Additional Cloud Based Jupyter Environments

-

 Welcome to the tutorial on "Additional Cloud-Based Jupyter Environments". By the end of this tutorial, you will be able to understand and work with cloud-based Jupyter environments effectively.


Introduction to Cloud-Based Jupyter Environments:


Cloud-based Jupyter environments provide a convenient platform for data scientists and developers to create, modify, and execute Jupyter notebooks online. These environments offer various data science features and tools to enhance productivity.


Key Features and Tools:


Computational Notebooks: Computational notebooks, such as Jupyter notebooks, combine code, computational output, explanatory text, and multimedia resources in a single document, facilitating interactive and reproducible data analysis.


Popular Cloud-Based Environments:


JupyterLite: JupyterLite is a lightweight tool built from JupyterLab components, designed to execute entirely in the browser. It offers support for interactive graphics and visualization libraries like Altair, Plotly, and ipywidgets.

Google Colaboratory (Google Colab): Google Colab is a free Jupyter notebook environment that runs entirely in the cloud. It provides seamless integration with Google Drive and GitHub, pre-installed machine learning and visualization libraries, and the ability to share and collaborate on notebooks effortlessly.

Navigating Cloud-Based Jupyter Environments:


To launch JupyterLite, simply open a browser and navigate to jupyter.org/try-jupyter/lab. Then, select the desired kernel and start working on your notebook.

For Google Colab, you can open a notebook by accessing Google Drive, selecting "More" from the menu, and then choosing "Google Colaboratory". Write and execute code directly within the notebook interface.

Identifying Tools in Cloud-Based Environments:


In JupyterLite, you can identify the kernel being used by checking the kernel indicator, which displays information such as the Python Pyodide kernel for running Python packages in the browser.

Google Colab offers a user-friendly interface for writing and executing code, with options to add code or text cells, as well as support for Markdown cells for rich text formatting.

Conclusion:


Cloud-based Jupyter environments like JupyterLite and Google Colab provide convenient and powerful platforms for data science and development tasks. By leveraging these environments, data scientists and developers can streamline their workflow, collaborate with others, and develop innovative data science applications with ease.


Additional Information:


Anaconda Navigator GUI can launch multiple applications, including JupyterLab and VS Code, providing additional flexibility and options for working with Jupyter environments.

Other open-source Jupyter environments, such as JupyterLab and VS Code, offer additional features and customization options to suit different user preferences and requirements.




Comments

Post a Comment

Popular posts from this blog

Lila's Journey to Becoming a Data Scientist: Her Working Approach on the First Task

-
  Education and Skill Acquisition: Lila, with an economics background, decides to transition to data science. Enrolls in the IBM Data Science Professional Certificate for comprehensive training. Gains proficiency in statistics, machine learning, and programming languages. Building a Strong Foundation: Develops a deep understanding of data manipulation and visualization using Python libraries. Visualization for Storytelling: Learns to create informative visualizations, aiding in effective communication of findings. Hands-On Experience: Engages in Kaggle competitions and personal data projects for practical experience. Establishes a GitHub profile to showcase her projects and skills. Data Wrangling and Preprocessing: Focuses on data cleaning, preprocessing, and handling missing data and outliers. Communication and Storytelling: Hones data storytelling skills using tools like Matplotlib and Plotly. Networking and Collaboration: Actively participates in data science communities, attend...
Read more

Notes on Hiring for Data Science Teams

-
  Unicorn Myth in Hiring: Companies often seek individuals with a myriad of skills for data science roles. Ideal candidates possess domain-specific knowledge, excel in data analysis, and have strong presentation and storytelling skills. However, finding such a "unicorn" is rare, and the focus should shift to other essential qualities. Resonance with Company DNA: The key is to identify applicants who resonate with the company's values and business domain. While analytics skills can be taught, passion for the business is intrinsic and contributes significantly to productivity. Curiosity Matters: Curiosity is a top priority in hiring for a data science team. Candidates should demonstrate a broad curiosity, showing interest in various aspects of their surroundings beyond just data science. Sense of Humor: A lighthearted approach is crucial, as being too serious may hinder collaboration and creativity. A sense of humor aids in navigating challenges and fosters a positive work ...
Read more

switch functions

-
  Nested IF Function : The  nested IF  function allows you to perform tests with multiple conditions and return different values based on the results of those tests. It is called “nested” because one IF function is embedded inside another IF function. Syntax of a generic nested IF formula: =IF(condition1, result1, IF(condition2, result2, IF(condition3, result3, result4) ) ) Each subsequent IF function is embedded into the  value_if_false  argument of the previous function. SWITCH Function : The  SWITCH  function is a more compact alternative to nested IF statements. It compares an expression against a list of values and returns the result according to the first matching value. Syntax of the SWITCH function: =SWITCH(expression, value1, result1, [default or value2, result2], ... [default or valueN, resultN]) It has four arguments: expression : The value to compare against the list of values. valueN : A value com...
Read more