Collaborative Data Science: Tools and Techniques

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Data Science: Python for Engineers - Part II

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

Scientific Programming for Engineers: Introduction to GIT

Covers the basics of scientific programming for engineers, emphasizing the importance of GIT for collaborative work and providing insights into challenges in scientific software development.

Python Test: Introduction

Introduces Python and Git for analyzing soil humidity data in an orchard.

Introduction to Renku

Introduces Renku, a platform for collaborative data science, emphasizing reproducibility, shareability, reusability, and security.

Big Data Best Practices and Guidelines

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

Data Science for Engineers: Part 2

Explores data manipulation, exploration, and visualization in data science projects using Python.

Version Control System: Basics and Workflow

Introduces the basics of Version Control Systems, focusing on Git operations and branching strategies.

Collaborative Data Science

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

Introduction to Jupyter Notebook

Introduces setting up the environment, Git basics, and Jupyter Notebook features with practical examples.

Introduction to Data Science

Introduces the basics of data science, covering decision trees, machine learning advancements, and deep reinforcement learning.