Data Handling and SQL for AI Engineers
Learn to load, clean, plot, and query data with NumPy, pandas, and SQL, and to split it without leakage, so every later AI result can be trusted.
What You'll Learn
Understanding Why the Bug Was Never in the Model
You have just joined acme-shop as its first AI engineer.
Understanding NumPy Arrays and Vectorized Operations
Why a Python list is not enough for numerical work A Python list stores each number as a full Python object, scattered in memory with pointers...
Performing Math and Aggregations with NumPy
Element-wise math versus matrix multiplication Adding two arrays with + combines them position by position.
Cleaning and Transforming Data with Pandas
Generating the dirty acme-shop dataset NumPy has no column names and holds one data type.
Visualizing Data Before You Trust It
Why plot before you model A summary number can hide a broken dataset.
Querying Data with SQL
Loading the data into SQLite Training and evaluation data usually lives in a database, not a CSV.
Skills You'll Master
Curriculum Index10 topics
Understanding Why the Bug Was Never in the Model
You have just joined acme-shop as its first AI engineer.
Understanding NumPy Arrays and Vectorized Operations
Why a Python list is not enough for numerical work A Python list stores each number as a full Python object, scattered...
Performing Math and Aggregations with NumPy
Element-wise math versus matrix multiplication Adding two arrays with + combines them position by position.
Cleaning and Transforming Data with Pandas
Generating the dirty acme-shop dataset NumPy has no column names and holds one data type.
Visualizing Data Before You Trust It
Why plot before you model A summary number can hide a broken dataset.
Querying Data with SQL
Loading the data into SQLite Training and evaluation data usually lives in a database, not a CSV.
Splitting Data Without Leaking the Future into the Past
What a train/test split protects against A model is only useful on data it has never seen.
Running the Hands-On Lab
You will use the files you created above. Everything runs inside ai-lab with the virtual environment active.
What You Built and What Comes Next
You now have a reusable ai-lab folder, a clean set of acme-shop orders, a SQLite database that confirms the same...
Quick Reference and Common Mistakes
Computing a fill value or scale on the full dataset before splitting is the classic leakage mistake.
Career Impact
Roles that use the skills in this module.
AI Engineer
MLOps Engineer
Platform Engineer
Data Engineer
Next Modules
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Practice on the Coding Sheet
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Open the Coding Sheet