Data Wrangling: Structuring and Cleaning Data

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Related lectures (32)

Page 3 of 4

Data Structuring: Intrarecord and Interrecord Techniques

Covers data structuring techniques, error detection, and functional dependencies within records.

Introduction to Spark Runtime Architecture

Covers the Spark runtime architecture, including RDDs, transformations, actions, and caching for performance optimization.

Elements of Collaborative Data Science

Introduces collaborative data science tools like Jupyter notebooks, Docker, and Git, emphasizing data versioning and containerization.

Data Science Innovations: Insights, Challenges, and Future

Explores advancements in data science, focusing on fast insights, data variety, and real-time intelligent data systems.

Data-Intensive Applications and Systems: Overview

Covers the exponential growth of data, challenges in processing technology, data variety, cleaning, approximate query processing, multi-query analytics, and hybrid transaction processing.

Data Stream Processing: Apache Kafka and Spark

Covers data stream processing with Apache Kafka and Spark, including event time vs processing time, stream processing operations, and stream-stream joins.

Big Data: Processing and Dimensions

Explores Big Data generation, storage, processing, and dimensions, along with challenges in data analytics, cloud computing elasticity, and security.

Data formats and data wrangling with Hadoop

Explores Apache Hive for data warehousing, data formats, and partitioning, with practical exercises in querying and connecting to Hive.

Feature Engineering: Polynomial Regression

Covers fitting linear regression on features of the original predictors for flexible feature representation.

Building DH Portal: Data Platform for Researchers

Showcases Haeeun Kim's internship experience in enhancing researchers' data cleaning and organizing processes.