Data visualization through graphs

Data is all around us. We generate and consume large amounts of data. Most of the time, data is in a form that is difficult for people to understand and appreciate.   Big Data and Data Analytics are current catch words and there is a lot of work that is happening in these areas. But these methods which try and analyze millions of data points, spread across thousands of variables, rely on sophisticated tools and require a sound knowledge of programming. Most of the data that we come in contact with and required to handle, are not 'Big'. But even then, the knowledge of data visualization is important.   Why visualisation? Humans, due to evolutionary traits, are good at recognizing small changes and patterns when presented as a picture. This can be thought of as a fall back, on the need for survival as one of the weakest mammals in the evolutionary chain. We can even spot small changes around us that are out of the ordinary or out of synch with what we expect. Essentially, human beings are visual learners. And large amounts of data that have an underlying pattern can be quickly grasped if same is presented visually rather than as a collection of facts and numbers. Data Visualization is the area that seeks to transform data that exists in a form that is confusing to humans to something that we can understand quickly.   One of the easiest ways of presenting data visually is through graphs. From upper primary itself we start drawing graphs and this continues throughout the education. However there is more to graphs than charting in a graph paper. Not all data are the same and not all graphs are the same.   This course (first part of a series) will address the following:   Types of data Various types of graphs (and charts) How to match the data type to the chart, for maximum impact How to create professional looking charts using Excel An introduction to other data tools Later parts of the course will cover advanced graphs and data interpretation tools, leading to understanding of visualizing and Interpreting complex data.