Jupyter Notebook

Mostly I use Jupyter Notebook to test some python code that I work on.

Jupyter Notebook is the most friendly software for testing and running python codes of all types. Its simple to illustrate and visualise and test a code line by line. Also if not downloaded you can still use it as a google collab notebook. Its easy and simple to use for data models, statistical visualisation and machine learning programs. I have been using it constantly in my research works.

I do all my python projects with it every day and the amount of convenience it provides is unmatchable. It's interface is very easy and self expressive which is why I recommend it the most. It comes with the code sharing feature which i use when I get stuck somewhere or having some bug or needed any advice from the team lead.

Jupyter Notebook is a coffee to me, a daily dose of it makes my day. I have been using Jupyter Notebook for various purposes from presenting a Proof of Concept to the client, to deploying it on the cloud, teaching new hires about their role, and explaining things to my team. Development is also super easy on Jupyter Notebook with its interactive environment where one can keep codes, comment, and plot images together on a single platform. It's readable and flexible. You can have host server-side also which is great and allows you to access your Notebook from anywhere around the globe. In my organization, Jupyter Notebook is used a lot for various reasons. It's presentable, powerful (not as powerful as IDE though but it has its own purpose), flexible, and most importantly its open source. [For] several years since the boom of Data Scientist fields Jupyter Notebook [has been] getting more and more popular among those who analyze data. I highly recommend Jupyter Notebook because it will make your life's work easy and fun, whether you are a manager who creates presentations for the clients, an engineer who develops code, or a Data Scientist/Analyst, Jupyter Notebook covers all.

Jupyter Notebook is one of the most important applications for my personal use and for the organization purpose also. It helps to develop open source software and for interactive computing across different programing languages. I'm using this since my college time, it supports Julia, Python and R. It allow us to create and share documents that contains live code and graphs.

Python is the new technology that is being deployed across organisation for data science. For every language, one must use a user friendly IDE that makes the code more streamlined, easy to understand and with added built in functionalities. Jupyter Notebook takes care of all these and adds a lot of value to the data science projects.

Jupyter notebooks are widely used by our Science and Analytics departments to analyze data, make forecasts, clean/wrangle data, graph visualizations, create machine learning models, and perform a suite of analyses to best understand our business landscape.

I use Jupiter Notebook to organize my workflows into manageable and readable chunks. The ability to streamline my work using markdown descriptions and title is extremely helpful for visually cueing transitions and reorienting the user to the pattern of the workflow outlined in the notebook. It also makes it easier to replicate the workflow for other projects.

Almost every large-scale enterprise today has started to have in-house ML models & my organization is no different. We have been making use of this wonderful tool for Data visualization purposes, consuming python libraries to get some insights from the larger datasets from different data sources. Also, we use it to train our ML models to make predictions & then use that to further fine-tune the models efficiently.

Jupyter Notebook is a really dynamic tool, currently used by the Engineering team at my company. The main focus of using it is to incorporate python programming while writing code to forecast sales. Jupyter gave me the flexibility of writing code in a line-by-line manner and help me see the output at each step.

It is used mostly by our Python and R programmers for programming. It is very interactive and easy to manage the programs and present them. Coding in it is very easy and we can also easily debug. We can do many things--change the cell types, use shortcuts, and if stuck somewhere restart the kernel.

As an organization, we are using Python for solving manual task creation by automatically doing this task. So for this we use the RPA technology, but for that we have to scrape the data from the PDF. So we use the Python library from which we read the data which in the form of image then create the CSV table by using the library.

We are an E-learning company. We use Jupyter Notebook in our lab's virtual environment.

Jupyter Notebook is used widely within our data science groups as a way to experiment with ML and other models. It's great for early stage data analysis as well as for training and sharing of new Python models within the group in a clear way. The interface is easy to use and onboard onto and has been a great way for members to easily share and onboard new associates onto their existing Python analysis scripts.

We are using Jupyter Notebook for data visualization of JSON and excel data for a banking customer. We are running multiple python scripts on Jupyter Notebook for in-depth data analysis to develop machine learning models.

Jupyter Notebook is used by the profiling department and we use it for data analysis and modeling.

Jupyter Notebook is used for interactive and literate programming for Python and R. It is deployed server side, and it is used across the organization to allow users to code through their web browser. It gives users access to Python and R programming, without needing the users to maintain it.

Jupyter Notebook was first used by me for a project for my Big Data Class to handle data in csv, excel, json, etc. I used python and a lot of flexible libraries in it to do data analysis on our banking information like documents, statements, reports etc. and draw visualizations on it.

We currently use Juptyer Notebooks across our organization. Jupyter Notebooks are our go-to experimenting environments for data pre-processing, creation, training and evaluation of machine learning and deep learning models. We also heavily use Jupyter for data visualization and exploratory data analysis. It also provides a great interactive interface which can be used for story telling to our clients and consumers.

Jupyter Notebook is an amazing way to see the code work and plots displayed in an awesome in-line perspective. We really enjoy it especially when plotting data analysis and comparisons. It is used by many of us at my department.

Jupyter Notebook is being used currently at my organization to handle data in any of the forms including csv, excel, json, etc. We use python and a lot of flexible libraries in it to do data analysis on our banking information like documents, statements, reports etc. and draw visualizations on it.

I use Jupyter Notebook to run Python codes for data science and visualization. It is being used by the data analytics department and it allows the team to work on analytics projects and write insightful reports.

I use Jupyter Notebook to run statistical analysis and develop machine learning models and deploy in production. Jupyter Notebook is widely adopted by data scientists in our organization. It's well integrated with our remote cloud desktop as well as production system. We use Jupyter Notebook to understand customer behaviors and how to improve customer experience.

I started using Jupyter to solve my everyday problems and to do things faster. I'm aware that there a few users already across the organization. I have been recommended to use it and I have also recommended this fantastic tool. With Jupyter, it's really easy to break the problem into small pieces and develop larger, more productive code. It's also easy to enhance without breaking the remaining part of code. I develop working code as and when time permits, integrate with different tools (through API, DB calls, etc), and even plot beautiful graphs all in one notebook. The best thing is that the results are saved when I reopen the notebook.