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# Extraordinary Data Visualisation — Circular Chart

It is very common to use a pie chart or a doughnut chart to represent proportions of different components of the whole. However, recently I met such data visualisation requirements:

• The chart needs to present several components
• Each component need to show how much the proportion with regards to the entire thing

Up to here, a pie chart or a doughnut chart still can be perfect options. However, pay attention to the rest of the requirements.

• The components might be overlapped
• Apart from showing the proportion, each component will start from a certain position from the baseline and end up with a certain terminal point.

There are some cases that really need such requirements. For example, representing segments of DNA in terms of their similarity to a baseline.

# Circular Chart

We can use a circular chart to visualise such relationships.

As shown above, the circular chart consists of

1. A circle representing the baseline
2. An indicate representing where is the start/end point. Because it is a circle, so the start and end points are actually the same.
3. Some degrees as ticks which will indicate the position, as well as showing it is clockwise or counterclockwise. To be intuitive, I would recommend using clockwise.
4. Finally, the components in different colours showing both the position (start and end) and the proportion (the radial length)

# Data to be Visualised

The data to be visualised is as follows.

``````gene_list = [
[0.1, 0.25],
[0.15, 0.3],
[0.6, 0.68]
]
``````

Here we got a 2-d array. Each sub-array represents a segment of the gene. The two numbers are the start point and the end point respectively.

For example, the first gene `[0.1, 0.25]` means this gene segment start from 10% of the baseline and end up at 25%. Therefore, the length is 0.15 of the original.

Now, let’s see how to use Plotly to draw this circular chart.

#data-science #plotly #data-analytics #python #data analysis

## Buddha Community

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If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition

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## Getting Started With Data Lakes

### Frameworks for Efficient Enterprise Analytics

The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.

This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.

### Introduction

As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).

#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management

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## Visualizing data with NGX-Charts in Angular

Data Science, Data Analytics, Big Data, these are the buzz words of today’s world. A huge amount of data is being generated and analyzed every day. So communicating the insights from that data becomes crucial. Charts help visualize the data and communicate the result of the analysis with charts, it becomes easy to understand the data.

There are a lot of libraries for angular that can be used to build charts. In this blog, we will look at one such library, NGX-Charts. We will see how to use it in angular and how to build data visualizations.

What we will cover:

1. Installing ngx-chart.

2. Building a vertical bar graph.

3. Building a pie chart.

4. Building an advanced pie chart.

## A brief introduction about NGX-Charts

NGX-Chart charting framework for angular2+. It’s open-source and maintained by Swimlane.

NGX-Charts does not merely wrap d3, nor any other chart engine for that matter. It is using Angular to render and animate the SVG elements with all of its binding and speed goodness and uses d3 for the excellent math functions, scales, axis and shape generators, etc. By having Angular do all of the renderings it opens us up to endless possibilities the Angular platform provides such as AoT, Universal, etc.

NGX-Charts supports various chart types like bar charts, line charts, area charts, pie charts, bubble charts, doughnut charts, gauge charts, heatmap, treemap, and number cards.

## Installation and Setup

1. Install the ngx-chart package in your angular app.

``````npm install @swimlane/ngx-charts --save
``````

2. At the time of installing or when you serve your application is you get an error:

``````ERROR in The target entry-point "@swimlane/ngx-charts" has missing dependencies: - @angular/cdk/portal
``````

You also need to install angular/cdk

``````npm install @angular/cdk --save
``````

3. Import NgxChartsModule from ‘ngx-charts’ in AppModule

4. NgxChartModule also requires BrowserAnimationModule. Import is inAppModule.

app.module.ts

``````import { BrowserModule } from '@angular/platform-browser';
import { NgModule } from '@angular/core';
import { AppComponent } from './app.component';
import { NgxChartsModule }from '@swimlane/ngx-charts';
import { BrowserAnimationsModule } from '@angular/platform-browser/animations';
@NgModule({
declarations: [
AppComponent
],
imports: [
BrowserModule,
BrowserAnimationsModule,
NgxChartsModule
],
providers: [],
bootstrap: [AppComponent]
})
export class AppModule { }
``````

Amazing! Now we can start using ngx-chart component and build the graph we want.

In the AppComponent we will provide data that the chart will represent. It’s a sample data for vehicles on the road survey.

#angular #angular 6 #scala #angular #angular 9 #bar chart #charting #charts #d3 charts #data visualisation #ngx #ngx charts #pie

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## How To Blend Data in Google Data Studio For Better Data Analysis

Using data to inform decisions is essential to product management, or anything really. And thankfully, we aren’t short of it. Any online application generates an abundance of data and it’s up to us to collect it and then make sense of it.

Google Data Studio helps us understand the meaning behind data, enabling us to build beautiful visualizations and dashboards that transform data into stories. If it wasn’t already, data literacy is as much a fundamental skill as learning to read or write. Or it certainly will be.

Nothing is more powerful than data democracy, where anyone in your organization can regularly make decisions informed with data. As part of enabling this, we need to be able to visualize data in a way that brings it to life and makes it more accessible. I’ve recently been learning how to do this and wanted to share some of the cool ways you can do this in Google Data Studio.

#google-data-studio #blending-data #dashboard #data-visualization #creating-visualizations #how-to-visualize-data #data-analysis #data-visualisation

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## How Has COVID-19 Impacted Data Science?

The COVID-19 pandemic disrupted supply chains and brought economies around the world to a standstill. In turn, businesses need access to accurate, timely data more than ever before. As a result, the demand for data analytics is skyrocketing as businesses try to navigate an uncertain future. However, the sudden surge in demand comes with its own set of challenges.

Here is how the COVID-19 pandemic is affecting the data industry and how enterprises can prepare for the data challenges to come in 2021 and beyond.

#big data #data #data analysis #data security #data integration #etl #data warehouse #data breach #elt