Python Courses
These Python courses teach essential syntax, data structures, and libraries like pandas and NumPy through interactive coding exercises. You’ll write scripts to automate tasks, clean data, and build robust applications from scratch.
Python Courses
These Python courses teach essential syntax, data structures, and libraries like pandas and NumPy through interactive coding exercises. You’ll write scripts to automate tasks, clean data, and build robust applications from scratch.
Showing 70 of 70 courses
- 19 courses 15 projects 97 hours 467K+
Junior Data Analyst
You'll begin with Excel, where you'll learn how to manipulate data using complex formulas, commands, and tools. Next, you'll transition into SQL, becoming familiar with querying, exploring, and handling data from multiple sources. Lastly, you'll dive into Python, learning the fundamentals of programming, statistical analysis, and data visualization. This progressive learning journey is designed for both aspiring data professionals and those looking to enhance their data skills. Best of all, you'll learn by doing - you'll write code and get feedback directly in the browser. You'll apply your skills to several guided projects involving realistic business scenarios to build your portfolio and prepare for your next interview.
Beginner Python SQLStart career path → - 38 courses 27 projects 171 hours 449K+
Data Scientist in Python Certificate Program
In this path, you'll develop key technical skills for data scientists, including object-oriented and functional programming with Python, along with libraries like scikit-learn, Matplotlib, NumPy, and pandas. You'll also learn web scraping, SQL queries, deep learning, machine learning, and predictive analysis. To help you stand out, you'll explore tools like the UNIX command line, Git, and GitHub for better collaboration. Best of all, you'll learn by doing - you'll write code and get feedback directly in the browser. You'll apply your skills to several guided projects involving realistic business scenarios to build your portfolio and prepare for your next interview.
Beginner Python SQLStart career path → - 27 courses 19 projects 144 hours 440K+
Data Analyst in Python
In this path, you'll learn the fundamentals of Python, as well as how to prepare and extract data by querying databases with SQL, how to create insightful data visualization, and how to perform descriptive and predictive statistical analysis. Best of all, you'll learn by doing - you'll write code and get feedback directly in the browser. You'll apply your skills to several guided projects involving realistic business scenarios to build your portfolio and prepare for your next interview.
Beginner Python SQLStart career path → - 12 courses 11 projects 77 hours 408K+
Probability and Statistics with Python
In this path, you'll learn the foundations of statistics such as sampling, working with variables, and understanding frequency distribution tables and the fundamentals of probability and how to use them for analysis. You'll also learn how to create and test hypotheses with significance testing, and how to make forecasts based on patterns and trends with real-world data. Best of all, you'll learn by doing - you'll write code and get feedback directly in the browser. You'll apply your skills to several guided projects involving realistic business scenarios to build your portfolio and prepare for your next interview.
Beginner Python Data ScienceStart skill path → - 9 courses 8 projects 64 hours 404K+
Data Cleaning with Python
In this path, you'll gain the fundamental skills to begin cleaning data, using the powerful tools offered by Python such as identifying and removing inaccurate records from a dataset. You'll learn how to manipulate, analyze, and visualize data using premier Python libraries such as Pandas and Numpy. Best of all, you'll learn by doing - you'll write code and get feedback directly in the browser. You'll apply your skills to several guided projects involving realistic business scenarios to build your portfolio and prepare for your next interview.
Beginner Python Data ScienceStart skill path → - 7 courses 6 projects 47 hours 399K+
Data Analysis and Visualization with Python
In this path, you will gain experience in manipulating, comparing, and presenting compelling and actionable data and you'll discover the best methods for visualizing data using line graphs, histograms, bar charts, scatter plots, and more. Best of all, you'll learn by doing - you'll write code and get feedback directly in the browser. You'll apply your skills to several guided projects involving realistic business scenarios to build your portfolio and prepare for your next interview.
Beginner Python Data ScienceStart skill path → - 4 courses 3 projects 21 hours 344K+
Learn Python
In this path, you'll explore the basics of Python programming from preparing data all the way to predicting trends from real-world data. You'll learn the fundamentals of Python, how to use Jupyter notebooks, work with numerical and text data and basic object-oriented programming. Best of all, you'll learn by doing - you'll write code and get feedback directly in the browser. You'll apply your skills to several guided projects involving realistic business scenarios to build your portfolio and prepare for your next interview.
Beginner Python Data ScienceStart skill path → - 30 courses 20 projects 183 hours 159K+
AI Engineer in Python
In this path, you'll build the technical skills AI engineers need, including Python programming, working with LLM APIs, and prompt engineering. You'll learn to build and deploy AI applications using FastAPI and Docker, then go deeper into machine learning, deep learning with PyTorch, embeddings, vector databases, and RAG systems. You'll also pick up essential tooling like the command line, Git, and virtual environments. Best of all, you'll learn by doing - you'll write code and get feedback directly in the browser. You'll apply your skills to several guided projects involving realistic business scenarios to build your portfolio and prepare for your next interview.
Beginner Python AI EngineeringStart career path → - 30 courses 14 projects 143 hours 137K+
Data Engineer
In this path, you'll master the mandatory technical skills for modern data engineering, including Python programming, distributed computing, containerization, and cloud deployment. You'll learn how to work with production databases like PostgreSQL, Snowflake, and MongoDB, process data at scale with PySpark, orchestrate workflows with Apache Airflow, and deploy containerized applications to cloud platforms using Docker and Kubernetes. Best of all, you'll learn by doing - you'll write code and get feedback directly in the browser. You'll apply your skills to several guided projects involving realistic business scenarios to build your portfolio and prepare for your next interview.
Beginner Python SQLStart career path → - 1 courses 3 hours 35K+
APIs and Web Scraping with Python
In this path, you'll learn how to use Python and Beautiful Soup to scrape the web and download data from APIs. If you've worked with Python and would like to add another powerful tool to your skill set, this is the perfect path for you. Best of all, you'll learn by doing - you'll write code and get feedback directly in the browser. You'll apply your skills to several guided projects involving realistic business scenarios to build your portfolio and prepare for your next interview.
Intermediate Python Data ScienceStart skill path → - 4 courses 11 hours 29K+
CLI and Git
In this path, you'll learn the command line and Git skills essential for data engineering. You'll go from basic shell navigation through advanced command line techniques and version control with Git, applying everything to practical, real-world data engineering workflows.
Beginner Command Line GitStart skill path → - 8 courses 4 projects 32 hours 24K+
Generative AI Fundamentals in Python
Gain the skills necessary for working with AI, from automating tasks to engaging with LLMs via API in Python, and progress to building AI-driven applications. This path is essential for professionals aiming to integrate AI into their toolkit.
Beginner Python Data ScienceStart skill path → - 7 courses 7 projects 25 hours 17K+
Machine Learning Using Python
In this path, you'll gain a strong understanding of supervised and unsupervised machine learning algorithms. You'll also learn some of the most important and used algorithms and techniques to build, customize, train, test and optimize your predictive models such as linear regression modeling, gradient descent, logistic regression modeling and decision tree and random forest modeling. Finally, you'll learn optimization techniques that will help you to improve efficiency and accuracy. Best of all, you'll learn by doing - you'll practice and get feedback directly in the browser. You'll apply your skills to several guided projects with realistic business scenarios to build your portfolio and prepare for your next interview.
Intermediate Python Data ScienceStart skill path → - 2 courses 1 projects 12 hours 11K+
Data Pipelines with Airflow
In this path, you'll learn how to build data pipelines in Python and orchestrate them with Apache Airflow. You'll go from foundational pipeline concepts to automating complex workflows through hands-on, real-world scenarios.
Intermediate Python Apache AirflowStart skill path → - 4 courses 2 projects 17 hours 10K+
LLM Fundamentals in Python
Learn to work with large language models through APIs, prompt engineering, and advanced patterns like function calling and MCP. Then put your skills to work building interactive AI-powered web applications with Streamlit.
Intermediate Python AIStart skill path → - 2 courses 9 hours 501+
Distributed Data Processing with PySpark
In this path, you'll learn how to use PySpark and Apache Spark to process data at scale. You'll work with RDDs, DataFrames, and Spark SQL to build and optimize production ETL pipelines through hands-on, real-world data engineering scenarios.
Intermediate PySpark Apache SparkStart skill path → -
Free
4 lessons 2 hours 264K+
Introduction to Python Programming
This interactive Python course for beginners develops fundamental data science skills to help you begin your journey to become a successful data professional.In this course, you'll learn to do basic arithmetic; write code using Python syntax; work with different types of data; and perform basic Python operations such as working with variables, processing numerical and text data, and manipulating lists. Best of all, you'll learn by doing - you'll write code and get feedback directly in the browser.
Beginner Python Data ScienceStart course → - 7 lessons 13 hours 109K+
Introduction to Pandas and NumPy for Data Analysis
In this course, you'll learn to use NumPy and pandas for data exploration, preparation, and analysis. You'll start this course by learning how NumPy can streamline your data science workflow with vectorized operations, ndarrays, and Boolean indexing. You'll then discover how pandas can super-charge your data exploration, preparation, and analysis. Finally, you'll bring everything you've learned to a data analysis project to test your skills. Best of all, you'll learn by doing - you'll practice and get feedback directly in the browser. You'll apply your skills to several guided projects involving realistic business scenarios to build your portfolio and prepare for your next interview.
Intermediate Pandas NumPyStart course → - 6 lessons 7 hours 82K+
Python Functions and Jupyter Notebook
This course expands on our Introduction to Python course, and our Basic Operators and Data Structures in Python course. You'll learn how to write Python functions, build functions that employ multiple return statements and return multiple variables, as well as installing and using Jupyter Notebook. You'll complete the course by creating a portfolio project on Profitable App Profiles for the App Store and Google Play Markets. Best of all, you'll learn by doing - you'll practice and get feedback directly in the browser. You'll apply your skills to several guided projects involving realistic business scenarios to build your portfolio and prepare for your next interview.
Beginner Python JupyterStart course → - 5 lessons 5 hours 80K+
Basic Operators and Data Structures in Python
This course builds upon the fundamentals of Python taught in Introduction to Python. You'll learn to repeat a process using "for loops"; how to use conditional statements such as if, else, and elif; how to employ logical operators and comparison operators. You'll also learn how to create Python dictionaries, which are important data structures in Python that help gather elements for identification using a key. Finally, you'll build frequency tables, which help to display the frequencies of different categories (particularly useful for understanding the distribution of values in a dataset). Best of all, you'll learn by doing - you'll practice and get feedback directly in the browser. You'll apply your skills to several guided projects involving realistic business scenarios to build your portfolio and prepare for your next interview.
Beginner Python Data StructuresStart course → - 5 lessons 8 hours 74K+
Intermediate Python for Data Science
This course builds upon Introduction to Python Programming, For Loops and Conditional Statements in Python, Dictionaries, Frequency Tables, and Functions in Python, and Python Functions and Jupyter Notebook. You'll not only learn how to manipulate text, clean messy data, and more but also how to work with object-oriented programming concepts, dates, and times in Python. Best of all, you'll learn by doing - you'll practice and get feedback directly in the browser. You'll apply your skills to several guided projects involving realistic business scenarios to build your portfolio and prepare for your next interview.
Beginner Python Data ScienceStart course → - 4 lessons 4 hours 57K+
Introduction to Python for Data Engineering
This Python course for beginners teaches Python fundamentals and helps you take your first steps to becoming a successful data engineer. In this course, you'll learn to write code using Python syntax; work with different types of data; and perform basic Python operations, such as working with variables, processing numerical and text data, and manipulating lists. Best of all, you'll learn by doing - you'll write code and get feedback directly in the browser.
Beginner Python Data ScienceStart course → - 6 lessons 11 hours 28K+
Data Cleaning and Analysis in Python
This course is for intermediate Python users, and it builds upon the essentials covered in our previous Python lessons. You'll learn how to leverage Python to supercharge your data analysis workflow. You'll learn how to manipulate, combine, transform, and merge data; manipulate strings; and work with missing values in Python - as well as new concepts and techniques to improve the speed and efficiency of your Python code. Best of all, you'll learn by doing - you'll practice and get feedback directly in the browser. You'll apply your skills to several guided projects involving realistic business scenarios to build your portfolio and prepare for your next interview.
Intermediate Python PandasStart course → - 6 lessons 8 hours 28K+
Introduction to Statistics in Python
In this course, you'll learn several techniques for sampling data, such as random sampling and cluster sampling; you'll also learn concepts such as discrete variables and random variables in the context of frequency distributions - and the different types of charts and graphs you might use to visualize frequency distributions. Best of all, you'll learn by doing - you'll practice and get feedback directly in the browser. You'll apply your skills to several guided projects involving realistic business scenarios to build your portfolio and prepare for your next interview. In the guided project, you'll investigate Fandango Movie Ratings to determine if Fandango is inflating movie ratings on its site. This project is a chance for you to apply the statistics skills you've learned and overcome common setbacks in practical data analysis.
Intermediate Python StatisticsStart course → - 6 lessons 7 hours 23K+
Introduction to Data Visualization in Python
In this course, you'll learn how to balance graph creation and statistics in your visualizations using tools such as Matplotlib and Seaborn. Throughout this course, you'll learn the most common methods and techniques to visualize data using a variety of Python libraries. Best of all, you'll learn by doing - you'll practice and get feedback directly in the browser. You'll apply your skills to several guided projects involving realistic business scenarios to build your portfolio and prepare for your next interview.
Intermediate Python MatplotlibStart course → - 6 lessons 7 hours 19K+
Data Cleaning Project Walkthrough
In your data science career, you'll rarely get a dataset that is in precisely the state you want. That's why data cleaning is such an invaluable skill in data science. This course builds on our previous Advanced Data Cleaning course and will make you a valuable asset to any data science team. After learning how to prepare the data for analysis, the real fun begins - you'll complete two data analysis and visualization guided projects using data from some of the biggest names in film culture.
Intermediate Python Data CleaningStart course → - 7 lessons 7 hours 19K+
PostgreSQL for Data Engineering
In this course, you'll learn about the SQL database management system PostgreSQL and what differentiates it from SQLite. You'll learn about proper data types for your data and why they're important. You'll also install PostgreSQL on your own machine and learn how to work with psycopg2, a Python database API for PostgreSQL that allows you to interact with PostgreSQL databases using Python. Best of all, you'll learn by doing - you'll practice and get feedback directly in the browser. At the end of the course, you'll complete a project that asks you to work on a real-life example - storing storm data in a PostgreSQL database.
Intermediate PostgreSQL SQLStart course → - 4 lessons 8 hours 18K+
Advanced Data Cleaning in Python
This course builds on basic data cleaning knowledge and requires intermediate familiarity with Python for data science. You'll learn how to clean and manipulate text data using basic and advanced regular expressions, how to resolve missing data, and how to employ lambda functions and list comprehension with pandas. Best of all, you'll learn by doing - you'll practice and get feedback directly in the browser.
Intermediate Python Data CleaningStart course → - 5 lessons 5 hours 17K+
Telling Stories Using Data Visualization and Information Design
This particular course is for intermediate Python users, and it builds upon the essentials covered in our previous Python visualization lessons. You'll learn how to use Python libraries like Matplotlib and Seaborn to transform raw data into compelling and actionable visualizations. You'll learn the most common data visualization techniques, and you'll use Python to generate beautiful, insightful, and meaningful visuals that will give new life to your data. Best of all, you'll learn by doing - you'll practice and get feedback directly in the browser. You'll apply your skills to several guided projects involving realistic business scenarios to build your portfolio and prepare for your next interview.
Intermediate Python MatplotlibStart course → - 5 lessons 3 hours 16K+
Dictionaries and Functions in Python
In this course, you'll explore the world of Python data engineering. You'll learn basic Python concepts such as dictionaries, functions, and default arguments. Best of all, you'll learn by doing - you'll practice and get feedback directly in the browser. The course will conclude with two guided projects: The first one teaches you to learn and install Jupyter Notebook The second one asks you to perform practical data analysis on profitable app profiles for the App Store and Google Play Market
Beginner Python Data ScienceStart course → - 5 lessons 7 hours 15K+
Introduction to Supervised Machine Learning in Python
In this course, you'll learn how to develop a machine learning workflow for classification tasks using scikit-learn. You'll learn how to build and implement the k-nearest neighbors algorithm using pandas and scikit-learn. Finally, you'll learn to train, validate, and improve your machine learning model for better performance and accuracy using techniques like tuning hyperparameters. Best of all, you'll learn by doing - you'll practice and get feedback directly in the browser. At the end of the course, you'll combine your new skills to complete a project to predict heart disease.
Intermediate Python Machine LearningStart course → - 6 lessons 8 hours 13K+
Intermediate Statistics in Python
In this course, you'll learn how to summarize distributions using the mean, the median, and the mode, as well as when to use them. It will teach you which statistic gives you the most information about a distribution so you know not only how to apply them but also why you should. You'll then learn to measure variability using variance or standard deviation, and how to locate and compare values using z-scores. We'll then explore range, mean absolute deviation, variance, and standard deviation. You'll also learn about z-Scores and how to use them to compare values across any distribution. Best of all, you'll learn by doing - you'll practice and get feedback directly in the browser. At the end of the course, you'll complete a guided project that asks you to find the best markets for advertising an e-learning platform that combines your data science programming skills and the statistical skills you've learned in this course.
Intermediate Python StatisticsStart course → - 4 lessons 6 hours 12K+
Data Analysis for Business in Python
In this course, you'll learn how to respond to key business needs using data, such as understanding churned customers, pricing, customer ratings, etc. You'll learn to work with ambiguous, imprecise, and subjective data - the "fuzzy" side of data - to present key business metrics like churn rate and net promoter score. Best of all, you'll learn by doing - you'll practice and get feedback directly in the browser. You'll also apply your skills to a guided project involving a realistic business scenario to build your portfolio and prepare for your next interview.
Intermediate Python Data InterpretationStart course → -
Free
1 lessons 0 hours 12K+
Querying SQLite from Python
Immerse yourself in the dynamic world of Python and SQL in our transformative course. Connect and query from SQLite databases using Python, turning raw data into actionable insights. The best part? It's all hands-on. You'll implement your newly acquired skills in real-world scenarios and receive interactive feedback. By the end of this course, you will have a unique skill set that puts you ahead in the rapidly evolving data industry.
Intermediate Python SQLiteStart course → - 5 lessons 4 hours 11K+
Building a Data Pipeline
In this course, you'll learn how to build a simple data pipeline using imperative and functional paradigms. You'll also learn how to use functional closures in Python, how to implement a well-designed pipeline API, how to write decorators, and how to apply them to functions. At the end of the course, you'll work on a real-world project, using a data pipeline to summarize Hacker News data. This project is a chance for you to combine the skills you learned in this course and build a real-world data pipeline from raw data to summarization.
Intermediate Python Data PipelinesStart course → - 5 lessons 4 hours 11K+
Introduction to Probability in Python
What You'll Learn in Probability Fundamentals As you might have guessed from the title, Probability Fundamentals is designed to give you a working understanding of critical concepts in probability that are relevant to the everyday work of data analysis and data science. Like all Dataquest courses, you'll work through this course in your web browser, writing code to apply what you're learning every step of the way. Working through the course, you'll use your Python programming skills and the statistics knowledge you're learning to estimate empirical and theoretical probabilities. You'll learn the fundamental rules of probability, and then work to solve increasingly complex probability problems. Finally, you'll learn about counting techniques like permutations and combinations before synthesizing all your new knowledge in a guided project building the logic for a mobile app that helps gambling addicts more accurately estimate lottery odds to help them overcome their addiction. By the end of the course, you'll understand the difference between theoretical and experimental probability. You'll have experience calculating the probabilities for a variety of different events, and you'll be able to calculate the number of permutations and combinations possible in experiment outcomes. Why Learn Probability and Statistics? Although a lot of data science work is experienced as programming, almost everything that data scientists do involves working with statistics. When data scientists make predictions, they're dealing with probabilities. The concept of probability might seem basic, but it's the foundation for even the most advanced predictive models. And while the actual mathematical operations are often baked into popular data science libraries for quick application, this convenience can be a double-edged sword. Just because a technique is easy to apply, after all, doesn't mean that it's correct to apply in every circumstance. That's why learning probability and statistics concepts, including those covered in this course, is so important for data scientists. When you understand the why, it becomes much easier for you to identify the correct statistical technique or calculation for the problem you're trying to solve. It also becomes easier to explain your analysis to others when you have a firm grasp of why you used the technique you chose.
Intermediate Python ProbabilityStart course → - 4 lessons 3 hours 10K+
Hypothesis Testing in Python
In this course, you'll learn about single and multi-category chi-square tests, degrees of freedom, hypothesis testing, and different statistical distributions. To learn about hypothesis testing and statistical significance, you'll work hands-on with multiple datasets on weight loss data - are patients losing weight due to pure luck, or is it a diet pill? You'll run the numbers and find out! At the end of the course, you'll complete a guided project in which you'll work with data from the American TV show Jeopardy. You'll analyze text and search for winning strategies. It's a chance for you to combine the skills you learned in this course, and to showcase a fascinating project in your portfolio. Best of all, you'll learn by doing - you'll practice and get feedback directly in the browser.
Intermediate Python Statistics and ProbabilityStart course → - 5 lessons 4 hours 9.5K+
Introduction to Python Programming
This interactive Python course for beginners develops fundamental web development skills to help you begin your journey to become a successful developer. In this course, you'll learn to do basic arithmetic; write code using Python syntax; work with different types of data; and perform basic Python operations such as working with variables, processing numerical and text data, and manipulating lists. Best of all, you'll learn by doing - you'll write code and get feedback directly in the browser.
Beginner Python Data ScienceStart course → - 5 lessons 5 hours 9.2K+
Introduction to Conditional Probability in Python
In this course, we'll build on the fundamentals of probabilities, including the theoretical and empirical probabilities, the probability rules ( the addition rule and the multiplication rule), and the counting techniques (the rule of product, permutations, and combinations). You'll learn to assign probabilities to events based on certain conditions by using conditional probability rules, to assign probabilities to events based on whether they are in a relationship of statistical independence or not with other events, and to assign probabilities to events based on prior knowledge by using Bayes's theorem. You'll also learn to create a spam filter for SMS messages using the multinomial Naive Bayes algorithm. Best of all, you'll learn by doing - you'll practice and get feedback directly in the browser.
Intermediate Python ProbabilityStart course → - 4 lessons 5 hours 6.8K+
Intermediate Python for Data Engineering
In this course, you'll expand your Python for data engineering knowledge. Using real-world data from the Museum of Modern Art, you' ll learn how to prepare text data, introduce uniformity into a messy dataset, and more. You'll also explore object-oriented programming (OOP) and how it powers Python. Finally, you'll learn new and exciting Python coding concepts for data engineering. Best of all, you'll learn by doing - you'll practice and get feedback directly in the browser.
Beginner Python Data ScienceStart course → - 5 lessons 6 hours 6.5K+
Python Dictionaries, APIs, and Functions
In this course, you'll explore the world of Python for development. You'll learn basic Python concepts such as dictionaries, APIs, functions, and default arguments. Best of all, you'll learn by doing - you'll practice and get feedback directly in the browser. The course will conclude with a guided project where you will create a food ordering app!
Beginner Python APIsStart course → - 6 lessons 7 hours 6.1K+
Introduction to Algorithms
Algorithms are at the center of almost any programming job - particularly in the world of data engineering, where this is a recurring topic in job interviews. In this course, you'll learn how to assess and model the time and space complexity of algorithms (i.e., how fast they'll be, how much memory they'll require), and you'll learn how to trade memory for speed. Best of all, you'll learn by doing - you'll practice and get feedback directly in the browser. At the end of the course, you'll put together what you've learned in a guided project that tasks you with building indices for a CSV using dictionaries.
Intermediate Algorithms PythonStart course → - 7 lessons 5 hours 5.8K+
Processing Large Datasets In Pandas
In this course, you'll learn how to reduce the memory footprint of a pandas DataFrame while working with data from the Museum of Modern Art. You'll learn how to work with DataFrame chunks, how to use them to increase processing speed in pandas, and how to optimize DataFrame types while exploring data from the Lending Club. You'll also learn how to augment pandas with SQLite to combine the best of both tools. Finally, you'll learn when to use disk space over in-memory space, as well as how to run SQL queries using pandas. Best of all, you'll learn by doing - you'll practice and get feedback directly in the browser. At the end of the course, you'll complete a project that asks you to work on a real-life example - using the pandas SQLite workflow to analyze startup fundraising deals using data from CrunchBase.
Intermediate Pandas SQLiteStart course → - 4 lessons 4 hours 5.4K+
Programming Concepts in Python
In this course, you'll build a critical understanding of the inner workings of Python and basic computation. You'll also explore basic number systems, methods of encoding data, how to work with text files, and the best way to optimize data usage. Finally, you'll learn how to develop simple techniques for reading and writing to files, converting between encodings, and optimizing data usage. This course will put you on your way to mastering Python for data engineering. Best of all, you'll learn by doing - you'll practice and get feedback directly in the browser.
Beginner Python Data ScienceStart course → - 6 lessons 5 hours 4.2K+
APIs and Web Scraping in Python for Data Science
This course is designed to equip you with the skills to gather and analyze data from the web like a pro. We start by introducing the basics of API structures, then progress to advanced data retrieval and analysis techniques. Our curriculum covers essential Python tools like the requests library, JSON data handling, data filtering, error management, authentications, and web scraping methods. By the end of this course, you'll be adept at extracting and analyzing data directly from web pages and integrating it with Pandas for thorough analysis and visualization. We believe in practical learning-each lesson is tailored to real-world applications. This way, you'll not only enhance your skills but also gain a deep understanding of AI's practical aspects. Best of all, you'll learn by doing-you'll write code, receive feedback directly in your browser, and apply your skills to several guided projects involving realistic scenarios. This hands-on approach will help you build your portfolio and prepare for your next interview. By the time you complete this course, you'll be an expert at sourcing and manipulating data from various online sources, ready to take on analytical and development roles.
Intermediate Python APIsStart course → - 5 lessons 4 hours 4.0K+
Optimizing PostgreSQL Databases
In this course, you'll learn how to write database descriptions. You'll discover how to manage meta information about databases and tables by using PostgreSQL Internals. You'll also learn how to debug your PostgreSQL queries using the EXPLAIN clause. You'll learn how to measure estimated and actual execution times of your queries and determine which SQL clause is the most computationally expensive to perform, as well as the biggest cause for long-running queries. You'll learn concepts such as indexing and how it can greatly reduce querying speed. You'll also learn what it means to vacuum a PostgreSQL database, how it reduces query speeds, and how to vacuum a database, as well as what ACID means for database transactions and why it's important for transaction blocks. Best of all, you'll learn by doing - you'll practice and get feedback directly in the browser.
Intermediate PostgreSQL SQLStart course → - 5 lessons 5 hours 3.8K+
Introduction to Unsupervised Machine Learning in Python
In this course, you'll learn the fundamentals of the k-means algorithm and how to use it to build a model to segment data. You'll also learn to work with clusters with activities such as finding the optimal number of clusters, creating new clusters using the k-means algorithm in scikit-learn, and interpreting the results from a k-means model. Best of all, you'll learn by doing - you'll practice and get feedback directly in the browser. At the end of the course, you'll combine your new skills to complete a project to perform a credit card customer segmentation.
Intermediate Python Machine LearningStart course → - 5 lessons 4 hours 3.7K+
Linear Regression Modeling in Python
Linear regression shows us how we can use data to predict the value of an outcome. This course covers the structure of a linear regression model, how to interpret it, how to determine if a model is appropriate, and how to use the model to predict values of new data. In this course, you'll learn to create single and multiple linear regressions, identify the different types of predictors, and identify a cost function for linear regression. You'll also learn how to interpret regression parameters, how to check linear regression fit, and how to apply linear regression models. You will use tools such as scikit-learn, statsmodels, pandas, NumPy and matplotlib. Best of all, you'll learn by doing - you'll practice and get feedback directly in the browser. At the end of the course, you'll combine your new skills to complete a project to predict insurance costs.
Intermediate Python Linear RegressionStart course → - 5 lessons 6 hours 3.4K+
Decision Tree and Random Forest Modeling in Python
Decision trees are known in the machine learning world for a particularly distinctive characteristic: their visualizations are easier to understand compared to other machine learning models, and for this reason, they are very suitable for explaining insights to non-technical audiences. In this course, you'll learn the foundations of Decision Trees including identifying the key components of trees, interpreting them, classifying new observations using decision trees and calculating optimal thresholds for both classification and regression trees. You'll also learn how to build and visualize decision trees by adapting a real-life dataset to train tree models, selecting the appropriate scikit-learn tools to build your model, and training, testing and visualizing decision trees. You'll be able to evaluate and optimize trees for better performance including activities such as establishing the optimal depth for a decision tree, using Prune decision trees to avoid overfitting, or manipulating sample distribution in nodes and leaves. Finally, you'll learn how to apply the cross validation and ensemble techniques for decision trees. You'll identify the differences between decision trees and random forest models, develop and customize random forest models and optimize the parameters of random forest. Best of all, you'll learn by doing - you'll practice and get feedback directly in the browser. At the end of the course, you'll combine your new skills in a project to predict employee productivity with tree models
Intermediate Python Tree ModelsStart course → - 5 lessons 4 hours 3.1K+
NumPy for Data Engineering
Python programming skills are critical for data engineering. But for many critical data analysis and processing tasks, using stock Python isn't the most efficient approach. That's where NumPy comes in. In this course, you'll learn how to manipulate data using NumPy - it's much more efficient than Python alone if you're working with large amounts of data. Best of all, you'll learn by doing - you'll practice and get feedback directly in the browser.
Intermediate NumPy PythonStart course → - 4 lessons 3 hours 2.9K+
Gradient Descent Modeling in Python
Gradient descent is one of the most commonly used optimization algorithms to train machine learning models, such as linear regression models, logistic regression, or even neural networks. It finds the minimum of any convex function by gradually converging toward it. In this course, you'll learn the fundamentals of gradient descent and how to implement this algorithm in Python. You'll learn the difference between gradient descent and stochastic gradient descent, as well as how to use stochastic gradient descent for logistic regression. Best of all, you'll learn by doing - you'll practice and get feedback directly in the browser. At the end of the course, you'll combine your new skills in a project to optimize a stochastic gradient descent algorithm on linear regression.
Intermediate Python Gradient DescentStart course → - 6 lessons 4 hours 2.9K+
Optimizing Machine Learning Models in Python
The amount of data and the complexity of machine learning models have grown exponentially which led to the development of additional methods and techniques to improve accuracy of predictive models. In this course, you will learn how to best select a model. You'll get a strong understanding of cross-validation in the machine learning workflow and how to use k-fold and LOOCV cross-validation techniques to check performance. Then, you'll learn how to use regularization in machine learning including activities such as using regularized versions of linear regression, identifying the difference between ridge and LASSO regression or standardizing the features using helper functions in scikit-learn. Finally, you'll go beyond linear models by implementing polynomial regression in scikit-learn, defining piecewise functions and splines, implementing regression splines in scikit-learn and establishing best practices concerning splines Best of all, you'll learn by doing - you'll practice and get feedback directly in the browser. At the end of the course, you'll combine your new skills in a project to optimize a predictive model.
Intermediate Python Machine LearningStart course → - 5 lessons 3 hours 2.9K+
Logistic Regression Modeling in Python
Logistic regression and linear regression are very similar, but the two have slightly different objectives. In linear regression, we try to predict losses in insurance claims. In logistic regression, we're trying to predict categorical outcomes, otherwise known as classification. In other terms, logistic regression is the classification-based equivalent of linear regression. In this course, you'll learn the logistic regression method. You'll learn how to interpret regression parameters, how to evaluate logistic regression models, and how to apply them. Best of all, you'll learn by doing - you'll practice and get feedback directly in the browser. At the end of the course, you'll combine your skills to complete a project to classify heart diseases.
Intermediate Python Logistic RegressionStart course → - 6 lessons 4 hours 2.8K+
Introduction to Data Structures
In this course, you'll learn the fundamentals of data structures. You'll explore linked lists and how using linked nodes is helpful in creating data structures. Then you'll learn about queues, the FIFO data structure (first in, first out) , and the FCPS process scheduling algorithm (first come, first serve). From there, you'll dig into stacks, LIFO (last in, first out), and LCFS process scheduling (last come, first serve) - and then dictionaries and parallel processing. By the end, you'll understand the performance difference between data structures such as hash tables, stacks, queues, and more. Best of all, you'll learn by doing - you'll practice and get feedback directly in the browser. You'll apply this knowledge by completing two real-world data projects: In the first one, you'll use stacks when implementing complex algorithms In the second one, you'll analyze stock prices using hash tables and by implementing various algorithms
Intermediate Python Data StructuresStart course → - 5 lessons 5 hours 2.6K+
Parallel Processing for Data Engineering
In this course, you’ll explore how to process large datasets efficiently using parallel processing and the MapReduce programming model. You’ll learn how to divide work across multiple processors, implement MapReduce workflows, and apply these techniques to common data engineering problems. Through hands-on practice, you’ll gain practical experience designing scalable solutions for data-intensive tasks.
Advanced Python Parallel ProcessingStart course → - 4 lessons 6 hours 2.4K+
Prompting Large Language Models in Python
In this course, you'll gain in-depth insights into the practical applications of large language models. Starting with the fundamentals of the OpenAI Chat Completions API, you'll journey through creating dynamic AI-driven interactions. You'll learn to maintain context in conversations by managing history effectively and use prompt engineering techniques to steer AI responses. Additionally, the course covers efficient token usage in scripting, ensuring your applications run smoothly. The blend of theoretical knowledge and hands-on practice in this course positions you at the forefront of AI interaction technology.
Intermediate Python LLMsStart course → - 6 lessons 12 hours 2.2K+
Intermediate Python for AI Engineering
This course focuses on intermediate Python skills needed for development and working with AI. Throughout the course, you'll dive into object-oriented programming tailored for applications, grasp the fundamentals of decorators, and work with regular expressions, list comprehensions, and lambda functions. Elevate the functionality and efficiency of your projects, and confidently tackle user input errors and typical programming challenges. You'll put it all together in a guided project where you build a garden simulator text-based game.
Beginner Python AIStart course → - 5 lessons 6 hours 2.2K+
Intermediate Python
This course focuses on intermediate Python skills needed for development and working with AI. Throughout the course, you'll dive into object-oriented programming tailored for applications, grasp the fundamentals of decorators, and harness the power of regular expressions. Elevate the functionality and efficiency of your projects, and confidently tackle user input errors and typical programming challenges. Most importantly, you'll learn by doing - practicing and receiving feedback directly in the browser. By the end, you'll be better equipped to take on advanced web development tasks with Python.
Beginner Python Data ScienceStart course → -
Free
1 lessons 1 hours 2.2K+
Querying Databases with SQL and Python
Immerse yourself in the dynamic world of Python, SQL, and data science in our transformative course. Connect, query, and visualize data directly from SQLite databases using Python, turning raw data into actionable insights. Harness the power of Pandas to structure and manipulate data, refining your queries to a level of finesse. The best part? It's all hands-on. You'll implement your newly acquired skills in real-world scenarios and receive interactive feedback. By the end of this course, you will have a unique skill set that puts you ahead in the rapidly evolving data industry.
Intermediate Python SQLStart course → - 4 lessons 4 hours 2.0K+
Designing Dynamic Python Applications with Streamlit
In this course, you'll learn the ins and outs of Streamlit. You'll begin by grasping the fundamentals of the Streamlit framework, followed by designing user interfaces with widgets like sliders, buttons, and text input. The course then moves into managing state within a Streamlit app and culminates with the integration of an LLM API for dynamic chatbot responses. The hands-on exercises and real-world scenarios provide an immersive learning experience, ensuring you gain practical skills and knowledge. Best of all, you'll learn by doing - you'll practice and get feedback directly in the browser. Engage in realistic business scenarios, from a customer service app for a coffee startup to an AI chatbot for a tech firm, building your portfolio and prepping for your next career move all while learning a new skill.
Intermediate Python StreamlitStart course → - 8 lessons 5 hours 1.9K+
Recursion and Trees for Data Engineering
In this course, you'll learn about recursion, binary trees, binary heaps, and more. By the end, you'll be able to explain the difference between iteration and recursion, build a binary heap to query large datasets, implement and query a dataset using Binary Search trees, and more. Best of all, you'll learn by doing - you'll practice and get feedback directly in the browser. At the end of the course, you'll complete a guided project in which you'll use a B-Tree to implement a key-value datastore in Python.
Advanced Python Data StructuresStart course → - 5 lessons 6 hours 1.1K+
Tooling Essentials for Python Users
Learn about essential tooling specifically tailored for Python enthusiasts. Starting with the basics, you'll navigate and manage files seamlessly using the command line, turning tasks that once seemed tedious into second nature. You'll then explore virtual environments and environment variables, ensuring that your Python projects remain isolated and customizable. Transitioning into the importance of version control, you'll harness Git's power to track your code changes, work collaboratively with peers, and maintain a systematic history of your projects. Lastly, understanding that the right workspace can make all the difference, you'll evaluate and set up an Integrated Development Environment (IDE) that complements your Python development needs. Best of all, you'll learn by doing - tackling hands-on exercises and getting feedback directly in the browser. Concluding the course, you'll have the confidence and skills to tackle Python projects with an enhanced and efficient toolset.
Beginner Python CLI ToolsStart course → - 6 lessons 12 hours 547+
Introduction to Deep Learning in PyTorch
Deep learning is a discipline in artificial intelligence that has recently garnered a lot of interest. It's used to solve complex problems in various fields such as computer vision, natural language processing, robotics, and others that might be difficult to solve using traditional machine learning methods. In this course, you'll start with the fundamentals of deep learning and PyTorch tensors, then advance to professional-grade techniques including proper data methodology, advanced regularization, and comprehensive evaluation practices. You'll learn to build robust models that generalize well to new data using batch normalization, dropout, and early stopping. Best of all, you'll learn by doing - you'll practice and get feedback directly in the browser. At the end of the course, you'll apply your advanced skills to build a regularized deep neural network that predicts IPO listing gains with sophisticated evaluation techniques.
Advanced Python Machine LearningStart course → - 4 lessons 3 hours 421+
Analyzing Large Datasets in Spark
Master Apache Spark, the leading framework for big data processing. This hands-on course teaches you to work with Spark's core data structures - RDDs and DataFrames - while understanding the distributed architecture that makes Spark 10-100x faster than traditional tools. You'll analyze real datasets including US Census data and Daily Show guests, learning when to use RDDs for custom transformations, DataFrames for optimized operations, and Spark SQL for complex queries. By the end, you'll confidently process datasets that don't fit on a single machine.
Intermediate Data Science Data AnalysisStart course → - 3 lessons 6 hours 207+
Introduction to Retrieval-Augmented Generation (RAG)
Retrieval-Augmented Generation (RAG) lets you build AI systems that answer questions grounded in real documents rather than relying on model memory alone. In this course, you'll build a RAG pipeline from scratch, improve retrieval with techniques like query expansion and reranking, and learn to diagnose common failure modes. The focus is on practical implementation: understanding how each stage of the pipeline works and how to make the system reliable.
Intermediate RAG LLMsStart course → - 3 lessons 6 hours 162+
PySpark for Data Engineering
Building PySpark notebooks is one thing. Building production pipelines that integrate with your company's cloud infrastructure is another. This course teaches you to write PySpark code that runs reliably every day in real environments. You'll start by building a complete ETL pipeline that cleans messy CSV data with inconsistent formats and quality issues. Then you'll learn systematic performance optimization, taking a slow pipeline and making it 10x faster by reading the Spark UI and applying targeted fixes. Finally, you'll explore the big data ecosystem—understanding managed Spark platforms like Databricks and how to integrate PySpark with cloud storage (AWS S3) and data catalogs (AWS Glue). By the end, you'll know how to build pipelines that work at scale, diagnose performance problems, and deploy on the platforms that companies actually use.
Intermediate PySpark SparkStart course → - 4 lessons 8 hours 151+
Building Data Pipelines with Apache Airflow
Manual scripts and cron jobs break down as data pipelines grow complex. Apache Airflow brings order to chaos through workflow orchestration—ensuring tasks run in the right order, at the right time, with proper failure handling and monitoring. This course teaches you to build production-grade data pipelines the way professional teams do. You'll start by understanding orchestration concepts and Airflow's architecture, then deploy a complete Airflow environment in Docker. Using the TaskFlow API, you'll build increasingly sophisticated workflows: from simple ETL processes to pipelines with dynamic parallel processing and database connections. You'll integrate Git-based version control and GitHub Actions CI/CD for automated deployment. Finally, you'll build a real-world pipeline that scrapes Amazon book data, cleans it with Python, and loads it into MySQL on a schedule—complete with monitoring and alerting. By the end, you'll have the skills to orchestrate complex data workflows reliably at scale.
Intermediate Airflow PythonStart course → - 3 lessons 6 hours 144+
Tool Use with LLMs in Python
Move from experimental prompts to reliable LLM systems. This course teaches you the engineering patterns that make LLM interactions dependable: structured outputs with validation, function calling for tool integration, and the Model Context Protocol for reusable tool servers. You'll learn how to handle messy LLM outputs, build agentic loops that execute multi-step tasks, and create maintainable components that work consistently.
Intermediate LLM Systems Function CallingStart course → - 5 lessons 10 hours 101+
Building AI Apps with FastAPI
AI applications need more than model code — they need APIs, containers, and orchestration. In this course, you'll build an LLM-powered API with FastAPI, containerize it with Docker, connect it to a database using Docker Compose, and apply production hardening patterns. You'll go from a working API endpoint to a fully orchestrated, deployment-ready application stack.
Intermediate FastAPI DockerStart course → - 4 lessons 8 hours 83+
Deep Learning Applications in PyTorch
Deep learning is applied differently depending on the type of problem you're solving. In this course, you'll explore how PyTorch is used across key application areas including sequence models, natural language processing, and computer vision. Rather than focusing on deep theory or production optimization, this course emphasizes understanding model structures, data representations, and common patterns so you can recognize how deep learning solutions are built in practice.
Advanced PyTorch Deep LearningStart course →
Related resources on Python
- Article
Python Skills You Need to Work with AI
Python skills have become a requirement for anyone looking to succeed in the constantly changing world of data science. Whether you're analyzing data, building AI applications, or automating tasks, Python is at the heart of it all.
16 min read View article → - Article
The Best Way to Learn Python (Verified by 100K+ Students)
After teaching over 100K students at Dataquest, I've discovered that the best way to learn Python is to start building projects as soon as possible. Most people waste months memorizing syntax from tutorials.
7 min read View article → - Article
88 Python Projects from Beginner to Advanced (2026)
Here’s a list of Python projects from beginner to advanced, designed to help you practice your skills, build real projects, and learn faster. New to Python?
17 min read View article →
Learn Python by building projects
-
Free
Project
Exploring Hacker News Posts
For this project, we’ll step into the role of data analysts to explore Hacker News submissions, analyzing trends using skills in string manipulation, object-oriented programming, and date handling in Python.
8 steps Start project → -
Free
Project
Profitable App Profiles for the App Store and Google Play Markets
For this project, we’ll assume the role of data analysts for a company that builds free Android and iOS apps. Our revenue depends on in-app ads, so our goal is to analyze data to determine which kinds of apps attract more users.
14 steps Start project → -
Free
Project
Exploring Financial Data using Nasdaq Data Link API
For this project, you’ll become a financial analyst exploring real-world economic data. You’ll use Python to interact with the NASDAQ Data Link API, retrieve financial datasets, then apply Pandas for data wrangling.
10 steps Start project → -
Free
Project
Kaggle Data Science Survey
For this project, we’ll act as a data analyst for Kaggle. Kaggle surveyed data scientists about their career status and skills.
8 steps Start project →
Frequently Asked Questions
How do I choose the right Python course for my goals?
The right Python course depends on your goals. If you want to focus on web development, automation, or data visualization, pick a course that emphasizes practical applications rather than just syntax.
Dataquest’s Python courses are designed for data roles and provide hands-on practice with tools like pandas and NumPy.
What is Python?
Python is a high-level python programming language designed to be easy to read and write. It is widely used for data analysis, automation, web development, and machine learning. Python is maintained by the Python Software Foundation, which supports its open-source development and ongoing improvement.
Is Python hard to learn?
Learning Python is not hard. Its clear and readable syntax makes it beginner-friendly, and core concepts like object-oriented programming are introduced gradually to build confidence. Dataquest reinforces learning with hands-on coding exercises so you practice as you go.
What are the best Python courses online?
The best Python courses online focus on active learning rather than passive video watching. Look for courses that let you write and test code throughout each lesson. Dataquest uses an interactive platform that provides instant feedback, helping learners improve quickly and build practical skills.
Are Python skills still in demand?
Yes, Python skills are still in high demand across technology and data-related fields. A strong python skill is essential for careers in data science, analytics, and artificial intelligence. Employers value professionals who understand how to apply Python to real business problems.
What jobs can you get with Python skills?
Python skills can lead to several in-demand roles, including:
- Data Scientist
- Data Analyst
- Python Developer
- Machine Learning Engineer
- Backend Developer
Dataquest focuses on teaching Python programming for data-focused careers that continue to grow.
Which programming language should I learn first?
Python is often recommended as the first programming language because it is easy to read and widely used. If your primary goal is database work, SQL may also be a good starting point. Dataquest offers beginner-friendly courses that let you explore both options in a structured way.
What is the difference between learning Python for development vs. data science?
Python for development is used to build applications and websites, often with frameworks like Django or Flask. Python for data science is used to analyze data, create models, and automate workflows using libraries like pandas, NumPy, and scikit-learn.
Dataquest focuses on teaching the Python skills most relevant for data-focused careers.
Do I need a technical background before starting Python courses?
No technical background is required to start learning Python. Many beginners start without any experience writing Python code. Dataquest assumes no prior knowledge and teaches concepts step by step.
What tools are commonly used with Python?
Common Python tools for data roles include Jupyter Notebooks, pandas, NumPy, Matplotlib, and scikit-learn. Some learners also use a Python cheat sheet for reference while practicing. Dataquest integrates these tools directly into the learning environment.
What is the best way to learn Python fast?
The best way to learn Python quickly is to practice writing code every day. Consistent practice reinforces concepts and improves retention. Dataquest supports this approach with short lessons and a coding challenge structure that encourages active learning.
How long will it take to become job-ready in Python?
Most learners become job-ready for data analysis roles in three to six months. Data science and engineering roles may require six to twelve months of study. Dataquest’s learning paths help learners stay focused while building practical Python scripts.
How much do Python courses cost?
The cost of Python courses varies by provider and format. Dataquest offers a subscription that includes access to its full curriculum, including Python, SQL, and R. Learners can try introductory content before committing.
Will I get a certificate, and does it help me stand out?
Yes, learners earn a certificate for each completed course, which can serve as a basic Python certification. More importantly, learners build a portfolio of projects that demonstrates real-world skills to employers.
Join 1M+ data learners on Dataquest.
- 1
Create a free account
- 2
Choose a learning path
- 3
Complete exercises and projects
- 4
Advance your career