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Related lectures (32)

Page 1 of 4

Covers best practices and guidelines for big data, including data lakes, architecture, challenges, and technologies like Hadoop and Hive.

Data Issues in Research

Explores challenges in data assumptions, biases, and more in research, including incomplete write-ups and frustrations of newcomers.

Water Consumption in Geneva

Explores water consumption data in Geneva, including charts on consumption and losses, available datasets, and data processing phases.

Data Wrangling with Hive: Managing Big Data Efficiently

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

Introduction to Spark runtime architecture

Introduces Apache Spark, covering its key features, history, RDDs, architecture, and distributed computing framework.

Data Cleaning Challenges: Optimizing Error Detection

Addresses challenges in data cleaning for analysis, proposing optimizations to reduce processing time.

Introduction to Spark Runtime Architecture

Introduces Apache Spark, covering its architecture, RDDs, transformations, actions, fault tolerance, deployment options, and practical exercises in Jupyter notebooks.

Handling Data: Intro to Pandas

Introduces the fundamentals of handling data, emphasizing the importance of Pandas and data modeling for effective analysis.

Data Processing

Covers the processing of data from a chemical experiment using Excel.

Data Representations and Processing in Machine Learning

Covers data representations and processing techniques essential for effective machine learning algorithms.