30 Days of Python Code: NumPy Challenge
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
Instructor
- 4.4 Instructor Rating
- 9,874 Reviews
- 398,340 Students
- 174 Courses
EN
Python Developer/AI Enthusiast/Data Scientist/Stockbroker
Enthusiast of new technologies, particularly in the areas of artificial intelligence, the Python language, big data and cloud solutions. Graduate of postgraduate studies at the Polish-Japanese Academy of Information Technology in the field of Computer Science and Big Data specialization. Master's degree graduate in Financial and Actuarial Mathematics at the Faculty of Mathematics and Computer Science at the University of Lodz. Former PhD student at the faculty of mathematics. Since 2015, a licensed Securities Broker with the right to provide investment advisory services (license number 3073). Lecturer at the GPW Foundation, conducting training for investors in the field of technical analysis, behavioral finance, and principles of managing a portfolio of financial instruments.
Founder at e-smartdata
PL
Data Scientist, Securities Broker
Jestem miłośnikiem nowych technologii, szczególnie w obszarze sztucznej inteligencji, języka Python big data oraz rozwiązań chmurowych. Posiadam stopień absolwenta podyplomowych studiów na kierunku Informatyka, specjalizacja Big Data w Polsko-Japońskiej Akademii Technik Komputerowych oraz magistra z Matematyki Finansowej i Aktuarialnej na wydziale Matematyki i Informatyki Uniwersytetu Łódzkiego. Od 2015 roku posiadam licencję Maklera Papierów Wartościowych z uprawnieniami do czynności doradztwa inwestycyjnego (nr 3073). Jestem również wykładowcą w Fundacji GPW prowadzącym szkolenia dla inwestorów z zakresu analizy technicznej, finansów behawioralnych i zasad zarządzania portfelem instrumentów finansowych. Mam doświadczenie w prowadzeniu zajęć dydaktycznych na wyższej uczelni z przedmiotów związanych z rachunkiem prawdopodobieństwa i statystyką. Moje główne obszary zainteresowań to język Python, sztuczna inteligencja, web development oraz rynki finansowe.
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