Python for Statistical Analysis
Python for Statistical Analysis
Master applied Statistics with Python by solving real-world problems with state-of-the-art software and libraries
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Python is a popular programming language that is widely used in the field of data science and data analytics. It is known for its simplicity, readability, and flexibility, making it a great choice for beginners and experienced programmers alike.
One of the main reasons why Python is so popular for statistical analysis is because of the many libraries and modules available for this purpose. Some of the most commonly used libraries for statistical analysis in Python include NumPy, Pandas, and SciPy.
NumPy is a library for working with large, multi-dimensional arrays and matrices of numerical data. It provides a variety of functions and tools for statistical analysis, such as linear algebra, random number generation, and statistical functions.
Pandas is a library for working with tabular data, such as data stored in spreadsheets or databases. It provides a variety of functions and tools for manipulating, cleaning, and analyzing data, such as filtering, aggregation, and pivot tables.
SciPy is a library that builds on NumPy and provides a variety of functions and tools for scientific and technical computing, including optimization, integration, interpolation, and signal and image processing.
Python is a powerful and versatile language for statistical analysis, and its libraries and modules make it easy to perform a wide range of statistical tasks, from basic descriptive statistics to more advanced statistical modeling and machine learning. If you're interested in using Python for statistical analysis, it's a good idea to familiarize yourself with these libraries and their functions and tools.
What you'll learn
- Gain deeper insights into data
- Use Python to solve common and complex statistical and Machine Learning-related projects
- How to interpret and visualize outcomes, integrating visual output and graphical exploration
- Learn hypothesis testing and how to efficiently implement tests in Python
