Euniwell_students December 2025

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What are the different types of Data Analytics?
by gsingh＠sevenmentor.com 24 Dec '25

24 Dec '25

Organisations these days are increasingly data-dependent because data helps in making informed decisions, advancing performance, and getting an edge over their competition. Data analytics is critical to the process of turning raw information into actionable insight and enabling better decision-making and business performance. It is utilized in every sector, from agriculture to manufacturing and logistics. That’s why people proficient in analytics are increasingly sought after by all types of organisations, causing many students to invest in a data analyst course to strengthen their analytical roots. In this space, you need to understand what different forms of data analytics are and how each contributes to decision-making. https://www.sevenmentor.com/data-analytics-course Understanding Data Analytics Data analytics is the process of systematically applying techniques to data to extract patterns, correlations, and actionable information. It requires statistics, some programming, some data visualization, and specific domain knowledge. Businesses use analytics to find out what has happened, why it happened, what could happen, and what actions to take. With these purposes in mind, data analytics is generally classified into four major types: SummarReport, Apply insights, Significance analysis, Reasons or drivers. Based on the above objectives, we can broadly classify each type of advanced analytics as follows: Descriptive Analytics, Diagnostic Analytics, Predictive Analytics, and Prescriptive Analytics. Each kind of history also has its own function, based upon the lessons of the one before. Descriptive Analytics: Understanding What Happened Type of Analytics 1 . Descriptive analytics: Descriptive analytics is the simplest and earliest stage of data analysis wherein summarization of historical data (from the past) is used to gain at Past understanding. It addresses the question: “What has taken place?” by monitoring the data on a predetermined basis. This kind of analytics practice is based on methods including data aggregation, reporting, dashboards, and simple statistical measures such as averages, percentages, and totals. For instance, an organisation might scrutinise monthly sales reports to see how well the company is doing in terms of revenue or its website traffic as a measure of user engagement. Descriptive analytics - Provides visibility and clarity on what is happening in their business. It is a factual picture of what has already happened –something that helps us see the patterns and the anomalous. It does not provide a reason for why something occurs or predict future events, but it can be used in further analysis. Basic descriptive analytics can be learned with data analyst courses, as they teach you basic tools such as Excel, SQL, and simple data visualization platforms. Diagnostic Analytics: This Is Why It Happened As descriptive analytics tells us what happened, diagnostics takes the next step in asking why. This approach to analytics aims at determining the underlying factors behind particular results or patterns. It includes such techniques as data drilling, correlation analysis, data discovery, and comparison studies. For example, if your company experiences an immediate sales decline, the diagnostic analysis can explain whether the decrease is due to changes in prices, competitive activity, industry and market conditions, or localisations, or even operational problems. Understanding these relationships and exposing the underlying insights hidden in the data are vital for diagnostic analytics. It allows companies to learn from historical data and not make the same mistakes twice. Data analyst training also provides professionals a chance to be more involved with diagnostic methods, collaborating on real-world data sets and business scenarios as they seek to uncover cause-and-effect relationships. Predictive Analytics: Seeing the Future in Big Data. Predictive analytics is all about predicting the future based on the past and present. It responds to he query, “What is more likely to occur subsequently?” through the application of statistical methods, machine learning algorithms, and data mining methods. Predictive analytics is commonly used in applications such as customer behavior, fraud detection, demand forecasting, and risk assessment across various domains/industries, e.g., the finance sector, the health industry, retail business market. For instance, e-commerce firms use predictive analytics to suggest products, and banks use it to assess credit risk. Organizations can prepare ahead of time by recognizing trends and probabilities, not only to anticipate events but also to act on them accordingly. Predictive analytics is central to many data analytics offerings as it leads learners directly into more advanced tools like Python, R, machine learning models, and time-series analysis. Prescriptive Analytics: Determining What Needs to Happen Prescriptive analytics is the final step of data analysis, and it concerns itself with advising users on the best course of action to achieve a goal. It responds to the question, “What do we do?” across descriptive, diagnostic, and predictive analytics. Optimization algorithms, simulation models, and decision analysis methods are leveraged in Prescriptive analytics to propose an optimal way forward. In supply chain management, prescriptive analytics can recommend the most appropriate inventory or distribution flow to minimize traffic costs and improve efficiency. For marketing, that can mean gleaning who will respond the best to a campaign based on anticipated customer reactions. With prescriptive analytics, the business can turn insights into action, which is a killer way to make strategy. Being a complicated subject, it is avoided in the basic modules of the course and taught rather as an advanced topic to foster strong analytical and problem-solving ability amongst learners. Data Analyst Course Details Relevance of Various Categories of Data Analysis All the data analytics types matter for decision-making. Descriptive analytics sheds light on how you performed in the past, diagnostic analytics gets to the root causes of trends, predictive gives a view into what will happen next (along with the ability to influence outcomes), and prescriptive takes it one step further by defining the best set of actions. Collectively, these methods give companies the insights they need to take action that is well-informed, planned, and strategic. Companies that make use of all four forms of analytics are more likely to respond to market volatility and succeed in the long term. Computer Programs and Techniques of Data Analysis Much like data scientists, data analysts are using many different tools and technologies: spreadsheets, databases, programming languages, and visualization platforms. Similar tools -Excel, SQL, Python, R; add Power BI and Tableau. Data analysts need to have good problem-solving skills, critical thinking, and excellent communication so they can effectively communicate what the data is telling stakeholders. A good data analyst course would provide a detailed insight into these tools and skills during the training, so that once you have completed it, you are already industry-ready. Career Opportunities in Data Analytics The rise of data-based decision-making has spawned scores of career opportunities in the field of analytics. Data analyst certification course, Business Analyst, Data Scientist, and Analytics Consultant are the most trending job roles in multiple industries. Experienced professionals who possess the right skill set and certifications often earn lucrative salaries and see continued career success. A lot of learners pursue a data analyst certification to confirm their skills and increase their credibility with employers. Learning Path and Training Options Structured programs provide a clear approach for those trying to break into the field of analytics. There are a number of data analytics courses, from beginner to professional certs. These tend to include data basics, statistical analysis, programming, visualization, and hands-on projects with real-world applications. Taking up a course on data analytics can expose learners to practical experience and hands-on work that is required for them to be confident and competent. Program fit Prospective students tend to think about a program in terms of its curriculum quality, its relevance, and its affordability. Being aware of the cost of data analyst course fees allows prospective candidates to choose a program that pairs with their own financial and career requirements. Plus, numerous schools have flexible learning formats — such as classes for budding data analysts that can be taken online and in the classroom — to fit into a working professional’s or busy student’s schedule. Conclusion Data analysis is an essential part of the business and technology world. It turns raw data into actionable insights that enable organizations to make smarter decisions and propel significant growth. The four types of analysis for data – descriptive, diagnostic, predictive, and prescriptive – each have their place in guiding decisions that are based on data. As the need for knowledgeable people in this area continues to grow, having exposure and experience is critical. Armed with the right understanding, resources, and education, future analysts will have great career opportunities and be instrumental in leading the charge for data-based decision-making.

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