Python Data Science with Pandas: Over 130 Exercises
What you'll learn
- solve over 130 exercises in Pandas
- 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 & Pandas
Description
This offers a comprehensive, exercise-based approach to mastering the Pandas library in Python. This course is perfect for individuals looking to improve their data wrangling and analysis skills for data science applications.
This course is divided into several sections, each focusing on a different aspect of the Pandas library. Topics covered include DataFrame creation, data cleaning, grouping and aggregation, merging and reshaping data, handling time series data, and more.
Each section consists of a set of curated exercises designed to reinforce and challenge your understanding of the covered concept. The exercises range from simple tasks to complex data manipulation problems, mirroring real-world data science scenarios. Detailed solutions are provided for each problem, allowing learners to compare their approach, understand alternative solutions, and learn efficient coding practices.
This course is ideal for anyone who has a basic understanding of Python programming and wants to enhance their data manipulation skills in Python using Pandas. Whether you are a data science enthusiast, a beginner in the field, or a seasoned professional looking for more practice, this course offers a practical and engaging way to learn.
Pandas - Data Empowered, Insights Unleashed!
Pandas is a powerful open-source library in Python that provides easy-to-use data structures and data analysis tools. It is widely used by data scientists, analysts, and researchers for data manipulation, cleaning, exploration, and analysis tasks. Pandas introduces two primary data structures, namely Series (one-dimensional labeled array) and DataFrame (two-dimensional labeled data table), which allow efficient handling of structured data. With Pandas, you can perform various data operations such as filtering, grouping, sorting, merging, and statistical computations. It also offers seamless integration with other libraries in the Python data ecosystem, making it a versatile tool for data wrangling and analysis.
Who this course is for:
- Aspiring Data Scientists
- Data Analysts and Business Intelligence Professionals
- Python Programmers Transitioning to Data Roles
- Students in Data-Focused Academic Programs
- Machine Learning and AI Practitioners
- Researchers and Academics
- Professionals Preparing for Technical Interviews or Certifications
- Lifelong Learners and Self-Taught Developers
Instructor
- 4.3 Instructor Rating
- 11,195 Reviews
- 431,744 Students
- 165 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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