Data analysis: types and examples

Data analysis has been an inseparable part of decision-making over the years across many domains, whether business, sciences or government. Knowing the different types of data analysis and examples can give you an insight into how to make your business more productive.  

In this article, we will study in depth the types and examples of data analysis, underlining the prominent role that statistical forecasting plays in it and ESIC University's Master’s Degree in Big Data and Business Analytics. 

What is data analysis? 

First, let us take a look at what data analysis is. Data analysis is a systematic transformation of data to information used for finding patterns or even extracting hidden relationships. Data Scientists use a variety of tools and techniques to analyze raw data that help them discover hidden patterns. 

Types of Data Analysis 

Different Types of Data Analysis Explanation Here are the main types: 

Descriptive analysis 

Descriptive analysis aims at describing the data in a summarized form to understand exactly what has happened. Using measures such as mean, median, mode and standard deviation to depict data is what descriptive analysis does. 

Diagnostic analysis 

This takes the diagnostic analysis a step further and actually looks at what happened in the past with script through causes. The process of discovering interesting patterns, correlations and anomalies in the data to generate high resolutive insights for why something happened. 

Predictive analysis 

Based on the history of these data, we can predict future events accurately using this system called Predictive analysis: the results of this analysis are used to model trends and patterns with statistical models / machine learning algorithms. 

Prescriptive analysis 

Prescriptive analysis recommends decisions on the grounds of past and predicted data. It suggests some strategies for decision-makers in order to get better results and make decisions that are aimed at reaching their objectives. 

Exploratory data analysis (EDA) 

EDA is an approach that helps summarize the main characteristics in data using visual methods. It is good for finding patterns, anomalies and testing hypotheses. 

Statistical analysis 

Statistical analysis is very important in the field of data analytics. Data is the raw material that you collect, analyze and synthesize. Some important methods we use for Statistical Analysis are:

• Regression analysis: This approach provides a full understanding of the relationship between a dependent and one or more independent variables. It helps us determine how a change in one variable affects another. 

• ANOVA (Analysis of Variance): It is used to compare the means between three or more groups to detect  if all groups are significantly different from each other. 

• Chi-Square test: The chi-square test is used to determine the relationship between two categorical variables. It is useful in checking if there are any relationships between the variables or not. 

• Correlation analysis: This is used to measure the strength and direction of an association between two continuous variables. 

Examples of Data Analysis 

Data analysis is commonly used to resolve issues and make choices in different fields. There are small indefinite quantities in different industries. 

• Business: Companies use data analytics to interpret the behavior of their customers, and to enhance marketing strategies and operations. Retailers use sales data to know which goods are popular and adjust the inventory accordingly. 

• Healthcare: Data analysis in healthcare helps to predict epidemics, improves the quality of patient care and even saves lives. Major hospitals use data on patients to make decisions about what works in a treatment and where trends are headed. 

• Finance: Data analysis is used in financial institutions to spot fraudulent activities, credit risk assessment and investment management. Analysts research market trends and economic data to decide where best to invest. 

• Education: Academic institutions use data analysis to enhance K-12 student outcomes, curriculum development and resource allocation. Within the school context, student data is analyzed to pinpoint areas of need where targeted interventions can be placed. 

• Sports: Sports teams use data analysis to track player performance, develop strategies and improve team play. Broadly speaking, coaches use match statistics to inform data-driven decisions. 

ESIC University's Master’s Degree in Big Data and Business Analytics 

The ESIC University Master’s Degree in Big Data and Business Analytics is good news for those who wish to learn data analysis and statistical analysis. The program enables students to analyze extensive data sets, and draw insights. 

Why choose ESIC University? 

ESIC University has many benefits for a data analyst: 

• Comprehensive curriculum: The program provides insights into various fields such as data mining, machine learning etc. Students receive practical training in many of the sophisticated tools and techniques employed in industry. 

• Expert Faculty: Our faculty has industry professionals, business academics and professional experts in big data and business analytics. The ESIC University Faculty is made up of professionals with extensive experience. 

• Industry connections: Exploiting the University's strong business connections with top companies provides students with exposure to internships, industry projects and networking events from the ESIC University network. 

• State-of-the-Art Facilities: Next-generation tools and equipment are provided in top-quality educational facilities, for peak hands-on learning. The campus is well equipped with the  modern technologies and infrastructure required to provide a high-quality  education. 

Career opportunities with a Master’s Degree in Big Data and Business Analytics 

Getting  a Master’s Degree in Big Data and Business Analytics at ESIC University leads to various job prospects. This program helps students to compete for positions such as data analysts, data scientists and business intelligence analysts, among others. This is a well-paid and future-proof career, as the need for data/algebraic analysis experts is rapidly growing. 

• Data Analysts: Data analysts collect, process and perform statistics on large amounts of data. They draw insights for organizations from the data they collect. 

• Data Scientists: They solve complex problems using advanced analytical techniques and machine learning algorithms. They create predictive models and share insights in a way that guides business expansion. 

• Business Intelligence Analysts: A business intelligence analyst analyzes data to identify trends and patterns that inform decision makers about the conditions of strategic opportunities. They also build reports and dashboards to communicate results effectively with stakeholders. 

• Data Engineers: They design, build and maintain data pipelines or architecture. They also make data available by storing them correctly for analysis. 

Challenges and ethical considerations in Data Analysis 

Data analysis leads to valuable insights, but also creates issues and ethical questions. The key part of understanding data analysis is recognizing these challenges and coming to terms with how we can solve them. 

• Data quality: It is important to underline the importance of data accuracy and completeness. Bad data leads to bad conclusions, and causes the damage. 

• Data privacy: Securing the data of individuals is a major concern. Analysts need to follow data protection rules and ethical frontiers in order to protect sensitive information. 

• Bias and fairness: Incorrect ways of doing the data analysis lead to bias. We need to identify any bias and account for it so that we can obtain accurate results. 

• Interpretation and misuse: The correct way to interpret data to prevent misjudging. To avoid data being used in an incorrect way, analysts must communicate clearly and responsibly. 

Learning the different types of data analysis, and identifying examples of data analysis are important for using information that leads to good decisions. ESIC University is one of the pioneering universities in this domain: It has a Master’s Degree program that provides students with education and growth opportunities that every beginner should consider.  

If you are interested in delving deeper into data analysis, we encourage you to learn more about our Máster en Big Data y Business Analytics. 

Harness the power of data with ESIC University and explore a new world!