Data analytics and statistics are both important in obtaining useful findings from data. Though they both involve scrutinizing data to identify trends and patterns, their procedures and methodologies differ.

While the former involves collecting, organizing, analyzing, interpreting, and presenting data; the latter relies on mathematical methods to collect summary data that leads to hypothesis testing involving probability theory.

Unlike statistics, data analytics incorporates more options for examining the data. This means it takes raw data analysis and turns it into an informed decision-making tool. Let’s dive into how is data analytics different from statistics

What is Data Analytics?

Data analysis includes data cleaning, processing, modeling, and transformation to discover patterns. It is a process that uses statistical analysis, machine learning algorithms, and data mining techniques in order to uncover reliable information from huge complex datasets.

To understand the findings of an analysis, it is important to develop visualizations like graphs, charts, or dashboards for better comprehension. Therefore, business operations can be improved and more effective strategies can be developed by the companies through data analytics. Cost savings and profitability may also result from increased productivity and efficiency improvement in processes.  

What is Statistics? 

Collecting, analyzing, organizing, interpreting, and presenting data is known as statistics. It is a segment of mathematics that involves numbers and the interpretation of data. Therefore, these collectible data may be obtained in different manners such as surveys, experiments, or observational studies. The collected data will be processed by statistical techniques to enable the drawing of conclusions for decision-making purposes.

It has immensely benefited science, business, economics, healthcare, and social sciences since the framework is an excellent basis for data-driven decision-making and research activities. Most importantly, it helps to reveal trends within large amounts of related information; patterns that can point out possible correlations between two or more variables thus providing deeper insights into better judgment-making processes.

How is data analytics different from statistics? 

Data analysis and statistics are related in that they involve gathering, examining, and interpreting data. Statistics is a mathematics branch that involves analyzing data. It uses mathematical techniques to summarize and interpret data, identify trends and patterns, make predictions, and draw conclusions. Statistical methods focus on inferring about a population from a sample of that population. It helps quantify uncertainty, assess risk, and make decisions based on data.

Conversely, though, data analytics is a broader concept that embraces different approaches used to scrutinize the meaning of information. Data analytics employs statistical methods as well as machine learning algorithms among other tools to derive insights from raw numbers, spot regularity or tendencies as well as departmental or organizational decision-making. Data analytics often requires one to dig very deep into the available information so as to be able to see what is really happening with the figures in front of us. When applied appropriately, this approach could be used for predicting future outcomes based on past patterns analyzed. Thus, it can be seen that by using this technique businesses can make more informed decisions at all appropriate levels within their organization structure.

The primary dissimilarity between data analytics and statistics is the direction of application. The core focus of statistics is building and implementing mathematical methods for processing data while data analytics involves the application of analysis techniques to solve practical issues derived from data. Big Data Tools and machine learning algorithms are often used in data analytics which makes it possible to process and analyze large datasets with more ease.

It’s visualization and communication that separate statistics from data analytics. Data analytics usually employs visualizations such as charts, graphs, or dashboards that help communicate findings and insights better to various stakeholders involved in a project. However, statistics focuses mainly on mathematically rigorous approaches and statistical theories.

Wrapping Up:

Analyzing statistics and data analytics both require doing the same thing, but their methodologies, target groups, fields of concentration, and aims are significantly different.  Statistics is a field that stresses mathematics-based theory since it deals with the formation and application of statistical models, however, another one is an applied area that employs many techniques to convert numbers into action. By partnering with big data analytics services businesses can identify the current trends in customer behavior. In a data-driven world, evaluating intricate information and supporting decision-making is impossible without either of these disciplines.

Click here to learn more detailed information about big data analytics and the data processing cycle.