Collaborative Data Science: Tools and Git Workflow

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Related lectures (32)

Page 3 of 4

Covers data science tools, Hadoop, Spark, data lake ecosystems, CAP theorem, batch vs. stream processing, HDFS, Hive, Parquet, ORC, and MapReduce architecture.

Critical Data Studies: Reproducibility and Renku

Explores the significance of reproducibility in data science and introduces Renku, a platform for managing data-driven projects.

Gaussian Mixture Models: Data Classification

Explores denoising signals with Gaussian mixture models and EM algorithm, EMG signal analysis, and image segmentation using Markovian models.

Distributed Version Control with Git

Explores Git's distributed version control, covering conflict resolution, collaboration management, and merging in software projects.

Introduction to GitHub Collaboration

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

Data Wrangling with Hive: Managing Big Data Efficiently

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

Monitoring with Icinga2

Explores the monitoring of a new VM and the use of Icinga2 for critical alerts and incident management.

Decision Tree Classification

Covers decision tree classification using KNIME Analytics Platform for data preprocessing and model creation.

Introduction to Git: Basics and Commands

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

Data Wrangling with Hadoop: Storage Formats and Hive

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