R and Python coding with Prython

R and Python coding with Prython, available at Free, has an average rating of 4.25, with 8 lectures, based on 21 reviews, and has 6898 subscribers.

You will learn about Learn how to use Prython for coding both R and Python projects Design complex data science projects in Prython This course is ideal for individuals who are Python and R practitioners with a focus on data science or ML engineers or Statisticians, engineers, and economists designing statistical models It is particularly useful for Python and R practitioners with a focus on data science or ML engineers or Statisticians, engineers, and economists designing statistical models.

Enroll now: R and Python coding with Prython

Summary

Title: R and Python coding with Prython

Price: Free

Average Rating: 4.25

Number of Lectures: 8

Number of Published Lectures: 8

Number of Curriculum Items: 8

Number of Published Curriculum Objects: 8

Original Price: Free

Quality Status: approved

Status: Live

What You Will Learn

Learn how to use Prython for coding both R and Python projects
Design complex data science projects in Prython

Who Should Attend

Python and R practitioners with a focus on data science
ML engineers
Statisticians, engineers, and economists designing statistical models

Target Audiences

Python and R practitioners with a focus on data science
ML engineers
Statisticians, engineers, and economists designing statistical models

In this course we will learn how to use Prython, which offers a different way of coding than existing R/Python IDEs. It allows us to drop our code into panels that we place and connect in a canvas. In a normal IDE your code will run linearly from start to end, making it really hard to create sub-experiments/tests, and also to organise your project clearly. In Prython each panel accepts multiple IN and OUT connections, effectively transforming it into a 2D Jupiter notebook. It also has a wide array of tools that complement this canvas functionality: such as displaying dataframes next to the panels that modified them, allowing you to freeze your outputs, attaching consoles, navigation markers, etc.

We assume that the student is already familiar with R or Python, and some familiarity with matplotlib, scikit-learn,or keras would be beneficial as well.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Installing

Chapter 2: Core Prython components

Lecture 1: Panels

Lecture 2: Consoles

Lecture 3: Markers

Lecture 4: Linkedpanels

Lecture 5: Extra tools for organising our projects

Lecture 6: Exporting

Instructors

Francisco Juretig
Mr

Rating Distribution

1 stars: 0 votes
2 stars: 3 votes
3 stars: 2 votes
4 stars: 8 votes
5 stars: 8 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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