Automated data science platforms

Here are three data stor­ies this week that are worth your time:

  1. Will auto­mated data sci­ence plat­forms take over? A dis­cus­sion on red­dit (12 min read)
  2. Which are the top LinkedIn groups for data sci­ence? An ana­lys­is on KDNuggest (7 min read)
  3. A his­tory of the last 1,000 years of European roy­al fam­il­ies (visu­al­isa­tion)

We’ll be share links every week. (But that could be ex­tra­pol­a­tion.)

Miami Herald publishes Gramener’s Debate Interactive

The ‘Miami Herald’, one of Florida’s ma­jor news­pa­pers, pub­lished Gramener’s Interactive on the fi­nal Presidential de­bate– ‘The Las Vegas Showdown’ . The full story with the in­ter­act­ive can be read here.


Data Wrangling: What, How and Why

Gramener’s CEO Anand and Senior Data Scientist Kathirmani along with the Upgrad team are run­ning a work­shop this Sunday (23 Oct, 11:30am) at Koramangala, Bangalore. The top­ic is “Data Wrangling – What, How and Why?”

Gramener's CEO Anand and Senior Data Scientist Kathirmani


Over 90% of data sci­ence is about clean­ing data – the pro­cess of load­ing, cor­rect­ing and pre­par­ing the data for ana­lys­is. This is a te­di­ous pro­cess. But what does it in­volve? What are the tricks of the trade? What tools and tech­niques make this easi­er?

Data is messy. (What a surpriise)
Data is messy. (If it sur­prises you, your ca­reer will be messy.)

Once we have the data ready, the toughest part is ask­ing the right ques­tions. Exploratory data ana­lys­is is about play­ing with data­sets in a struc­tured way to ex­tract as many of the im­port­ant in­sights as pos­sible in a given time. What are ef­fect­ive EDA tech­niques? Is there a struc­ture to this?

This talk is ideal for people in­ter­ested in learn­ing about data ana­lys­is, as well as ana­lysts who are look­ing to im­prove their data wrangling skills. Join us!