Data science is an umbrella term for a broad set of techniques to analyse and process large amounts of data. Data science has become increasingly important, given the ever-growing volume and variety of data available today. Data scientists do not necessarily need a technical background or extensive mathematical knowledge to start. However, they need good analytical skills and a strong understanding of business analytics principles and practices.
Data analytics can help companies make better decisions. But it’s not just about data, and it’s not just about making decisions. Data analytics has become a buzzword, with dozens of start-ups promising to turn your company’s data into gold. But most people don’t know what data analytics is, how it works, or how it can be used. Data analytics is a broad term that encompasses several types of computerised data processing. The data analyst is responsible for extracting information from the data and making it useful to the business decision-maker.
Here’s a breakdown of the four key types of data analytics you can use individually or in tandem.
Different Types of Data Analytics
1. Descriptive Analytics: Descriptive analytics focuses on collecting data from one specific source and analysing it at a granular level, such as by-product or customer segment. For example, suppose you want to understand the performance of your mobile business in more detail. Descriptive analytics is a branch of data science that focuses on analysing large amounts of data to extract meaningful patterns. It is used to analyse the behaviour and characteristics of people, organisations, and other entities. Descriptive analytics is used to describe the current state of things. The goal is to make predictions about what will happen in the future based on the current data.
2. Diagnostic Analytics: Diagnostic analytics is a form of data analytics that focuses on understanding the system’s current state and identifying all the factors causing it to perform poorly. Diagnostic analytics aims to identify all the issues causing a problem, such as where they might be occurring, what information is missing from the system, and how to resolve them. It can be used for troubleshooting, incident management, and configuration management. The main function of diagnostic analytics is to provide insight into problems within a system. This can help you determine why an error occurred and how it can be prevented from happening again.
3. Predictive Analytics: Predictive analytics is a data science branch that predicts future events. It refers to using statistical algorithms, mathematical models, and computer programs to make predictions about future outcomes. Predictive analytics is used in various applications, from fraud detection, credit scoring, and customer relationship management (CRM) to marketing, fraud prevention, and risk assessment. Predictive analytics is a type of analytics that aims to anticipate future events or trends. This can be done by past modelling data, which is called historical data analytics, or by predicting future outcomes based on current trends and knowledge.
4. Prescriptive Analytics: Data science is analysing data to make better predictions. Predictive analytics is a field within data science that seeks to make predictions about consumer behaviour, business performance, and more. Predictive analytics is an important skill set used to understand a phenomenon from past data and predict future outcomes. Predictive analytics is a decision support system (DSS) or statistical modelling.
Popular Data Science Courses
Data science is a new field of study that can be learned to various degrees. Most of the courses are available online, and you can study them from anywhere in the world. You may also consider certification courses that Emeritus India offers in collaboration with reputed institutes as a stepping stone to launch your career.
Why Choose Data Science & Analytics Courses from Emeritus India?
Data Science & Analytics courses from Emeritus India are designed to help you learn the skills employers are looking for. These courses are a great way to gain a competitive edge in the job market.
Our data science & analytics courses are designed to help students develop their analytical skills, whether they want to learn how to conduct data analysis or use statistical techniques to solve problems.
The courses cover data exploration, data management, statistical modelling, and business intelligence. The course material is based on real-world examples from companies like Google and LinkedIn, so you can apply what you learn immediately upon completion of the course.
The Data Science & Analytics courses that Emeritus India offers are designed to help students to develop a strong foundation in data science and analytics. We have been offering our courses to students worldwide, including in India, USA, UK, and Canada Experienced professors from the Data Science Association (DSA) teach these courses. The faculty has worked with companies such as Google, Microsoft, and Amazon.