🧠 Core Programming and Foundations

🎯 What You’ll Learn

• Python mindset Write clean, readable code that is easy to test.
• Essential libraries Use NumPy, Pandas, and plotting tools with confidence.
• Data access Talk to relational and NoSQL databases smoothly.
• Data structures Pick the right structure for speed and clarity.
• Math toolkit Recall the key math ideas behind machine learning.
• Service delivery Wrap models inside simple REST APIs for real teams.

📖 Overview

Core programming skills keep every MLOps project moving. Python holds the workflow together, SQL brings the data, and math explains the model behavior. A strong base means you can debug faster, spot bad assumptions, and share results with less friction. This guide keeps the language simple and focuses on real actions you can take today.

🧩 Skill Map

• Programming Python, shell basics, modular code habits.
• Data Access SQL joins, NoSQL queries, file handling.
• Computation Vector math, matrix operations, gradient intuition.
• Architecture Functions, classes, API design, packaging.
• Collaboration Version control basics, code reviews, documentation.

🐍 Python Essentials

• Readable code Follow PEP 8, use descriptive names, and keep functions short.
• Virtual environments python -m venv .venv keeps dependencies tidy.
• Type hints Add hints like def score(data: pd.DataFrame) -> float: to guide reviewers.
• Logging Use the built-in logging module instead of print statements in production.
• Error handling Catch expected errors with try and raise clear messages.
• Testing Start with pytest and aim for small, reliable unit tests.
• Packaging Use pyproject.toml or setup.cfg for repeatable installs.

📦 Library Cheat Sheet

• NumPy Fast array math, random sampling, linear algebra utilities.
• Pandas DataFrame operations, groupby, joins, time-series resampling.
• Matplotlib / Seaborn Quick plots for checking distributions and trends.
• Scikit-learn Classic algorithms, preprocessing pipelines, evaluation helpers.
• TensorFlow & PyTorch Deep learning frameworks with GPU support.
• Requests Simple HTTP client for calling or building APIs.
• FastAPI & Flask Light frameworks to expose models as services.

🗂️ Project Structure Template

• ${project}/
• src/ holds packages like src/features/, src/models/, src/services/.
• tests/ mirrors src/ to keep test coverage clear.
• data/ optional, but avoid committing large raw files.
• configs/ store YAML or JSON settings used across runs.
• notebooks/ for exploration with clear naming like 2025-01-08_churn.ipynb.
• README.md explains setup, commands, and contact info.
• pyproject.toml or requirements.txt list dependencies.

🧠 Data Structures Refresher

• Lists Keep ordered values; great for small collections but slow for membership checks.
• Tuples Immutable pairs or triples that work well as dictionary keys.
• Dictionaries Fast lookups using key-value pairs, ideal for feature maps.
• Sets Store unique items, perfect for deduplication checks.
• Queues / Deques From collections, they support fast append and pop from both ends.
• Heaps From heapq, handy for priority queues or top-k selections.
• Namedtuples / Dataclasses Express records with readable attributes.

🧮 Math & Stats Essentials

• Linear algebra Understand vectors, matrices, dot products, eigenvalues.
• Calculus Grasp derivatives and gradients to interpret model training steps.
• Probability Know mean, variance, Bayes rule, conditional probability.
• Statistics Confidence intervals, hypothesis testing, p-values.
• Optimization Gradient descent, learning rates, convergence criteria.
• Distance metrics Euclidean, cosine, Manhattan; choose based on data scale.

🗄️ SQL Basics

• Select pattern SELECT columns FROM table WHERE filters ORDER BY sort;
• Joins Practice inner, left, right, and cross joins with sample data.
• Aggregations SUM, AVG, COUNT, GROUP BY, HAVING for filtered groups.
• Window functions Use ROW_NUMBER, RANK, LAG, LEAD for comparisons.
• CTEs Common Table Expressions keep SQL readable for complex logic.
• Indexes Suggest index creation when queries run slowly.
• Transactions Use BEGIN, COMMIT, ROLLBACK to protect data integrity.

🛢️ NoSQL Patterns

• MongoDB Flexible documents; design schema for reading patterns.
• Cassandra Wide-column store; model around query access, not normalization.
• DynamoDB Key-value with predictable read and write units.
• Redis In-memory cache for fast feature lookup or rate limiting.
• ElasticSearch Search engine for text, logs, and analytics.
• Consistency choices Understand eventual vs strong consistency trade-offs.

🧪 Data Handling Tips

• File formats CSV for quick wins, Parquet for columnar analytics, JSON for APIs.
• Validation Use pydantic or cerberus to enforce schema contracts.
• Missing data Decide between fill, drop, or flag; document reasoning.
• Scaling Fit scalers on training data only to avoid leakage.
• Feature encoding Use categorical encoders like one-hot or target encoding carefully.
• Time zones Convert to UTC early and document localized views.

☁️ Working with Filesystems

• Local vs cloud Abstract storage so code can reach S3, GCS, Azure Blob.
• Pathlib Use Path objects instead of string paths for clarity.
• Streaming Process large files in chunks to avoid memory issues.
• Permissions Secure secrets outside version control.
• Compression Read and write gzip or parquet for smaller storage footprints.

🧰 CLI Productivity

• Makefiles Provide shorthand commands like make test or make train.
• Pre-commit hooks Run linting automatically with black, isort, ruff.
• Task runners Use invoke or nox for repeatable automation tasks.
• Shell scripts Keep deployment commands in scripts/ with comments.

🌐 REST API Basics

• FastAPI starter ```python from fastapi import FastAPI app = FastAPI()

@app.get("/health") def health(): return {"status": "ok"} `` - **Input models** Define request schemas usingpydantic.BaseModelto validate payloads. - **Response structure** Return dictionaries or data classes that serialize cleanly to JSON. - **Status codes** Use200for success,400for bad requests,500for server errors. - **Middleware** Add logging, timing, or authentication layers as functions. - **Testing APIs** Hit endpoints withpytestplushttpxorrequestsclients. - **Documentation** FastAPI auto-generates docs at/docs` for easy demos.

🔐 Security Basics

• Secrets Load API keys from environment variables, not code.
• Dependency updates Patch libraries regularly to avoid known CVEs.
• Input validation Sanitize all external inputs before use.
• Least privilege Give each service only the permissions it needs.
• Transport layer Use HTTPS everywhere; avoid plain HTTP in production.
• Audit logs Record access and data changes for investigations.

🔄 Version Control Habits

• Small commits Track logical changes to simplify review.
• Branch naming Use clear names like feature/add-churn-service or fix/metric-bug.
• Pull requests Document intent and testing steps before merging.
• Code reviews Focus on clarity, correctness, and maintainability.
• Tag releases git tag v1.2.0 marks stable snapshots used in deployments.

🧪 Testing Checklist

• Unit tests Validate functions and classes with known inputs.
• Integration tests Check data access, model loading, API responses.
• Performance tests Measure runtime and memory for critical paths.
• Regression tests Protect against known bugs returning.
• Mocking Use unittest.mock or pytest fixtures to isolate components.
• Continuous testing Trigger tests in CI before merges or releases.

📊 Example Scenario

A small analytics squad prepares a churn model. Python scripts pull data using SQL queries stored in version control. They clean data with Pandas, train a scikit-learn pipeline, and save the model with joblib. A FastAPI service loads the model and exposes a /predict endpoint. Simple math checks confirm probability outputs sum to one. Unit tests protect the scoring logic, while integration tests mock database connections. The team documents KPI definitions and shares them with stakeholders.

💡 Best Practices

• ✅ Keep code simple Prefer clear loops over clever one-liners when readability wins.
• ✅ Document decisions Note why you chose a certain feature, threshold, or method.
• ✅ Automate setup Provide scripts or Make targets so others can run the project quickly.
• ✅ Monitor dependencies Use Dependabot or Renovate to track library updates.
• ✅ Share notebooks Export key graphs and results to markdown for easy review.
• ✅ Teach teammates Run short demos so everyone understands core modules.

⚠️ Common Pitfalls

• 🚫 Hidden assumptions Forgetting to record preprocessing steps causes production drift.
• 🚫 Copy-paste SQL Unreviewed queries can break when schemas change.
• 🚫 Untested math Complex formulas without tests are hard to trust.
• 🚫 Global state Avoid singletons that make debugging difficult.
• 🚫 Silent failures Always log errors with stack traces for faster resolution.

🧩 Related Topics

• Previous Topic 10_End_to_End_Project.md
• Next Topic 12_Data_Engineering_and_Big_Data.md

🧭 Quick Recap

Step	Purpose
Strengthen Python	Make pipelines cleaner and easier to maintain.
Practice SQL & NoSQL	Pull the right data faster and with fewer errors.
Refresh math	Explain model behavior and spot anomalies.
Structure projects	Help teams onboard and debug quickly.
Ship APIs	Deliver ML value inside real applications.

🖼️ Assets

• Diagram idea Flow showing data going from SQL to Python scripts to an API.
• Template idea Provide a starter repo with folders and Make targets.

🗓️ Study Plan

• Morning routine Spend 30 minutes reading code from an open-source project.
• Lunch break Practice two SQL queries on a sample dataset.
• Evening slot Watch one short video on math or algorithms and take notes.
• Weekly goal Build a tiny REST API and write tests for one new endpoint.
• Monthly review Share lessons learned with a teammate and update documentation.

✍️ Practice Ideas

• Code kata Rewrite a simple function in multiple styles to compare readability.
• Database drill Design a schema for a small app, then query it using joins and windows.
• Math check Explain gradient descent to a friend in less than five sentences.
• API test Use pytest and requests to send fake payloads and assert responses.
• Refactor day Clean up a notebook by moving logic into reusable modules.

📚 Glossary

• Idempotent Running the same command many times gives the same result.
• Serialization Turning objects into text or bytes so they can travel over networks.
• Latency Time delay between sending a request and getting a response.
• Schema Description of data fields, types, and constraints.
• Dependency External package or service that your project relies on.

🔁 Continual Improvement

• Track habits Keep a simple checklist of daily learning tasks.
• Pair often Work with peers to spot blind spots quickly.
• Ask for feedback Invite comments on pull requests and notes.
• Keep examples Save useful snippets in a shared cookbook repository.
• Stay curious Subscribe to newsletters and podcasts on Python, SQL, and MLOps.

📘 References

• Official docs https://docs.python.org/3/
• Official docs https://pandas.pydata.org/docs/
• Official docs https://docs.sqlalchemy.org/
• Blog posts https://realpython.com/
• Blog posts https://mode.com/sql-tutorial/
• GitHub examples https://github.com/tiangolo/fastapi
• GitHub examples https://github.com/pallets/flask