Our Courses

Python for Data Science, AI & Development

Kickstart your learning of Python with this beginner-friendly self-paced course taught by an expert. Python is one of the most popular languages in the programming and data science world and demand for individuals who have the ability to apply Python has never been higher. This introduction to Python course will take you from zero to programming in Python in a matter of hours—no prior programming experience necessary! You will learn about Python basics and the different data types.

• Course by
• Self Paced
• 27 hours
• English

Algorithms, Part I

This course covers the essential information that every serious programmer needs to know about algorithms and data structures, with emphasis on applications and scientific performance analysis of Java implementations. Part I covers elementary data structures, sorting, and searching algorithms. Part II focuses on graph- and string-processing algorithms. All the features of this course are available for free.

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• Self Paced
• 54 hours
• English

Data Structures & Algorithms I: ArrayLists, LinkedLists, Stacks and Queues

Work with the principles of data storage in Arrays, ArrayLists & LinkedList nodes. Understand their operations and performance with visualizations. Implement low-level linear, linked data structures with recursive methods, and explore their edge cases. Extend these structures to the Abstract Data Types, Stacks, Queues and Deques.

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• English

Data Structures & Algorithms II: Binary Trees, Heaps, SkipLists and HashMaps

Become familiar with nonlinear and hierarchical data structures. Study various tree structures: Binary Trees, BSTs and Heaps. Understand tree operations and algorithms. Learn and implement HashMaps that utilize key-value pairs to store data. Explore probabilistic data structures like SkipLists. Course tools help visualize the structures and performance.

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• Self Paced
• English

Data Structures & Algorithms III: AVL and 2-4 Trees, Divide and Conquer Algorithms

Learn more complex tree data structures, AVL and (2-4) trees. Investigate the balancing techniques found in both tree types. Implement these techniques in AVL operations. Explore sorting algorithms with simple iterative sorts, followed by Divide and Conquer algorithms. Use the course visualizations to understand the performance.

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• Self Paced
• English

Java Built in Data Structures

In this project you will read weather data from a file and populate a Java ArrayList with the file data. The data is then uniquified in a Set data structure and stored in a HashMap for accessing data by year. Java's collections framework contains data structures that are built for efficiency. They are used to store and retrieve data in various formats. A list such as an ArrayList can be used in place of an array to contain data where the size cannot be determined ahead of time.

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• Self Paced
• 2 hours
• English

How to Code: Complex Data

Learn how to design more complex programs, using new data structures, abstraction, and generative recursion.

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• English

Data Structures Fundamentals

Learn about data structures that are used in computational thinking – both basic and advanced.

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• Self Paced
• English

Algorithms and Data Structures Capstone

Synthesize your knowledge of algorithms and biology to build your own software for solving a biological challenge.

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• Self Paced
• English

Data Structures & Algorithms Using C++

• Course by
• Self Paced
• 6
• English

R Programming Basics for Data Science

This course introduces you to R language fundamentals and covers common data structures, programming techniques, and how to manipulate data all with the help of the R programming language.

• Course by
• 13
• English

Python for Beginners: Data Structures

This Guided Project "Python for Beginners: Data Structures" is for individuals who are new to programming and want to learn the basics of Python data structures. In this 2-hour long project-based course, you will learn about lists, tuples, sets, and dictionaries and how to use them to solve real-world problems through a student performance analysis short project.

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• Self Paced
• 3 hours
• English

Scripting with Python and SQL for Data Engineering

In this third course of the Python, Bash and SQL Essentials for Data Engineering Specialization, you will explore techniques to work effectively with Python and SQL. We will go through useful data structures in Python scripting and connect to databases like MySQL. Additionally, you will learn how to use a modern text editor to connect and run SQL queries against a real database, performing operations to load and extract data. Finally, you will use extracted data from websites using scraping techniques.

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• Self Paced
• 18 hours
• English

Probability & Statistics for Machine Learning & Data Science

Newly updated for 2024! Mathematics for Machine Learning and Data Science is a foundational online program created by DeepLearning.AI and taught by Luis Serrano. In machine learning, you apply math concepts through programming. And so, in this specialization, you’ll apply the math concepts you learn using Python programming in hands-on lab exercises.

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• Self Paced
• 29 hours
• English

Computing in Python III: Data Structures

Learn more complex ways of handling data, including files, lists, and dictionaries for building complex programs.

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• Self Paced
• English

Functional Program Design in Scala (Scala 2 version)

In this course you will learn how to apply the functional programming style in the design of larger Scala applications. You'll get to know important new functional programming concepts, from lazy evaluation to structuring your libraries using monads. We'll work on larger and more involved examples, from state space exploration to random testing to discrete circuit simulators. You’ll also learn some best practices on how to write good Scala code in the real world.

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• Self Paced
• 34 hours
• English

Dynamic Programming, Greedy Algorithms

This course covers basic algorithm design techniques such as divide and conquer, dynamic programming, and greedy algorithms. It concludes with a brief introduction to intractability (NP-completeness) and using linear/integer programming solvers for solving optimization problems. We will also cover some advanced topics in data structures. This course can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform.

• Course by
• Self Paced
• 38 hours
• English

Data Science with NumPy, Sets, and Dictionaries

Become proficient in NumPy, a fundamental Python package crucial for careers in data science. This comprehensive course is tailored to novice programmers aspiring to become data scientists, software developers, data analysts, machine learning engineers, data engineers, or database administrators. Starting with foundational computer science concepts, such as object-oriented programming and data organization using sets and dictionaries, you'll progress to more intricate data structures like arrays, vectors, and matrices.

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• Self Paced
• 31 hours
• English

Approximation Algorithms

Many real-world algorithmic problems cannot be solved efficiently using traditional algorithmic tools, for example, because the problems are NP-hard. The goal of the Approximation Algorithms course is to become familiar with important algorithmic concepts and techniques needed to effectively deal with such problems. These techniques apply when we don't require the optimal solution to certain problems, but an approximation that is close to the optimal solution. We will see how to efficiently find such approximations.

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• Self Paced
• 15 hours
• English

Windows OS Forensics

The Windows OS Forensics course covers windows file systems, Fat32, ExFat, and NTFS. You will learn how these systems store data, what happens when a file gets written to disc, what happens when a file gets deleted from disc, and how to recover deleted files. You will also learn how to correctly interpret the information in the file system data structures, giving the student a better understanding of how these file systems work. This knowledge will enable you to validate the information from multiple forensic tools properly.

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• Self Paced
• 8 hours
• English

Plots Creation using Matplotlib Python

By the end of this project, you will be able to add the data in the CSV file to Pandas data frame, plot the graph, and set marker type and color. You will also be able to apply labels, change font size, add grid lines and legends. Finally, you will be able to create the boxplot and save the graph as an image using the matplotlib and seaborn libraries, which are the most important libraries in python that are used for Data Visualization. You can create bar-plots, scatter-plots, histograms, and a lot more with them. This guided project is for people in the field of data and data analysis.

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• Self Paced
• 3 hours
• English

Getting Started with R

In this 2 hour-long project, you will learn the basics of R programming language. In addition, you will take your first steps in the use of R programming language for Data Analysis. By the end of this 2-hour long project, you will understand how to use the R GUI called R studio. By extension, you will learn the different data types and data structures used in R. Finally, you will learn how to install packages and how to import data sets into the R studio work space. This course is aimed at learners who are looking to get started with the R programming language.

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• Self Paced
• 4 hours
• English

Introduction to Java and Object-Oriented Programming

This course provides an introduction to the Java language and object-oriented programming, including an overview of Java syntax and how it differs from a language like Python. Students will learn how to write custom Java classes and methods, and how to test their code using unit testing and test-driven development. Topics include basic data structures like Arrays and ArrayLists and overloading methods.

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• Self Paced
• 19 hours
• English

Process Map Data using C++ Adjacency List Shortest Path

In this project you will use the adjacency list data structure and other data structures to find the shortest distance between a set of towns loaded from a file. The shortest path problem is well known in the field of computer science. An adjacency list is probably the best data structure to represent a set of connected vertices to find the shortest path from one vertex to another. One application for shortest paths is in computer networking routing.

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• Self Paced
• 2 hours
• English

Get Started with Python

This is the second of seven courses in the Google Advanced Data Analytics Certificate. The Python programming language is a powerful tool for data analysis. In this course, you’ll learn the basic concepts of Python programming and how data professionals use Python on the job. You'll explore concepts such as object-oriented programming, variables, data types, functions, conditional statements, loops, and data structures.

• Course by
• Self Paced
• 31 hours
• English