Pandas for Data Wrangling: Core Skills for Data Scientists, Master data analysis with Pandas and Python through hands-on projects and real-world case studies..
Course Description
Welcome to the “Data Analysis with Pandas and Python” course! This course is designed to equip you with the essential skills and knowledge required to proficiently analyze and manipulate data using the powerful Pandas library in Python.
Whether you’re a beginner or have some experience with Python programming, this course will provide you with a solid foundation in data analysis techniques and tools. Throughout the course, you’ll learn how to read, clean, transform, and analyze data efficiently using Pandas, one of the most widely used libraries for data manipulation in Python.
From understanding the basics of Pandas data structures like Series and DataFrames to performing advanced operations such as grouping, filtering, and plotting data, each section of this course is crafted to progressively enhance your proficiency in data analysis.
Moreover, you’ll have the opportunity to apply your skills in real-world scenarios through case studies and projects, allowing you to gain hands-on experience and build a portfolio of projects to showcase your expertise.
By the end of this course, you’ll have the confidence and competence to tackle a wide range of data analysis tasks using Pandas and Python, empowering you to extract valuable insights and make informed decisions from diverse datasets. Let’s embark on this exciting journey into the world of data analysis together!
Section 1: Pandas with Python Tutorial
In this section, students will embark on a comprehensive journey into using Pandas with Python for data manipulation and analysis. Starting with an introductory lecture, they will become familiar with the Pandas library and its integration within the Python ecosystem. Subsequent lectures will cover practical aspects such as reading datasets, understanding data structures like Series and DataFrames, performing operations on datasets, filtering and sorting data, and dealing with missing values. Advanced topics include manipulating string data, changing data types, grouping data, and plotting data using Pandas.
Section 2: NumPy and Pandas Python
The following section introduces students to NumPy, a fundamental package for scientific computing in Python, and its integration with Pandas. After an initial introduction to NumPy, students will learn about the advantages of using NumPy over traditional Python lists for numerical operations. They will explore various NumPy functions for creating arrays, performing basic operations, and slicing and dicing arrays. The section then seamlessly transitions to Pandas, where students will learn to create DataFrames from Series and dictionaries, perform data manipulation operations, and generate summary statistics on data.
Section 3: Data Analysis With Pandas And Python
This section focuses on practical data analysis using Pandas and Python. Students will learn about the installation of necessary software, downloading and loading datasets, and slicing and dicing data for analysis. A case study involving the analysis of retail dataset management will allow students to apply their newfound skills in a real-world scenario, gaining valuable experience in data management and analysis tasks.
Section 4: Pandas Python Case Study – Data Management for Retail Dataset
In this section, students will delve deeper into a comprehensive case study involving the management of a retail dataset using Pandas. They will work through various parts of the project, including data cleaning, transformation, and analysis, gaining hands-on experience in handling large datasets and deriving actionable insights from them.
Section 5: Analyzing the Quality of White Wines using NumPy Python
The final section introduces students to a specific application of data analysis using NumPy and Python: analyzing the quality of white wines. Through file handling, slicing, sorting, and gradient descent techniques, students will learn how to analyze and draw conclusions from real-world datasets, reinforcing their understanding of NumPy and Python for data analysis tasks.
