Showing posts with label data analytics. Show all posts
Showing posts with label data analytics. Show all posts

Thursday, March 28, 2019

Capabilities of python


What  special things can be done using python?
Recently I happen to  analyse a set of csv files atleast 100.
Each file contains several fields- variables, among them the most is the purchaser Id, purchase date .
The task is,
List all the purchaser’s id and find them in the successive date  files, to identify whether the purchaser has visited again.
If available list the dates and the value of all purchases he has made.
The illustration
Day1 – 01-12-2018
Ids          value of purchase
123         10
234         15
456         25
Get the summary – 3 customers , Rs 50 /-

Find these ids in the next day – 02-12-2018
If available get the summary- only one purchaser id is available – value is  Rs 20
Extend this for the next 99 days. This is cycle 1.
Pick up the list of purchasers in the day 2 and continue this process for the next 98 days.

WoW , what a wonderful tool is this ?
It is calculating the number of files available, running  the iterations – say 100 factorial, tabulating   summarizing  the results  as output in excel format, and what else, the capability is still more.




Monday, March 5, 2018

Data Analysis and analytics


Is data analysis and data analytics the same?

While many seek the clarification between these , I have made a small video to make the viewers understand the difference .
The process  in which  we have full control over  the data  and the outcome is  not going to depend on external factors it is analysis.
In the case of example I have given in farming, the moisture of the soil, area, type of soil are the information which is known( Internal), Rain, water flow, weather are few things which are external.
prediction parameter: Yield
In the second example: the prediction is whether the person will have / chances to have  a disease of a particular kind.

DATA ANALYSIS  
DATA ANALYTICS
Ex 1
Farming
Type of soil ,moisture
Weather, water flow , rain, manure composition
Ex 2
 health care
Age, Gender
History of illness, lifestyle, location, occupation
Ex 3
Banking
Age ,gender ,occupation
Annual income, sector

Watch , share and give your comments.


Thanks : Rohit kumar - IBM for the inputs