This course provides an overview of skills needed for reproducible research and open science using the statistical programming language R. Students will learn about data visualisation, data tidying and wrangling, archiving, iteration and functions, probability and data simulations, general linear models, and reproducible workflows. Jeffrey Leek Johns Hopkins Bloomberg School of Public Health. They are by no means perfect, but feel free to follow, fork and/or contribute.Please reach out to s.xing@me.com if you have any questions. Git-lfs allows you to have much more space than putting binaries in your repo, but it isn’t unlimited or free. Thus, I added an additional major in Computer Science. Computational History. This course covers the basic ideas behind machine learning/prediction Study design - training vs. test sets; Conceptual issues - out of sample error, ROC curves 15-388/688: Practical Data Science (Spring 2018) 15-780: Graduate Artificial Intelligence (Spring 2017) 15-388/688: Practical Data Science (Fall 2016) 15-780: Graduate Artificial Intelligence (Spring 2016) 15-381: Artificial Intelligence (Fall 2015) 15-780: Graduate Artificial Intelligence (Spring 2014) The extraordinary spread of computers and online data is changing forever the way decisions are made in many fields, from medicine to marketing to scientific research. (Full Course) 15-388 Practical Data Science. About this course. Statistics for the Humanities (2014). Overview. Data Visualization, Web Development, & Related. Practical Data Science (95-885) From empirical, to theoretical, to computational science, we are at the dawn of a new revolution---a fourth paradigm of science driven by data. Many of the approaches and tools for data viz come from the natural and social sciences. Computational History is a branch of digital history that carries out historical studies via machine learning and other data-heavy and computational approaches like network analysis.. For further information see: How the New Science of Computational History Is Changing the Study of the Past MIT Technology Review (June 23, 2016). Practical Data Science 10/30/2013 20 . Hello! Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the “data-analytic thinking” necessary for extracting useful knowledge and business value from the data you collect. Compressed data files (e.g. • Over 10 years experience as consultant and trainer in data science • A principal consultant at Win-Vector LLC (a data science consultancy) • Also an author of Practical Data Science with R • Ph.D. in computer science from Carnegie Mellon University • Contributor to the Win-Vector blog Like archaeological remnants, data, by its very nature, is a marker of what happened in the past. Data Visualization for Social Science: A practical introduction with R and ggplot2 (2018). Happy Learning All notes are written in R Markdown format and encompass all concepts covered in the Data Science Specialization, as well as additional examples and materials I compiled from lecture, my own exploration, StackOverflow, and Khan Academy.. Kieran Healy. So don’t just go crazy. Reference works on the best practices of data visualization include: The Visual Display of Quantitative Information (1983) Edward R. Tufte. K Means Clustering . Data Mining: Practical Machine Learning Tools and Techniques, 4th edition (2016). I am passionate about creating things that make impacts, and turning ideas into reality. I entered CMU as a Civil & Environmental Engineering major, but after being surrounded by people focused on computers and technology, I too developed an interest in programming and computer engineering. If you go to Settings for your github account, and look at Billing, you will see a “Git LFS Data” quota. One of the main challenges for businesses and policy makers when using big data is to find people with the appropriate skills. HDF5 with compression) are your friend! Overview: Carnegie Mellon's Interdisciplinary Approach to Data Science. Data Science For Business. For some discussion of how best to apply visualization techniques to humanistic research, see humanities visualization. John Canning. Witten, Frank, Hall, & Pal. I am Aiqi Cui (Chelsea), a senior at Carnegie Mellon University majoring in Information Systems with a secondary major in Statistics & Machine Learning. Data Science and Big Data Analytics are exciting new areas that combine scientific inquiry, statistical knowledge, substantive expertise, and computer programming. I entered this university persuing a Bachelor of Science in Computer Science and Technology ().Upon entry I was accepted to the Honors program in which I had to learn a new language, german in my case, and was required to take more advanced courses. Recorded lectures are here and archived here; Introduction to the 'full stack' of data science analysis: data collection and processing, data visualization and presentation, statistical model building using machine learning, and big data …