Data Visualization with Python & R for Engineers (Coursera)

Data Visualization with Python & R for Engineers (Coursera)

The primary objective of this course is to offer students an opportunity to learn how to use visualization tools and techniques for data exploration, knowledge discovery, data storytelling, and decision making in engineering, healthcare operations, manufacturing, and related applications. This course covers basics of data mining and visualization, and Python. It also introduces students to static visualization charts and techniques that reveal information, patterns, interactions.

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

Syllabus

Introduction to Data - Part 1
In this module, we will delve into the fundamental aspects of data, exploring its definition, significance, and the transformative journey from raw information to actionable insights. Through a series of engaging videos, we will unravel the mysteries of structured and unstructured data, unveiling their unique characteristics and applications. As we progress, the module unfolds the intricate steps of the data workflow, guiding through the pivotal stages of framing objectives, preparing data, analysis, interpretation, and effective communication of findings. Additionally, our exploration extends to the vast landscape of Big Data, unraveling its complexities through the lens of the Five Vs: Volume, Velocity, Variety, Veracity, and Value. By the end of this module, We will not only have a comprehensive understanding of the foundational concepts of data but also possess the essential skills to navigate the data-driven landscapes of today's digital era. Get ready to unlock the power of data and discover its profound impact on our world!

Introduction to Data - Part 2
In this module, we will dive into the world of data analytics. We'll learn how to find the right data for data analysis, considering factors like relevance and timeliness. Then, we'll explore the crucial step of preprocessing, where we’ll learn to clean and organize raw data effectively. From handling missing values to spotting outliers, we'll pick up essential skills to ensure the analysis is accurate and reliable. By the end of this module, we'll be all set to confidently select, process, and analyze data like a pro. Let's get started!

Introduction to Visualization
In this module, we'll explore how data visualization turns complex data into engaging stories. Building on our understanding of data's significance, we'll discover how visualization simplifies information and connects with diverse audiences. We’ll delve into creating various visualizations, from statistical plots to geographical graphs. By grasping different statistical graphs and their applications, you'll enhance your skills in sharing meaningful insights. Get ready to unlock the potential of visualization and enhance your ability to tell compelling data stories. Let's dive into this visually enlightening journey!

Basics of Python
In this module, we'll delve into the fundamentals of Python coding. We'll explore key concepts such as variables, data types, and structures — crucial components in creating robust code. Throughout your Python learning journey, you'll acquire the skill of decision-making through if-else statements, navigate data using loops, and enhance your code with custom functions. Whether you're a coding novice or have some prior knowledge, this course ensures hands-on, practical experience. Let's explore, learn, and become experts in the key principles of Python programming. Get ready to bring your coding ideas to life!

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

Related Courses

Uso de bases de datos con Python (Coursera) Coursera
University of Michigan

Uso de bases de datos con Python (Coursera)

Este curso presentará a los estudiantes los conceptos básicos del lenguaje de consulta estructurado (Structured Query Language, SQL), así como el diseño básico de bases de datos para almacenar datos como parte de una iniciativa de varios pasos para recopilar, analizar y procesar datos. El curso utilizará SQLite3 como base de datos. También crearemos rastreadores web y procesos de visualización y recopilación de datos de varios pasos. Utilizaremos la biblioteca D3.js para realizar la visualización básica de datos.

Aug 17th 2026
5-12 Weeks
Design Computing: 3D Modeling in Rhinoceros with Python/Rhinoscript (Coursera) Coursera
University of Michigan

Design Computing: 3D Modeling in Rhinoceros with Python/Rhinoscript (Coursera)

Why should a designer learn to code? As our world is increasingly impacted by the use of algorithms, designers must learn how to use and create design computing programs. Designers must go beyond the narrowly focused use of computers in the automation of simple drafting/modeling tasks and instead explore the extraordinary potential digitalization holds for design culture/practice.

Aug 17th 2026
5-12 Weeks
Estructuras de datos de Python (Coursera) Coursera
University of Michigan

Estructuras de datos de Python (Coursera)

Este curso presentará las estructuras de datos básicas del lenguaje de programación Python. Veremos los conceptos básicos de la programación de procedimientos y exploraremos cómo podemos usar las estructuras de datos integrados de Python, como listas, diccionarios y tuplas, para realizar análisis de datos cada vez más complejos. Este curso abarcará los capítulos 6 a 10 del libro de texto “Python para todos”. Este curso cubre Python 3.

Aug 17th 2026
5-12 Weeks
Understanding and Applying Text Embeddings (Coursera) Coursera
DeepLearning.AI

Understanding and Applying Text Embeddings (Coursera)

The Vertex AI Text-Embeddings API enhances the process of generating text embeddings. These text embeddings, which are numerical representations of text, play a pivotal role in many tasks involving the identification of similar items, like Google searches, online shopping recommendations, and personalized music suggestions.

Aug 17th 2026
1 Week
Introduction to PySpark (Coursera) Coursera
Edureka

Introduction to PySpark (Coursera)

Welcome to Introduction to PySpark, a short course strategically crafted to empower you with the skills needed to assess the concepts of Big Data Management and efficiently perform data analysis using PySpark. Throughout this short course, you will acquire the expertise to perform data processing with PySpark, enabling you to efficiently handle large-scale datasets, conduct advanced analytics, and derive valuable insights from diverse data sources.

Aug 17th 2026
1 Week
Experimentation for Improvement (Coursera) Coursera
McMaster University

Experimentation for Improvement (Coursera)

We are always using experiments to improve our lives, our community, and our work. Are you doing it efficiently? Or are you (incorrectly) changing one thing at a time and hoping for the best? In this course, you will learn how to plan efficient experiments - testing with many variables. Our goal is to find the best results using only a few experiments. A key part of the course is how to optimize a system.

Aug 17th 2026
5-12 Weeks
Interprofessional Healthcare Informatics (Coursera) Coursera
University of Minnesota

Interprofessional Healthcare Informatics (Coursera)

Interprofessional Healthcare Informatics is a graduate-level, hands-on interactive exploration of real informatics tools and techniques offered by the University of Minnesota and the University of Minnesota's National Center for Interprofessional Practice and Education. We will be incorporating technology-enabled educational innovations to bring the subject matter to life. Over the 10 modules, we will create a vital online learning community and a working healthcare informatics network.

Aug 17th 2026
5-12 Weeks
Machine Learning Rapid Prototyping with IBM Watson Studio (Coursera) Coursera
IBM

Machine Learning Rapid Prototyping with IBM Watson Studio (Coursera)

An emerging trend in AI is the availability of technologies in which automation is used to select a best-fit model, perform feature engineering and improve model performance via hyperparameter optimization. This automation will provide rapid-prototyping of models and allow the Data Scientist to focus their efforts on applying domain knowledge to fine-tune models. This course will take the learner through the creation of an end-to-end automated pipeline built by Watson Studio’s AutoAI experiment tool, explaining the underlying technology at work as developed by IBM Research.

Aug 17th 2026
4 Weeks