Raw data often contains missing values, outliers, and irrelevant features that can hinder the success of data science projects. In this course, Data Science with Python: Munging, Outliers, and Feature Engineering, you’ll gain the ability to preprocess and engineer features from raw data effectively using Python. First, you’ll explore techniques for handling missing data and imputing missing values to ensure your datasets remain informative and reliable for analysis. Next, you’ll discover methods for detecting and treating outliers using statistical and machine learning approaches, developing strategies to handle them appropriately. Finally, you’ll learn how to create meaningful features from raw data, transform categorical variables, and optimize datasets for efficient analysis by dropping irrelevant columns and reordering them for better readability and processing. When you’re finished with this course, you’ll have the skills and knowledge of data munging and feature engineering needed to tackle real-world data science problems, transform raw data into clean, informative datasets, and enhance the performance of your machine learning models.
Author Name: Dr. Ali Feizollah
Author Description:
Hello, I’m Ali. I have a PhD in computer science. I am currently conducting research on natural language processing (NLP) using Python and machine learning. I have a YouTube channel, named Flutterware, that shares tutorial on Google’s Flutter platform that is used to develop mobile applications for Android and iOS with one base code. I love research and exploring knowledge. I think acquiring knowledge without sharing it is useless. I hope that you find my classes helpful.