Data Science notebooks have been around for over 10 years and are used by most data scientists. These interactive computing tools can be used for writing codes, executing them, visualising results and sharing insights. A sound knowledge of data science notebooks is necessary if you already have experience in data science and aspire to climb the professional ladder. Most organisations have heavily invested in data science and other tools that allow them to work efficiently and experiment with data rapidly. Data Science notebooks are at the crux of this, as they are part of most innovations seen in data stacks today. A data science certification course gives you the basic notebook data science training, helping you understand how these can be helpful to you in the future.
Notebooks have a rich history that can be traced back to the early 1980s, predating data science. Some of the most popular data science notebooks today are Jupyter or RMarkdown; data scientists have used these for a long time.
In 1984, Donald Knuth introduced literate programming, which was meant to create programs humans can read. The concept was that program logic was written out in human language while snippets of code and macros were in a separate comment, and this was called the WEB.
The preprocessor would parse the WEB and create a source code and documentation. Axiom is an example of literate code that is still being used even today, and it is the crucial ideals behind literate programming that have helped in the development of what we now call notebooks.
The first notebooks, Wolfram Mathematica and Maple were released in the late 1980s. These operated with a kernel in the back end and a front end. While both contained the basic ideas that significantly influenced the way modern data science notebooks are designed, they had a few subtle differences in the display of mathematical operations.
Previously, the cost was one of the critical barriers to the widespread adoption of these tools, as they were expensive and needed a licence. As they say, necessity is the father of creation, and this problem led to the creation of the Open Source Initiative in 1998, which brought about the many free tools that most of us use today.
It was around 2001 that IPython and SciPy were released, and just two years later, Matplotlib followed. While SciPy allowed you to carry out a range of scientific calculations in Python, IPython improved your terminal experience and provided added support for distributed computing.
In 2005, a few available open-source tools were brought together into a new one called SageMath. The end goal for this was to provide you with an alternative to Mathematica and Maple. This building and combining other features and tools over existing ones and providing them as open-source web-based components have become a staple in most data science notebooks.
Jupyter was born out of IPython in 2014, and this introduced the notebook interface and the concept of taking some languages that it supports. Data science notebooks like Julia, R.Jupyter, and Python are some of the most widely used notebooks today and are the go-to options for data scientists today.
Creating apps and separate front ends that users can access are two of the main areas of development in recent years. As the data science notebooks became more popular and were adopted by more data scientists, a new wave of individuals started using modern notebooks to make their work easier.
Going forward, you, as a data scientist, must keep an eye on three significant developments in the data science notebook space.
A citizen data scientist has the technical skills needed to be a data scientist, a bit different from the traditional training and background associated with statistics, data science, and computer science.
As data science becomes more popular, you may notice increasing branching out of it into other fields. Citizen data scientists are considered the face of data-driven organisations today, and data science notebooks are the perfect tool to facilitate experimentation and shareable insights.
Data science notebooks allow you to explore and analyse datasets quickly. While they were previously lacking in aspects like sharing and experimentation, newer versions have been able to solve these problems to some extent. Here is how modern data science notebooks can boost productivity, collaboration, and efficiency.
One of the most common collaboration tools used today is Google Docs. You can edit documents and allow others to view and edit your papers or make suggestions. You can also save the changes regularly to ensure you do not lose out on any data by mistake.
Similarly, a few companies allow data science notebooks to be shared between multiple users. You and your team can run, edit, and leave comments on a notebook in real-time, making the process more efficient. Anyone who works with data knows how essential collaboration is for work, and it can result in reducing data silos and sharing expertise and knowledge between the team.
While some forms of collaboration between the team are slow, many modern tools can seamlessly be integrated into the existing tech stack to allow cooperation between groups.
Suppose you are already in the field of data science. In that case, you may be well aware of the difficulties that arise when you share your analysis with others, regardless of whether they are business stakeholders or technically-minded individuals. Previously data scientists would have to share their insights via Slack, emails, custom-built web applications, or presentations.
With modern data science notebooks, you can seamlessly democratise insights and share them with team members and business associates. Some applications also allow you to change the graphics and make the pictures more interactive. You can also turn a notebook into a hosted web app that allows your users to interact with the information without seeing any code.
The field of data science is as wide as it is deep; with so many different areas to learn and understand, it is next to impossible for one individual to know everything there is to learn about data science.
Data and infrastructure are the critical links between the different data science sectors. A model can only work well with good data, and if the infrastructure supports the development of the model and data cleaning. Citizen data scientists can use and modify a cloud-based infrastructure without engineering skills, as anyone can use and modify a cloud-based infrastructure.
Packages and environments can be managed and updated well by most users. This feature helps bridge the gap between engineers and practitioners, as most people who use the systems do not need to know any technical knowledge. Most techniques used in data science are fully compatible with notebooks. Using modern data science notebooks, you can train the data models, build data pipelines, and plot 3D graphs.
Integration was previously a significant pain point for data science notebooks. However, modern notebooks for data science provide native SQL support, allowing you to set up a more secure connection and integrate different tools that make your analysis easier.
Here are a few simple guidelines to follow while creating data science notebooks:
Doing this can result in less interactive results and can hide details that the users might need. Since a data science notebook can have unlimited cells, you can create new cells freely and explore more data.
The structure of the notebook is of great importance. A transparent system will allow readers to:
The KISS principle allows you to maintain the simplicity of your structure yet have all the critical areas.
Could you make sure any plots you create are fully descriptive and can be easily interpreted?
You can fully use notebooks' benefits by filling any gaps in understanding the code with markdown cells. These cells should help users understand the overall goals of the steps in the notebook.
In some cases, you added a code to a cell and executed it but also modified another cell before it, leading to inconsistencies in the data. To ensure you don't have these errors, please run the code every time you finish a section of the project.
Instead of using repetitive codes in your notebook, using a function as much as possible is better.
A data science notebook is a core stack piece for most data-driven teams. While it is exciting to see what the future holds for this technology, individuals in this field need to update their skills and keep up with the rapidly changing field. We at StarAgile, have specially curated courses that help you gain a better understanding of the minute details of the field so if you are a fresher looking for a viable career opportunity or a working professional that wants to upskill for a different role we have the courses for you. Sign up for any of our multiple data science certificate courses and build a successful career in this industry.
1. Which laptop is best for Data Science Notebooks?
Ans: A laptop with dedicated graphics cards and memory with CUDA cores is preferred when using data science notebooks. As data science demands a fair amount of processing power, the system should have between 8 and 16 GB RAM.
2. How long does it take to complete a data science certificate course?
Ans: Most data science certificate courses are around 6 months which teaches you the basics of data science that you need to become a professional in the field.
3. What is a notebook for Python?
Ans: Jupyter or IPhyton Notebook is an interactive web application that is used for creating and sharing computational documents.
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