Did you actually know that the first idea to present data in a visual form dates back to the 17th century? (I am always amazed when I see how some modern hypes are rooted in long-existing ideas)
While the world is drowning in data, with 2.5 quintillion bytes produced daily, while organisations are competing to employ the best talents to draw data insights and the data engineers are working hard to outsmart these requirements, there was a point in time when the only way to structure and understand data was reading tables on pieces of paper.
It only became possible to present statistical data in a visual form after the French mathematician Rene Dekart invented a two-dimensional coordinate system for displaying values in the 17th century. Later on, a Scottish man William Playfair pioneered with the line charts, bar charts and pie charts to present financial and statistical economic data.
_“I confess I was very anxious to find out if I was actually the first who applied the principles of geometry to matters of finance, as it had long before been applied to chronology with great success.” — _William Playfair, Lineal Arithemtic, 1798
In the picture below you can find a simple line chart, which he used to argue against England’s policy of financing colonial wars.
William Playfair, public domain
Now, the exciting part about this graph, is indeed the speed and precision with which you can identify the trend of growing debt.
#data-analysis #design #tableau #data-visualization #visual studio code
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
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
With possibly everything that one can think of which revolves around data, the need for people who can transform data into a manner that helps in making the best of the available data is at its peak. This brings our attention to two major aspects of data – data science and data analysis. Many tend to get confused between the two and often misuse one in place of the other. In reality, they are different from each other in a couple of aspects. Read on to find how data analysis and data science are different from each other.
Before jumping straight into the differences between the two, it is critical to understand the commonalities between data analysis and data science. First things first – both these areas revolve primarily around data. Next, the prime objective of both of them remains the same – to meet the business objective and aid in the decision-making ability. Also, both these fields demand the person be well acquainted with the business problems, market size, opportunities, risks and a rough idea of what could be the possible solutions.
Now, addressing the main topic of interest – how are data analysis and data science different from each other.
As far as data science is concerned, it is nothing but drawing actionable insights from raw data. Data science has most of the work done in these three areas –
#big data #latest news #how are data analysis and data science different from each other #data science #data analysis #data analysis and data science different
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.
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).
This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.
#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management
EDA is a way to understand what the data is all about. It is very important as it helps us to understand the outliers, relationship of features within the data with the help of graphs and plots.
EDA is a time taking process as we need to make visualizations between different features using libraries like Matplot, seaborn, etc.
There is a way to automate this process by a single line of code using the library Pandas Visual Analysis.
Let’s understand the different sections in the user interface :
#data-analysis #machine-learning #data-visualization #data-science #data analysis #exploratory data analysis