Data preparation is perhaps the most critical step in data science research, exploratory analysis, or data visualization work. It refers to collecting, cleaning and transforming raw data before its usage and application, and well before any processing or further study. It could also mean aggregating data from one or more sources with disparate structures and formats.
Data preparation allows for efficient analysis, improves consistency, reduces errors, identifies redundancies, and eliminates any discrepancies during the data collection process. Most importantly, the process helps standardize the desired format and estimate the effort required to transform the unprocessed data into a usable form…
Creating compelling data visualization is both a science and an art. It should not only uncover insights and awareness about the subject in question but also communicate it effectively. It must strike a balance between the art and science elements of it, which means visualization must both:
Accomplishing both can be a bit of a challenge. First, data visualization isn’t just about representing data. It’s about presenting data in a way the target audience can absorb easily and follow the cue along the way — that’s where the real…
With the growing complexity of AI models, the critical need for understanding their inner-workings has increased
Deep learning has led to unprecedented breakthroughs in many areas such as computer vision, voice recognition, and autonomous driving. It has proved very powerful at solving large-scale real-world problems in recent years and adopted in many large-scale information processing applications like image recognition, language translation and automated personalization. There is now hope that these same techniques will be able to diagnose deadly diseases, make trading decisions, and do many other things that will potentially transform our lives and many industries.
While a deep neural…
Deep learning has led to unprecedented breakthroughs and innovation in areas such as computer vision, speech recognition, autonomous driving and many more. Deep learning is a specific set of techniques from the broader field of machine learning that uses artificial neural networks to learn structured representations of data, including images, audio and video. It is used for classifying patterns using large data sets and multi-layer neural networks.
The origin of deep learning traces back to the dawn of artificial intelligence in the 1950s, when there were two competing visions for how to create an AI system: one vision was focused…
Geospatial data (or Geodata) is a data set used to collectively refer to geographic data and information. It’s all around us. Weather reports, location coordinates, geotagged tweets, suggested routes on Google Maps or Google Earth, location coordinates, roadways, airline routes, etc, are also considered geospatial data. It can be in the form of coordinates, address, city, or ZIP code. It combines location information, in the form of coordinates, along with related attributes and other temporal details (time or lifespan of location and distance).
Generally, there are four concepts behind working with geospatial data: Shapefiles, GDAL, GeoJSON and TopoJSON.
- Thirty-seven percent Rule
How many people should you date before truly finding the “one” or deciding to settle down with? It’s a tricky question, and as with many tricky questions, math has an answer of some sort, which tells you Its 37% of the way through your search!
This answer has its origin in a famous puzzle in mathematics known as ‘The Secretary Problem’. The strategy is, say you’re interviewing a group of applicants for a position, how do you maximize the chances of hiring the single best applicant in the pool. …
Inspired by 100 days coding challenge #100DaysOfCode, I decided to take on a year-long challenge, a weekly writing series that explores some interesting and famous algorithms out there in the world of computer science.
So every fortnight, I’d pick an algorithm concept, do a bit of research and write a post explaining the main idea and how it helps solve a particular problem and also discuss its origin, facts, and efficiency in terms of big-O
I’ll illustrate the concepts with a specific example and the source code of the examples can be found in my GitHub repository. I’ll be coding…
CSS3 Transition can be used to created content transitions, animate complex navigation, reveal off-screen menus and chain multiple effects for an element. It can enhance simple actions like showing and hiding content visually richer. It offers better control through its properties including speed, direction, acceleration, and iteration-count.