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The Open Source Data Science Masters (FREE)

The Open Source Data Science Masters (FREE)
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Code Avengers Learn Data Science by nborwankar Mahi / DAT7 · GitLab DAT7 Course Repository Course materials for General Assembly's Data Science course in Washington, DC (6/1/15 - 8/12/15). Instructor: Kevin Markham Course Project Python Resources Codecademy's Python course: Good beginner material, including tons of in-browser exercises.DataQuest: Similar interface to Codecademy, but focused on teaching Python in the context of data science.Google's Python Class: Slightly more advanced, including hours of useful lecture videos and downloadable exercises (with solutions).A Crash Course in Python for Scientists: Read through the Overview section for a quick introduction to Python.Python for Informatics: A very beginner-oriented book, with associated slides and videos.Beginner and intermediate workshop code: Useful for review and reference.Python 2.7x Reference Guide: Kevin's beginner-oriented guide that demonstrates a ton of Python concepts through short, well-commented examples.Python Tutor: Allows you to visualize the execution of Python code. What's next?

CS109 Data Science Predicting Hubway Stations Status by Lauren Alexander, Gabriel Goulet-Langlois, Joshua Wolff Learning from data in order to gain useful predictions and insights. This course introduces methods for five key facets of an investigation: data wrangling, cleaning, and sampling to get a suitable data set; data management to be able to access big data quickly and reliably; exploratory data analysis to generate hypotheses and intuition; prediction based on statistical methods such as regression and classification; and communication of results through visualization, stories, and interpretable summaries. We will be using Python for all programming assignments and projects. All lectures will be posted here and should be available 24 hours after meeting time. The course is also listed as AC209, STAT121, and E-109. Lectures and Sections Lectures are 2:30-4pm on Tuesdays & Thursdays in Science Center B First week collective section Friday 9/4/ 10am-12pm in MD G115 Section times on schedule page Staff

Data Science Cheat Sheet I will update this article regularly. An old version can be found here and has many interesting links. All the material presented here is not in the old version. This article is divided into 11 sections. 1. Hardware A laptop is the ideal device. Even if you work heavily on the cloud (AWS, or in my case, access to a few remote servers mostly to store data, receive data from clients and backups), your laptop is you core device to connect to all external services (via the Internet). 2. Once you installed Cygwin, you can type commands or execute programs in the Cygwin console. Figure 1: Cygwin (Linux) console on Windows laptop You can open multuple Cygwin windows on your screen(s). To connect to an external server for file transfers, I use the Windows FileZilla freeware rather than the command-line ftp offered by Cygwin. You can run commands in the background using the & operator. $ notepad VR3.txt & A few more things about files Other extensions include File management 3. Examples Miscellaneous 4.

MDN-Web-Dokumentation A (small) introduction to Boosting – Sachin Joglekar's blog What is Boosting? Boosting is a machine learning meta-algorithm that aims to iteratively build an ensemble of weak learners, in an attempt to generate a strong overall model. Lets look at the highlighted parts one-by-one: 1. Weak Learners: A ‘weak learner’ is any ML algorithm (for regression/classification) that provides an accuracy slightly better than random guessing. 2. 3. 4. How does Boosting work? Usually, a Boosting framework for regression works as follows: (The overall model at step ‘i’ is denoted by 1. , usually predicting a common (average) value for all instances. 2. from to do: 2.a) Evaluate the shortcomings of , in terms of a value for each element in the training set. 2.b) Generate a new weak learner based on the s. 2.c) Compute the corresponding weight . 2.d) Update the current model as follows: where denotes the learning rate of the framework. 3. as the overall output. For example, Gradient Boosting computes as the gradient of a Loss function (whose expression involves target output . . and

Courses Free Data Science Courses | Data Science Academy Free Data Science Courses The Little List of Free #DataScience Courses Free Online Data Science Courses & Data Science Training Click on the free data science courses links below: The Open Source Data Science Masters Harvard CS109 Data Science Introduction to Data Science by Jeff Hammerbacher at UC, Berkeley Introduction to Data Science @coursera Introduction to Data Science @UofWashington Data Science Course @ColumbiaUni notes by @mathbabe An Introduction to Data Science at Syracuse University ( pdf) Applied Data Science: An Introduction @SyracuseUni Data Science and Analytics at UCBerkeley Process Mining: Data Science in Action @TUEindhoven Learning from Data at California Institute of Technology Statistical Thinking and Data Analysis @MIT Data Analysis and Statistical Inference @DukeUni Introduction to Data Mining @MIT Mining Massive Datasets @Stanford Pattern Discovery in Data Mining @UoIllinois Introduction to Data Wrangling at the School of Data Making Sense of Data @Google Openintro to Statistics

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