Course overview
Description generated based on course syllabus and open data.
The material covers foundational and applied Python for web development with Jango (Django), Data Science, and ML using Jupyter Notebook and common libraries.
Structure and key topics in Python, Data Science and ML
Includes core syntax and working tools for data processing, visualization, and building machine learning models.
Core libraries: NumPy, Pandas, Matplotlib, Scikit-learn in Jupyter Notebook
- NumPy: arrays, vectors, basic computations.
- Pandas: tables, filtering, aggregation, data preparation.
- Matplotlib: charts, plots, visualization styling.
- Scikit-learn: model selection, metrics, pipelines.
- Jupyter Notebook: experiments, notes, reproducibility.
Syntax and paradigms: variables, lists, dicts, classes, loops, modules, virtual environments
- Data structures and collections.
- Functional and object-oriented programming.
- Project layout, modules, dependencies, virtualenv.
Web in Python with Jango (Django)
Essentials of building web applications: routing, views, templates, and working with data models.
Who this Python + Jango (Django), Data Science and ML course suits and who it does not
Suitable for
- Beginners in programming and data analytics.
- Professionals switching from other languages to Python.
- Analysts, engineers, and researchers working with data.
- QA/BI specialists automating analysis and reporting.
Not suitable for
- Those expecting solutions without writing code.
- Those seeking only a narrow focus (e.g., only web or only ML) without Python fundamentals.
- Those not planning to set up environments and libraries.
Problem → consequence → approach in Python, Data Science and ML
- Problem: fragmented materials → Consequence: gaps in fundamentals → Approach: sequential study of syntax, data structures, and paradigms.
- Problem: difficult data preparation → Consequence: inaccurate models → Approach: Pandas/NumPy workflows, data quality checks, reproducible steps in Jupyter.
- Problem: model choice and evaluation → Consequence: unreliable conclusions → Approach: Scikit-learn, cross-validation, metrics, pipelines.
Comparison with alternatives: approaches to Python, Jango (Django), Data Science and ML
- Books: in-depth but often without interactive notebooks.
- Standalone videos: quick but lacking structure and cohesive examples.
- Narrow programs (only Django or only ML): depth in a single area without a holistic Python base.
- Other languages (R, JavaScript): strong in their niches with different ecosystems and libraries.
Covered learning outcomes in Python, Jango (Django), Data Science and ML
- Core Python syntax and collection handling.
- Data cleaning and analysis with Pandas/NumPy.
- Data visualization using Matplotlib.
- Model building and evaluation in Scikit-learn.
- Working practices in Jupyter Notebook.
- Building simple web apps with Jango (Django).
- Project organization: modules, environments, dependencies.
- Applying OOP and functional approaches in code.
Course Description
Python is the simplest programming language in the world. But at the same time, Python is a powerful tool with which you can solve a huge range of different tasks, from file processing to machine learning, data processing, games and web applications. Thus, studying Python, you can choose a profession from a wide range of vacancies or use Python to create your own applications and solve your own problems. This course includes many practical tasks, as well as tasks that must be completed independently. Python is an object-oriented programming language. Python is also a language with a huge number of functions, but in order to know how to write Python code, you need to understand the key concepts of Python. That's what I'll focus on with you in this course. Before writing code and running examples, you will receive explanations and answers from me to the questions WHY and WHY, and only after that HOW to write code. I will not waste your time and therefore I have created the most effective course structure. All the examples I will explain and run are written by me before the course, but you will write and run the code yourself. The duration of all video lectures in this course is more than 45 hours, but expect to spend about 300 hours mastering all the topics of the course, including the independent implementation of all practical tasks.
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