Hands-on Labs

Getting Started with Data Science – Jupyter, R on Ubuntu Machine

Whether you're a student, professional, or enthusiast looking to enter the field of data science, this course provides a comprehensive introduction to Jupyter, R, and data science principles.

Lab Description

The "Getting Started with Data Science - Jupyter, R on Ubuntu Machine" hands-on lab is a comprehensive program designed to provide participants with the necessary knowledge and practical skills to embark on their data science journey using Jupyter notebooks and the R programming language on an Ubuntu machine.


Getting Started with Data Science - Jupyter, R on Ubuntu Machine lab is suitable for beginners and individuals with some programming experience who are interested in entering the field of data science.

Learning objective

Throughout the lab, participants will be introduced to the fundamentals of data manipulation, analysis, and visualization using Jupyter Notebooks. They will gain a solid understanding of the data science workflow, starting from data preprocessing and exploration to statistical analysis, machine learning, and data visualization.
The lab will cover essential topics in data science, including data cleaning, feature engineering, hypothesis testing, regression analysis, classification, clustering, and dimensionality reduction. Participants will learn how to perform these tasks using the R programming language and leverage its vast ecosystem of packages and libraries for data analysis and modeling.

Lab Exercises
Instruction with the Lab environment
Log in to JupyterLab Portal
Execute Jupyter Notebooks with Python
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A hands-on lab is a virtual environment where you can actively engage in practical exercises and simulations related to a specific subject or technology. It allows you to gain real-world experience and develop practical skills.

Upon enrolment, student will receive voucher code  and access instructions to launch the lab environment.

No, generally, MOC Hands-On Labs are designed to be completed in one continuous session. Once you start a lab, it's recommended to allocate enough time to finish it in one go.

Only a system with internet access and a compatible web browser.

Yes, CloudLabs Hands-on labs often come with built-in tracking and reporting features. These features allow you to monitor trainee progress, track completion rates, and generate reports on lab performance. This enables you to assess individual or group achievements and measure the effectiveness of your training initiatives.

CloudLabs have the SLA of 15 days to update the Labs.

Learners are typically allowed to access the lab once.

We have CloudLabs Shadow which is an embedded feature integrated into the CloudLabs platform that lets Instructors shadow the learners environment.

Cloud Labs support team is available 24/7 for 365 days over email and live chat.

  1. Email Support: labs-support@spektrasystems.com 
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This Hands-on lab includes
Getting Started with Data Science - Jupyter, R on Ubuntu Machine
01 Day
03 Exercises

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