Best data science courses in 2026

Demand for data science skills is reaching new limits as organizations lean into data-driven decision-making and aim to harness increasing volumes of data for competitive advantage. And as AI and machine learning continue to accelerate, data scientists are crucial for generating actual business value from those technologies. In fact, the Bureau of Labor Statistics predicts that hiring for data science roles will increase 34% between 2024 and 2034, much faster than the average growth of other occupations.

Here are some of the best Pluralsight data science courses and curated learning paths to help you develop the expertise to thrive as a data scientist.

 

What is data science?

Data scientists extrapolate meaning and insights from the wealth of data available to organizations. They empower their businesses to make better decisions by building predictive and prescriptive models that extract insights from structured and unstructured data. But they also go beyond analysis: like other researchers, data scientists develop, implement, and test new theories and processes to help organizations understand patterns, predict trends, and more.

 

Best data science courses for beginners

Introduction to SQL: SQL is a critical language to learn for data scientists. This learning path introduces you to SQL and takes you all the way from database fundamentals and through writing queries, functions, and even generative AI tools.

Business Intelligence Tools for Data Analysts: This course teaches you how to use business intelligence tools to support data-driven decision-making. You’ll explore Excel, Tableau, and Power BI, all through the lens of transforming data into insights. 

Data Science with Python: Foundations of Machine Learning: Jump into machine learning by understanding how to use Python to identify problems ML can solve, train models using data, and evaluate the performance of those models.

Data Analysis for Storytelling: This course teaches you how to pull insights from data and weave those insights into a narrative that helps businesses solve problems.

Becoming a Data Scientist: Dive into the responsibilities of a data scientist, the skills necessary to thrive in this role, and how you can transition into this career.

Python Data Essentials: Programming Fundamentals: Learn the essential skill of controlling the flow of a code evaluation by exploring conditional statements and types of operators, how to loop over iterable structures and reuse repetitive code, and more.

 

Best intermediate data science courses

Identifying and Understanding Business Problems for Data Scientists: Where the storytelling course above teaches you how to tell stories with data, this course teaches you how to pinpoint the actual business problems that data science can solve. Then you’ll learn how to articulate that solution and its concrete objectives, driven by data.

SQL for Data Engineers: Yes, this course is aimed at data engineers, but it will provide data scientists with a stronger technical SQL foundation by diving into analysis, query optimization, workflow integration, data lakes, and more.

Data Preparation in Microsoft Excel: As one of the most common tools employed by data scientists, mastery of Microsoft Excel is crucial. Learn how to clean, transform, and format data and make it easier to interpret using Excel.

Exploratory Data Analysis for Data Scientists: Learn advanced Exploratory Data Analysis (EDA) techniques in order to interpret complex data sets and uncover deeper insights and patterns in data.

Transform Data Using PySpark: This course teaches you how to manipulate and analyze large-scale data sets using PySpark. You’ll learn how to read and write data using various formats, perform advanced transformations, and create custom functions.

 

Best advanced data science courses

Image Classification with PyTorch: Learn one of the most common applications of neural networks with the most common tool for building those networks: PyTorch. This course will teach you how to build enterprise-grade image classification systems using a range of neural network architectures.

Scale Data in Snowflake: In this course, you’ll learn how to efficiently scale data operations in Snowflake—one of the most common data platforms—through auto-scaling, mini-cluster warehouses, and scaling monitoring for virtual warehouses.

Implementing Data Engineering Solutions Using Microsoft Fabric (DP-700): Built to prepare you for the DP-700 exam, this learning path covers configuring analytics solutions, ingesting and transforming data, and monitoring and optimizing data systems. At the end of this path, you’ll be able to build and monitor data engineering solutions in Microsoft Fabric.

 

Conclusion

Data science continues to evolve rapidly, and staying competitive means building skills that span the full spectrum—from foundational tools like SQL and Python to advanced techniques in machine learning and large-scale data systems. 

Whether you’re just starting out or honing your expertise, these courses and learning paths can position you to turn data into meaningful insights and drive value in an increasingly data-driven world.