Difference between Data Science and Statistics
Data science is one of the rapidly emerging trends in computing and is a vast multi-disciplinary area. Data science combines the application of subjects namely computer science, software engineering, mathematics and statistics, programming, economics, and business management. Data science is based on the collection, preparation, analysis, management, visualization, and storage of large volumes of information. Data science in simple terms can be understood as having strong connections with databases including big data and computer science. A data scientist is an individual with adequate domain knowledge relevant to the question addressed.
Big data is closely integrated with data science and in fact, has evolved with big data in different applications and use cases. We are aware that, big data is mostly available in unstructured formats and contains non-numeric data. Useful information easily gets buried in big data which is made up of blogs, audio/video files, images, text messages, social networks, and so on. All this data is just noise unless it is analyzed and useful information is extracted from them. In addition, nowadays businesses consider the internet as their primary information channel due to the growing role of social web and for its business potential. All this data is of much interest to a data scientist because by using these data many problems can be solved for organizations, and also societies.
Data science is a specialized skill and can be understood as:
- Design and implementation in 4A’s – Data Architecture, Acquisition, Analysis and Archival
- Applying advanced techniques in mathematics and statistics to model data for deep analysis
- Adequate programming and development skills, algorithm development skills
- Analytical and ethical reasoning skills
- Communication and business skills
Therefore, it is apparent that data science is an interdisciplinary area and needs varied skill sets to gain mastery in this domain. Use cases in data science are similar to data analytics – they begin with a clear problem statement and decision to finally end with well-defined metrics. Therefore, data scientists are considered to be familiar with business models and paradigms, who ask good business questions to obtain meaningful insights from given data sets.
Statistics is another broad subject which deals with the study of data and is widely applied in numerous fields. Statistics provides the methodology for making conclusions from data. It gives different methods to gather data, analyze them and interpret results and is widely used by scientists, researchers, and mathematicians in solving problems. Statistics is synonymous with data-intensive activities – collecting, processing and interpretation of processed data.
Though statistics provides the methods for data collection and analysis, it helps to obtain information from numerical and categorical data. Categorical data refers to unique data, examples are blood group of a person, marital status, etc. Statistics is highly significant in data related studies because it helps in,
- Deciding the type of data required to address a given problem
- Organizing and summarizing data
- Analysis to be done to draw conclusions from data
- Assessing the effectiveness of results and to evaluate uncertainties
The methods provided by statistics include,
- Design for planning and conducting research
- Descriptions which implies exploring and summarizing data
- Making predictions and inference using the phenomena represented by data
Head to Head comparison between Data Science vs Statistics (Infographics)
Below is the Top 5 comparison between Data Science vs Statistics
Key differences between data science vs statistics
- Data science combines multi-disciplinary fields and computing to interpret data for decision making whereas statistics refers to mathematical analysis which use quantified models to represent a given set of data.
- Data science is more oriented to the field of big data which seeks to provide insight information from huge volumes of complex data. On the other hand, statistics provides the methodology to collect, analyze and make conclusions from data.
- Data science use tools, techniques, and principles to sift and categorize large data volumes of data into proper data sets or models. This is contrary to statistics which confines itself with tools such as frequency analysis, mean, median, variance analysis, correlation, and regression, and so on, to name a few.
- Data science will investigate and inspect data to deduce factual, quantitative and statistical inference. This is opposed to statistics which focuses on analysis using standard techniques involving mathematical formulas and methods.
- A data scientist must have skill sets to analyze and simplify problems using complex data sets to figure out information, whereas a statistician will use the techniques of numeric and quantitative analysis.
Data Science vs Statistics Comparison Table
The differences between data science vs statistics are explained in the points presented below
|Basis for Comparison||Data Science||Statistics|
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Conclusion – Data science vs statistics
In summary, it may be noted that Data science and statistics are indistinguishable and are closely linked. It is clear that statistics is a tool or method for data science, while data science is a wide domain where a statistical method is an essential component. Data science and statistics will continue to exist and there is a big overlap between these two disciplines. Also to note, all statisticians cannot become data scientists and vice-versa. Data science has developed recently with big data and will continue to grow in the coming years as data growth seems to be never-ending.
This has been a guide to Data Science vs Statistics, their Meaning, Head to Head Comparison, Key Differences, Comparision Table, and Conclusion. You may also look at the following articles to learn more –