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How to do data science in notebooks

By Nick Barth

Updated on March 6, 2024

Data platforms are technology solutions that collect, store, manage, and analyze large amounts of data. They handle the growing volume, velocity, and variety of data from digital activities. These platforms provide infrastructure for data warehousing, big data processing, and analytics, enabling organizations to gain insights and make data-driven decisions.

How to do data science in notebooks

Data science has transformed the way industries operate by turning data into valuable insights and predictions. Jupyter notebooks have become a staple in the data science community, known for their flexibility and interactive computing environment. Here, we will guide you through the essentials of performing data science within these notebooks, focusing on key concepts and leveraging popular tools and libraries.

Key data science concepts

Data exploration

Before diving into complex analyses and machine learning models, it's important to explore and understand the data. Data exploration involves:

  • Identifying the main features of datasets
  • Detecting outliers or anomalies
  • Understanding the distribution and relationship between variables
  • Cleaning and preprocessing the data for further analysis

Machine learning

Machine learning allows us to make predictions or draw insights based on historical data. It typically involves:

  • Selecting appropriate algorithms for your data and problem
  • Training models using historical data
  • Validating models to ensure their reliability and accuracy
  • Using the model to make predictions on new, unseen data

Data visualization

Visualization is a powerful tool for understanding data and communicating results. Effective data visualization often includes:

  • Creating plots to show relationships between variables
  • Designing dashboards to track key metrics
  • Using graphs to identify trends and patterns within the data


Deepnote is a collaborative notebook platform designed specifically for data scientists. It offers a real-time collaborative environment, making it easier for teams to work together on data science projects. Deepnote integrates seamlessly with popular data science libraries and tools.

Essential tools and libraries

To conduct data science in a notebook environment, you'll need certain tools and libraries that enable you to work with data effectively.


Pandas is an open-source data analysis and manipulation tool built on top of the Python programming language. It offers data structures and operations for manipulating numerical tables and time series, which are essential for data exploration and preprocessing.


Matplotlib is a widely used Python library for creating static, interactive, and animated visualizations. It works well with pandas and other computational tools to provide a rich set of features for data visualization.

When you integrate these libraries into a notebook platform like Deepnote, you can create an interactive document that combines code, visualizations, and text annotations. This allows for agile data exploration and communication of results within a team dynamic.


Data science in notebooks is about combining interactive coding with robust tools to unravel stories hidden in the data, predict trends, and make informed decisions. By following a systematic approach—beginning with data exploration, moving through machine learning, and communicating results through effective visualization—you'll harness the full potential of data science.

Whether you're a novice or a seasoned data scientist, leveraging notebooks, Deepnote, Pandas, and Matplotlib enables you to streamline your workflows, collaborate effectively, and generate insights that can make a significant impact.

Nick Barth

Product Engineer

Nick has been interested in data science ever since he recorded all his poops in spreadsheet, and found that on average, he pooped 1.41 times per day. When he isn't coding, or writing content, he spends his time enjoying various leisurely pursuits.

Follow Nick on LinkedIn and GitHub

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