MACHINE LEARNING ENGINEER ROADMAP · STAGE 01 OF 04
Programming and data foundations
Build the coding, data, and quantitative foundations used throughout machine learning.
Prepare and inspect data with readable Python, SQL, and basic statistical reasoning.
CORE SKILLS
Key concepts in programming and data foundations
Python for data work
Write modular Python that reads, transforms, and validates data before it reaches a model.
Topics include
- Functions, modules, and exceptions
- NumPy and tabular data concepts
- Reproducible environments
Data structures and algorithms
Choose appropriate structures and estimate their cost when processing growing datasets.
Topics include
- Lists, sets, dictionaries, and arrays
- Searching and sorting basics
- Time and space complexity
SQL and data inspection
Query datasets to understand their schema, quality, and relationship to the prediction task.
Topics include
- Filtering, aggregation, and joins
- Nulls and duplicate records
- Train and inference feature consistency
Probability and statistics
Use statistical ideas to reason about uncertainty, samples, distributions, and measured model results.
Topics include
- Distributions and summary statistics
- Sampling and confidence
- Correlation versus causation
APPLY WHAT YOU LEARN
Practical project
Create a small, tested Python pipeline that loads a public tabular dataset, profiles missing values, validates types, and produces a reproducible train/test split.
CHECK YOUR UNDERSTANDING
You are ready to move on when
- Can explain the shape and quality of a dataset before training.
- Can write and test a transformation without leaking target information.