When your data can't be explained by plain words.
Scientists consider that AI will revolutionize our world exactly the way electricity did a century ago. The integration of AI technology in Medical and Healthcare is a major leap forward. Although Healthcare and Medical AI will add extensively to the development and emergence of swift possibilities, it also faces certain challenges. Dexascan is considered to be one of the leading organizations that faced numerous challenges in healthcare Body Fat, Muscle & Bone Health and tried to overcome them at every possible step.
When Helical Insight first announced a couple of years ago that they were releasing an Open Source Business Intelligence (BI) tool, it really caught my interest and I reached out to founder Nikhilesh Tiwari to find out more about what he was doing. I spent a little time with the product and really liked where it was going and was determined to do more of a deep dive in the future, and with their release of version 3.0, that time is now.
Nowadays, the amount of data grows exponentially, and the more information we see, the harder it gets to process it. That’s why we need data visualization — in charts and dashboards, preferably interactive. It helps us humans save a lot of time and effort to view, analyze, and understand data, and make the right, informed decisions based on that.
Despite being a python developer for years only recently have I needed to interact with Django. While exploring Django, I decided I wanted to learn a little more about Bokeh the visualisation library. I tried to integrate it into my django project and found it challenging to find a complete tutorial. I thought I would create a post outlining the steps to integrate Bokeh into Django in case anyone finds it useful.
When you tend to use one library for a certain period of time, you get used to it. But, you need to evolve and learn something new every day. If you are still stuck up with Matplotlib(Which is amazing), Seaborn(This is amazing too), Pandas(Basic, yet easy Visualization) and Bokeh, You need to move on and try something new. Many amazing visualization libraries are available in python, which turns to be very versatile. Here, I’m going to discuss about these amazing libraries:
This tutorial will show you how to convert publicly available shapefiles to TopoJSON to create interactive maps with d3/d3-geo. I will show you how to do this without the use of the command line so you can get into experimenting with d3 as fast as possible. If you are interested in working with shapefiles and TopoJSON in a more advanced fashion, I suggest you take a look at Mike Bostock’s recent multipart tutorials on command-line cartography.
Unsupervised learning may sound like a fancy way to say “let the kids learn on their own not to touch the hot oven” but it’s actually a pattern-finding technique for mining inspiration from your data. It has nothing to do with machines running around without adult supervision, forming their own opinions about things. Let’s demystify!
In an IT firm, there are many Employee Architectures available. Some IT firms or at particular departments or certain levels follow the chief programmer structure, in which there is a “star” organisation around a “chief” position designated to the Engineer who best understands the system requirements.
Say, you’re building a Django-powered web application and you have some data you want to visualize. How do you do it? The most popular option is to pick a front-end charting library, have the back-end send the dataset (either through an API or directly passing it to the template) to the front-end, and render the chart in the browser. This approach allows the front-end to do most of the heavy lifting, thereby reducing the strain on your server.
Classification is a very common and important variant among Machine Learning Problems. Many Machine Algorithms have been framed to tackle classification (discrete not continuous) problems. Examples of classification based predictive analytics problems are:
(This is part 2. Read part 1 on augmented reality visualizations here.)
People often ask me “How can I learn Data Analytics?” and I often stumble upon this question ‘How to become a Data Analyst” on Quora too. The answer is pretty much clearly available all over the internet. The actual issue is not how to become a data analyst but it is if we are ready to become one?
As a developer, I don’t usually sit in on our brand strategy meetings, but seeing as this particular meeting was about 20nine’s brand, I was able to get a taste of how they work. One of our assignments was to come up with a character (real or fictitious) that best represented the personality of the company.
By the definition: A Pareto chart, is a type of chart that contains both bars and a line graph, where individual values are represented in descending order by bars, and the cumulative total is represented by the line.
React-simple-maps is a react component library to help make SVG mapping with d3-geo, TopoJSON, and React easier. One of the strengths of using
react-simple-maps is that it gives React full control over the DOM and does not treat the SVG map as a black box. This means that
react-simple-maps can easily take advantage of the entire React ecosystem and all the good things that come with it.
For those of you who are getting started with Machine learning, just like me, would have come across Pandas, the data analytics library. In the rush to understand the gimmicks of ML, we often fail to notice the importance of this library. But soon you will hit a roadblock where you would need to play with your data, clean and perform data transformations before feeding it into your ML model.
One of my favorite things about being a data scientist is creating new tools that make it easier to interpret data and models. I especially like to think about new ways to visualize data that could help solve a tough problem and be useful to my team. Visualizations and interactive interfaces tend to elevate how we work, and short-term investment in new tooling accelerate our analyses and enhance our understanding of the data.
Okay, so here’s another article about data visualization(also my first article on Medium). Well, its kind of because its such an important topic(and also because I have nothing else to write about :p )
Last summer MapD open-sourced their technology and made it available for everybody. At that moment me and my colleagues at Dimebox where working on a POV for a potential big client which we had to impress. Our data analytics capabilities where already advanced, but couldn’t handle a lot of data due to the fact it worked client side. We decided to hop on the MapD-train and the results thus far are pretty amazing.
This is Part 2 of a three-part series about creating visualizations for dissecting data and models. Part 1 can be found here and code, including a Jupyter Notebook with the visualizations in this post, is on GitHub.
34. Small Team, Big Success. Part 1: amCharts is Building the Future of Data Visualization on the Web
For the last 5 years I’ve been dreaming about starting a podcast where I would interview indie developers who made it big and still decided to stay small and frugal rather than shoot for some imaginary stars just because our society considers it to be the “right” way. Why after creating a successful “indie” product developers would decide to prioritize freedom and happiness over a chance to manage thousands of people is a question I can relate to and like to explore.
It’s summer in our nation’s capital. The humidity and the political climate are unbearably oppressive, and the denizens of D.C. do not give a fuck — at least, not literally. How do we know? I, along with fellow data scientist Rebecca Meseroll, collected over 10.7 million tweets from the contiguous 48 states and found out that ‘fuck’ appears in approximately 21 out of every 1000 tweets. In other words, slightly over 2% of all American tweets contain at least one variant of the word ‘fuck.’ Our analysis reveals a dearth of fucks in the District relative to the rest of the nation; the local fuck frequency in D.C. is a scant 11.7 per 1000 tweets. Language in other locales is not so chaste, however. Wyomingites, Californians, and Nevadans liberally peppered their tweets with profanity, exceeding 25 fuck-containing tweets per 1000 — more than twice their D.C. counterparts.
EDA for Data Analysis or Data Visualization is very important. It gives a brief summary and main characteristics of data. According to a survey, Data Scientist uses their most of time to perform EDA tasks.
Have you ever wanted to create an interactive data visualization map? In my most recent side project, I created a pretty cool visualization for how a virus might spread across the United States. If you want to check out the finished site, you can click here:
Data visualization is the practice of converting data from raw figures into a graphical representation such as graphs, maps, charts, and complex dashboards. Let’s see what makes it important (meaning), how it has developed (history), and exactly how it can work in real life (examples). Join us on Data Visualization 101, an introduction to dataviz and its power.
(This is part 1. Read part 2 on creating silent augmented reality here.)
In 2012, I saw the most amazing visualization in the New York Times. It was created by one of my favorite engineers, Mike Bostock, and his team of data visualization specialists to give readers a deeper look into the most polarizing issues of Obama’s reelection campaign.
Programmers rarely agree on whether or not coding is a creative profession. My interest in coding always stemmed from what I could create with the code. Seeing an interesting visual result from my efforts is usually the most satisfying part. Most programmers are less concerned with how their app looks and more concerned with the functionality. Usually, as long as the app works the way it is supposed to, most programmers are satisfied.
Lately I have been using D3 for visualizing data for a React project and it got my attention for a while. I was especially interested as to the scope of this very powerful tool that has a great problem solving ability range related to any kind of data visualization.
Agile Analytics is structured on a set of guiding principles and core values. It is not a robust or prescriptive methodology; while it is a way of constructing data marts, data warehouse, analytics applications, and BI applications that aim at primary and consistent productivity of business value all through the development life-cycle. Practically, agile analytics has a set of highly disciplined techniques and practices, a few of which are tailored to enhance the unique (DW/BI) project needs found in your organization.
From the most popular seats to the most popular viewing times, we wanted to find out more about the movie trends in Singapore . So we created PopcornData — a website to get a glimpse of Singapore’s Movie trends — by scraping data, finding interesting insights, and visualizing them.
A few weeks ago I was asked to give a talk at one of the most distinguished elite technological units in the Israeli Defense Forces. The goal was to expose them to product development and entrepreneurship in the startup world. I thought it through and decided to try and visualize my experience through dimensional thinking and data points which to depict the entrepreneurial experience. I used scientific language and data visualization to tell my story as an entrepreneur.
For you to be reading this, it surely would be no news that data visualization has become a very critical part of the IT world today. The huge amount of data being generated by different web technologies need to be properly refined and visualized for the world to use and gain insights from.
In the era of information explosion, more and more data piles up. However, these dense data are unfocused and less readable. So we need data visualization to help data to be easily understood and accepted. By contrast, visualization is more intuitive and meaningful, and it is very important to use appropriate charts to visualize data.
The 2019–20 coronavirus pandemic is an ongoing pandemic of coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The outbreak was first identified in Wuhan, Hubei, China, in December 2019, and was recognized as a pandemic by the World Health Organization (WHO) on 11 March 2020.
For a business getting a customer is exciting, not only because it helps you ‘secure the bag’ by bringing in much needed revenue, but it also creates an opportunity to create loyalty with this new found customer which in turn could help you ‘secure more bags’ through repeat purchases.
What if you could instantly visualize the political affiliation of an entire city, down to every single apartment and human registered to vote? Somewhat surprisingly, the City of New York made this a reality in early 2019, when the NYC Board of Elections decided to release 4.6 million voter records online, as reported by the New York Times. These records included full name, home address, political affiliation, and whether you have registered in the past 2 years. The reason according to this article was:
Photo credit, HackerNoon AI