Description
Python Numpy For Data Science Course. This course provides a comprehensive and practical introduction to the NumPy core library, an essential tool for high-performance numerical computing in Python and data science. NumPy is considered the foundation for numerical computing in Python and is a vital tool in every data scientist’s toolbox. This course is designed to provide a thorough and practical introduction to NumPy, focusing on its key features and applications in data science. Participants will learn how to use NumPy to write cleaner, faster, and more efficient code for working with large datasets, building machine learning models, and preparing data for statistical analysis. The course begins by exploring the structure and functionality of NumPy arrays, explaining the fundamental differences between these arrays and standard Python lists and why they are superior for high-speed numerical computing. It then covers basic concepts and operations, including array creation, indexing, and slicing, the concept of broadcasting for performing operations on arrays of different dimensions, and vectorized operations for eliminating loops and increasing speed. It then moves on to more advanced topics such as performing statistical calculations, linear algebra operations, and optimal memory management techniques. Throughout the course, learners will directly engage with real-world data science problems and scenarios, using NumPy to perform tasks such as data cleaning, data transformation, and data analysis. By the end of this course, students will not only be proficient in using the NumPy library effectively, but will also have a clear understanding of how it integrates and interacts with other popular data science libraries in Python, such as Pandas, Matplotlib, and Scikit-learn. This course is ideal for those interested in and seeking careers in data science, data analytics, and machine learning.
What you will learn
- Master the core features of NumPy, including arrays, indexing, slicing, reshaping, and broadcasting.
- Write efficient, vectorized Python code for numerical and data-driven tasks, avoiding slow loops.
- NumPy applications in real-world data science workflows, including descriptive statistics, simulations, and linear algebra operations.
- Build a strong foundation for advanced data science libraries like pandas, scikit-learn, and TensorFlow by understanding the underlying structure of NumPy.
This course is suitable for people who:
- This course is for aspiring data scientists, analysts, developers, and students who want to learn more about data processing.
- People who want to understand the connection between NumPy and modern data science and machine learning workflows.
Course details
- Publisher: Udemy
- Instructor: Daniel Yoo
- Training level: Beginner to advanced
- Training duration: 2 hours and 23 minutes
- Number of lessons: 14
Course headings
Prerequisites for the Python Numpy For Data Science course
- No Programming Experience Needed: You will learn everything you need to know throughout the course.
Course images
Sample course video
Installation Guide
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Download link
Downloadly
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File(s) password: www.downloadly.ir
File size
729 MB
