Data science course Udemy: the 10 best in 2026

Best for aspiring data scientists who want to master data prep before modelling. Alice Zhao of Maven Analytics covers scoping a project, gathering data from files, Excel and SQL, cleaning data types, missing values and outliers, exploratory analysis and visualisation, and preparing features for machine learning, playing a junior data scientist at a music streaming service.

It has 4.7 stars from more than 2,100 reviews and more than 14,000 students. It runs about 8.5 hours with seven quizzes, 16 homework assignments, two projects and a 190-page ebook, and was updated in August 2026.

Best for complete beginners who first need to learn Python for data work. Kirill Eremenko teaches the core principles of programming, variables and data types, loops and data visualisation with Seaborn, including histograms, KDE plots and violin plots, through real-life analytical challenges and homework.

It has 4.6 stars from more than 30,000 reviews and more than 176,000 students. It runs about 11 hours with six quizzes, but was last updated in January 2025.

Best for learners who want to master regression, classification and clustering with Pandas. It teaches Python 3 and Pandas for advanced data handling, then regression and prediction, classification with ensemble models, and unsupervised cluster analysis, with Scikit-learn, Statsmodels, Matplotlib and Seaborn.

It has 4.9 stars from more than 1,100 reviews and more than 6,100 students. It runs about 52 hours with 81 resources and was updated in October 2026. Its topics overlap heavily with Data Science and Machine Learning Fundamentals [2026] (opens in a new tab), so you only need one of the two.

Best for learners who want a deep foundation in core modelling techniques. It covers regression from linear to multivariate polynomial models, classification with random forest and voting ensembles, seven clustering algorithms, automatic model creation, text and emotion mining, and Python and Pandas for data handling, using Anaconda's cloud notebooks.

It has 4.9 stars from more than 1,700 reviews and more than 9,300 students. It runs about 60 hours with 94 resources and was updated in October 2026. Only basic arithmetic is required.

Best for committed learners who want the theory and maths behind every model. It covers machine learning algorithms, deep learning, reinforcement learning, NLP and transformer models, the linear algebra, calculus and probability behind them, and tools like TensorFlow, PyTorch and scikit-learn, with real-world projects in healthcare, finance and e-commerce.

It has 4.5 stars from more than 28,000 reviews and more than 169,000 students. At about 101 hours it is the longest course here, with 20 quizzes and 167 resources, and was updated in August 2026.

Best for programmers who want to move from data science into AI engineering. It starts with a Python crash course and statistics, then covers classical machine learning, reinforcement learning, deep learning with TensorFlow and Keras, transformers, the OpenAI API, fine-tuning, RAG, LangChain, multi-agent systems with the OpenAI Agents SDK, and Apache Spark.

It has 4.6 stars from more than 36,000 reviews and more than 247,000 students. It runs about 20 hours and was updated in August 2026.

Best for analysts who want hands-on, business-style data science without heavy coding. Kirill Eremenko takes you through cleaning and preparing messy data, visualisation, modelling and curve fitting, and presenting your findings, using SQL, SSIS, Tableau and Gretl, with pre-planned pathways through the sections.

It has 4.6 stars from more than 34,000 reviews and more than 225,000 students. It runs about 21 hours with challenging homework exercises, and was updated in January 2026.

Best for learners who want to build a portfolio of real machine learning projects. Daniel Bourke and Andrei Neagoie cover Python, NumPy, Pandas, Matplotlib and Seaborn, Scikit-Learn, supervised learning, decision trees and random forests, hyperparameter tuning, deep learning and transfer learning with TensorFlow and Keras, and Kaggle competitions.

It has 4.7 stars from more than 31,000 reviews and more than 172,000 students. It runs about 43 hours, offers separate tracks for beginners and programmers, and was updated in February 2026. Projects include heart disease detection, a bulldozer price predictor and a dog breed image classifier.

Best for learners who want the whole data career path in one course. It moves from data analytics and Python, including APIs and web scraping, through data cleaning, EDA and visualisation, probability and hypothesis testing, machine learning from regression to XGBoost, deep learning with TensorFlow, NLP and generative AI, plus SQL with MySQL, Power BI and Excel.

It has 4.8 stars from more than 5,300 reviews and more than 12,000 students. It runs about 65 hours with 154 quizzes, two practice tests and 11 capstone projects, including bank churn analysis and several GenAI apps, and was updated in September 2026.