code by adding comments for complex steps and structuring it logically.

These tips may seem basic, but it’s astonishing how disorganized I can become when busy, neglecting to keep my work orderly and documented. Regarding optimization, I often realize towards the end of a project that I should have considered efficiency at every step, rather than leaving it for last. This is an ongoing area for improvement for me.