Tip (2) for R to Python and Vice-Versa seamlessly

In continuation to my earlier R to Python tips, in order to deal with both Python and R simultaneously for client requests; this time with respect to plots where both schools as of now by large distinct in their plotting styles; Plotnine a new python package for grammar of graphics will help us with ease for two language enthusiasts.

Plotnine which is almost in same style (> 90%) as ggplot2, claims to be an "implementation of a grammar of graphics in Python, it is based on ggplot2 and allows users to compose plots by explicitly mapping data to the visual objects that make up the plot". Below is a quick example from the new API page:


## Installation
Conda - "conda install -c conda-forge plotnine"
(or)
PIP - "pip install plotnine"

## quick example from API page

from plotnine import ggplot, geom_point, aes, stat_smooth, facet_wrap
from plotnine.data import mtcars

(ggplot(mtcars, aes('wt', 'mpg', color='factor(gear)'))
 + geom_point()
 + stat_smooth(method='lm')
 + facet_wrap('~gear'))

Those who have not yet experience this can try out now and let me know your experiences at mavuluri.pradeep@gmail.com

Happy R and Python Programming!

Hard-nosed Indian Data Scientist Gospel Series - Part 2 : Certificate (or Degree) Mania

This is second in series, first is here.

Again, whole past decade before & after recession seems to be & seeming to be revolving around a mania called certificate or degree’s around some topic / tool. Let it be subject / concept namely., Analytics or Machine Learning or Data Science etc. and tool / technology namely., SAS or SPSS or R or Python etc. (where price of such unequal to (s) ranged from 0,000’s to 000,000’s).

This always reminded and reminds me that most of marketers duped aspirants around data science by hiding its important characteristic namely., “multi-disciplinary one”, that led to ending up with partial learning or incomplete or incompetent learning which couldn’t cater industry needs.

Author undertook several projects, courses and programs in data sciences for more than a decade, views expressed here are from his industry experience. He can be reached at mavuluri.pradeep@gmail or besteconometrician@gmail.com for more details.

Find more about author at http://in.linkedin.com/in/pradeepmavuluri

Lynchpins for Analytical Skill Development

As business are adopting more and more data-driven strategies (analytics) in their day to day life, I keep on listening from leadership or concerned people that training provided towards it, are not having anticipated impact. Herein, pragmatic confession would be happy with thought that 'it is not a pure science' (or) let’s appreciate the concepts and different relationships involved for their success:

Author has developed and undertook several programs towards analytical talent development, views expressed here are from his industry experience that lead him to develop/design analytical training's as fun concepts with games having clues. He can be reached at mavuluri.pradeep@gmail for more details.

Big Data Analytical Services Environment (Success Struggles)

 

Observations are author's personal views after observing big data space over a period of time, he can be reached at mavuluri.pradeep@gmail for further discussion on this topic.

Appropriate Training Objectives for Strong Analytical Foundation

Organizations likes to invest in developing their analytical talent in such a way that they can compete for global competencies, for that training objectives should be appropriately identified and cannot ignore strong foundation. Herein, they should cater for both semi-skilled (graduates of M.S.) who has awareness but lack of domain/practical experience and non-skilled (other graduates). Below are the three important objectives for strong analytical foundation.

1. Ability to link learned analytical capabilities to real-world situations.
2. Knowledge of basic analytical concepts, and their applications.
3. Obtain cognizance to synthesize the constituents of an analytical project and convey the results in a clear manner to whom so ever.

Reach me at mavuluri.pradeep@gmail.com for more details. 

Key Non-ignorable Points for Agile Analytical Services.

Taking workforce to immediate and next levels of analytical requirements

In today’s world, where the demand for analytical skilled workers is greater than the supply, employers’ needs to provide critical trainings to their workforce for two very good reasons (short-term) that enable current key deliveries for them, and (long-term) innovative and career development oriented that helps workforce to stay with them.

 

Reach me at mavuluri.pradeep@gmail.com for more details on analytical trainings.