Statistics for Data Analysis
Learn core statistics for data analysis, including probability, regression, and experiment design. Build practical Python skills to analyze data and prepare for analyst and data scientist roles.
Python for Data Science Courses
Python is the standard programming language in data science. Udacity's Python for data science courses cover the libraries and workflows data professionals use most, including NumPy, Pandas, Matplotlib, and Seaborn for data wrangling and visualization, plus statistical methods like hypothesis testing, linear and logistic regression, and data preparation. You'll work through practical projects that reflect real data science workflows, building skills in writing clean, production-ready Python for analytical tasks. Courses go from Python fundamentals through advanced data analysis techniques.
Browse Python for Data Science Courses
Numpy, Pandas, Matplotlib
Strengthen your Python data skills by working with NumPy, Pandas, and Matplotlib to analyze, transform, and visualize data using industry-standard tools used across data teams.
Advanced Data Wrangling
Data wrangling is a set of processes for turning raw and messy data into a clean format to answer interesting questions from the data. In this course, you will learn the three phases of data wrangling: gathering, assessing, and cleaning data.
Introduction to Python for Data Science
Learn Python programming fundamentals such as data types and structures, variables, loops, and functions. Work with Python libraries and packages, manipulate and analyze data with Pandas and Polars, compute statistics, and create effective visualizations to uncover patterns, trends, and relationships. Apply industry-standard tools and workflows to explore datasets, generate insights, and communicate findings through data-driven storytelling and decision-making.
Data Visualization with Matplotlib and Seaborn
Learn to apply sound design and data visualization principles to the data analysis process. Learn how to use analysis and visualizations to tell a story with data.
Preparing for Data Analysis
Refine your approach to data preparation. Dive into reprocessing techniques, feature engineering strategies, and exploratory analysis with Pandas, matplotlib, and Plotly to uncover insights and enhance machine learning outcomes.
Introduction to Python
In this Nanodegree, you will develop a strong foundation in Python programming, build professional-level coding skills, and learn how to leverage AI-assisted tools to write efficient, effective code. The program is designed to guide you from beginner concepts to practical, real-world applications.
Linear and Logistic Regression
The linear and logistic regression course offers a detailed introduction to fundamental statistical and machine learning algorithms, particularly focusing on regression techniques. The course begins with simple linear regression and progresses to multiple linear regression, equipping students with the ability to analyze relationships between multiple variables. Finally, it covers logistic regression, a powerful tool for classification problems.
Hypothesis Testing
Hypothesis testing is one of the most important topics in all of statistics because it tells us whether our conclusions are statistically significant. In this course, you will learn about the fundamental role statistics plays in hypothesis testing as well as how to implement statistical concepts in Python.
Complementary Course Collections
Python is one part of a complete data workflow. Add SQL Fluency, Data Analyst, and Machine Learning & Deployment courses to access data at the source, analyze it, and put predictive models into production.
SQL Fluency
Expand your SQL fluency by pairing database fundamentals with advanced cloud, machine learning, and systems coursework. Deepen your understanding of how data is stored, processed, and deployed, linking strong querying and modeling foundations to scalable infrastructure, intelligent applications, and end-to-end data systems.
Machine Learning & Deployment
Combining Machine Learning fundamentals with cloud platforms, DevOps, and continuous deployment courses equips you with the tools to build, deploy, and manage intelligent applications end-to-end. This integrated approach boosts efficiency, automates workflows, and drives faster innovation.
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