Category: Analytics

#fixfacebook rather than #deletefacebook

While I have written articles on mining Facebook data using R (here https://decisionstats.com/2014/05/10/analyzing-facebook-networks-using-rstats/ ) I view it as a place to share photos (from Instagram and Whatsapp) and keep in touch. Deleting facebook means deleting most of my adult memories- or deleting  baby along with bath water. FB has smart people. Surely they can find the energy and focus to fix this before it goes worse

Reinstalling Dependent Packages in R

You can use the miniCRAN packages to reinstall the dependent packages in R


install.packages("miniCRAN")
library("miniCRAN", lib.loc="~/R/win-library/3.4")
pkgDep(‘sqldf’)
a=pkgDep(‘sqldf’)
install.packages(a)

I love my company but I hate my boss

 

 

(a parable )

The principal–agent problem, in political science and economics, (also known as agency dilemma or the agency problem) occurs when one person or entity (the “agent”) is able to make decisions on behalf of, or that impact, another person or entity: the “principal”.[1] This dilemma exists in circumstances where agents are motivated to act in their own best interests, which are contrary to those of their principals, and is an example of moral hazard. (from https://en.wikipedia.org/wiki/Principal%E2%80%93agent_problem)

Why do people join companies and startups but leave bosses? because a BOSS does not have the same ownership or value system as the company has. A project manager is focused on expanding his project not how profitable it is or long term effects.

A Boss is more concerned with his bonus- and will let go of good people to meet his cost /billing ratio targets. By doing so he can say he achieved profit by replacing high quality high cost resources with others.But in truth he may have damaged the company’s image as a potential recruiter 

The real challenge then arises for company founders/ owners to create metrics to avoid agency conflict. or to align individual metrics to global company metrics. This is true because many teams need to work together but they have different goals.

The product team wants to push the  latest release of product to get feedback on bugs, but the client services team wants to resist it and stick to stable builds (an example)

However the sum of a local optima is rarely a global optimum, mathematically speaking. A local optimum is whatever is best for the performance of an individual part, whereas the global optimum is what is best for the performance of the system as a whole.  Hat tip – http://bit.ly/2Iyp3pc for amazing insights and images

many people unknowingly subscribe to a defunct management philosophy: that you can improve the performance of a company as a whole by individually improving the performance of its parts.

from the Boss’s perspective, probably the local optima is their global optima as it keep them safe from being overtaken by people better than them.

the job of founders and senior management to neutralize this very human insecurity

That is why culture eats strategy for lunch. But how do you quantify culture?  (to be continued)

Amazing Binder turns github repositories to a collection of interactive notebooks

 

Just go to https://mybinder.org/

 

 

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 https://github.com/decisionstats/pythonfordatascience

to

https://hub.mybinder.org/user/decisionstats-p-nfordatascience-vhxwj6n2/tree

How it works

1
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.
2
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.
3
Interact with your notebooks in a live environment!
A 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

https://jupyterhub.readthedocs.io/en/latest/

JupyterHub

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 http://jupyterlab.readthedocs.io/en/stable/index.html

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

https://www.linkedin.com/feed/update/urn:li:activity:6371209812172201984

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.