Businesses need to evolve and pace up with the ongoing dawn of the digital era. While all businesses — big and small — now understand the need to digitize their businesses, it can be quite challenging for those who can’t find solutions through traditional methods.

For businesses that are in the initial phases of incorporating machine learning can leverage low-code options. Not only are they easy to use but they also accelerate the development process given that you don’t have to write the entire code manually. Low-code platforms are the best solution for less experienced data science teams.

With the…

Who are promoters, detractors, and passives? What is the role of NPS in improving customer experience? How to calculate NPS? We are answering all the important NPS-related questions in this ultimate Net Promoter Score guide.

“On a scale of zero to 10, how likely are you to recommend [Company Name] to a friend or colleague?”

All of us have seen this question at least once in our inboxes. It could be from the car rental agency you just used or your internet security software company. Your answer to this question is the foundation of the Net Promoter Score, an authoritative, widely accepted gauge of customer satisfaction and customer experience.

First, a little bit of history. Fred Reichheld of Bain & Company was the first to introduce Net Promoter Score (NPS) in 2003. He wrote an article in Harvard Business Review titled, “The One Number You Need to Grow”. …

In our previous blog on Geospatial Workflows, we went through the process of creating geospatial workflows to estimate a city’s population.

To summarize, here are the steps we followed on a Jupyter Notebook to estimate the population of Fort Portal, the tourism town of Uganda at a 100x100m granularity:

  1. Load GeoJSON and population data
  2. Polygonize: Use the Python convert raster into a vector image
  3. Gridify: Breakdown the tiles into 100x100m resolution
  4. Load building footprints dataset: Buildings are used as a proxy for the human population
  5. Compute building statistics for each grid
  6. Remove extra grids and adjust the population
  7. Compute population

geospatial workflow to estimate a city’s population using jupyter notebook
geospatial workflow to estimate a city’s population using jupyter notebook

How many people are there per square mile of New York? How can the number of people in a locality be identified during a hurricane? Enter Geospatial Analytics, which uses a combination of satellite images and data analysis.

Geospatial AI and Spatial Data Analysis have a more visual flavor to it than numbers. The output is visual, but there are several steps involved to get there. …

Creating a Bar Chart using Vega JS library is shown here.

The sample data in the above tutorial has alphabets as Labels. In the real-world, data labels can be lengthy. For example, a bar chart showing sales of various products.

"data": [
"name": "table",
"values": [
{"category": "Meat and Seafood", "amount": 28},
{"category": "Milk and milk products", "amount": 55},
{"category": "Ice cream", "amount": 43},
{"category": "Cereals and Breakfast Foods", "amount": 91},
{"category": "Nuts and seeds", "amount": 81},
{"category": "Seasoning and spices", "amount": 53},
{"category": "Sweets, candy, chocolate", "amount": 19},
{"category": "Thin crispy breads", "amount": 87}

A look inside MoMA’s collection reveals the changing landscape of artists and artworks.

By Priti Pandurangan

The Museum of Modern Art (MoMA) is a pioneering institute that exhibits modern, contemporary artworks. Established in 1929 in New York, MoMA has grown to be the largest and the most influential museums of modern art with a growing collection of over 200,000 artworks by more than 25,000 artists.

MoMA has published its collection spanning the last 150 years of artists and artworks. This catalogue paints a rich picture of the idiosyncratic paths that have led diverse artists to exhibit at MoMA as well as the evolving tastes in art.

Art is intrinsically a human endeavour and…

Nutan Bhattiprolu — From the Leaders’ Desks

Artificial Intelligence (AI) will transform companies, industries and businesses. That is the future, and not a very distant one. Having said that, it’s not current reality either. At least, not yet in a complete way.

AI is a broad area of knowledge which is delivered by numerous promising technologies, tech stacks, and companies. For a business user it can be tedious, time consuming and possibly even never ending to explore the technologies available and plan for the right ones which fit the need.

The following few paragraphs are aimed towards business leaders to…

We’ve had a lot of fun at Gramener in 2017 creating some exciting dataviz. It is that time of the year to look back with satisfaction and also spark the desire to do even better dataviz in 2018. This post is a round-up of some of our favorite work this year.

Two of our dataviz properties found their way to Andy Kirk’s ‘Best of the Visualization Web’ in 2017. The interactive Elvis Presley Jukebox was a result of one of our experiments in creating data portraits.


Gramener is a design-led data science company that solves complex business problems with compelling data stories using insights and a low-code platform, Gramex.

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