data science

An interdisciplinary, applied data science master’s degree with no application. Pass a series of placement tests and get started right away. The Curriculum This degree will draw on CU Boulder faculty expertise in statistics, data science, computer science, geospatial analytics, and natural...

MOOC platform Coursera recently announced the launch of two more MOOC-based degrees in partnership with the University of Colorado Boulder: a Master of Science in Electrical Engineering (MS-EE) and a Master of Science in Data Science (MS-DS)*. This latest announcement was made during Coursera's...

With 2000 courses from Harvard, MIT and more available on MOOC platform edX, sometimes it can be hard to know what course to take. To help you save time searching, here is a list of the top 35 courses in Programming, Data Science, Math and Engineering you can enrol in right now. <!-- START...

Data Science: R Basics Provided by Harvard University (HarvardX) Build a foundation in R and learn how to wrangle, analyze, and visualize data. What You Will Learn Basic R syntax Foundational R programming concepts such as data types, vectors arithmetic, and indexing How to perform operations...

Overview How can citizen science create positive change in the world? Join this online course to discover soil and food growing data and results generated by citizen scientists like you who are collaborating with the GROW Observatory. Become familiar with datasets, learn to identify patterns...

According to Coursera, Data Science is one of their top 3 most popular fields of learning. The MOOC platform has recently shared their 10 most popular Data Science courses of 2018, and this holiday season is the perfect time to build your skills with some of these top courses available now on...

Overview Understand what Linked Data is Linked Data, a term coined by Sir Tim Berners-Lee, is a way of publishing data online so it can be easily interlinked and managed using semantic queries. This helps the exposure and interlinking of datasets so that data can be exchanged, reused and...

Overview 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...

Overview Linear models, as their name implies, relates an outcome to a set of predictors of interest using linear assumptions. Regression models, a subset of linear models, are the most important statistical analysis tool in a data scientist’s toolkit. This course covers regression analysis...

Overview This statistics and data analysis course will pave the statistical foundation for our discussion on data science. You will learn how data scientists exercise statistical thinking in designing data collection, derive insights from visualizing data, obtain supporting evidence for...

Overview The job of a data scientist is to glean knowledge from complex and noisy datasets. Reasoning about uncertainty is inherent in the analysis of noisy data. Probability and Statistics provide the mathematical foundation for such reasoning. In this course, part of the Data Science...

Overview The world is full of uncertainty: accidents, storms, unruly financial markets, noisy communications. The world is also full of data. Probabilistic modeling and the related field of statistical inference are the keys to analyzing data and making scientifically sound predictions...

Overview Statistics is the science of turning data into insights and ultimately decisions. Behind recent advances in machine learning, data science and artificial intelligence are fundamental statistical principles. The purpose of this class is to develop and understand these core ideas on firm...

Overview As data collection has increased exponentially, so has the need for people skilled at using and interacting with data; to be able to think critically, and provide insights to make better decisions and optimize their businesses. This is a data scientist, “part mathematician, part...

Overview This course will introduce the learner to the basics of the python programming environment, including fundamental python programming techniques such as lambdas, reading and manipulating csv files, and the numpy library. The course will introduce data manipulation and cleaning techniques...

Overview This course will introduce the learner to information visualization basics, with a focus on reporting and charting using the matplotlib library. The course will start with a design and information literacy perspective, touching on what makes a good and bad visualization, and what...

Overview This course will introduce the learner to text mining and text manipulation basics. The course begins with an understanding of how text is handled by python, the structure of text both to the machine and to humans, and an overview of the nltk framework for manipulating text. The second...

Overview This course will introduce the learner to network analysis through tutorials using the NetworkX library. The course begins with an understanding of what network analysis is and motivations for why we might model phenomena as networks. The second week introduces the concept of...

Overview In this course you will get an introduction to the main tools and ideas in the data scientist's toolbox. The course gives an overview of the data, questions, and tools that data analysts and data scientists work with. There are two components to this course. The first is a conceptual...

Overview In this course you will learn how to program in R and how to use R for effective data analysis. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a...