Amazing Binder turns github repositories to a collection of interactive notebooks


Just go to



Turn a GitHub repo into a collection of interactive notebooks

Have a repository full of Jupyter notebooks? With Binder, open those notebooks in an executable environment, making your code immediately reproducible by anyone, anywhere.

I turned my repo from  to


How it works

Enter your repository information
Provide in the above form a URL or a GitHub repository that contains Jupyter notebooks, as well as a branch, tag, or commit hash. Launch will build your Binder repository.
It builds a Docker image of your repository
Binder will search for a dependency file, such as requirements.txt or environment.yml, in the repository’s root directory (more details on more complex dependencies in documentation). The dependency files will be used to build a Docker image. If an image has already been built for the given repository, it will not be rebuilt. If a new commit has been made, the image will automatically be rebuilt.
Interact with your notebooks in a live environment!
JupyterHub server will host your repository’s contents. We offer you a reusable link and badge to your live repository that you can easily share with others


JupyterHub, a multi-user Hub, spawns, manages, and proxies multiple instances of the single-user Jupyter notebookserver. JupyterHub can be used to serve notebooks to a class of students, a corporate data science group, or a scientific research group.

jupyterlab is here!

Get your copy at

From command line do the following

  • Upgrade Jupyter notebook

pip install notebook —upgrade 

  • Install  Jupyterlab

pip install jupyterlab

  • Launch Jupyter Lab

jupyter lab

A very nice design and excellent for working- lets see how fast people adapt to it along with other kernels in Julia, R, SAS et al


The Journey of a Data Scientist

I began my data science career 15 years ago in 2003. First I learnt SAS. That lasted me till 2008. I then learnt R. That got me to 2015. Then I started using Python. In 2017 I started with PySpark. I love how data science means continuous learning , as learning is fun. We data scientists are also lucky to be in a high demand career. 

SAS and Python Together

A software called saspy helps SAS and Python work together

 Python coders can now bring the power of SAS into their Python scripts. The project is SASPy, and it’s available on the SAS Software GitHub. It works with SAS 9.4 and higher, and requires Python 3.x.

SAS/STAT object in SASPy





and its available at Github

A Python interface to MVA SAS

This module allows a python process to connect to SAS 9.4 and run SAS code, generated by the supplied object and methods or explicitly user written, and returns results as text, HTML5 documents (via SAS ODS), or as Pandas Data Frames. It supports running analytics and returning the resulting graphics and result data. It can convert between SAS Data Sets and Pandas Data Frames. It has multiple access methods which allow it to connect to local or remote Linux SAS, IOM SAS on Windows or Linux (Including Grid Manager), and local PC SAS. It can run w/in Jupyter Notebooks, in line mode python or in python batch scripts. It is expected that the user community can and will contribute enhancements.


Clearly SAS has made tremendous progress in reaching out to the open source community from releasing the free SAS University Edition to latest products like SAS Viya ( )

With SAS Viya, it’s now possible to integrate all elements needed to
build and deploy analytics – whether they are defined in SAS, written
with other programming languages like Python, Java, R or Lua, or
called from public REST APIs.

But is too little too late for SAS or is it the other way around for R, with Python usage increasing rapidly and R’s much vaunted libraries ported with ease, much better documentation and enterprise customer support in SAS and Python than in R.

As they say, time will tell? Meanwhile the data science and big data market is booming and there  seems enough for all to share slices of market share

Sentiment Manipulation using fake entities in social media

Some examples of sentiment manipulation are

  1. Reviews (at Amazon for Books and Rotten Tomatoes)- By writing a few bad reviews early on, the fake reviewer can choke sales. This is similar to the fake facebook page to give Bad reviews to Black Panther recently ( see and
  2. Sustained sentiment manipulation by Twitter tweets and Facebook groups. Since all social media depend on email for authentication and since the email providers rarely share IP address login information with social media networks, this enables trolls to create a few email addresses every hour followed by few social media accounts every hour. Tor and One Touch VPN are examples of IP address masking
  3. Network effects- people tend to infer that social media accounts having larger number of followers or a retweet having larger retweets is credible compared to smaller accounts. This is thus a ripe area for deception

(to be continued-)

Why Datacamp scores above other online learning

Here are some reasons I recommend over online learning platforms in Data Science

  1. World class faculty specific to each sub course and module
  2. High Quality -quizzes and videos make a high quality automated self learning environment
  3. Really really cheap – At 29$ / month its much cheaper than say paying 25000 rs for six weeks (65rs= 1$) or 2830 Rs versus 25000 Rs

Note I am not associated with Datacamp.