Data Science and Statistics both have analytical backgrounds with a strong foundation in Mathematics. However, Statistics is essentially a part of the broader area of Data Science, while the latter calls for certain core competencies in multiple areas.
As a student, IT professional, or analytics practitioner you may be wondering about the difference between Data Science and Statistics, or why you should opt for a Data Science course. What is the difference between the two degrees, and can you switch roles from Statistics to Data Science? Read on to learn more.
Difference between Data Science and Statistics Degrees
Data science is a vast multi-disciplinary field that combines a variety of subjects ranging from computer science, mathematics, statistics, economics, programming to project management. Statistical and mathematical techniques are applied to model and analyze data deeply.
Data science deals with the collection, extraction, analysis, visualization, and storage of data to solve real-world problems. Not just structured data, but also unstructured data like tweets, images, audio/video files, etc. are analyzed by data scientists.
Statistics, on the other hand, is a subject that handles the mathematical analysis of data. It covers techniques to analyze and draw conclusions from data, based on mathematical models and formulas. Statistics deals with only numerical or structured data.
For proficiency in the data science domain, you need to have expert knowledge of multiple domains like statistics, programming, algorithm development, data architecture, and reasoning. However, for statistics, you need to be good at theoretical statistics and modeling.
A Data Science degree requires knowledge of these above-mentioned subjects, as well as expertise with tools and software. The certification or post-grad diploma is a more inclusive course and equips you with the necessary knowledge and skill sets. Whereas a degree in Statistics is just like Math and all you need is to have a graduate or postgraduate degree. In some cases, those with a background in statistics can have additional training in data science through a degree program or even a Bootcamp and start a data science career.
Data Scientist vs. Statistician
The Data Scientist is several roles combined into one. The role is cross-functional requiring multidisciplinary knowledge, technical and non-technical skill sets. It is not sufficient to have analytical rigor and soft skills such as the ability to ask questions, communicate, teamwork, presenting findings, and explanation of results to stakeholders, but also have technical skill sets like proficiency in programming languages and toolboxes like R and Apache Spark.
The role of a Statistician has been around for a longer time and refers to Statistics experts in academia, industry, economics, healthcare, etc. The job role of a Statistician includes experimental design, conducting studies, creating estimations, and developing surveys. A strong background in mathematics and experimental design are must-haves. Soft skills required for this position are the ability to report findings to stakeholders and making the changes needed as a result of those statistical findings.
Data Science vs. Statistics: all you wanted to know
Data Science and Statistics, both, have many similarities. Both focus on extracting data and using it for analysis and solving real-world problems.
Both require knowledge of mathematics, the ability to understand problems, conduct exploratory data analysis, analyze trends and patterns, make forecasts and visualizations and ultimately report the findings to non-technical users.
However, there is a great deal of overlap between the two.
As a discipline
Data Science is a multidisciplinary specialized field that applies scientific methods, processes, and tools, to extract knowledge from data using many disciplines, one of which is statistics. You need varied skill sets to gain mastery in this domain. In contrast, Statistics is a mathematical-based discipline that collects and interprets quantitative data. Statistics deals with the study of data, widely applied in numerous fields.
Data handled
Data Science handles a huge amount of structured and unstructured data. However, Statistics focuses on small chunks of numerical data.
Concept
Data Science is based on scientific computing. It uses advanced Mathematics and Statistics to extract new information from big data. Data Science uses machine learning and other advanced analytical processes. In contrast, Statistics is a science used to estimate an attribute, apply statistical functions or algorithms to determine values.
Approach
In Data Science, huge datasets are broken using scientific methods before using them to solve problems. Problems are solved using predictive models. The best model is selected to solve the problem. In contrast, statistics problems are solved using a simple linear model, which is then improved to give the best results. Mathematical formulas, models, and concepts are used and values are estimated for different data attributes. As the data used is not very large, the problems are solved using statistical tools such as median, variance analysis, regression, etc.
Potential Applications
Data Science has various applications, especially in the fields where predictive data analysis is needed, such as healthcare, fraud, market analysis, finance, manufacturing, and engineering. Statistics applications are found in commerce and trade, finance, economics, astronomy, physical sciences, and psychology.
Data Science vs. Statistics: Which is right for you?
A data science degree provides learning across a range of disciplines, including data analysis, machine learning, statistical theory, and advanced programming. It is most relevant in dynamic enterprise settings and academia. However, a statistics degree may be ideal for those with an aptitude in mathematics and an interest in working in a government or university setting conducting research.
Conclusion
So what is the difference between data science and statistics? The two fields differ in their processes, knowledge base, the size of data handled, the types of problems studied, and the language used. Ultimately, your aptitude, academic background, ability with numbers and coding, and where you see yourself a few years from now will help you decide between pursuing Data Science or Statistics.
