Online Courses Catalog

The Georgia Institute of Technology

Delve into Pattern Matching algorithms from KMP to Rabin-Karp. Tackle essential algorithms that traverse the graph data structure like Dijkstra’s Shortest Path. Study algorithms that construct a Minimum Spanning Tree (MST) from a graph. Explore Dynamic Programming algorithms. Use the course visualization tool to understand the algorithms and their performance.

The University of California, San Diego

Learn about high-performance data structures and supporting algorithms, as well as the fundamentals of theoretical time complexity analysis through an interactive online text.

The University of California, San Diego

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

The Georgia Institute of Technology

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.

The Georgia Institute of Technology

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.

The Georgia Institute of Technology

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.

IITBombay

Learn the best way to structure and represent data.

IITBombay

Learn how to write correct and efficient data structures manipulation using existing standard template library (STL) of C++. Get introduced to the power of STL and make your code more solid, reusable, and robust.

IBM

Build efficient programs by learning how to implement data structures using algorithmic techniques and solve various computational problems using the C++ programming language.

Adelaide University

Learn the core concepts of computational thinking and how to collect, clean and consolidate large-scale datasets.

Adelaide University

Learn how to apply fundamental programming for data science concepts, computational thinking and data analysis techniques to solve real-world data science problems.

Université de Montréal

Les données sont partout et il faudra rapidement savoir comment les analyser pour en dériver des connaissances sur lesquelles nous pourrons prendre des décisions et des actions plus éclairées.

Universitat Politècnica de València

Aprende a utilizar las visualizaciones avanzadas, a personalizar los formatos de informe y a usar las opciones de trabajo colaborativo en Power BI.

Universidades Anáhuac

Toma decisiones en marketing basadas en los datos.

Universidades Anáhuac

Desarrolla tus habilidades en el manejo, visualización, interpretación y comunicación de datos y pruebas descriptivas fundamentadas en los resultados de investigación clínica y de salud pública.

Universidades Anáhuac

En este curso pondremos en práctica el potencial que nos ofrece el lenguaje R para darle solución a diversos problemas de los negocios actuales. Desarrollarás diversos proyectos que te permitan formar a tu primer portafolio de proyectos relacionados con la ciencia de datos.

Tsinghua University

As a pilot course and cognitive course for data science, this course is dedicated to popularizing the basic knowledge, core concepts and thinking models related to data mining and big data for students through a vivid teaching model, from engineering technology, legal norms, and application practice. Describe the beautiful blueprint of data science from different angles.

Statistics.com

Concern about the harmful effects of machine learning algorithms and AI models (bias and more) has resulted in greater attention to the fundamentals of data ethics. This data science ethics course for both practitioners and managers provides guidance and practical tools to build better models and avoid these problems. The course offers a framework data scientists can use to develop their projects and an audit process to follow in reviewing them. Case studies with Python code are provided.

Statistics.com

AI’s popularity has resulted in numerous well-publicized cases of bias, injustice, and discrimination. Often these harms occur in machine learning projects that have the best of goals, developed by data scientists with good intentions. This course, the second in the data science ethics program for both practitioners and managers, provides guidance and practical tools to build better models and avoid these problems.

RWTH Aachen University

"Basics of Data Science" gives a comprehensible overview of many fundamental concepts and tools of data science, including data quality and data preprocessing, supervised and unsupervised learning techniques including their evaluation, frequent itemsets and association rules, sequence mining, process mining, text mining, and responsible data science.

Statistics.com

Most data science projects fail. There are various reasons why, but one of the primary reasons is the challenge of deployment. One piece to the deployment puzzle is understanding how to automate your pipeline’s functions and continuously optimize its performance, which is why we developed this course, MLOps2 (GCP): Data Pipeline Automation & Optimization using Google Cloud Platform.

Massachusetts Institute of Technology

Become a data explorer – learn how to leverage data and basic machine learning algorithms to understand the world.