A Compilation of my articles on various Data Visualisation toolsParul PandeyBlockedUnblockFollowFollowingFeb 12Photo by Patrick Schneider on Unsplash“By visualizing information, we turn it into a landscape that you can explore with your eyes.
A sort of information map.
And when you’re lost in information, an information map is kind of useful.
” — David McCandlessI have been writing in the Data Science domain for quite some time now.
In fact, it has been almost six months and during this time I have written on a multitude of topics.
Wow, this is in itself a whole new topic.
Anyways, while going through my previous articles, I couldn’t ignore the fact that I have a number of articles on Data Visualisation in my kitty.
So, I thought of compiling them into an article of their own which will make it easier for me to locate them and also for others who’ll be interested in them.
The various articles can be grouped on the basis of the tools used for the visualisation purpose.
Data Visualisation with Python1.
PyViz: Simplifying the Data Visualisation process in Python.
If you work with data, then Data Visualisation is a vital part of your daily routine.
And if you use Python for your analysis, you ought to be overwhelmed by the sheer amount of choices present in form of Data Visualisation libraries.
This article is an overview of the PyViz ecosystem to make data visualizations in Python easier to use, learn and more powerful.
2.
Visualising Machine Learning Datasets with Google’s FACETS.
A Machine Learning dataset sometimes consists of data points ranging from thousands to millions which in turn may contain hundreds or thousands of features.
Additionally, real-world data is messy comprising of missing values, unbalanced data, outliers etc.
Visualising the data can help in locating these irregularities and pointing out the locations where the data actually needs cleaning.
FACETS is an open source tool from Google to easily learn patterns from large amounts of data.
This tool helps us to understand the various features of data and explore them without having to explicitly code.
3.
Exploratory Data Visualisation with AltairThere are a few well-developed visualization packages in Python, but they often have very imperative APIs.
This means the user is required to focus more on the mechanics of the visualization — axis limits, legends, etc.
— rather than the important relationships within the data.
Altair is a package designed for exploratory visualization in Python that features a declarative API, allowing data scientists to focus more on the data than the incidental details.
Altair is based on the Vega and Vega-Lite visualization grammars, and thus automatically incorporates best practices drawn from recent research in effective data visualization.
4.
Visualising Geospatial data with Python using FoliumThe beauty of using Python is that it offers libraries for every data visualisation need.
One such library is Folium which comes in handy for visualising Geographic data (Geo data).
Geographic data (Geo data) science is a subset of data science that deals with location-based data i.
e description of objects and their relationship in space.
This article is an overview of the Folium library to visualize Geospatial data.
DataVisualisation with RA Comprehensive Guide to Data Visualisation in R for BeginnersR is a language and environment for statistical computing and graphics.
R is also extremely flexible and easy to use when it comes to creating visualisations.
One of its capabilities is to produce good quality plots with minimum codes.
This article is primarily meant for beginners and deals with the visualisation capabilities of R.
The article begins with basic plots and moves on to more advanced ones later in the article.
Data Visualisation with Tableau1.
Data Visualisation with TableauTableau is a data analytics and visualization tool used widely in the industry today.
In this tutorial, I have explained how to analyze and display data using Tableau and make better, more data-driven decisions.
This is a pretty comprehensive tableau tutorial dealing with all its aspects.
Apart from this, I have also touched upon the concept of Tableau’s integration with R, Python and SQL.
2.
Recreating Gapminder in Tableau: A Humble tribute to Hans RoslingIn this article, I have tried to recreate Hans Rosling’s famous visualisation to analyse how Life Expectancy in years (health) and GDP per capita (wealth) have changed over time in the world for various countries.
All the data and files related to the above articles have been consolidated into a single Github Repository titled Data-Visualisation-libraries.
Data Visualisation is not merely a tool, it’s an art of storytelling.
A story told with data can change the way we see the world, creating a conviction that may even call us to action.
Hopefully, you will find the above articles and libraries of use to make more data-driven decisions.
Through this article, I would also like to thank each and everyone who read, liked, clapped, commented on my articles.
This is the sole motivation which encourages me to write articles that aim to break down the jargons of data science for everybody.
Keep reading and I’ll keep writing.
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