Lectures related to Data transformation (computing)

Data Modeling: Concepts and Applications

Explores data modeling concepts, SQL implementations, and practical applications in handling missing data.

Data Wrangling: Transforming Data for Analysis

Covers the process of data wrangling, focusing on transforming and preparing data for analysis.

Data Wrangling: Structuring and Wrangling Issues

Covers data wrangling stages, structuring techniques, and common issues in data preparation.

Offers a comprehensive introduction to Data Science, covering Python, Numpy, Pandas, Matplotlib, and Scikit-learn, with a focus on practical exercises and collaborative work.

Data Wrangling and Analysis

Covers a homework assignment on data wrangling and analysis using Python's pandas library for real-world datasets.

Data Wrangling with Hive: Managing Big Data Efficiently

Covers data wrangling techniques using Apache Hive for efficient big data management.

Data Science: Python for Engineers - Part II

Explores data wrangling, numerical data handling, and scientific visualization using Python for engineers.

Entity Resolution Techniques

Explores entity resolution techniques, data deduplication, similarity metrics, computational cost, blocking techniques, and scaling out similarity joins.

Nexus Forge: Building and Using Knowledge Graphs Made Easy

Explores Nexus Forge, a tool for creating and utilizing knowledge graphs effortlessly.

Temporality and Entity Resolution

Explores challenges in data temporality and techniques for entity resolution.

Data Wrangling with Hadoop: Advanced Techniques

Covers advanced data wrangling techniques using Hadoop, focusing on Hive and HBase integration.

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.

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.

Data Wrangling Techniques: HBase and Hive Integration

Covers data wrangling techniques using HBase and Hive, focusing on integration and practical applications.

Data Wrangling: Structuring and Cleaning Data

Explores data wrangling techniques, error detection, functional dependencies, denial constraints, and data temporality.

Data Wrangling with Hadoop

Covers data wrangling techniques using Hadoop, focusing on row versus column-oriented databases, popular storage formats, and HBase-Hive integration.

Collaborative Data Science: Tools and Techniques

Introduces collaborative data science tools like Git and Docker, emphasizing teamwork and practical exercises for effective learning.

Data Wrangling with Hadoop: Storage Formats and Hive

Explores data wrangling with Hadoop, emphasizing storage formats and Hive for big data processing.

Data Wrangling: ETL Process and Wrangling Issues

Explores the ETL process, data wrangling stages, and common issues.

Data Accuracy: Assessing Faithfulness and Error Detection

Explores data accuracy through faithfulness assessment, error detection, outlier handling, correlations, functional dependencies, violation detection, denial constraints, and data repairing techniques.