CENG 303/503-Wk-3A-Data Cleaning and Exploratory Data Analysis

This video, part of a Data Science & Analytics course provides an overview of Data Cleaning and Exploratory Data Analysis (EDA), two essential stages of the data science workflow. This session highlights the importance of preparing accurate, consistent, and reliable data before analysis and introduces common data quality challenges such as missing values, duplicate records, inconsistent formats, incorrect data types, and outliers. The video also explores the purpose of exploratory data analysis, including the use of descriptive statistics, data distributions, correlations, visualizations, and feature relationships to understand a dataset. You will gain a clear picture of how data cleaning and EDA help uncover trends, detect anomalies, generate insights, and prepare data for statistical analysis and machine learning. #DataScience #DataAnalytics #DataCleaning #ExploratoryDataAnalysis #EDA #Python #Pandas #DataVisualization #MachineLearning #DataPreprocessing #Statistics #JupyterLab