Course overview
Description generated based on course syllabus and open data.
Python Developer: scope and context
Python ranks among the top three programming languages. It powers Backend, DevOps, Data Science, and Machine Learning. The server side of YouTube, Instagram, and Pinterest uses Python; it is also applied at Tesla, NASA, and IBM.
What Python enables
- Building websites and mobile applications.
- Creating social networks, audio/video services, and games.
- Data analysis, numerical computing, neural networks.
- Designing server-side logic and automation.
Who the Python Developer path fits / who it does not
Fits
- Beginners seeking a universal language for Backend and Data Science.
- Analysts and engineers needing ML and data processing tools.
- Developers switching from other languages for rapid prototyping.
Does not fit
- Those expecting purely visual development without coding.
- Users not ready to work with the terminal, packages, and containers.
Problem → outcome (for the Python Developer path)
- Fragmented knowledge → structured progression: syntax, OOP, files, and modules.
- Data handling difficulties → EDA, statistics, validation, and model assessment practices.
- Deployment hurdles → databases, Docker, dependency management (Poetry).
- Unclear direction → overview of Backend, DS, and ML tools.
Comparison with alternatives for a Python engineer
- JavaScript/Node.js: strong in frontend and real-time; Python is simpler for DS/ML.
- Java: high performance and static typing; Python is faster for prototyping.
- R: statistics and visualization; Python is more universal for production.
- C++: peak performance; Python is more convenient for fast iterations and integrations.
Program contents for a Python Developer
Python Core (≈2.5 months)
- Introduction, syntax, and data types.
- Control flow, functions, strings, date and time.
- Files, modules, packages; object serialization and copying.
- Functional style and built-in modules.
- Advanced OOP: classes, inheritance, protocols.
Data Science and Machine Learning (≈4.5 months)
- Poetry, Docker; working environments.
- SQL and MongoDB; data modeling.
- EDA and basic statistics; validation.
- Classical ML: classification, regression, other supervised algorithms.
- Unsupervised learning; clustering and dimensionality reduction.
- Neural networks, CNNs, NLP basics; hyperparameter tuning.
- Web scraping; Dash for interactive web apps.
- Recommender systems; time series.
Outcomes after completing the Python Developer track
- Python Core understanding: syntax, OOP, modules, files, and serialization.
- Data practices: EDA, statistical basics, model quality evaluation.
- ML and DL: core algorithms, CNNs, NLP basics, hyperparameter tuning.
- Infrastructure: SQL, MongoDB, Docker, dependency management (Poetry).
- Tooling: web scraping, creating simple interactive dashboards with Dash.
Course Description
Python Core 2.5 months
Introduction to Python
Flow control and functions
Working with date, time and advanced work with lines
Working with files and modular system
Functional programming and built-in Python modules
Advanced Object-Oriented Programming in Python
Serialize and copy objects to Python
Data Science and Machine Learning4.5 months
Development. Poetry. Docker
Learning without a teacher
Database. SQL, MongoDB
Recommendation systems
WebScraping
Neural networks and deep learning
Introduction to Data Science Programming
Selection of NM hyperparameters
EDA + Basics of Statistics
Convolutional neural networks
Classical machine learning
Numerical series study models
Task classification and evaluation of the model
Classic examples of neural networks and the basics of NLP
Other learning algorithms with teacher
Dash Interactive Web Applications
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