Coursera

NoSQL systems (Coursera)

Offered by Universidad Nacional Autónoma de México,
NoSQL systems (Coursera)

Welcome to the specialization course of NoSQL Systems. This course will be completed on six weeks, it will be supported with videos and exercises that will allow you to identify the differences between the relational and NoSQL databases.

Class Deals by MOOC List - Click here and see Coursera's Active Discounts, Deals, and Promo Codes.

As part of these alternative technologies the student will learn the main characteristics and how to implement the typical NoSQL databases, such as Key-value, columnar, document and graph.
Let's start!
After completing this course, a learner will be able to
● Identify what type of NoSQL database to implement based on business requirements (key-value, document, full text, graph, etc.)
● Apply NoSQL data modeling from application specific queries
● Use Atomic Aggregates and denormalization as data modelling techniques to optimize query processing
Software to download:
MongoDB
Neo4j
SAPIQ
Cassandra
In case you have a Mac / IOS operating system you will need to use a virtual Machine (VirtualBox, Vmware).
Course 3 of 4 in the Database systems Specialization.

Syllabus

WEEK 1
NOSQL Systems
Welcome to the specialization course of NoSQL Systems.
This course will be completed on six weeks, it will be supported with videos and exercises that will allow you to identify the differences between the relational and NoSQL databases. As part of these alternative technologies the student will learn the main characteristics and how to implement the typical NoSQL databases, such as Key-value, columnar, document and graph. Let's start!

WEEK 2
Key-value database
Welcome to the module key-value database. We will learn the components and types of a key-value database, its properties, scalability and indexing. Let's start!

WEEK 3
Columnar Databases
Welcome to the session columnar databases from the NoSQL course.
The learner will understand why a columnar database performs better than a relational in the case of analytical queries.

WEEK 4
Document databases with MongoDB
Welcome to the session document databases with MongoDB.
The learner will identify the advantages of storing semistructured data with MongoDB.

WEEK 5
Graph Databases
Welcome to the session graph databases from the NoSQL course.
The learner will understand that a graph database is a perfect solution for information systems where the relationships between entities are more like graphs or trees which are structures more flexibles.

WEEK 6
How to design reliable, scalable and maintainable applications
Welcome to the session How to design reliable, scalable and maintainable applications. The student will identify which database or repository is the best option according to response time, amount of data, type of data and analysis.
The student will learn the last database technologies such as in-memory database, multi-model database, etc. and how these approaches can help to design reliable, scalable and maintainable applications.

Go to Class
MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Related Courses

Predictive Modeling and Analytics (Coursera) Coursera
University of Colorado Boulder

Predictive Modeling and Analytics (Coursera)

Statistics & Data Analysis Data Science

Welcome to the second course in the Data Analytics for Business specialization! This course will introduce you to some of the most widely used predictive modeling techniques and their core principles. By taking this course, you will form a solid foundation of predictive analytics, which refers to tools and techniques for building statistical or machine learning models to make predictions based on data. You will learn how to carry out exploratory data analysis to gain insights and prepare data for predictive modeling, an essential skill valued in the business.

Jun 8th 2026
4 Weeks
Data Analysis Analytics Data Cleaning
Details
Cloud Computing Applications, Part 2: Big Data and Applications in the Cloud (Coursera) Coursera
University of Illinois at Urbana-Champaign

Cloud Computing Applications, Part 2: Big Data and Applications in the Cloud (Coursera)

Security & Networking

Welcome to the Cloud Computing Applications course, the second part of a two-course series designed to give you a comprehensive view on the world of Cloud Computing and Big Data! In this second course we continue Cloud Computing Applications by exploring how the Cloud opens up data analytics of huge volumes of data that are static or streamed at high velocity and represent an enormous variety of information. Cloud applications and data analytics represent a disruptive change in the ways that society is informed by, and uses information.

Jun 8th 2026
4 Weeks
Cloud Machine Learning Big Data
Details
Foundations of strategic business analytics (Coursera) Coursera
ESSEC Business School

Foundations of strategic business analytics (Coursera)

Management & Leadership Business

Who is this course for? This course is designed for students, business analysts, and data scientists who want to apply statistical knowledge and techniques to business contexts. For example, it may be suited to experienced statisticians, analysts, engineers who want to move more into a business role. You will find this course exciting and rewarding if you already have a background in statistics, can use R or another programming language and are familiar with databases and data analysis techniques such as regression, classification, and clustering.

Jun 8th 2026
4 Weeks
Data Analysis Analytics Presentations
Details
Big Data Modeling and Management Systems (Coursera) Coursera
University of California, San Diego

Big Data Modeling and Management Systems (Coursera)

Statistics & Data Analysis Data Science

Once you’ve identified a big data issue to analyze, how do you collect, store and organize your data using Big Data solutions? In this course, you will experience various data genres and management tools appropriate for each. You will be able to describe the reasons behind the evolving plethora of new big data platforms from the perspective of big data management systems and analytical tools.

Jun 8th 2026
5-12 Weeks
Big Data Data Management Data Modeling
Details
Effective Problem-Solving and Decision-Making (Coursera) Coursera
University of California, Irvine

Effective Problem-Solving and Decision-Making (Coursera)

Business

Critical thinking – the application of scientific methods and logical reasoning to problems and decisions – is the foundation of effective problem solving and decision making. Critical thinking enables us to avoid common obstacles, test our beliefs and assumptions, and correct distortions in our thought processes. Gain confidence in assessing problems accurately, evaluating alternative solutions, and anticipating likely risks. Learn how to use analysis, synthesis, and positive inquiry to address individual and organizational problems and develop the critical thinking skills needed in today’s turbulent times. Using case studies and situations encountered by class members, explore successful models and proven methods that are readily transferable on-the-job.

Jun 8th 2026
4 Weeks
Data Analysis Decision Making Critical Thinking
Details
Inferential Statistics (Coursera) Coursera
University of Amsterdam

Inferential Statistics (Coursera)

Statistics & Data Analysis Data Science

Inferential statistics are concerned with making inferences based on relations found in the sample, to relations in the population. Inferential statistics help us decide, for example, whether the differences between groups that we see in our data are strong enough to provide support for our hypothesis that group differences exist in general, in the entire population. We will start by considering the basic principles of significance testing: the sampling and test statistic distribution, p-value, significance level, power and type I and type II errors. Then we will consider a large number of statistical tests and techniques that help us make inferences for different types of data and different types of research designs.

Jun 8th 2026
5-12 Weeks
Statistics Social Sciences Data Analysis
Details
Pattern Discovery in Data Mining (Coursera) Coursera
University of Illinois at Urbana-Champaign

Pattern Discovery in Data Mining (Coursera)

Statistics & Data Analysis Data Science

Learn the general concepts of data mining along with basic methodologies and applications. Then dive into one subfield in data mining: pattern discovery. Learn in-depth concepts, methods, and applications of pattern discovery in data mining. We will also introduce methods for data-driven phrase mining and some interesting applications of pattern discovery. This course provides you the opportunity to learn skills and content to practice and engage in scalable pattern discovery methods on massive transactional data, discuss pattern evaluation measures, and study methods for mining diverse kinds of patterns, sequential patterns, and sub-graph patterns.

Jun 8th 2026
4 Weeks
Algorithms Data Mining Data Analysis
Details
The Structured Query Language (SQL) (Coursera) Coursera
University of Colorado Boulder

The Structured Query Language (SQL) (Coursera)

Data Science

In this course you will learn all about the Structured Query Language ("SQL".) We will review the origins of the language and its conceptual foundations. But primarily, we will focus on learning all the standard SQL commands, their syntax, and how to use these commands to conduct analysis of the data within a relational database. Our scope includes not only the SELECT statement for retrieving data and creating analytical reports, but also includes the DDL ("Data Definition Language") and DML ("Data Manipulation Language") commands necessary to create and maintain database objects.

Jun 9th 2026
5-12 Weeks
Databases SQL Data Science
Details
Introducción a Data Science: Programación Estadística con R (Coursera) Coursera
Universidad Nacional Autónoma de México

Introducción a Data Science: Programación Estadística con R (Coursera)

Statistics & Data Analysis Data Science

Este curso te proporcionará las bases del lenguaje de programación estadística R, la lengua franca de la estadística, el cual te permitirá escribir programas que lean, manipulen y analicen datos cuantitativos. Te explicaremos la instalación del lenguaje; también verás una introducción a los sistemas base de gráficos y al paquete para graficar ggplot2, para visualizar estos datos. Además también abordarás la utilización de uno de los IDEs más populares entre la comunidad de usuarios de R, llamado RStudio.

Jun 8th 2026
4 Weeks
Data Analysis Data Science R Language
Details
Statistical Inference (Coursera) Coursera
Johns Hopkins University

Statistical Inference (Coursera)

Statistics & Data Analysis Data Science

Statistical inference is the process of drawing conclusions about populations or scientific truths from data. There are many modes of performing inference including statistical modeling, data oriented strategies and explicit use of designs and randomization in analyses. Furthermore, there are broad theories (frequentists, Bayesian, likelihood, design based, …) and numerous complexities (missing data, observed and unobserved confounding, biases) for performing inference.

Jun 8th 2026
4 Weeks
Statistics Probability Data Analysis
Details