Check out ready-to-run tutorials to get started with Deepnote
Snowflake provides a single platform for data warehousing, data lakes, data engineering, data science, data application development, and secure sharing and consumption of real-time and shared data
Data science and analytics
Data science and analytics are interdisciplinary fields that involve extracting insights and knowledge from data through various techniques, including statistical analysis, machine learning, data mining, and visualization.
What is exploratory programming?
What is exploratory data analysis (EDA)?
What is advanced analytics?
Efficient data cleaning strategies in Jupyter notebooks
What's the real difference between data science and data analytics?
Centralized vs distributed data teams
What is a data workspace?
How to collaborate efficiently in a data team
Introduction to data science for managers
Roles in a modern data team
How to become a data team lead
Introduction to managing data teams
Pandas vs Polars
Jupyter is an open-source project that provides a web-based interactive computing platform. The name "Jupyter" is derived from the core programming languages it initially supported: Julia, Python, and R.
How to run a Jupyter notebook in the cloud
How to schedule a Jupyter notebook
How to optimize Jupyter Notebooks for machine learning projects
How to open a Jupyter notebook
How to open an ipynb file
How to run Jupyter notebooks automatically
Where to host a Jupyter notebook
How to import a Jupyter notebook
How to export a Jupyter notebook
How to convert Jupyter to Python
How to check the Python version in Jupyter
How to run Jupyter in Docker
With an intuitive, real-time collaborative environment, teams can seamlessly work together from anywhere. Deepnote simplifies visualizing complex datasets with features like connecting to various data sources and showing them to stakeholders.
Collaboration within data teams is critical as it pools diverse expertise and perspectives, leading to more comprehensive and robust analyses. By working together, team members can validate findings and reduce the risk of oversight or bias in data interpretation.
Google Cloud is a suite of cloud computing services provided by Google that runs on the same infrastructure that Google uses internally for its end-user products, such as Google Search, Gmail, file storage, and YouTube. It offers a range of services including computing, data storage, data analytics, and machine learning. Organizations of all sizes can utilize Google Cloud to create anything from simple websites to complex applications.
How to connect to BigQuery with Python
How to work with BigQuery in Jupyter
How to visualize data in BigQuery
How to schedule queries in BigQuery
How to create a table from query in BigQuery
How to move data from Cloud SQL to BigQuery
How to export results to CSV in BigQuery
What are saved queries in BigQuery?
Amazon Web Services (AWS) is a comprehensive and widely adopted cloud platform that offers over 200 fully featured services from data centers globally. AWS provides infrastructure and various services such as computing power, storage options, and networking that can be used to build applications with increased flexibility, scalability, and reliability.
SQL, or Structured Query Language, is a powerful tool for managing and manipulating relational databases, allowing for complex data querying and analysis. It is the standard language for relational database management systems, used widely in various data-driven applications.
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.
Versioning with notebooks involves maintaining a record of changes and updates made to the documents, making it easier to track revisions and collaborate with others.
How to use state of the art AI with Data Science & Deepnote
A collection of guides for specific use cases
How to run your first docker container
How to analyze ecommerce purchases
How to estimate feature importance and perform feature selection with XGBoost in Python
How to use `folium.colormap` in choropleths
How to capture output with `%%capture` in IPython
How to implement interactive and animated figures in Jupyter Notebooks
How to explore netCDF datasets using xarray
How to use Python statistics functions
How to analyze San Francisco city employee salary data with Pandas