Data, AI, and Cloud Courses

In this course, youll learn how to use tree-based models and ensembles for regression and classification using scikit-learn.

You will investigate a dataset from a fictitious company called Databel in Excel, and need to figure out why customers are churning.

Learn about modularity, documentation, and automated testing to help you solve data science problems more quickly and reliably.

Learn the fundamentals of AI agents, their components, and real-world use—no coding required.

Predict housing prices and ad click-through rate by implementing, analyzing, and interpreting regression analysis in R.

Improve data literacy skills by analyzing remote working policies.

Learn about Excel financial modeling, including cash flow, scenario analysis, time value, and capital budgeting.

Learn the essentials of VMs, containers, Docker, and Kubernetes. Understand the differences to get started!

Boost your Excel skills with advanced referencing, lookup, and database functions using practical exercises.

Learn how to work with dates and times in Python.

Learn about fundamental deep learning architectures such as CNNs, RNNs, LSTMs, and GRUs for modeling image and sequential data.

Learn about the world of data engineering in this short course, covering tools and topics like ETL and cloud computing.

Learn how to efficiently transform, clean, and analyze data using Polars, a Python library for fast data manipulation.

Build robust, production-grade APIs with FastAPI, mastering HTTP operations, validation, and async execution to create efficient data and ML pipelines.

Dashboards are a must-have in a data-driven world. Increase your impact on business performance with Tableau dashboards.

Learn how to clean and prepare your data for machine learning!

In this course, you will learn the fundamentals of Kubernetes and deploy and orchestrate containers using Manifests and kubectl instructions.

Consolidate and extend your knowledge of Python data types such as lists, dictionaries, and tuples, leveraging them to solve Data Science problems.

Learn the fundamentals of working with big data with PySpark.

Explore data ethics with this comprehensive introductory course, covering principles, AI ethics, and practical skills to ensure responsible data use.

Discover modern data architectures key components, from ingestion and serving to governance and orchestration.

Learn how to manipulate and visualize categorical data using pandas and seaborn.

Learn to retrieve and parse information from the internet using the Python library scrapy.

Learn the skills needed to create impactful dashboards. Understand dashboard design fundamentals, visual analytics components, and dashboard types.

Learn about the power of Databricks Lakehouse and help you scale up your data engineering and machine learning skills.

Master your skills in NumPy by learning how to create, sort, filter, and update arrays using NYC’s tree census.

Master AWS cloud technology with hands-on learning and practical applications in the AWS ecosystem.

Learn cutting-edge methods for integrating external data with LLMs using Retrieval Augmented Generation (RAG) with LangChain.

Gain a clear understanding of data privacy principles and how to implement privacy and security processes.

Learn how to use graphical and numerical techniques to begin uncovering the structure of your data.