A clear understanding of what data science is will help you focus your career
Data science has generated a lot of buzz in many industries in recent years. For leaders and managers, it has become an important tool for meeting short and long-term objectives. For many professionals, it is now a skill that is well worth putting on your CV.
With this increased visibility and importance, however, has also come some confusion over what exactly data science is – and what it is not. Keep reading to learn more about how data science compares to other key data-related concepts, and how data science training will help you distinguish between them throughout your career.
What Are Data Science Fundamentals?
Put simply, the fundamentals of data science encompass the cleansing, preparation, and aligning of data, often beginning with a large pool of unstructured data. Professionals will usually go through an interconnected process as they progress through their work, starting with a data question, collecting raw data, filtering it, analysing it, and finishing with a ‘data product.’
Data scientists will use quantitative methods and coding skills in languages like Python and Java to complete these tasks. They will also often be required to build and use database platforms like Hadoop and SQL.
Computer and quantitative skills are necessary for successful data science work
A Career in Data Science is Different From Big Data
Any individual who wants to learn data science online is likely to find a lot of information about the role of big data in many businesses. Although a lot of the vocabulary used to describe big data is similar to that used when discussing data science, the two terms refer to different ideas.
Bid data is primarily concerned with the collection of large amounts of data, often to be used by large web applications. Any process that focuses more on big data than data science will likely acquire large amounts of raw data, but may not be capable of processing this data at an immediately useful scale, or be aiming to do so. This is where data science is key: it allows you not only to gather and extract data, but to convert it from its raw form into a useful end product or model.
Data science takes big data a step further, producing new results and insights
Machine Learning is a Big Part of Data Science, But Not Its Only Component
For those with a particularly specialised interest in data science, data gathering, and computer science, machine learning is another subject that likely appears alongside data science quite frequently. Machine learning and data science are certainly related, but they are not quite the same thing.
Machine learning is the process by which a program can be designed to improve its performance as it gains experience – or, as it ‘learns.’ For example, when your smartphone keyboard provides you with increasingly accurate text predictions as you use it, this is the result of machine learning.
When you take data science fundamentals courses, machine learning is just one of the many data analysis techniques you will be introduced to. While machine learning is an important tool for data scientists, it is not the only one at their disposal and it can also be used in other contexts. By undergoing data science training, you will gain the skills you need to work with techniques like machine learning within a more comprehensive data science framework, increasing their power and effectiveness.
Are you ready to hone in on a specialised career in data science?
Contact us at the Southampton Data Science Academy to learn more about our professional development programmes.