Python Data Science with NumPy: Over 100 Exercises
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
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
Instructor
- 4.5 Instructor Rating
- 7,845 Reviews
- 320,609 Students
- 118 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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