Python is a high-level, interpreted, general-purpose programming language. Its design philosophy emphasizes code readability with the use of significant indentation.
Python is dynamically-typed and garbage-collected. It supports multiple programming paradigms, including structured (particularly procedural), object-oriented and functional programming. It is often described as a “batteries included” language due to its comprehensive standard library.
Most popular ibraries:
- pandas – fast, powerful, flexible and easy to use open source data analysis and manipulation tool, built on top of the Python programming language. pandas is a software library written for the Python programming language for data manipulation and analysis. In particular, it offers data structures and operations for manipulating numerical tables and time series. It is free software released under the three-clause BSD license. More about pandas
- NumPy is the fundamental package for scientific computing in Python. It is a Python library that provides a multidimensional array object, various derived objects (such as masked arrays and matrices), and an assortment of routines for fast operations on arrays, including mathematical, logical, shape manipulation, sorting, selecting, I/O, discrete Fourier transforms, basic linear algebra, basic statistical operations, random simulation and much more. More about NumPy
- Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python. Matplotlib makes easy things easy and hard things possible. More about Matplotlib
I refresh my Python skills at Datacamp. Datacamp has Python courses, tutorials at all levels, projects, and a workspace to ensure my progression on my Python journey.
As a Data Analyst, Data Engineer or Data Scientist I can:
- import, clean, manipulate, and visualize data with some of the most popular Python libraries, including pandas, NumPy, Matplotlib, Seaborn and many more,
- build an effective data architecture, streamline data processing, and maintain large-scale data systems,
- create data engineering pipelines, automate common file system tasks, and build a high-performance database.
My Python Learning Path
With Data Camp, Kaggle and others I build my skills and experience and validate my knowledge:
- Machine Learning Fundamentals with Python (Datacamp) – in progress
- Supervised Learning with scikit-learn 21.04.2022
- Unsupervised Learning in Python
- Linear Classifiers in Python
- Case Study: School Budgeting with Machine Learning in Python
- Introduction to Deep Learning in Python
- Data Manipulation with Python (Datacamp) – in progress
- Python Fundamentals (Datacamp) 11.04.2022
- Data Types for Data Science in Python (Datacamp) 20.06.2022
- Introduction to Data Science in Python (Datacamp) 04.05.2022
- Intro to Machine Learning (Kaggle) 20.04.2022
- Python (Kaggle) 17.03.2022
- Dictionaries, Frequency Tables, and Functions in Python (Dataquest) 15.03.2022
- For Loops and Conditional Statements in Python (Dataquest) 14.03.2022
- Variables, Data Types, and Lists in Python (Dataquest) 14.03.2022
- Python 2020 (Linkedin Learning) 06.01.2022