Data wrangling is a crucial step in the data analysis process, involving the transformation and preparation of raw data into a suitable format. The "Fundamental Tools of Data Wrangling" course offered by University of Colorado Boulder provides participants with essential skills and knowledge to effectively manipulate, clean, and analyze data. This comprehensive course delves into the world of data manipulation using Python as the primary programming language, covering data structures, NumPy, and pandas. The course encompasses hands-on exercises and practical examples to ensure participants gain the necessary proficiency to work with various data formats and effectively prepare data for analysis.
Upon completion of the course, participants will be able to describe the fundamentals of programming in Python, identify data structures for efficient organization and manipulation of data, and practice using NumPy and pandas for numerical computing, data manipulation, and analysis.
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The "Fundamental Tools of Data Wrangling" course comprises modules covering Python fundamentals, data structures, NumPy basics, pandas, and a case study, providing participants with comprehensive knowledge and hands-on experience in data manipulation and analysis.
The Python module provides a comprehensive overview of Python programming, covering fundamentals, functions, and packages. Participants will gain practical experience through hands-on exercises and quizzes, enabling them to apply Python for data manipulation effectively.
The Data Structures module delves into various data structures, including lists, strings, sets, and dictionaries. Participants will learn the essential concepts of organizing and manipulating data efficiently using different data structures, with practical demonstrations and quizzes to reinforce learning.
The NumPy module introduces participants to the basics and advanced features of NumPy, including masks. Through practical demonstrations, practice labs, and quizzes, participants will develop proficiency in numerical computing and working with multi-dimensional arrays and matrices using NumPy.
The Pandas module focuses on Pandas Series, DataFrames, and advanced features, providing participants with a versatile library for data manipulation and analysis. Practical demonstrations, practice labs, and quizzes will enable participants to learn various techniques to clean, reshape, and aggregate data using Pandas.
The Case Study module offers participants the opportunity to apply the knowledge and skills acquired throughout the course to a real-world case study, enhancing their ability to derive valuable insights from messy datasets and effectively prepare data for analysis.
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