Individual Courses
Browse our standalone courses across data science, analytics, programming, and visualization. Sign up to take your first course for free.
Individual Courses
Browse our standalone courses across data science, analytics, programming, and visualization. Sign up to take your first course for free.
Showing 115 of 115 courses
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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 → -
Free
3 lessons 3 hours 50K+
Introduction to Data Analysis in Excel
This course will help you gain the practical skills in Excel to perform data analysis and visualization - and ultimately help organizations make more-informed decisions. We designed it for aspiring data professionals with little experience or learners who use basic Excel in their daily jobs and want to enhance their skills. Best of all, you'll learn by doing - you'll practice and get feedback directly in the browser.
Beginner Excel Data LiteracyStart course → - 5 lessons 3 hours 48K+
Introduction to Data Analysis in R
This interactive R course for beginners teaches fundamental data analysis skills and helps you begin your journey to become a successful data professional. In this course, you'll learn to use basic arithmetic; write code using R syntax; and work with different data types, values, and vectors in the data analysis workflow, including data exploration, manipulation, analysis, and visualization with R. Best of all, you'll learn by doing - you'll write code and get feedback directly in the browser.
Beginner R Data ScienceStart course → - 7 lessons 4 hours 29K+
Introduction to SQL and Databases
This interactive SQL course for beginners will teach you how to code and perform fundamental data science tasks using SQL - and it will help you begin your journey to become a successful data professional. In this course, you'll learn how to write data queries and how to use statements and clauses - as well as the critical role of SQL in routine data science tasks. 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 real-world scenarios to build your portfolio and prepare for your next interview.
Beginner SQL 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 → - 5 lessons 4 hours 25K+
Command Line for Data Science
In this course, you'll learn how to navigate the filesystem, how to alter permissions for different users, and how to create and run a Python script from the command line. You'll also learn how to use the terminal on UNIX machines and how to use the command line's powerful text processing tools like awk and sed. The lack of a graphical user interface (GUI) also makes the command line faster than other approaches for many tasks. Best of all, you'll learn by doing - you'll practice and get feedback directly in the browser. When you finish the course, you'll have enough hands-on practice that you'll be comfortable using the command line in your day-to-day data analysis tasks.
Intermediate Shell Command LineStart 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 4 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 4 hours 16K+
Text Processing for Data Science
This course builds on the Command Line for Data Science course. You'll learn how to read documentation, how to inspect files, how to perform basic text processing using the command line, how to redirect and pipe output, and how to access documentation for different commands if you get stuck. Best of all, you'll learn by doing - you'll practice and get feedback directly in the browser.
Intermediate Shell Text ProcessingStart 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 → - 5 lessons 6 hours 15K+
Data Structures in R
In this course, you'll learn the most common basic data structures you'll encounter in the data analysis workflow. You'll learn how to work with vectors, matrices, lists, and DataFrames. You'll be able to index vectors and matrices to extract specific elements and apply functions to vectors and matrices to perform calculations. You'll also learn how to create and index lists to extract objects. Finally, you'll learn how to define a tibble, filter and subset the data in a tibble and employ piping with tibbles. Best of all, you'll learn by doing - you'll practice and get feedback directly in the browser. The course will wrap up with a guided project that will enable you to perform a data analysis by investigating COVID-19 Virus Trends
Beginner R Data StructuresStart 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 3 hours 13K+
Control Flow, Iteration, and Functions in R
In this course, you'll begin by learning control flow (including if-else statements) and conditionals. You'll then move into learning about functions and functional programming. You'll also learn about iteration, how to write for loops and while loops, and when and why you might choose to write a for loop instead of employing vectorization in R. 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 work on the first part of a guided project that will enable you to apply control flow loops and functions to create a reusable data workflow.
Beginner R Data ScienceStart 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 → - 4 lessons 2 hours 12K+
Summarizing Data in SQL
In this course, you'll learn several techniques for sampling data, such as random sampling and cluster sampling. You'll also learn about 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. As you learn about these concepts and how to use them for more robust data analysis, you'll be working with a dataset about basketball players in the WNBA (Women's National Basketball Association) that contains general information about players, along with their metrics for the 2016-2017 season. 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 portfolio project that asks you to investigate Fandango Movie Ratings to determine if Fandango is inflating movie ratings on its site. This is an opportunity to learn to identify and overcome common setbacks in practical data analysis.
Beginner SQL Data ScienceStart 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 3 hours 11K+
Introduction to Git and Version Control
This course teaches you how to use Git, one of the most popular version control systems. You will learn how Git is helpful in the context of data analysis and data science. We'll cover the fundamentals, including how to clone a project to your local machine, how to iterate on the project by creating branches, and how to push your work to Git remotes like GitHub. You'll learn how Git automatically creates "merge conflicts" to prevent catastrophic mistakes. By the end of this course, you'll know how to install Git on your local machine. An active GitHub account is crucial for making your data analysis and data science projects available to potential employers. Best of all, you'll learn by doing - you'll practice and get feedback directly in the browser.
Intermediate Git ShellStart course → - 4 lessons 3 hours 11K+
Combining Tables in SQL
Joins in SQL allow you to combine datasets from multiple tables using a single query. In this course, you'll learn how joins work, how to combine data from more than one table using inner joins, how to select columns from different tables, and how to alias tables. Best of all, you'll learn by doing - you'll practice and get feedback directly in the browser.
Intermediate SQL JoinsStart 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 → - 6 lessons 6 hours 10K+
SQL Subqueries
A subquery is a query nested inside another query. Subqueries are useful to data practitioners for scaling and making more powerful queries. The main reason we have subqueries is the need to combine information from multiple tables. Information in a relational database isn't stored in a single table; it's shared between several tables. In this course, you'll learn various types of subqueries, how to use them, and why they are so valuable: scalar subquery, multi-rows subquery and multi-columns subquery. You'll also learn how to write common table expressions in SQL. Best of all, you'll learn by doing - you'll practice and get feedback directly in the browser.
Intermediate SQL SubqueriesStart course → - 6 lessons 6 hours 10K+
Preparing Data in Excel
This course is part of the "Introduction to Data Analysis with Excel" skill path, which we designed for anyone who wants to gain the practical skills in Excel to perform data analysis and visualization, and ultimately help organizations make better informed decisions. We designed it for aspiring data professionals with little experience and learners who use basic Excel in their daily jobs and want to enhance their skills. Preparing data involves cleaning and organizing. In this course you'll not only learn how to prepare data with Excel by organizing data into a spreadsheet using worksheets and tables but also how to clean data by removing duplicates and irrelevant data. You'll also learn how to consolidate the data to prepare it for analysis. Best of all, you'll learn by doing - you'll practice and get feedback directly in the browser.
Beginner Excel Data PreparationStart course → - 3 lessons 2 hours 9.8K+
Calculus For Machine Learning
Calculus is one of the core mathematical concepts behind machine learning, and enables us to understand the inner workings of different machine learning algorithms. It plays an important role in the building, training, and optimizing machine learning algorithms. In this course, you'll learn to work with linear and nonlinear functions, including decomposing a linear equation into slope and y-intercept or defining slope. You'll also learn to use limits, including representing slope using limits, defining defined and undefined limits, and computing limits using SymPy. Finally, you'll identify extreme points in a nonlinear function and compute the derivative of a nonlinear function. Best of all, you'll learn by doing - you'll practice and get feedback directly in the browser.
Intermediate Calculus Machine LearningStart course → - 5 lessons 4 hours 9.4K+
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 → - 7 lessons 3 hours 9.3K+
Intermediate Command Line for Data Science
In our intermediate command line course, you'll learn to improve your data analysis workflow with concepts such as piping and redirecting output into a file; searching files for a string; and cleaning, exploring, and consolidating data using the command line. You'll also learn to work with Jupyter console, an enhanced Python interpreter, to develop scripts. And you'll learn how to clean and explore data using csvkit, a suite of command line tools for converting and working with CSV formats. Then you'll build a project that combines your Python data skills with your new command line expertise - you'll write Python scripts to compute summary statistics and then run the scripts directly from the command line. When you finish this course, you'll be able to add "UNIX Command Line" skills to your data science resume!
Intermediate Shell Command LineStart 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 2 hours 8.8K+
Linear Algebra For Machine Learning
Linear Algebra is a key branch of mathematics that is concerned with vectors, matrices, planes, and lines, and it helps to build blocks of machine learning algorithms. In this course, you'll learn how to define linear systems using linear algebra, how to represent a problem as a linear system, and how to solve linear systems by elimination. You'll learn how to define vectors using geometry, as well as how to perform vector operations and identify the link between linear combinations and solutions to linear systems. You'll also learn how to perform matrix operations in NumPy, how to define the matrix inverse and transpose, and how to solve the matrix inverse in higher dimensions. Finally, you'll learn to identify the different solution sets to linear systems and define homogeneous and nonhomogeneous systems. Best of all, you'll learn by doing - you'll practice and get feedback directly in the browser.
Intermediate Linear Algebra Machine LearningStart course → - 5 lessons 4 hours 8.5K+
Introduction to Data Visualization in R
In data science, it's not enough to be able to analyze data. You must also be able to create compelling visualizations to share your insights and help people understand your findings. In this course, you'll learn the ggplot2 package, a powerful data visualization library for R. You'll also learn how to add and work with multiple plots in your code to show different visualizations. By the end of this course, you'll be able to create visualizations such as line charts, bar plots, scatter plots, histograms, and box plots to help others understand your data. Best of all, you'll learn by doing - you'll practice and get feedback directly in the browser. At the end of the course, we'll conclude with a guided data science project that uses real-world data about forest fires in Portugal.
Intermediate R ggplot2Start course → -
Free
3 lessons 2 hours 7.1K+
AI Chatbots: Harnessing the Power of Large Language Models with Chandra
Artificial Intelligence is redefining the landscape of technology and communication. In this course, you'll gain a foundational understanding of AI, machine learning, deep learning, natural language processing, and chatbots. Discover how to craft effective prompts and interact with chatbots like Chandra to maximize their potential in educational, work, and personal projects. By the end of this course, you'll have hands-on experience with Chandra and be inspired to explore further into AI and data science.
Beginner AI LLMsStart 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 → - 4 lessons 3 hours 6.7K+
Specialized Data Processing in R
First, you'll learn techniques like string indexing and concatenation to interpret, process, and analyze text data. Next, you'll leverage the lubridate package to overcome the unique difficulties of working with dates and times in R. Finally, you'll learn how to vectorize a function using the map function. 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 work on the second part of a guided project that will enable you to apply control flow loops and functions to create a reusable data workflow.
Beginner R Data ProcessingStart 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 6 hours 6.2K+
Introduction to Data Cleaning in R
Data cleaning is a necessary skill for anyone who wants to work in a data-related field. You'll start this course by learning how to identify data cleaning needs prior to analysis, how to use functionals for data cleaning, how to practice string manipulation, how to work with relational data, and how to reshape data using tools from the tidyverse. You'll create correlation matrices to identify trends in your data, and then you'll then learn how to deal with missing values in your dataset. 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 work on a guided project to analyze parents', students', and teachers' perceptions of NYC schools. You'll learn to work with survey data - specifically how to import, simplify, and reshape the data. You'll also learn about R Notebooks and how you can use them to showcase your work.
Intermediate R Data CleaningStart 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 → -
Free
3 lessons 1 hours 5.4K+
Neural Network Fundamentals
The course has several objectives. First, you will learn what a GPT model is (Generative Pre-trained Transformer) and how it operates. Additionally, the course will help you refresh your knowledge of linear algebra and calculus concepts that are crucial for deep learning. You'll learn how to manipulate matrices, perform vector calculus, and solve optimization problems. Finally, you will learn how gradient descent is used in deep learning. You'll learn how to train a linear regression model using gradient descent, and you'll learn how gradient descent is used to optimize neural network parameters. You'll also gain practical experience by implementing gradient descent algorithms from scratch using Python. By the end of this course, you'll have a solid understanding of the fundamental concepts of deep learning. You'll be well-prepared to continue your deep learning journey and move on to more advanced topics such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), leading up to training your own GPT model.
Intermediate Neural Networks Deep LearningStart course → - 7 lessons 5 hours 5.2K+
Introduction to Statistics in R
In this course, you'll learn several techniques for sampling data, such as random sampling and cluster sampling. You'll also learn about 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. As you learn about these concepts and how to use them for more robust data analysis, you'll be working with a dataset about basketball players in the WNBA (Women's National Basketball Association) that contains general information about players, along with their metrics for the 2016-2017 season. 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 portfolio project that asks you to investigate Fandango Movie Ratings to determine if Fandango is inflating movie ratings on its site. This is an opportunity to learn to identify and overcome common setbacks in practical data analysis.
Intermediate R StatisticsStart course → - 7 lessons 6 hours 4.3K+
Window Functions in SQL
Embark on an exciting journey into the world of SQL Window Functions and amplify your data analysis skills. In this course, you'll master aggregate, ranking, distribution, and offset window functions to streamline intricate queries and extract valuable information. Best of all, you'll learn by doing – practice and receive feedback directly in the browser. You'll apply your expertise to a captivating real-world project, fortify your portfolio, and stand out in the competitive data landscape.
Intermediate SQL Window FunctionsStart 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 5 hours 4.1K+
Visualizing Data in Excel
This course is part of the "Introduction to Data Analysis with Excel" skill path, which we designed for those seeking the practical skills in Excel to perform data analysis and visualization - and ultimately help organizations make better-informed decisions. We designed it for aspiring data professionals with little experience and learners who use basic Excel in their daily jobs and want to enhance their skills. In this course, you'll learn how to use graphs and charts to design insightful data visualizations for your audience. You'll learn how to use Gestalt principles and pre-attentive attributes and more. Best of all, you'll learn by doing - you'll practice and get feedback directly in the browser.
Beginner Excel Data VisualizationStart 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 → - 6 lessons 2 hours 4.0K+
Data Preparation in Tableau
In this course, you'll learn how to connect Tableau to multiple data sources such as text files, csv files, or Excel workbooks. You'll also learn to import and connect datasets from multiple sources by building complete data models and configuring relationships and joins. Finally, you'll learn how to clean and filter your data by hiding unnecessary fields and using only the data you need. Best of all, you'll learn by doing - you'll practice and get feedback directly in the browser.
Beginner Tableau Data PreparationStart course → - 7 lessons 3 hours 3.8K+
Learn Data Modeling in Power BI
This interactive course will help you take your first steps in Data Modeling with Microsoft Power BI. You'll learn how to design data models, define key components like dimensions and fact tables, and optimize performance to build robust models for decision-making. No advanced Power BI experience is necessary - we'll start with the basics and guide you every step of the way
Beginner Power BI Data ModelingStart 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 → - 6 lessons 5 hours 3.8K+
Analyzing Data in Excel
This course is part of the "Introduction to Data Analysis with Excel" skill path, which we designed for those seeking the practical skills in Excel to perform data analysis and visualization - and ultimately help organizations make better-informed decisions. We designed it for aspiring data professionals with little experience and learners who use basic Excel in their daily jobs and want to enhance their skills. In this course, you'll learn how to develop business insights using PivotTables, how to identify trends using time-series analysis, and how to create data visualizations to tell meaningful stories. You'll also learn how to summarize and visualize relationships between categorical and quantitative variables. Best of all, you'll learn by doing - you'll practice and get feedback directly in the browser.
Beginner Excel Data InterpretationStart 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 4 hours 3.7K+
Exploring Data in Excel
This course is part of the "Introduction to Data Analysis with Excel" skill path, which we designed for those seeking the practical skills in Excel to perform data analysis and visualization - and ultimately help organizations make better-informed decisions. We designed it for aspiring data professionals with little experience and learners who use basic Excel in their daily jobs and want to enhance their skills. In this course, you'll learn why we need descriptive statistics, how to explore and apply multiple summary statistics to a spreadsheet in Excel, how to identify which statistics apply to which data type, and how to apply descriptive statistics to groups of data. Best of all, you'll learn by doing - you'll practice and get feedback directly in the browser.
Beginner Excel Descriptive StatisticsStart 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 → - 6 lessons 2 hours 3.2K+
Linear Regression Modeling in R
In this course, you'll learn how and when to use linear regression models to make predictions. You'll learn how to build linear regression models, how to interpret their output, and how to assess model accuracy. You'll also explore the limitations of linear regression models when data isn't linear. Finally, you'll learn to use programming tools to fit and visualize many linear regression models at once. 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 to practice your skills with a subset of condominium sales data from all five boroughs of New York City.
Intermediate R Linear RegressionStart 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 → - 5 lessons 4 hours 2.9K+
Introduction to Data Analysis Using Microsoft Power BI
In this course, you'll learn the essentials of Microsoft Power BI. You'll start by exploring the Power BI interface, components, and workflow. Then, you'll learn how to import, load, clean, and transform data to prepare it for analysis. Along the way, you'll discover how to organize and simplify your models to make data more manageable. Best of all, you'll learn by doing - you'll practice hands-on tasks and receive instant feedback directly in the browser.
Beginner Power BI Data PreparationStart 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 3 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 → - 4 lessons 6 hours 2.9K+
Advanced Data Cleaning in R
You'll learn about regular expressions (regex), a powerful tool that allows you to match and manipulate text data with precision. You'll also learn to work with JSON data, a common format you'll encounter when pulling data from web APIs. Then you'll dive into map and anonymous functions, two intermediate-to-advanced concepts in R that can speed up your data cleaning. You'll also learn how to resolve missing values in your data, a critical part of almost every data analysis project. Rather than dropping rows or columns, which reduces the amount of data you have to work with, you'll learn statistical techniques to impute missing data, and you'll also learn how to insert data from outside sources. Best of all, you'll learn by doing - you'll practice and get feedback directly in the browser.
Intermediate R Data CleaningStart 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 → - 5 lessons 2 hours 2.5K+
Learn to Visualize Data in Power BI
In this course, you'll learn the essentials of creating and working with Power BI visuals. You'll start by exploring how to design visuals that bring your data to life and tell compelling stories through reports. Then, you'll learn to design report layouts, enhance navigation, and build interactive dashboards to share insights effectively. Best of all, you'll learn by doing - you'll practice creating visuals, designing reports, and building dashboards, with hands-on tasks and instant feedback directly in the browser.
Beginner Power BI Data VisualizationStart course → - 6 lessons 6 hours 2.5K+
Introduction to Deep Learning in TensorFlow
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 you'll explore the TensorFlow library. Then, you'll learn to build, train, and evaluate deep learning regression and classification models using the TensorFlow framework. 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 new skills to a project to build a deep neural network model that can predict the listing gains of IPOs on the Indian market.
Intermediate TensorFlow Deep LearningStart course → - 4 lessons 2 hours 2.4K+
Introduction to APIs in R
Although there are many datasets available in convenient formats like CSVs, there is also a large amount of data that is accessible only via an API. If you want to analyze streaming data from Twitter, for example, or dig into posting trends on Reddit, you need to get that data from the relevant APIs. In this course, you'll learn the fundamentals of APIs, such as connecting to an open API and interpreting different status codes. You'll also learn how to work with the JSON data format in R (since most data from APIs will be in JSON format). Then, you'll tackle more complex tasks like authenticating with private APIs and submitting more complex requests. Best of all, you'll learn by doing - you'll practice and get feedback directly in the browser. The course concludes with a guided project that asks you to assemble a full data analysis project using an API to get data on New York's solar energy resources.
Intermediate R APIsStart course → - 6 lessons 1 hours 2.4K+
Introduction to Machine Learning in R
In this course, you'll learn key concepts such as KNN Algorithms (K-Nearest Neighbors), error metrics including the Mean Squared Error and the Root Mean Squared Error and caret, a machine learning library for the R programming language. You'll learn how to optimize machine learning algorithms for better accuracy and performance of trained models using hyperparameter optimization. You'll then dig into performing rigorous model testing using k-fold cross-validation. As you learn these new skills, you'll be working with AirBnB prices data from Washington D.C. to predict the optimal price for generating profit from a Washington D.C. home rental. 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 to predict car prices using the K-Nearest Neighbors algorithm. This project is a chance for you to combine the skills you learned in this course and practice a machine learning workflow.
Advanced R Machine LearningStart 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 → -
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 → - 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 → - 5 lessons 1 hours 2.1K+
Hypothesis Testing in R
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! 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 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.
Intermediate R StatisticsStart course → - 6 lessons 2 hours 2.1K+
Intermediate Statistics in R
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. You'll learn 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 in which you'll 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 R StatisticsStart course → -
Free
1 lessons 0 hours 2.0K+
Querying Databases with SQL and R
Immerse yourself in the dynamic world of R and SQL in our transformative course. Connect and query from SQLite databases using R, 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 R 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 1 hours 1.9K+
Manage Workspaces and Semantic Models in Power BI
A workspace is a space where you can collaborate with others to create collections of reports and dashboards.In this course, you'll learn how to create workspaces in the Power BI service, how to deploy Power BI artifacts to the service, how to share them with other users, and how to connect Power BI reports to on-premises data sources. By the end of this course, you'll be able to use workspaces to house reports and dashboards for collaboration across multiple teams; use share and present reports and dashboards in a single environment; and maintain security by controlling who can access semantic models, reports, and dashboards.
Beginner Power BI WorkspacesStart course → - 5 lessons 1 hours 1.8K+
Introduction to Probability in R
In this course, you'll learn the difference between theoretical and experimental probability, and you'll calculate the probabilities for a variety of different events. You'll also learn the number of permutations and combinations possible in experiment outcomes. You'll apply this knowledge in a guided project to build the logic for a mobile app that estimates lottery odds. Best of all, you'll learn by doing - you'll practice and get feedback directly in the browser.
Intermediate R ProbabilityStart course → - 4 lessons 2 hours 1.6K+
Introduction to Web Scraping in R
Although there are many datasets available in convenient formats, there's also a lot of data that's more difficult to access, like a table on a web page. To get this data, we'll need to use web scraping. In R, we can do that with the rvest scraping package. In this course, you'll learn about web page structure, including the basics of HTML and CSS. You'll also learn how to get the code from a page into your R workflow for further parsing and cleaning. Then, you'll dig deeper into scraping, learning to use the CSS Selector to get precisely the data you want. 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 use web scraping to analyze movie ratings.
Intermediate R Web ScrapingStart course → - 5 lessons 1 hours 1.5K+
Conditional Probability in R
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, how to assign probabilities to events based on whether they are in a relationship of statistical independence with other events, and how 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 R ProbabilityStart course → - 5 lessons 1 hours 1.5K+
Introduction to Interactive Web Applications in Shiny
While notebooks are great for sharing data work, they're not very user-friendly for viewers without programming skills. With the popular Shiny package, we can showcase our data work in a more attractive and accessible way by building interactive, data-based dashboards and projects for the web! In this course, you'll learn the fundamentals of working with Shiny. Then you'll explore more complex topics such as learning about programming server logic for Shiny and programming the UI for your interactive dashboards. You'll also learn various ways to customize the design of elements you build in Shiny. Finally, you'll learn how to get your apps online and how to share 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 will complete a guided project that asks you to build a portfolio website for yourself using Shiny!
Intermediate R ShinyStart course → - 5 lessons 5 hours 1.4K+
Data Visualization Fundamentals in Tableau
In this course, you'll learn the fundamentals of creating visualizations, common pitfalls, and best practices. You'll also learn how to use the various elements of the Tableau interface, how to create different charts (and their practical uses), and how to transform data using calculated fields and filters. Best of all, you'll learn by doing - you'll practice and get feedback directly in the browser.
Beginner Tableau Data VisualizationStart course → - 5 lessons 5 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 → - 5 lessons 3 hours 996+
Sharing Insights in Tableau
In this course, you'll learn how to draw an audience's attention to a specific story point or insight by effectively using colors and graph size, and by leveraging annotations and options within the analysis pane. You'll also learn how to make your dashboards more effective by using additional elements such as titles, buttons, images, and webpages. Finally, you'll learn to make your dashboards interactive so that everyone can explore your analysis and conduct deeper data analysis. Best of all, you'll learn by doing - you'll practice and get feedback directly in the browser.
Beginner Tableau DashboardsStart course → - 4 lessons 2 hours 961+
Visual Analytics in Tableau
In this course, you'll learn how to employ quick table calculations, add secondary table calculations, and write custom advanced calculations. You'll also learn the various levels of data granularity as well as the aggregation methodology to write LOD expressions (level of detail). You'll learn to make your dashboards interactive using sets, parameters, viz in tooltip and other elements - and you'll also learn to improve their readability by adding and configuring trend lines, reference bands, and distributions. Best of all, you'll learn by doing - you'll practice and get feedback directly in the browser.
Beginner Tableau Data InterpretationStart course → -
Free
3 lessons 0 hours 662+
Network Architectures
This is the second course in a series of courses that will take you from no knowledge of deep learning to training your own GPT model (Generative Pre-trained Transformer). You'll gain a deeper understanding of different network architectures, and you'll learn to build neural networks from scratch using Python and to make predictions for both categorical and sequential outcomes. After you have completed this course, you will understand different neural network architectures. You will be able to build a dense neural network from scratch using Python, train a neural network on a classification task, and predict sequence data using recurrent neural networks. Best of all, you'll learn by doing - you'll practice and get feedback directly in the browser!
Intermediate Deep Learning Neural NetworksStart course → - 3 lessons 2 hours 619+
Power BI Analytics
In this course, you'll learn how to use data analytic functions, explore statistical summaries, identify outliers in your data, group data together, bin data for analysis, and perform time series analysis. Best of all, you'll learn by doing - you'll practice and get feedback directly in the browser.
Beginner Power BI Data InterpretationStart course → - 6 lessons 5 hours 603+
Natural Language Processing for Deep Learning
First, you'll explore the concepts and terms necessary for working with sequential models in TensorFlow. You'll discover recurrent neural networks (RNN) and how they compare with convolutional neural networks (CNNs), as well as some of the most common RNN applications. Then, you'll learn how to build, train, evaluate, and improve a basic RNN to predict song popularity using regression. You'll also learn to use Gated Recurrent Units (GRU) and Long Short-Term Memory (LSTM) techniques to improve model performance. You'll implement these to predict the sentiment of the review (good or bad) on a dataset of IMDB reviews. Next, you'll combine convolutional neural networks with sequential models to add a convolutional layer to your LSTM model and compare its predictive performance before and after on a dataset of IMDB reviews, predicting sentiment. Finally, you'll optimize the tools already used for time-series forecasting on a dataset of movie ticket sales to prepare you for the guided project. 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 new skills to a project to build a model to better forecast how the S&P 500 futures index will move based on its behavior over the past several years.
Intermediate Deep Learning NLPStart course → - 6 lessons 6 hours 572+
Sequence Models for Deep Learning
First, you'll explore the concepts and terms necessary for working with sequential models in TensorFlow. You'll discover recurrent neural networks (RNN) and how they compare with convolutional neural networks (CNNs), as well as some of the most common RNN applications. Then, you'll learn how to build, train, evaluate, and improve a basic RNN to predict song popularity using regression. You'll also learn to use Gated Recurrent Units (GRU) and Long Short-Term Memory (LSTM) techniques to improve model performance. You'll implement these to predict the sentiment of the review (good or bad) on a dataset of IMDB reviews. Next, you'll combine convolutional neural networks with sequential models to add a convolutional layer to your LSTM model and compare its predictive performance before and after on a dataset of IMDB reviews, predicting sentiment. Finally, you'll optimize the tools already used for time-series forecasting on a dataset of movie ticket sales to prepare you for the guided project. 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 new skills to a project to build a model to better forecast how the S&P 500 futures index will move based on its behavior over the past several years.
Intermediate Deep Learning Sequence ModelingStart course → - 6 lessons 12 hours 539+
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 → - 6 lessons 12 hours 522+
Convolutional Neural Networks for Deep Learning
First, you'll learn the relevance of CNN in the field of computer vision, and you'll implement both basic and complex CNN for multi-class classification tasks in TensorFlow. You'll then advance to understanding how a CNN model learns different features across its layers and attempts to improve the model's performance. You'll learn different regularization techniques to tackle overfitting when building deep learning models in TensorFlow. Next, you'll see the importance of complicated models like ResNet for computer vision tasks, and you'll implement a ResNet-based, pre-trained model on advanced CNN architecture. Finally, you'll learn how to use previously trained models for other similar tasks on a different dataset than the original model was trained on. 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 new skills to a project to create a computer vision model that can detect if a patient has pneumonia using an X-ray scan.
Advanced Deep Learning Computer VisionStart course → - 4 lessons 3 hours 417+
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 → -
Free
2 lessons 0 hours 397+
Optimizing Network Parameters
In this course, you'll dive into the world of deep learning by gaining a deeper understanding of backpropagation and building a simple deep learning framework from scratch. As part of your journey, you'll learn to optimize network parameters and employ regularization techniques to improve your models, creating a strong foundation in core deep learning concepts. The course will also explore the essential role of optimizers in adjusting neural network parameters. You'll delve into gradient descent and learn about batch size, learning rate schedules, weight decay, and momentum. Additionally, you'll discover the popular Adam optimizer, which extends the idea of momentum, and learn how to optimize hyperparameters for neural networks while monitoring and comparing their performance. By the end of this course, you'll have a comprehensive understanding of fundamental deep learning concepts and be well-equipped to continue your deep learning journey.
Intermediate Deep Learning OptimizationStart course → - 3 lessons 6 hours 202+
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 172+
Docker Fundamentals
Modern data engineering requires reproducible environments that work the same on every machine. Docker creates isolated containers that bundle everything your code needs to run—dependencies, databases, configuration—eliminating "it worked on my machine" problems. This course takes you from Docker fundamentals through production-ready containerization. You'll start by running PostgreSQL in a container, connecting to it, and persisting data with volumes. Then you'll use Docker Compose to orchestrate complete data pipelines: a Python ETL script that connects to a database, all defined in a single file and started with one command. Finally, you'll learn the production patterns that DevOps teams expect—health checks that prevent startup race conditions, multi-stage builds that create slim images, security hardening with non-root users, and proper secret management with environment files. By the end, you'll build containerized data workflows that are portable, maintainable, and ready for production deployment.
Intermediate Data Science Data AnalysisStart course → - 3 lessons 6 hours 160+
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 → - 3 lessons 6 hours 148+
Understanding Embeddings
Embeddings transform text into numerical vectors that capture semantic meaning, enabling AI systems to understand context beyond simple keyword matching. In this course, you'll learn how to generate embeddings using both open-source models and APIs, visualize them in reduced dimensions, measure similarity between vectors, and build semantic search systems that power modern AI applications.
Intermediate Embeddings Semantic SearchStart course → - 4 lessons 8 hours 145+
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 137+
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 → - 4 lessons 8 hours 114+
Introduction to Cloud Computing
Cloud computing transformed technology from owning infrastructure to renting it on demand — eliminating the complexity and cost of managing physical servers. This course teaches you the fundamentals. You'll understand service models: IaaS for maximum control, PaaS for faster development, and SaaS for turnkey solutions. You'll explore deployment strategies — public clouds for scalability, private for security, hybrid for flexibility — and compare AWS, Azure, and GCP to understand what each platform offers. By the end, you'll have the conceptual foundation to make smart cloud architecture decisions and be ready to deploy real pipelines to AWS and GCP in the next course.
Intermediate Cloud Computing AWSStart course → - 3 lessons 6 hours 113+
Production Database Tools
Production data systems require more than traditional SQL databases. This course takes you beyond PostgreSQL into the tools that power modern data infrastructure at scale. You'll start with Snowflake, learning how its cloud-native architecture separates storage from compute to handle massive datasets efficiently. Then you'll explore the NoSQL landscape—understanding when document, key-value, column-family, and graph databases solve problems that SQL can't. Finally, you'll get hands-on with MongoDB, building a flexible review system that handles schema changes without migrations and connects to Python analytics workflows. By the end, you'll understand how companies like Netflix and Uber combine multiple database types in production, and you'll be able to choose the right tool for each part of your data pipeline.
Intermediate Data Science Data AnalysisStart course → - 6 lessons 12 hours 92+
Vector Databases and Search
Vector databases enable semantic search at scale by using approximate nearest neighbor algorithms instead of brute-force comparison. In this course, you'll learn to build production-ready vector search systems using ChromaDB, implement document chunking and metadata filtering strategies, compare production databases, apply semantic caching patterns, and create a complete knowledge base search system combining hybrid search and performance optimization.
Intermediate Vector Databases Semantic SearchStart course → - 5 lessons 10 hours 91+
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 → - 3 lessons 6 hours 89+
Introduction to Kubernetes
Kubernetes transforms container management from manual tasks into automated systems. This course teaches you to orchestrate containerized applications at scale through hands-on practice with realistic scenarios. You'll start by deploying applications to local clusters and watching Kubernetes automatically replace crashed pods. Then you'll solve the networking puzzle—how applications find each other when pods constantly restart with new IP addresses—using Services and performing rolling updates for zero-downtime deployments. Finally, you'll add production safeguards: health checks that prevent broken applications from receiving traffic, resource limits that protect clusters from runaway workloads, and ConfigMaps and Secrets for secure configuration management. By the end, you'll understand when Kubernetes adds value over Docker Compose and how to build applications that are good citizens in shared production clusters.
Intermediate Data Science Data AnalysisStart course → - 4 lessons 8 hours 78+
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 → - 4 lessons 8 hours 56+
Data Transformation with dbt
dbt has become essential for transforming data in modern analytics workflows. In this course, you'll learn to build transformation pipelines from the ground up—starting with core concepts like models and DAGs, then adding testing, documentation, and incremental processing. You'll finish with production patterns that make pipelines maintainable and deployable, always with an eye toward knowing when added complexity is justified.
Intermediate dbt SQLStart course → - 5 lessons 10 hours 54+
Deploying to the Cloud
You understand cloud fundamentals — now deploy for real. This course takes you through hands-on deployment to both AWS and GCP. On AWS, you'll provision S3 buckets, RDS databases, and deploy Airflow to ECS with Fargate — a managed container architecture with load balancing and auto-scaling. On GCP, you'll take the same pipeline and deploy it to a Compute Engine VM with Docker Compose, configure service accounts for secure access, and upload pipeline output to Cloud Storage. You'll see how the same Airflow and Docker skills transfer across platforms while the infrastructure layer changes. By the end, you'll have deployed working pipelines to both major cloud providers — giving you the versatility to work on any team, with any stack.
Intermediate Cloud Computing AWSStart course → - 3 lessons 6 hours 50+
Using AI to Work with Data
AI is becoming a common tool for working with data, even for non-technical roles. In this course, you'll learn how to use AI tools to explore data, improve communication, and support everyday data tasks—without writing code or building models. You'll gain practical skills for prompting AI effectively and understanding its strengths and limitations in real data work.
Beginner AI Literacy Data CommunicationStart course → -
Free
3 lessons 6 hours 17+
Foundations of Data Communication
Being data-driven isn't about writing code—it's about understanding and communicating insights clearly. In this course, you'll learn how to tell effective data stories, choose the right charts, and design clear visuals that help others understand what the data is saying. This course is designed for non-technical learners who want to build data literacy and communicate insights with confidence in reports, presentations, and everyday work.
Beginner Data Communication Data LiteracyStart course →
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