Python Test: Introduction

Related lectures (32)

Page 3 of 4

Introduces the basics of Git, covering setting up repositories, committing changes, and pushing modifications.

Covers the basics of GIT, including cloning, committing modifications, managing branches, and using remotes.

Showcases the basics of collaborative work on GitHub through examples of creating repositories, making commits, and managing branches.

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

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

Introduces NumPy and Matplotlib, essential tools for scientific computing and data visualization in Python.

Covers the analysis of air pollution data, focusing on R basics, visualizing time series, and creating summaries of pollutant concentrations.

Covers collaborative data science tools, big data concepts, Spark, and data stream processing, with tips for the final project.

Covers the fundamentals of big data ecosystems, focusing on technologies, challenges, and practical exercises with Hadoop's HDFS.

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