If you have been working in data science for some time, you must be aware of the recent buzz around analytics. Most companies are looking for data scientists to help them make sense of their data, understand their business and customers better, and build predictive models. In today’s job market, data science is, without a doubt, in high demand. As a part of this boom, we have needed clarification about the role played by data scientists. Analytics combines computer science, statistics, mathematics, and operations research. The subject matter ranges from applied statistics to machine learning algorithms.

In recent years, the field of Data Science has gained immense popularity. Most companies are looking for data scientists to help them make sense of their data, understand their business and customers better, and build predictive models. It has become one of the hottest professions in the modern economy.

An essential aspect of data science is its integration of computer science, statistics, and other related disciplines, such as mathematics and operations research. It’s commonly referred to as data analytics. A subset of data analytics known as data science uses advanced statistical techniques for collecting, storing, and processing large volumes of structured and unstructured data (i.e., raw data).

There may be opportunities for data scientists to work with a business team or within an IT department on projects that involve developing predictive models directly from customer-identified requirements across numerous dimensions such as demographics, psychographics, etc.

With the boom in data science, it has become essential to clarify the role played by data scientists. Analytics is a field that combines computer science, statistics, and other related areas such as mathematics and operations research. It covers various topics, including applied statistics and machine learning algorithms. Data Analysis, Data Analytics, and Data Science are the other terms used in this discussion. These three roles require a wide range of skills, and statistics is one field used in these roles, although many more contribute to the analysis and interpretation of Big Data.

Data analysis involves using statistical techniques such as regression analysis and clustering algorithms to extract useful information from large amounts of data. Humans perform these analyses, but they may also be performed automatically with computer programs (for example, databases). In this context, “data” refers to numerical values, text documents (that may contain numbers), images, and videos.

## Statistics and Analytics differ, but what are the differences?

Let’s start with an overview of the two terms before we move on to understanding how they are connected and where they differ from each other.

“What’s the difference between analytics and statistics?” That is a common question that many individuals have been asking. Although it is easy to understand the two terms, some need help to use them correctly or tell which to use during specific situations. With this in mind, here are some explanations on correctly using analytics and statistics.

**An overview of statistics is data collection, analysis, interpretation, and presentation.** It is necessary to examine a population or process before applying statistics. A population can be a person, a country, or an atom in a crystal. The planning of data collection includes surveys and experiments.

It is possible to divide the statistical analysis into two categories: descriptive statistics, which analyzes data from samples based on the standard deviation or mean, and inferential statistics, which examines data based on random variation (e.g., observational errors, sampling variation).

The two main components of descriptive statistics (sample or population) are central tendency (or location) and dispersion (or variability): central tendency determines the distribution’s central or typical value, while dispersion indicates how far each member of the distribution departs from its center. In probability theory, analyzing random phenomena is the basis for mathematical statistics. The sample is used to evaluate the performance of one or more estimators on an unknown quantity.

One common thread connects analytics and statistics: they use statistical methods and analyses. However, in contrast to statistics, an analytics scientist often conducts studies where there is no assumed null hypothesis and subsequently applies machine learning algorithms to these analyses. It is essential to understand this distinction to understand my perspective. For example, I rarely use a null hypothesis when performing predictive modeling. Nevertheless, I perform statistical analyses with a null hypothesis at the forefront-this difference is subtle but significant.

**In analytics, meaningful patterns are discovered and communicated.** The goal of analytics is to find meaningful patterns in data. Statisticians, machine learning, computer programmers, and operations researchers combine to quantify performance and predict outcomes. Visualization is a common way to communicate analytics insights.

The goal of analytics is to improve the performance of a business using business data. Analytics encompasses predictive analytics, enterprise decision management, retail analytics, optimizing stock-keeping units for store assortments, optimizing marketing, modeling marketing, web analytics, sizing sales forces, pricing and promotion, predictive science, credit risk analysis, and fraud analysis. Algorithms and software for analytics incorporate computer science, statistics, and mathematics methods.

Statistics and analytics are indeed different entities. But can they both be used in the same manner to track and monitor data? Yes, they can. Analytics and statistics should never become interchangeable, but they shouldn’t be considered two completely different animals. They depend on one another – statistics gives context to analytics, and analytics provide meaning to statistics – and together, they build a bridge between data and decision-making.

Statistical analysis includes procedures used to assess data and understand their meaning. At the same time, Analytics is a broader term that consists of collecting, organizing, and analyzing data to demonstrate a correlation or causality—collecting Data and Displaying it in a Graph or Figure is not analytics but can form part of analytic processes.

With all of these definitions in mind, you should now understand the difference between Analytics and Statistics and why it is essential to use the words accurately. Whether you are working for an organization that uses both or are planning to start a career in either field, you will find helpful information in this blog post.

Well, yes and no. Despite similarities, both are different fields and should be understood separately. Statistics are just a part of the globe around which you find analytics, but analytics can quickly play a role in anything. It’s another tool of the trade that helps us understand and learn more about the world around us.

You need to know the differences for each to make an informed decision about which you’ll use most effectively. For example, Google Analytics could be the ideal solution if you’re trying to find metrics on your website. On the other hand, if you’re researching a business statistics project, try looking at Solver. Both of these products might be able to do the job for you—but there are clear distinctions between them.

When choosing a job in this space or determining if you are qualified for a particular job opening, a clear understanding is necessary. Often people need to learn what they need, what they have, or what they can do with it.

## Conclusion

According to the descriptions I have provided here, statistics and analytics are different disciplines, though they do share some characteristics. There is no intention here to denigrate statisticians’ work. It is evident that the statistician is highly professional, understands their discipline exceptionally well, and provides valuable, focused analyses. Statisticians are specialists in the “focused” area of statistics, while analytics professionals are generalists to some degree. When statistical hypotheses are paramount in studies, analytics professionals will often ask statistics professionals for assistance.

Last but not least, analytics is the process of predicting future events based on data science. An algorithm analyses past data using various statistical techniques, such as linear regression, to predict what will happen in the future.