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Python Data Science with NumPy: Over 100 Exercises
64,131 students
Python Data Science with NumPy: Over 100 Exercises
Level up Your Data Science Skills in Python - Unleash the Power of Numerical Computing and Analysis!
Created byPaweł Krakowiak, Tomasz Krakowiak
Last updated 4/2025
English
What you'll learn
- solve over 100 exercises in NumPy
- deal with real programming problems in data science
- work with documentation and Stack Overflow
- guaranteed instructor support
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Coding Exercises
This course includes our updated coding exercises so you can practice your skills as you learn.
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Course content
15 sections • 116 lectures • 46m total length
Requirements
- Completion of all courses in the Python Developer learning path
- Completion of all courses in the Data Scientist learning path
- Basic knowledge of NumPy library
Description
The course "Python Data Science with NumPy: Over 100 Exercises" is a practical, exercise-oriented program aimed at individuals who want to strengthen their Python data science skills, with a particular focus on the powerful NumPy library. It caters to learners eager to dive deep into the functionalities that NumPy offers for handling numerical data efficiently.
Each section of the course contains a set of carefully curated exercises designed to consolidate the learners' understanding of each concept. Participants will get to tackle real-life problems that simulate challenges faced by data scientists in their everyday roles. Each exercise is followed by a detailed solution, helping students understand not just the 'how' but also the 'why' of each solution.
The "Python Data Science with NumPy: Over 100 Exercises" course is suited for individuals at various stages of their data science journey - from beginners just starting out, to more experienced data scientists looking to refresh their knowledge or gain more practice working with NumPy. The primary prerequisite is a basic understanding of Python programming.
NumPy - Unleash the Power of Numerical Python!
NumPy, short for Numerical Python, is a fundamental library for scientific computing in Python. It provides support for arrays, matrices, and a host of mathematical functions to operate on these data structures. This course is structured into various sections, each targeting a specific feature of the NumPy library, including array creation, indexing, slicing, and manipulation, along with mathematical and statistical functions.
Who this course is for:
- Aspiring Data Scientists
- Python Developers Expanding into Data Analytics
- Data Analysts and Business Intelligence Professionals
- Machine Learning Enthusiasts
- Students in Computer Science, Engineering, or Data Science Programs
- Professionals Preparing for Technical Interviews
- Researchers and Academics in Scientific Domains
- Financial Analysts and Quants
- Self-Taught Programmers and Career Changers