30 Days of Python Code: NumPy Challenge Take the 30-Day NumPy Challenge: Dive into Python Code – Level Up Your Skills and Master NumPy for Data Manipulation!
What you’ll learn
- solve over 200 exercises in Python & NumPy
- deal with real programming problems
- work with documentation & Stack Overflow
- guaranteed instructor support
Requirements
- basic knowledge of Python & NumPy
Description
The “30 Days of Python Code: NumPy Challenge” course is a unique, hands-on program designed to elevate your Python programming skills by honing in on one of Python’s most powerful libraries: NumPy. This course is ideal for those already comfortable with Python basics and are looking to deepen their knowledge of numerical computing within the Python ecosystem.
Over the course of 30 days, you’ll undertake a range of coding exercises designed to familiarize you with the power and flexibility of the NumPy library. The course covers NumPy’s core features such as arrays, array indexing, datatypes, array math, broadcasting, and more. Each day presents a new challenge, pushing you to apply and reinforce what you’ve learned, ensuring that your understanding of NumPy is comprehensive and well-rounded.
The course is highly interactive, allowing you to learn by doing, which is widely recognized as one of the most effective ways to learn programming. This approach fosters practical problem-solving skills and creativity, as you are tasked with finding solutions to real-world programming problems.
In addition, the course provides detailed solutions and explanations for each coding exercise, enabling you to compare your solutions with best practices. This way, you not only learn about the correct approach, but also gain insight into the reasoning behind it, improving your coding and debugging skills.
This “30 Days of Python Code: NumPy Challenge” course is perfect for anyone aiming to use Python for data analysis, data science, or machine learning, and wants to leverage the power of NumPy to work with numerical data efficiently.
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.
Topics you will find in the basic exercises:
- arrays creation
- shapes, reshaping arrays
- dimensions
- size
- indexing
- slicing
- arrays manipulation
- math, statistic & calculations
- dates
- random
- comparing arrays
- broadcasting
- saving, loading & exporting
- appending, concatenating & stacking arrays
- sorting, searching & counting
- filtering
- boolean mask
- image as an array
- dealing with missing values
- iterating over arrays
- linear algebra
- matrix multiplication
- polynomials
- solving systems of equations
- arrays with characters
- functional programming & universal functions
- dummy encoding
- and other
Who this course is for:
- Python programmers or developers who want to enhance their skills and knowledge in NumPy, a powerful library for numerical computing in Python
- data scientists or analysts who work with large datasets and want to learn how to efficiently manipulate and analyze data using NumPy
- students or individuals studying computer science, data science, or related fields who want to gain practical experience in working with NumPy arrays and perform data manipulations and computations
- professionals in non-programming roles who want to learn NumPy and its capabilities for data analysis and numerical computing to enhance their job skills
- Python enthusiasts or hobbyists who enjoy coding challenges and want to explore the capabilities of NumPy through hands-on exercises
- recruiters or hiring managers who want to evaluate the NumPy skills and competency of job candidates applying for positions that require data manipulation and analysis using Python
