Visualizations Speak
Data Visualization — Graphical Representation of Data
This is the standard definition of Data Visualization which you will find everywhere but have you ever wondered that is it just the graphical representation of data or is there more to it.
What is Data Visualization?
I think of Data Visualization as ‘Story Telling’ . Data visualization exists, in large part, to help create a compelling narrative. It generates a communication between the data and the target. In recent times Data Visualization has become quite notable and has gained a lot of significance in almost all the fields.
Why is Data Visualization important?
On a daily basis more than 2.5 quintillion bytes of data is generated!
From the first step of data collection till the final step of making decisions there are a series of steps involved out of which Data Visualization plays a key role. Data Visualization is not only a graphical representation of your data but much more than that.
Data Visualization helps you in analyzing the data better as well as drawing better predictions from it. It also helps in making good data driven decisions and also makes decision-making swift.
It helps you to understand complex structured or unstructured data and makes it more accessible and usable.
It leads to an easy distribution of information that increases the opportunity to share insights with everyone involved.
It makes any kind of data more interactive and intuitive.
In a nutshell Data Visualization is the key/solution to all data driven problems and decisions.
Where is Data Visualization Used?
Sales and Marketing :
Online selling, buying and marketing has gained a lot of significance. As a result, companies must pay attention to their sources of web traffic and how their web properties generate revenue. Data visualization makes it easy to see traffic trends over time as a result of marketing efforts. It also helps in analyzing the buying trends of the buyer and helps to find solutions to these questions :
“What does he/she shop quite often?” or “How often does he/she shop online?”
Healthcare :
Data Visualization is widely used in the field of healthcare.
Healthcare professionals frequently use choropleth maps to visualize important health data .For example , to analyze the mortality rate or birth rate of a geographical region or the best example being the current pandemic situation where on a daily basis there are thousands of visualizations on the number of cases , number of vaccinations etc.
Data Visualization in the field of healthcare has helped healthcare professionals to analyze data collected on a daily basis and make predications about the future.
Scientists or Researchers :
Scientific visualization, allows scientists and researchers to gain greater insights from their experimental data. Research in any field be it IoT, AI, etc.
,data visualization helps you understand the effectiveness and constructiveness of your solution.
For example , in the field of IoT when you connect multiple sensors and devices the data that has been collected, if represented graphically it gives you a better understanding than just reading random numbers.
If you think of AI algorithms, when you plot your data and results,it helps in understanding the behaviour of your model.
Finance :
This is another field where Data Visualization plays a very important role. Finance professionals must track the performance of their investment decisions when choosing to buy or sell an asset. Candlestick charts are used as trading tools and help finance professionals analyze price movements over time, displaying important information, such as securities, derivatives, currencies, stocks, bonds and commodities. By analyzing how the price has changed over time, data analysts and finance professionals can detect trends.
Data Visualization is used in many more fields such as aviation , logistics ,etc.
I personally feel these are the fields where Data Visualization plays a vital role enhancing the functionality of organizations or individuals working in these fields.
What Makes A Good Visualization?
The above diagram depicts the different steps to obtain a successful visualization , which I personally believe is the only path for a successful visualization.
The first step is to collect the “correct and required” data/information.
You must explore and then collect the data that is apt for your goal.
This is the most important step because all the other steps depend on the whether or not you collect the right data.
The second step is about data story that puts the information/data into context and communicates why it matters and what actions must be taken. In other words, data stories connect the audience with the data.
The third step is the goal or function of your data visualization and how you work on it. You must be clear about this step even before collecting your data.
This step involves deciding and choosing the right tools, charts , techniques that you will use for data analysis and decision making.
The fourth or final step is presentation of your visualization.
Best Tools for Data Visualizations?
Choosing the correct Data Visualization tool plays a vital role in reaching your goal , analyzing the data better , faster decision making and presentation of your graphs. Data visualization tools provide data visualization designers with an easier way to create visual representations of large data sets.
These data visualizations can then be used for a variety of purposes, dashboards, annual reports, sales and marketing materials, investor slide decks, and virtually anywhere else information needs to be interpreted.
There are many tools used in Data Visualization like
- Visme
- Tableau
- Infogram
- Python
- R
- Data Wrapper
- Google Data Studio
- Power BI
- Sisense
- DataBox
and many more…..
I would like to share my personal favourites!
1. TABLEAU
Tableau is where I started exploring data visualization for the first time.
Tableau is one of the world’s leading analytics platforms. Tableau Public is a popular visualization application that allows you to create a wide range of charts, graphs, maps, and other graphics.
The visualizations you create can be conveniently inserted into any web page and can be shared with your friends, organizations, peers in the industry, and so on. It is easy to understand and easy to present.
Tableau supports data of various types(integers, geographical, strings, date, time, etc.) and has a fantastic dashboard with great features such as filters, marks card , measures, dimensions, etc.
It helps you to create maps ,bar charts , pie charts, line charts, heat maps, waffle charts, etc. It also helps you with statistical analysis like clustering, regression, correlation, etc.
Tableau’s public gallery contains a wide ranges of visualizations created by the community.
Tableau Public is designed for scientists, academics, or anyone who wants to create and explore the journey of data visualization.
It offers unparalleled data visualization with fully functional and interactive graphics. It provides a large variety of visualizations as well as statistical analysis tools.
2. PYTHON
Python is a highly popular general-purpose programming language and it is extremely useful for Data Scientists to create beautiful visualizations. Python provides the Data Scientists with various packages.
I feel Python is a useful tool when data visualization has to integrated with other applications as well.
For Example, in an IoT system that contains sensors, microcontrollers and many other devices ,the data that has been collected from the sensors can be plotted real time in Python. So, Python helps in fetching the sensor data from the microcontroller then plotting the values and also in analyzing it.
Python helps in data mining , data preprocessing , data warehousing , data visualization , data analytics and much more.
Python is the best language in the field of data science because unlike other tools Python can be used for other applications as well along side Data Visualizations. When we talk about Data Visualization Python provides multiple built in libraries with interactive charts and graphs.
- Matplotlib
- Plotly
- Seaborn
- GGplot
- Altair
- Bokeh
- Pygal
- Geoplotlib
3. R
R is another programming language used for Data Visualization. The popularity of R has reduced in these times…but I believe R is still a good visualization tool.
Other than data visualization R programming is also useful for statistical analysis like hypothesis ,regression , clustering ,correlation etc.
R has multiple data visualization libraries like ggplot , dplyr , quantmod , dygraph , plotly ,etc. R generates interactive and alluring plots useful for data analysis. R generates basic charts like bar charts , box plots, etc. and advanced charts like heat maps, correlograms ,etc.
I believe after reading this you would have got an insight in field of Data Science. I hope that you start your data science journey soon!!
