course Data Science Python Basics is designed for beginners in computer science. It covers setting up your work environment, writing your first lines of code in Python, using numerical libraries, and data visualization techniques. It can be used both as an independent source of knowledge and as a starting point for a career in Machine Learning.
- Beginners in programming and data science
- IT professionals interested in getting started in data analysis with Python
- Setting up the Jupyter Notebook and Google Colab environment
- Basic Python syntax: variables, conditions, functions, collections, exception handling
- Object-oriented programming in Python: classes, inheritance, polymorphism
- Data visualization with Matplotlib and Seaborn
- Data manipulation with Pandas: Series, DataFrame, aggregation and processing operations
- Working with arrays and mathematical/statistical operations in NumPy
- Building a preprocessing pipeline for machine learning algorithms
- There are no special prerequisites.
- Environment setup
- Jupyter Notebook, Google Collabor
- Basic Python syntax
- Variables, conditions, functions, collections, exception handling
- Object-oriented programming
- Classes, methods, inheritance, polymorphism
- Data visualization
- Simple and advanced graphs with Matplotlib and Seaborn
- pandas
- Series, DataFrame, indexing, grouping, data cleaning
- NumPy
- Arrays, mathematical and statistical operations
Project:
- Pipeline preprocessing for Machine Learning algorithms
- DevOps Artisan – Machine Learning Fundamentals
- DevOps Artisan – Python for Development
- DevOps Artisan – Basics Data Science in Python
There are no recommendations at this time.
