Introduction to Data Visualization

In this lecture, we are going to understand the implications of good UX and bad UX in data visualization.

We are going to understand the key points which a Data Visualization designer should consider before creating a design.

In this lecture, we will go through the timeless UX principles which can be used in a Data Visualization design, website or mobile App.

We will understand what are the challenges which are involved in Data visualization and how the different forms of communication can help in solving the challenges.

In this lecture, we will look into two main forms of visualization that are explanatory and exploratory, why they are different and when to use each of them.

A great Data visualization is grounded by the timeless principles of human mind and how it can be triggred effectively to communicate.

Gestalt Psychology explains those principles in a very simple way.

In this lecture, we are going to show you, with examples, how these differentiation points are implemented in data visualization.

One of the key challenges in data visualization is to understand how to show it in a way that users can differentiate among different data points.

We are going to see what are these differentiation points which we can use in our data visualization.

In this lecture, we are going to show you, with examples, how these differentiation points are implemented in data visualization.

Finally, regarding the differentiation points, there are a few points which people often miss out. We will show what not to do in a data visualization.

The most important decision in data visualization design is to answer which chart types to select.

In this lecture, we will see bar charts, line charts, timelines and area charts, in detail.

We are going to continue our discussion of previous lecture, with scatter plot, which can show positive and negative correlations, and bubble chart.

Finally, we are going to understand the pie chart and see when to use it and when not to use it.

In this lecture, we will understand why a simple thing as scale can have such large implications and how to be not bias in selecting scales.

Legends are something often overlooked. We are going to see what are legends and how to use them.

For high-dimensional data, we need to understand the advance chart types like box plot, heatmaps, graphs, flow diagram, etc, and see examples of them of how they are used.

When it comes to start the work in data visualization, creative people fall in the trap of going for perfection and somehow starting the work becomes very difficult.

in this lecture, we are going to understand what techniques can be used for that.

Storytelling is the key to great visualization, where you immerse the users in a story where they can get the point more effectively and remember it longer.

In this lecture, we are going to show how to use this techniques in your data visualization.

Customers are key to the businesses.

In this lecture, we are going to show you the steps to deliver a successful data visualization project.

In this lecture, we are going to see what are the factors that we need to consider before selecting a technology for our data visualization.

At the end of this lecture, we are going to see the technological choices available to us and when to select each of them.