Ementorhub

What you'll learn

Describe the Data Scientist’s tool kit which includes: Libraries & Packages, Data sets, Machine learning models, and Big Data tools

Utilize languages commonly used by data scientists like Python, R, and SQL

Demonstrate working knowledge of tools such as Jupyter notebooks and RStudio and utilize their various features

Create and manage source code for data science using Git repositories and GitHub.

Skills you'll gain

Version Control

Machine Learning

Query Languages

Cloud Computing

Git (Version Control System)

GitHub

Application Programming Interface (API)

Data Visualization Software

Big Data

Other Programming Languages

Data Science

Jupyter

Statistical Programming

R Programming

There are 7 modules in this course

You will become familiar with the Data Scientist’s tool kit which includes: Libraries & Packages, Data Sets, Machine Learning Models, Kernels, as well as the various Open source, commercial, Big Data and Cloud-based tools. Work with Jupyter Notebooks, JupyterLab, RStudio IDE, Git, GitHub, and Watson Studio. You will understand what each tool is used for, what programming languages they can execute, their features and limitations. This course gives plenty of hands-on experience in order to develop skills for working with these Data Science Tools. With the tools hosted in the cloud on Skills Network Labs, you will be able to test each tool and follow instructions to run simple code in Python, R, or Scala. Towards the end the course, you will create a final project with a Jupyter Notebook. You will demonstrate your proficiency preparing a notebook, writing Markdown, and sharing your work with your peers.

Languages of Data Science

Packages, APIs, Data Sets, and Models

Jupyter Notebooks and JupyterLab

RStudio & GitHub

Create and Share your Jupyter Notebook

[Optional] IBM Watson Studio