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# MSc in Statistics vs MSc in Data Science: Which One Should You Pick

Statistics and data science are so closely related that many definitions for one discipline can be applied to the other.

Statistics and data science have a lot in common, to the point where many definitions from one subject might be applied to the other. However, there is a significant distinction between a data science and a statistics degree and the opportunities and skill-sets that each offers. Thus, while the two degrees are fundamentally identical, obtaining a data science degree versus a statistics degree might lead to radically distinct employment trajectories.

Statistics is a mathematical discipline concerned with the collection and interpretation of quantitative data. By comparison, data science is an interdisciplinary field that uses scientific methods, procedures, and systems to derive knowledge from a variety of types of data. Data scientists employ techniques drawn from a variety of fields, including statistics. However, the fields are distinct in terms of their methodologies, the types of problems researched, and a variety of other characteristics.

Key differences between a master’s in statistics and data science:

### Statistics Curriculum

Professionals seeking a deep understanding of the theoretical foundations of statistical theory and advanced training in model development are well-suited to pursue a master’s in statistics. Individuals pursuing this degree will surely acquire data science principles as well, as this new field is founded on statistical theory.

A master’s degree in statistics often includes courses in programming languages such as SAS, R, and Python. Additionally, students will learn to evaluate a variety of data types and, ideally, will have the opportunity to apply newly acquired abilities to real-world data sets. The master in statistics programme develops professionals who are confident in their abilities to provide corporate leadership with a validated and comprehensive data analysis evaluation.

### Data Science Curriculum

A master’s degree in data science is suitable for individuals interested in learning how to mine data in order to generate predictions and data-driven decisions, most likely in a commercial setting. The programme will teach students how to extract knowledge from massive amounts of data in order to eliminate errors and improve business acumen. Moreover, the programme often prepares students to not only discover patterns in data but also to acquire, constantly rearrange, and manage data.

The master’s in data science prepares students to make accurate predictions and decisions based on the validity of acquired data, whereas the master’s in statistics teaches students to comprehend data correlations and associations through the application of statistical theorems.

### Career Possibilities

Statistics degrees emphasise the collection, organisation, analysis, and interpretation of numerical data to solve business problems. Calculus, mathematics, and statistics, including statistical modelling, are frequently covered in the course, whereas a data science degree emphasises data analysis, machine learning, statistical theory, and advanced programming abilities. These programmes educate students on developing novel data modelling and data processing techniques and how to use cutting-edge technologies to analyse, monitor, manage and visualise huge datasets.

Due to the increased demand for data analytics skills, data science and statistics degrees appeal to students interested in math, statistics, and problem-solving. So, what’s the difference between statistics and data science? The areas vary in their modelling approaches, the size of their data sets, the types of problems they study, the backgrounds of its practitioners, and the language they employ. However, the two fields are inextricably linked. Both statistics and data science ultimately seek to extract knowledge from data.

Nivash holds a doctorate in information technology and has been a research associate at a university and a development engineer in the IT industry. Data science and machine learning excite him.

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