Data Cleaning and Preprocessing using Python

About this course

Have you ever wanted to apply Machine Learning, create impactful visualizations, or generate solid reports… but realized that your data is messy, incomplete, or poorly structured?This course was designed to help you avoid those obstacles from the very beginningThroughout this course, you will learn—simply and step by step—the basics of Python and everything necessary to clean and preprocess data like a professionalWhat will you learn?Fundamentals of Python and data structuresHow to install and work with Jupyter Lab and Anaconda PromptTechniques for importing, exploring, and transforming CSV, Excel, JSON files, and moreWorking with key data structures such as lists, dictionaries, arrays, series, and DataFramesCleaning null, duplicate, and incorrect dataTo detect outliers using different techniquesPreprocessing to leave your data ready for models and analysisIn addition, each video includes downloadable scripts and example files so you can practice directly in your own environment, without needing to type everything from scratchThis course is ideal for:People who are just starting in data scienceStudents in tech- or business-related fieldsProfessionals who need to process data but don’t come from a technical backgroundThroughout the course, we’ll be using a carefully selected set of Python tools and libraries that are widely adopted in the data science industry such as Pandas, Numpy, scikit-learn, Matplotlib and Seaborn, using Jupyter Lab to document the entire process clearly and to debug our codeWe will cover both basic data cleaning techniques and more sophisticated methods used in machine learning<