Data science includes the fields of statistics, scientific methods, artificial intelligence (AI) and data analysis. Every day, huge amounts of data are collected from our usage of the internet, our phones, the Internet of Things, and other objects embedded with sensors that provide data sources. This mass of information can be used by data scientists to create algorithms for data mining and data analysis from machine learning.
Once machines are conversant in what they’re looking for, they can potentially create their own algorithms from looking at raw data. This is called deep learning, which is a subset of machine learning. It usually requires some initial supervised learning techniques, for example, allowing the machine to scan through labelled datasets created by data scientists. However, because the machine will be powered by a neural network of at least three layers, its thinking simulates that of the human brain, and it can start noticing patterns beyond its specific training.
What’s the difference between data science and computer science?
Computer science and data science are sometimes used interchangeably. Pure computer science is focused on software, hardware and offering advances in the capacity of what computers can do with data collection. Data science is more interdisciplinary in scope, and involves aspects of computer science, mathematics, statistics and knowledge of specific fields of enquiry. Computer scientists use programming languages like Java, Python, and Scala. Data analysts are likely to have basic knowledge of SQL – the standard language for communicating with databases – as well as potentially R or Python for statistical programming.
Data analytics is concerned with telling a compelling story based on data that’s been sorted by machine learning algorithms. Although data analysts are expected to have some programming skills, their role is more concerned with interpreting and presenting clear and easily understandable data visualisations. This could be data that supports an argument, or data that proves an assumption wrong.
Data engineers are part of the data analytics team, but they work further up the pipeline (or lifecycle as it’s sometimes known) overseeing and monitoring data retrieval, as well as storage and distribution. Hadoop is the most used framework for storing and processing big data. The Hadoop distributed file system (HDFS) means that data can be split and saved across clusters of servers. This is economical and easily scalable as data grows. The MapReduce functional programming model adds speed to the equation. MapReduce performs parallel processing across datasets rather than sequential processing, which significantly speeds things up.
Why data science is important
Data science is most commonly used for predictive analysis. This helps with forecasting and decision-making in a wide spectrum of areas from weather to cybersecurity, risk assessment and FinTech. Statistical analysis helps businesses make decisions with confidence in an increasingly unpredictable world. It also offers up insight into broader trends or helps zero-in on a particular consumer segment, which can give businesses a competitive advantage. Big names like McKinsey and Nielsen use data to report on larger sector-wide trends and provide analysis on the effects of geopolitical and socio-economic events. Many organisations pay good money for these reports so that they can plan and stay ahead of the curve.
In the 21st century, AI and big data are revolutionising whole industries such as insurance, financial services and logistics. In healthcare, big data enables faster identification of high-risk patients, more effective interventions, and closer monitoring. Public transport networks can function more economically and sustainably thanks to data analysis. As the climate crisis increases the frequency of extreme weather, improved forecasting can help to mitigate the worst of the damage.
Data science is the fastest growing job area on LinkedIn and is predicted to create 11.5 million jobs by 2026 according to the US Bureau of Labour Statistics. Many leading tech-based companies like LinkedIn, Facebook, Netflix and Deliveroo rely heavily on data science and are driving demand for analysts.
How to learn data science
Data science tutorials can be found all over the internet and you can get a reasonable understanding of how it works from these, as well as certification – for example, from Microsoft on Azure. However, for professionals, a qualification like an MSc Data Science or a postgraduate degree in an associated subject area like an MSc Computer Science with Data Analytics is highly valued by employers. This can be studied full-time or part-time while you gain work experience in the area you wish to specialise in. Academia can only take you so far in understanding the theory but working hands-on in the world of data science will help you in the practice of this subject, honing your skills. It’s not one of the prerequisites for taking on a role, but it will help you stand out from the crowd in a competitive job market.
Data science is a burgeoning field that can complement most of the social sciences and there is an increasing demand for expertise in this area. Data scientists can come from a wide variety of backgrounds such as the fields of psychology, sociology, economics, and political science, because data and statistics are valuable and applicable to all these areas.
A score of 6.5 in IELTS (the International English Language Testing System) is one of the entry requirements for a degree in data science or computer science. This is because English is considered a first language in data science internationally, but also because natural language processing works off the English language as a primary reference point when programming in Python.
Ready to discover more about the world of data?
If you’ve reached a point in your career where you want to specialise in data analytics, now is the time to explore an online MSc Computer Science with Data Analytics from the University of York. Offering knowledge in machine learning, data analytics, data mining and text analysis, you’ll also create your own data analytics project in an area of interest.
Find out more about the six start dates throughout the academic year and plan your future.