Udacity

Data Analysis and Visualization (Udacity)

Offered by Georgia Institute of Technology, Udacity,
Data Analysis and Visualization (Udacity)

Data and visual analytics is an emerging field concerned with analyzing, modeling, and visualizing complex high dimensional data. This course will introduce students to the field by covering state­-of-­the-art modeling, analysis and visualization techniques. It will emphasize practical challenges involving complex real world data and include several case studies and hands-on work with the R programming language.

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

Why Take This Course
You should take this course if you want to cover the state of the art in data modeling and visualization techniques using the R programming language.

What You Will Learn

Lesson 1
Programming in R

  • The R Programming Language
  • R Programming Syntax
  • R Programming and Data Structures

Lesson 2
Data Analysis

  • Data Preprocessing
  • Data Processing
  • Data Visualization

Lesson 3
Regression

  • Logistic Regression
  • Linear Regression
  • Regularization

Prerequisites and Requirements

  • Programming experience
  • Mathematics: basic linear algebra, calculus, introductory probability
  • No background in machine learning is required
Go to Class
MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Related Courses

Leadership Through Marketing (Coursera) Coursera
Northwestern University

Leadership Through Marketing (Coursera)

Management & Leadership

The success of every organization depends on attracting and retaining customers. Although the marketing concepts for doing so are well established, digital technology has empowered customers, while producing massive amounts of data, revolutionizing the processes through which organizations attract and retain customers. In this course, students will learn how to identify new opportunities to create value for empowered consumers, develop strategies that yield an advantage over rivals, and develop the data science skills to lead more effectively, allocate resources, and to confront this very challenging environment with confidence.

Jun 14th 2026
4 Weeks
Marketing Strategy Leadership
Details
Bioinformatic Methods II (Coursera) Coursera
University of Toronto

Bioinformatic Methods II (Coursera)

Sci: Biology & Life Sciences Health & Society

Large-scale biology projects such as the sequencing of the human genome and gene expression surveys using RNA-seq, microarrays and other technologies have created a wealth of data for biologists. However, the challenge facing scientists is analyzing and even accessing these data to extract useful information pertaining to the system being studied. This course focuses on employing existing bioinformatic resources – mainly web-based programs and databases – to access the wealth of data to answer questions relevant to the average biologist, and is highly hands-on.

Jun 8th 2026
5-12 Weeks
Biology Bioinformatics Molecular Biology
Details
Real-Time Analytics with Apache Storm (Udacity) Udacity
Udacity,Twitter

Real-Time Analytics with Apache Storm (Udacity)

Statistics & Data Analysis Data Science

The world is trending in real time! Learn from Twitter to scalably process tweets, or any big data stream, in real-time to drive d3 visualizations using Apache Storm, the "Hadoop of Real Time." Storm is free, open source, and fun to use! Learn from Karthik Ramasamy, about the distributed, fault-tolerant, and flexible technology used to power Twitter’s real-time data flow pipeline. Twitter open sourced Storm in 2011, and it graduated to a top-level Apache project in September, 2014.

Self Paced
Self-Paced
Data Analysis Hadoop Data Science
Details
Reproducible Research (Coursera) Coursera
Johns Hopkins University

Reproducible Research (Coursera)

Statistics & Data Analysis Data Science

This course focuses on the concepts and tools behind reporting modern data analyses in a reproducible manner. Reproducible research is the idea that data analyses, and more generally, scientific claims, are published with their data and software code so that others may verify the findings and build upon them. The need for reproducibility is increasing dramatically as data analyses become more complex, involving larger datasets and more sophisticated computations.

Jun 8th 2026
4 Weeks
Data Analysis Data Science Reproducible Research
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
Data Engineering with Rust (Coursera) Coursera
Duke University

Data Engineering with Rust (Coursera)

Data Science

Are you a data engineer, software developer, or a tech enthusiast with a basic understanding of Rust, seeking to enhance your skills and dive deep into the realm of data engineering with Rust? Or are you a professional from another programming language background, aiming to explore the efficiency, safety, and concurrency features of Rust for data engineering tasks? If so, this course is designed for you.

Jun 11th 2026
4 Weeks
Data Structures Software Testing Data Processing
Details
Statistics (Udacity) Udacity
Udacity,San Jose State University

Statistics (Udacity)

Sci: Mathematics Statistics & Data Analysis

The Science of Decisions. We live in a time of unprecedented access to information...data. Whether researching the best school, job, or relationship, the Internet has thrown open the doors to vast pools of data. Statistics are simply objective and systematic methods for describing and interpreting information so that you may make the most informed decisions about life.

Self Paced
Self-Paced
Math Statistics Probability
Details