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Snowflake

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

introduction

  1. Introduction to Snowflake for data analysts

  2. How to collaborate in Snowflake

  3. How to share queries in Snowflake

  4. What file formats does Snowflake support?

  5. Snowpark: A Beginner’s Guide

Other

  1. Connecting to Snowflake with Python

  2. How to export Snowflake query to Pandas dataframe

  3. How to connect to Snowflake using SQLAlchemy

  4. Connecting to Snowflake from a Jupyter notebook

  5. How to use Python in Snowflake

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.

introduction

  1. What is exploratory programming?

  2. What is exploratory data analysis (EDA)?

  3. What is advanced analytics?

  4. Efficient data cleaning strategies in Jupyter notebooks

  5. What's the real difference between data science and data analytics?

  6. Centralized vs distributed data teams

  7. What is a data workspace?

  8. Modern data team collaboration

  9. Introduction to data science for managers

  10. Roles in a modern data team

  11. How to become a data team lead

  12. Introduction to managing data teams

  13. Pandas vs Polars

  14. How to analyze Google ads data in Python using Deepnote

  15. How to analyze data from QuickBooks in Python using Deepnote

Jupyter

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.

introduction

  1. What is a Juptyer notebook?

  2. What is JuptyerLabs?

  3. What is JupyterHub?

  4. What is a data notebook?

  5. How to use Pandas in Jupyter notebooks

collaboration

  1. Real time collaboration in Jupyter

  2. How to add comments to a Jupyter notebook

versioning

  1. How to version a Jupyter notebook

other

  1. How to run a Jupyter notebook in the cloud

  2. How to schedule a Jupyter notebook

  3. How to optimize Jupyter Notebooks for machine learning projects

  4. How to open a Jupyter notebook

  5. How to open an ipynb file

  6. How to run Jupyter notebooks automatically

  7. Where to host a Jupyter notebook

  8. How to import a Jupyter notebook

  9. How to export a Jupyter notebook

  10. How to convert Jupyter to Python

  11. How to check the Python version in Jupyter

  12. How to run Jupyter in Docker

Dashboarding

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.

Juptyer dashboards

  1. How to turn a Juptyer notebook into an app

  2. 8 Best visualization libraries for Jupyter notebooks

  3. How to present data in a Jupyter Notebook

Other tools

  1. How to use Jupyter with Tableau

  2. How to use Jupyter with PowerBI

Collaboration

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.

Multiplayer with Deepnote

  1. How to run Jupyter in multiplayer mode

  2. How to collaborate in a Jupyter notebook

  3. How to share a Jupyter notebook

  4. Real time collaboration in Jupyter

  5. How to add comments to a Jupyter notebook

Google Cloud

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.

Introduction

  1. What is VertexAI?

  2. What's the difference between Colab and Vertex AI?

  3. What is Google Colaboratory?

Google Drive

  1. How to connect to Google Drive from Python

  2. How to work with Google Drive in Juptyer

  3. How to download files from Google Drive in Python

Google Sheets

  1. How to connect to Google Sheets

Google BigQuery

  1. How to connect to BigQuery with Python

  2. How to work with BigQuery in Jupyter

  3. How to visualize data in BigQuery

  4. How to schedule queries in BigQuery

  5. How to create a table from query in BigQuery

  6. How to move data from Cloud SQL to BigQuery

  7. How to export results to CSV in BigQuery

  8. What are saved queries in BigQuery?

AWS

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.

Sagemaker

  1. What is Sagemaker?

Redshift

  1. Connecting to Redshift from Python

  2. How to connect Redshift to Juptyer

Athena

  1. How to connect to Amazon Athena with Python

  2. How to connect to Amazon Athena from Jupyter

S3

  1. How to connect to S3 with Python

  2. How to use Amazon S3 with Juptyer

SQL

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.

Introduction

  1. What is a SQL editor

  2. How to collaborate with SQL

  3. Introduction to SQL in Jupyter notebooks

Other

  1. How to connect to a SQL database in a Juptyer notebook

Data platforms

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.

Introduction

  1. Data science in notebooks

  2. How to do data analytics in notebooks

  3. How to do data engineering in Notebooks

  4. How to do machine learning in a notebook

  5. How to schedule notebooks in Databricks

ChatGPT

  1. How to use ChatGPT for data analytics

  2. How ChatGPT handles data security

  3. How to use ChatGPT with enterprise data

Data ops

    Versioning

    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.

    With Notebooks

    1. What are checkpoints in a Jupyter notebook

    2. How to use Git with Jupyter / IPYNB

    ChatGPT

    How to use state of the art AI with Data Science & Deepnote

    ChatGPT & Data science

    1. How to use ChatGPT for data analysis

    2. How to use ChatGPT for data science

    3. How to use AI for data analysis

    4. How to use AI for data science

    5. What is conversational analytics

    Notebook Integrations

    Introduction to custom environments in notebooks

    introduction

    1. Rust kernel in Deepnote for blazingly fast notebooks

    2. Using JavaScript in Deepnote

    3. Enhancing data analysis with Deepnote and Scala

    4. Mastering R in Deepnote: A comprehensive guide to collaborative data science

    5. Boosting data processing in Deepnote: A guide to setting Up C++ kernels

    6. Importing function from another notebook

    Platform solutions

    Solutions to common issues on the platform

    Introductions

    1. Building a Streamlit app with LangChain and OpenAI in Deepnote

    2. Use Poetry in Deepnote instead of dealing with local packages

    3. Using Folium in Deepnote: A guide to displaying maps

    4. Selenium web-driver exception in Deepnote

    5. Connecting to Oracle Cloud database with Deepnote

    6. Resolving pyodbc connection Issues in Deepnote

    Notebook tutorials

    How-to guides on getting the most out of your notebook.

    advanced

    1. Wavelength denoising with PyWavelets in Deepnote

    2. Extracting tabular data from PDFs with Deepnote

    3. TripAdvisor datasets in Deepnote

    4. Guide BJH analysis in Deepnote

    5. Modern Python ORM for data science

    6. Linear regression in Deepnote

    Introductions

    1. Python functions and variable scope with Deepnote

    2. Cheat sheet for markdown in Deepnote notebooks

    3. Python conditional statements in Deepnote

    4. Exploring exponentials, radicals, and logarithms with Deepnote Notebooks

    5. Deepnote Pandas puzzle: A comprehensive guide to mastering Pandas

    6. Deepnote Notebooks for data analysis

    Analysis

    1. Analyzing salaries dataset with Deepnote

    2. Analyzing e-commerce purchases data in Deepnote

    Tutorials

    How-tos for specific niches

    Banking and finance

    1. Anti-money laundering in Python

    2. Automated forecasting in Python

    3. Introduction to stock trading in Python

    4. Introduction to Python for banking

    5. Introduction to credit card fraud detection in Python

    6. Introduction to Python for retail analysis using Deepnote

    Real estate

    1. Introduction to Python for real estate investment analysis in Deepnote

    2. Introduction to Python for real estate market analysis in Deepnote

    3. Introduction to Python for property valuation in Deepnote

    Health care and pharmaceuticals

    1. Introduction to Python for health care in Deepnote

    2. Introduction to Python for drug development and discovery

    3. Introduction to Python for the Pharmaceutical Industry in Deepnote

    Insurance

    1. Automating insurance claims processing in Python using Deepnote

    2. Introduction to Python for insurance risk assessment in Deepnote

    3. Fraud detection in insurance using Python in Deepnote

    Manufacturing

    1. Introduction to Python for data science for manufacturing in Deepnote

    Transportation

    1. Transportation analytics in Python

    2. Introduction to supply chain management in Python using Deepnote

    Telecommunications

    1. Introduction to Python for telecom network optimization in Deepnote

    2. Customer churn prediction in telecom using Python in Deepnote

    Utilities and energy

    1. Energy management in Python using Deepnote

    Marketing

    1. Introduction to marketing automation in Python using Deepnote

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