Python is a versatile language for backend, automation, and data analysis. Below: use cases, program structure, and competencies for a Python Developer.
Language of course:
Ukrainian
Format of the event:
Video lectures
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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.
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