Introduction
Big data science is a new discipline that combines many disciplines, competencies, skills, tools, and techniques. To process, analyze, and visualize big and voluminous data. In recent times, the need for data scientists has been drastically felt. Consequently, many tools and platforms have been created. One of the tools that have been extensively used in the last years is Jupyter Notebook. Here we will discuss Jupyter notebook for data scientists along with pros and Coins.
Jupyter Notebook is a web based interactive development platform for over 40 languages including Python, R and Julia. It’s code and comments, as well as an interactive visualization in one document, shareable with anyone—it’s a must have tool for data scientists everywhere. In this article we’ll be outlining Jupyter notebook for data scientists.The pros and cons of Jupyter Notebook for data scientists.
What are the benefits of using Jupyter Notebook for data scientists?
1. Two things that can be linked to this are interactivity and reproducibility.
I think one of Jupyter Notebook’s strengths is the flexibility to do all of these things in one notebook. Write and intermingled code, long form notes, and visualizations. It’s easy to share work, because the other data scientists can understand your work and replicate your work as well. Additionally, because it will be defined in section, this type of tool is extremely engaging as users can enter code in a notebook and see the results right away. This enables the switching back and forth and trying, which is an important part of the data science process.
2. Multilingual support
Data Scientists need programming languages depending on their specialization, and currently Jupyter Notebook supports about 40+ languages. It also serves as a language support for the purpose of writing, testing and visualization of the coded language for the users’ interest. It is also helpful for the data scientist to learn languages and or change the process followed to meet the need as is required.
3. Combined Libraries and Tools
An extensive library and tool set is complimented by the Jupyter Notebook, which can be imported and used directly within the notebook interface. NumPy, SciPy, Pandas, and Matplotlib as libraries are used by data scientists to manipulate and visualize data, for example. Jupyter Notebook, also, works well with several visualization tools such as Plotly, Bokeh, and Seaborn. Through which users can produce attractive and high-quality graphs.
4. Easy to use when you want to share files or work together.
Tools such as GitHub/GitLab and JupyterHub can be used to share and publish Jupyter Notebooks themselves to the web. This feature enables teams to work on projects irrespective of their geographical location and to review and contribute to the projects. Moreover, the work done in Jupyter Notebooks can be exported to HTML, PDF and LaTEX formats for sharing with stakeholders, sharing at seminars or conferences.
Reasons why not Jupyter Notebook for data scientists?
1. Performance Issues
Jupyter Notebook for data scientists with disadvantages that the program is slow. However, it’s a browser-based app so it can be a little slow with its execution. For example, when you are performing calculations on large data sets. However, this can lead to an undesirable user experience and can restrict productivity amongst the users
2. Limited User Interface
One of the disadvantages of Jupyter Notebook for data scientists is that it gives its users an articulate user interface. Which may not feel friendly and easy to work with as other data analysis software as a whole. As the number of cells becomes large, the basic interface could get noisy. Due to this users might find it difficult to find their code or the specific visualization components.
3. Compatibility Issues
However, Jupyter Notebook gives language support, so it can be a compatibility issue with many libraries and other tools. There are Likely library collisions at times that can pause the whole functioning of the notebook.
4. Security Concerns
Note that Jupyter Notebook was developed by the University of Michigan and therefore, it is open source environment . It can have security issues when working in notebooks on the internet. There are possibilities of leakage of this information and so users should be very careful with the notebooks. Users share with other users containing the information that should be kept private.
Conclusion
Jupyter Notebook is an inalienable asset for every data scientist apart from that, it has several advantages. It is very easy to collaborate with others, the working process is interactive, and the results can be reproduced. However, it’s important to understand its inconvenience such as lag, restraint in graphics interface, compatibility issues and security risk. Now, having discussed the advantages and disadvantages of Jupyter Notebook for data scientists can then decide whether or not to make it part of their processes and if not, they can look for something else that will better fit their data science project.