Introduction to dbt
This course introduces dbt for data modeling, transformations, testing, and building documentation.
Follow short videos led by expert instructors and then practice what you’ve learned with interactive exercises in your browser.
This course introduces dbt for data modeling, transformations, testing, and building documentation.
You will investigate a dataset from a fictitious company called Databel in Excel, and need to figure out why customers are churning.
In this course, you will learn to read CSV, XLS, and text files in R using tools like readxl and data.table.
Master your skills in NumPy by learning how to create, sort, filter, and update arrays using NYC’s tree census.
Bring your Google Sheets to life by mastering fundamental skills such as formulas, operations, and cell references.
Consolidate and extend your knowledge of Python data types such as lists, dictionaries, and tuples, leveraging them to solve Data Science problems.
Master AWS cloud technology with hands-on learning and practical applications in the AWS ecosystem.
Learn about fundamental deep learning architectures such as CNNs, RNNs, LSTMs, and GRUs for modeling image and sequential data.
Learn to retrieve and parse information from the internet using the Python library scrapy.
Learn how to use graphical and numerical techniques to begin uncovering the structure of your data.
Learn the essentials of VMs, containers, Docker, and Kubernetes. Understand the differences to get started!
Learn the fundamentals of working with big data with PySpark.
Learn how to manipulate and visualize categorical data using pandas and seaborn.
In this course youll learn the basics of working with time series data.
Learn to combine data across multiple tables to answer more complex questions with dplyr.
Learn how to translate business questions to well-formed analytical questions and select the right analytical solutions.
Discover how to extract business value from AI. Learn to scope opportunities for AI, create POCs, implement solutions, and develop an AI strategy.
Learn the fundamentals of neural networks and how to build deep learning models using Keras 2.0 in Python.
To understand Fabric’s main use cases, you will explore various tools in the seven Fabric experiences.
Learn the key components of building a strong data culture within an organization.
Dive into the exciting world of APIs as we introduce you to the basics of consuming and working with Web APIs using Python.
Learn cutting-edge methods for integrating external data with LLMs using Retrieval Augmented Generation (RAG) with LangChain.
Learn about modularity, documentation, and automated testing to help you solve data science problems more quickly and reliably.
Learn about the world of data engineering in this short course, covering tools and topics like ETL and cloud computing.
Learn the role Generative Artificial Intelligence plays today and will play in the future in a business environment.
In this course, you will learn the fundamentals of Kubernetes and deploy and orchestrate containers using Manifests and kubectl instructions.
In this course you will learn the basics of machine learning for classification.
Build robust, production-grade APIs with FastAPI, mastering HTTP operations, validation, and async execution to create efficient data and ML pipelines.
Learn to use the KNIME Analytics Platform for data access, cleaning, and analysis with a no-code/low-code approach.
In this course, you will be introduced to unsupervised learning through techniques such as hierarchical and k-means clustering using the SciPy library.