Data analytics is a trending domain, and people are attracted to it. The reason is not one but many. Data analytics offers high-paying jobs and gives you many options to choose from when choosing an industry. Since data analytics is needed for every industry, you can be anywhere or transition to any industry.
However, you must know that there are four types of data analytics before you make a career in Data Analytics. So, let’s explore these four types of data analytics in this blog.
Descriptive Analytics
The first type of data analytics is descriptive Analytics, which deals with questions like What happened? In Descriptive Analytics historical data is analyzed to know the trend in the past. The process of Descriptive analytics included structuring the data and identifying patterns and trends.
In this process, the relationship between various events around the targeted event is also analyzed. In business, there is no need to explain why it is essential. Many events keep occurring in businesses, and identifying them can help create a strategy in advance if the signs of the side event are seen.
For example, a company did Descriptive analytics where they found that their sales reached all-time highs in June and July for the last three years. So, based on this insight, the company can make informed decisions on their production, delivery, and marketing in June and July. They will increase production and also make the supply smooth. Moreover, they may also cut budgets on marketing in this period as the sales are already up.
Diagnostic Analytics
Now, from what happens, let’s move to why it happened. Answering why it happened is also important for many reasons, such as dodging a negative event that occurs again. Similar to the day, the oast data is also analyzed in Diagnostic Analytics. There are two further types of Diagnostic Analytics: “query and drill down” and “discovery and alert”.
Query and drill down are basically when a query or an event is available to study, and the past is drilled down to know the cause. Similarly, the discovery and alert are done the same way, but it is most used in businesses to know the events, their cause and their potential of happening again. So, the signs are also evaluated in discovery and alert, which can let the concerned people know that the event can occur again.
A simple example of Diagnostic Analytics is: let’s say a company, ABC Private Limited, gets to know that their sales went down for the last month, but the cause is unknown as there were no changes in demand, supply, production, etc.
However, when the company used Diagnostic Analytics, the insights showed that there were more public holidays in the previous month, which does not happen every month. This example of Diagnostic Analytics is overly simplified, but it gives you an idea of its use in a business.
Predictive Analytics
Predictive analytics again uses historical data but can also use present data to make predictions. It is one of the most used data analytics today as it has various uses in businesses and other organizations. It can predict consumer behavior, weather forecasting, financial ups and downs, etc. The technology used in predictive analytics is AI, ML, regression, predictive modelling, time series models and many more.
Prescriptive Analytics
Now, let’s come to the present from the past. There are various decisions to make in a company. Moreover, making informed decisions is indeed essential. So, here, the prescriptive data analytics model is used. The best decision to make is found in Prescriptive Analytics, using Statistical Algorithms, AI, ML and human input. The data which is used in Prescriptive Analytics is mostly the data that is generated in real-time.
For example, if there is a decision to make whether or not to increase the production of a product launched a few days back. Then, the decision will be made based on the product’s performance data, such as sales. Prescriptive Analytics is number three as it answers what will happen, which is exactly what a person will think knowing what did happen.
How to learn data analytics?
Learning data analytics isn’t a big deal today. You can learn analytics by enrolling in a data analytics course online. Many companies offer a data analytics course online, but you must learn it from the best source.
One of the best data analytics courses is the “Best Data Analytics Course Online” by Coding Ninjas. This online data analytics course has more than 50 hours of content, which will give you an understanding of important tools, languages and concepts required for data analytics, such as SQL, excel, python, tableau, ML, etc.
In addition, you will also get 8+ guided projects, which will give you real-world experience in data analytics. What else you get is one on one doubt-solving support, cheat days, self-paced learning and many more.
What is a threaded binary tree in data structure?
Data structures are used to create programs that are totally efficient and are the best ones to solve a specific problem while being in a multi program environment. One such thing is a threaded binary tree in the data structure, which makes the program more efficient. A threaded binary tree in the data structure contains threads in the place of empty child pointers.
The threaded binary tree in the data structure is useful as it can be transversed without needing a stack or recursion. So, it makes the threaded binary tree in the data structure better than a binary tree to use in various programs.
Final Words
In conclusion, you must identify which type of data analyst you want to become as the jobs you will apply for will have different needs. The skills or expertise also vary in these four types of data analytics. So, if you want to become a data analyst, enroll in the best data analytics course online by Coding Ninjas.
