Top data visualization techniques that help in effective decision-making

Making wise decisions is one of the key building blocks to success in any sector and industry. It’s essential to have visible information at your fingertips to express ideas effectively, regardless of your position as a high or low-level decision-maker within your organization. When businesses expand, the amount of data they produce also increases. Data visualization puts information into a visual context, such as a map or graph and makes it easier for the human brain to grasp and draw conclusions. Its major objective is to simplify seeing patterns, trends, and outliers in big data sets. Terms like information graphics, visualization, and statistical graphics are frequently used interchangeably. 

Analysts can extract the most knowledge from a research or presentation when data is presented visually appealing, making examining a set of information simple. Data visualization may make quicker decisions, and users can better understand patterns and trends. The importance of data visualization in decision-making is now clearly visible. It is accessible to various roles within your organization, even those who may not be subject-matter experts. Even though they might not be specialists at reading data themselves, your sales personnel can comprehend customer behavior and attitudes if you use the proper data visualization technologies.  

Common data visualization techniques

• Line graphs:  These visuals, widely used in predictive analytics, demonstrate change in one or more values by showing a series of data points across time. Line graphs use lines to show these changes. 
• Bar graphs: Another popular and simple data visualization technique is the standard bar chart, sometimes known as a bar graph. The categories being compared are shown on one axis of the chart, while the measured value is shown on the other. The length of the bar represents how each group performs about the value.
• Pie charts: Pie charts may also show how a categorical variable is split into several categories. Yet, we must exercise extreme caution while employing this type of representation. This is because they are typically difficult to interpret precisely. 
• Scatter plots: A scatter plot is another way of displaying data utilized. A scatter plot represents data for two variables as points placed on the horizontal and vertical axes. This sort of data visualization may be used to discover patterns or correlations in data by demonstrating the links between variables. 
• Heatmaps: A heat map is a visualization that uses color to indicate contrasts in data. Color is used in these charts to express values, allowing the observer to detect patterns immediately. Heat maps may be used in a variety of ways. 
• Treemaps: This approach displays stacked hierarchical data. Each category’s rectangle size is proportionate to its percentage of the total. Treemaps work well when there are several categories, and the purpose is to compare different aspects of a whole.
• Word clouds: A word cloud, also known as a tag cloud, is a visual representation of text data in which the size of each word corresponds to its frequency. The larger a certain term appears in the graphic, the more frequently it appears in the dataset. Words may seem stronger or follow a certain color scheme based on frequency and size.

How an MBA in Analytics and Data Science helps you learn data visualization techniques

Data science focuses on understanding the underlying data to get insightful business knowledge. Recent years have seen a rise in the relevance of big data, and organizations are looking to use data to redesign their operations and make better decisions. The MBA in Data Science and Analytics covers machine learning for business, data analysis and AI, data visualization, and many more topics. The training also aids in your development of managerial and decision-making skills. Data science is becoming more and more in demand. The primary cause of this is the development of digitalization throughout time. 

Data analysts are in high demand nowadays, with practically every organization seeking them. Also, you may have the opportunity to work with some of the biggest employment agencies, like TCS, Wipro, KPMG, Deloitte, and many others. Companies mostly seek individuals with data analysis abilities to help them foresee and comprehend potential business difficulties.

You should also check out MBA in Banking & Financial Services vs. MBA in Finance.

Learn important data visualization techniques with MUJ’s online MBA in Analytics and Data Science

Manipal University Jaipur (MUJ) is a reputed institution with a NAAC A rating and UGC recognition. It provides MBA programs with various electives, including one in analytics and data science. The online MBA program by Manipal University Jaipur offers the best academic content taught by knowledgeable professors. You get free access to Coursera’s courses after you enroll in the program, in addition to the massive amount of online learning resources. You may go over any concepts you missed previously and make many adjustments with the aid of the recorded lessons. Most importantly, you will learn about the common data visualization mistakes to avoid.

The MBA in Analytics and Data Science program seeks to build strong conceptual and application design foundations that will enable students to reevaluate existing career pathways and create new opportunities for professional advancement. 

Also, read, can I grow as a domain leader after completing an MBA in Business Analytics?

Conclusion

The demand for analytics and data science is expanding rapidly right now. There has never been a better opportunity to harness the quantity of data we have access to today to get insights. It will result in better employment opportunities and options for professional progression. An MBA in Analytics and Data Science aims to equip students with the necessary knowledge and skills to excel in the domain and establish a lucrative career.