Skip to main content

Building Solutions on Data Analysis with Python and Jupyter Notebook

To collect, analyze, and organize data to make future predictions, and make informed data-driven decisions using Python and Jupyter Notebooks.
Jupyter Notebook is an open-source web application that provides an interactive computational environment. It generates documents that compile inputs (code) and outputs into a single file (notebooks). With Jupyter Notebook, you can create an interactive document that can be shared, reused, and edited by others by combining code, comments, multimedia, and visualizations.

Let's Discuss Now
Jumpstart your project today the  smart  way
Let's Discuss Now

Jupyter Notebook Services

Data importing
Data pre-processing
Data cleansing
Statistical analysis
Exploratory data analysis
Data Visualization
Machine learning Model
Data Automation

Libraries we can use

Sci-kit learn

Feature of   Jupyter Notebook


Code sharing

Code-sharing options are offered by cloud services like GitHub and Pastebin, however, they are primarily non-interactive. You can examine code, run it, and see the results in your web browser by using a Jupyter Notebook.

Live interactions with code

The code in Jupyter Notebooks is dynamic; it may be changed and run incrementally again in real time while receiving feedback right in the browser. Additionally, user controls that can be used as code input sources can be incorporated into notebooks (such as sliders or text input fields).

Documenting code samples

You could include some code in a Jupyter Notebook if you wanted to show how it functions line by line with real-time feedback. The code will still function properly, allowing you to add interaction and explain things while also showcasing them.

Data visualizations

Most individuals are first introduced to Jupyter Notebook through a data visualization, which is a shared notebook that includes a graphic representation of a particular data collection. You can create visualizations with Jupyter Notebook, share them with others, and make interactive changes to the shared code and data collection.

Functions in Jupyter Notebook

Exchange notebooks

You may now share your notebooks with others using email, Dropbox, GitHub, and the Jupyter Notebook Viewer thanks to this feature.

Producing Interactive Results

Generate interactive content effortlessly, including HTML, images, videos, LaTeX, and unique MIME types. Includes high-quality inline figures and seamless integration of mathematical notations with MathJax.

Easily Removed

Now that you can edit the content before publishing your book online, you can also edit the code only, allowing the image and other outputs to still appear

Code Execution

Additionally, this has the capability of executing scripts directly from the browser while keeping track of computation results in the code that originally generated them.


Rich text can be edited in-browser using the Markdown markup language, which is not just restricted to plain text but also gives commentary for the code. To give your postings cool effects, you may also embed photos, HTML, and other outputs inside them.

Language Selection Varieties

Since the notebook supports more than 40 programming languages, including Python, R, Julia, and Scala, you can select the language you like.

Why Use a Jupyter Notebook?

All in one Place

In a single document, you might combine code, text, photos, animations, plots, maps, figures, GUI, and videos.


Programming languages

Over 40 programming languages, including R, Python, JavaScript, Haskell, and others, are supported by Jupyter notebooks.


Simple to convert and share

Because notebooks are saved in the JSON format and include a unique application that can convert them into HTML and PDF, sharing them is simple.


Easy Customization

The Jupyter interface makes it simple to design a completely unique experience.


Powerful Learning and Teaching Tools

By utilizing features like IPython Blocks and others, it can be used for teaching in addition to data analysis or scientific study.


Integration of Big Data

You can use various big data tools with it, including Scala and Apache Spark.


Tools that We Use

Jupyter Notebook
Google colab

Affordable, Reliable, Responsive, Smart - BitCot is all of those things, which were and are important to my growing company's needs. They have knowledgeable resources that really know technology. I look forward to working a lot more with them in the future for Website and Apps Development!

Cat KomFounder, Studio SWEAT onDemand

I discussed the concept with Raj, he really helped me to map out the plan & strategy to get my concept into a real software platform. If you have an idea and you want to make it real, I would recommend Raj and his team.

Patrick HadleyFounder, Biglio

Raj and his team at BitCot has made me a believer. BitCot delivers quality products, timely communication and most importantly hits committed timelines. I was so impressed with BitCot’s speed and standards that I’ve asked them to begin a second project.

Joe RobertsFounder, Poured

BitCot have been excellent development partners for Stomp Sessions. BitCot's rapid development approach works in concert with our highly iterative process.

Robert SuarezCo-Founder- StompSessions