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Updated in [July 27th, 2023]

This course provides an introduction to data cleaning and preprocessing in Python. It covers common issues with data, such as missing values, noise values or univariate outliers, multivariate outliers, data duplication, and improving the quality of data through standardizing and normalizing it, as well as dealing with categorical features. The course provides theoretical explanation, mathematical evaluation, and code for each concept. Lectures are organized into sections, with the first number referring to the section number and the second number referring to the lecture number within the section. All code is written in Python using Jupyter Notebook. Data cleaning is essential for building reliable machine learning models that can produce good results. Without sorting out the issues in the data, the results of even the most efficient models would be unreliable. This course provides the skills necessary to make useful analysis with business data acquired from multiple online sources.

Course Syllabus

Introduction

Detecting Missing and Noise Values (Univariate Outliers)

Handling Missing and Noise Values (Univariate Outliers)

Multivariate Outliers

Anomalies in Textual data

Structuring Textual Documents

Feature Scaling (Normalization)

Handling Categorical Features

Machine Learning Overview

Data Acquisition