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Python Data Science with NumPy: Over 100 Exercises

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Level up Your Data Science Skills in Python - Unleash the Power of Numerical Computing and Analysis!
4.6
4.6/5
(187) Ratings
61,649 students
Created by Paweł Krakowiak
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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
This course includes:
1 total hour on-demand video
108 articles
0 downloadable resources
115 lessons
Full lifetime access
Access on mobile and TV
Certificate of completion
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Course content

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:

  • data analysts or data scientists who want to enhance their skills in data manipulation and numerical computing using the NumPy library in Python
  • students or individuals with a background in data analysis, statistics, or related fields who want to gain practical experience in using NumPy for data manipulation and analysis
  • programmers or software developers who are interested in data science and want to learn how to use the NumPy library to efficiently handle large datasets and perform numerical computations
  • professionals working with scientific or numeric data who want to leverage the power of NumPy to perform advanced calculations, data transformations, and statistical analysis
  • self-learners who are passionate about data science and want to develop proficiency in using NumPy for data manipulation, analysis, and numerical computations
  • researchers or scientists in fields such as physics, biology, or engineering who want to apply numerical methods and data analysis techniques using NumPy in Python
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