**Data science** is a broad multidisciplinary field that is one of the most rapidly expanding developments in computing. Computer science, software engineering, mathematics and statistics, programming, economics, and business management are all applied in data science. The gathering, preparation, analysis, management, visualization, and storage of massive amounts of data are all part of data science. In simple terms, data science is defined as having strong ties to databases, especially big data and computer science.

Data science is a specialized skill and can be understood as:

• Design and implementation in the 4A’s - Data Architecture, Acquisition, Analysis, and Archival

• Using advanced mathematics and statistical tools to model data for in-depth analysis

• Appropriate programming and development skills, as well as algorithm creation skills

As a result, it is clear that data science is an interdisciplinary field that requires a diverse set of skills to grasp.

Data science use cases are comparable to data analytics in that they start with a well-defined issue statement and conclusion, and end with well-defined metrics.

As a result, data scientists are thought to be conversant with business models and paradigms, as well as capable of asking excellent business questions in order to extract valuable insights from provided data sets.

**Statistics** is another vast discipline that deals with data analysis and is used in a variety of fields. The approach for drawing inferences from data is provided by statistics. It provides several ways for gathering data, analyzing it, and interpreting the results, and it is widely utilized in problem-solving by scientists, researchers, and mathematicians. Statistics is associated with data-intensive operations such as data collection, processing, and interpretation.

In data-related studies, statistics is extremely important because it aids in,

• Choosing the sort of data needed to solve an issue

• Organizing and summarizing data

• Conducting analysis to generate conclusions from data

• Evaluating the usefulness of the results and assessing uncertainties

The methods provided by statistics include,

• Design for planning and executing research

• Descriptions, which entails investigating and summarizing data

• Making predictions and inferences based on the phenomena represented by data are some of the approaches provided by statistics.

**Key Differences Between Data Science and Statistics**

The following are the main distinctions between Data Science and Statistics:

• Data science combines multi-disciplinary fields and computing to understand data for decision-making, whereas statistics is a mathematical analysis that use quantified models to represent a set of data.

• Data science is mainly focused on the topic of big data, which aims to extract insights from large amounts of complex data. Statistics, on the other hand, provides the methods for gathering, analyzing, and drawing conclusions from data.

• Data science is the process of sorting and categorizing enormous amounts of data into appropriate data sets or models using tools, techniques, and ideas. Statistics, on the other hand, is limited to methods such as frequency analysis, mean, median, variance, correlation, and regression, to mention a few.

• Data science will examine and analyse data in order to draw factual, quantitative, and statistical conclusions. Statisticians, on the other hand, focus on analysis using established procedures such as mathematical formulas and methods.

• A data scientist will use numeric and quantitative analysis techniques to examine and simplify problems using complicated data sets, whereas a statistician will use numeric and quantitative analysis techniques.