I recently managed to get a copy of SAS University Edition.
1) Here were some problems I had to resolve- The download size is 1.5 gb of a zipped file ( a virtual machine image). Since I have a internet broadband based in India it led to many failed attempts before I could get it. The unzipped file is almost 3.5 gb. You can get the download file here http://www.sas.com/en_us/software/university-edition/download-software.html.
Secondly the hardware needed is 64 bit, so I basically upgraded my Dell Computer. This was a useful upgrade for me anyway.
2) You can get an Internet Download Manager to resume downloading in case your Internet connection has issues downloading a 1.5 gb file in one go. For Linux you can see http://flareget.com/download/
and for Windows http://www.internetdownloadmanager.com/download.html
3) I chose VM Player for Linux because I am much more comfortable with VM Player ( Desktop free version). I got that from here ~200 MB https://my.vmware.com/web/vmware/free#desktop_end_user_computing/vmware_player/6_0
4) Finally I installed VM Player and Open an Existing Virtual Machine to boot up SAS University Edition
I was able to open the SAS Studio at the IP Address provided.
I downloaded a Dataset from this collection here
6) Then I uploaded it to within the SAS Studio System
7) Lastly I was able to run some basic commands
I was really impressed by the enhancements made to the interface, the ability to search command help through a drop down, the color coded editor and of course the case insensitive SAS language (though I am not a fan of the semi colon I loved using Ctrl + / for easy commenting and uncommenting)
- For a SAS turned R turned SAS coder- here are some views
- SAS has different windows for coding, log and output. R generally has one
- SAS is case insensitive while R is case sensitive. This is a blessing especially for variable and dataset names.
- SAS deals with Datasets than can be considered the same as Rs Data Frame.
- R’s flexibility in data types is not really comparable to SAS as it is quite fast enough.
- SAS has a Macro Language for repeatable tasks
- SQL is embedded within SAS as Proc SQL and in R through sqldf package
- You have to pay for each upgrade in SAS ecosystem. I am not clear on the transparent pricing, which components does what and whether they have a cloud option for renting by the hour. How about one web page that lists product description and price.
- SAS University Edition is a OS agnostic tool, for that itself it is quite impressive compared to say Academic Edition of Revolution Analytics.
- R is object oriented and uses  and $ notation for sub objects. SAS is divided into two main parts- data and proc steps, and uses the . notation and var system
- SAS language has a few basic procs but many many options.
- How good a SAS coder you are often depends on what you can do in data manipulation in SAS Data Step
- Graphics is still better in R ggplot. But the SAS speed is thrilling.
- RAM is limited in the University Edition to 1 GB but I found that still quite fast. However I can upload only a 10 mb file to the SAS Studio for University Edition which I found reasonable for teaching purposes.
I really liked the mature design, and foundation of this charitable organization. While it is similar to FOAS in many ways (http://www.foastat.org/projects.html) I like the projects . Excellent projects and some of which I think should be featured in Journal of Statistical Software– (since there is a seperate R Journal) unless it wants to be overtly R focused.
In the same manner I think some non Python projects should try and reach out to NumFocus (if it is not wanting to be so PyFocus-ed)
Here it is NumFocus
NumFOCUS supports and promotes world-class, innovative, open source scientific software. Most individual projects, even the wildly successful ones, find the overhead of a non-profit to be too large for their community to bear. NumFOCUS provides a critical service as an umbrella organization which removes the burden from the projects themselves to raise money.
Money donated through NumFOCUS goes to sponsor things like:
- Coding sprints (food and travel)
- Technical fellowships (sponsored students and mentors to work on code)
- Equipment grants (to developers and projects)
- Conference attendance for students (to PyData, SciPy, and other conferences)
- Fees for continuous integration and other software engineering tools
- Documentation development
- Web-page hosting and bandwidth fees for projects
SciPy is open-source software for mathematics, science, and engineering. It is also the name of a very popular conference on scientific programming with Python. The SciPy library depends on NumPy, which provides convenient and fast N-dimensional array manipulation. The SciPy library is built to work with NumPy arrays, and provides many user-friendly and efficient numerical routines such as routines for numerical integration and optimization.
2D plotting library for Python that produces high quality figures that can be used in various hardcopy and interactive environments. matplolib is compatiable with python scripts and the python and ipython shells.
High quality open source python shell that includes tools for high level and interactive parallel computing.
SymPy is a Python library for symbolic mathematics. It aims to become a full-featured computer algebra system (CAS) while keeping the code as simple as possible in order to be comprehensible and easily extensible. SymPy is written entirely in Python and does not require any external libraries.
Cython is a language based on Pyrex that makes writing C extensions for Python as easy as writing them in Python itself. Cython supports calling C functions and declaring C types on variables and class attributes, allowing the compiler to generate very efficient C code from Cython code.
pandas is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language.
PyTables is a package for managing hierarchical datasets and designed to efficiently and easily cope with extremely large amounts of data. PyTables is built on top of the HDF5 library, using the Python language and the NumPy package. It features an Pythonic interface combined with C / Cython extensions for the performance-critical parts of the code. This makes it a fast, yet extremely easy to use tool for very large amounts of data. http://pytables.github.com/
Free high-quality and peer-reviewed volunteer produced collection of algorithms for image processing.
Module designed for scientific pythons that provides accesible solutions to machine learning problems.
Statsmodels is a Python package that provides a complement to scipy for statistical computations including descriptive statistics and estimation of statistical models.
Interactive development environment for Python that features advanced editing, interactive testing, debugging and introspection capabilities, as well as a numerical computing environment made possible through the support of Ipython, NumPy, SciPy, and matplotlib.
Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently.
NumFOCUS is currently looking for representatives to enable us to promote the following projects. For information contact us at: info@NumFOCUS.org.
Open source mathematics sofware system that combines existing open-source packages into a Python-based interface.
NetworkX is a Python language software package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks.
Free scientific and engineering development software used for numerical computations, and analysis and visualization of data using the Python programmimg language.
Quote of the Day-
it is impossible to be a data scientist without knowing iris
Revolution Analytics has been nice enough to provide both datasets and code for analyzing Big Data in R.
Site was updated so here are the new links
while the Datasets collection is still elementary, as a R Instructor I find this list extremely useful. However I wish they look at some other repositories and make .xdf and “tidy” csv versions. A little bit of RODBC usage should help, and so will some descriptions. Maybe they should partner with Quandl, DataMarket, or Infochimps on this initiative than do it alone.
Overall there can be a R package (like a Big Data version of the famous datasets package in R)
But a nice and very useful effort
Revolution R Datasets
Also a recent project made by a student of mine on Revolution Datasets and using their blog posts.
Note how much more better the above project is than use the mini and super clean datasets within R (like Boston)