Lesson 9: Principles of Data Science by Mohammad Hajiaghayi: Data Wrangling and Cleaning
In this session, guest lecturer Dr. Arefeh Nasri talks about data wrangling and cleaning which is a very important task in data science. More precisely we focus on data wrangling, cleaning, and entity resolution. Preceded by previous lectures on data modeling and databases, we now delve into preparing data for analysis. Data wrangling involves structuring data to facilitate analysis, often consuming the bulk of a data scientist's time. Various sources provide data, from web pages to surveys, with platforms like data.gov offering open datasets. However, transforming and cleaning this data for analysis requires careful consideration of inconsistencies and missing values, typically utilizing ETL (extract, transform, load) processes.
Data cleaning involves addressing inconsistencies and errors in datasets, a crucial step before analysis. Issues like inconsistent units of measurement or duplicate entities must be resolved. Additionally, entity resolution is vital for identifying and linking related data points across different datasets. This process involves clustering similar records and determining representative entities, often a complex task in large datasets with noisy data.
Ultimately, efficient data wrangling and cleaning are essential for reliable analysis. While time-consuming, these steps ensure that data is structured and accurate, laying the groundwork for meaningful insights. In this session, we explore techniques for handling data inconsistencies, linking related entities, and clustering data for analysis, setting the stage for more advanced data science tasks.
#datawrangling
#DataCleaning #EntityResolution #DataPreparation #ETL #DataAnalysis #DataScience #OpenData #DataQuality #StructuredData, #DataInconsistencies #MissingValues #DataClusters #DataRepresentation #DataInsights #DataTransformation #DataCuration #DataSources #DataAccuracy #ETLProcesses #DataQualityIssues #NoisyData.
