What is data science?

Data science is a field of study in which data is collected and analysed. With the help of machine learning, the amount of data which can be searched for patterns and insights has grown exponentially. 

These large amounts of raw data are referred to as big data. The grouping of this data into datasets is then held within a database. All this information is meaningless until it is sorted for data analysis, which is where algorithms and machine learning come in. Artificial intelligence (AI) takes this a step further, introducing computers that are self-learning and which spot anomalies or similarities without training. Artificial intelligence is used in many sectors, including the automotive sector to develop self-driving cars and in healthcare to help effectively identify cancers at an early stage. It’s also used in natural language processing which informs functions like autocorrect and autocomplete as well as digital assistants like Siri and Alexa.

Deep learning is allowing AI robotics to progress rapidly, offering numerous potential applications and making various forms of manual labour more of a reality. As well as aiding in the development of AI, data science and data analytics provide business intelligence that supports problem-solving and decision-making in organisations.

Is machine learning data science?

Machine learning is at the heart of data science as it helps us to visualise patterns and glean knowledge that would otherwise require thousands of man hours, collating and sorting data. Data science and machine learning are not interchangeable; however, they are reliant on one another. Artificial intelligence is a more advanced branch of machine learning that involves artificial neural networks.

Machine learning is key to helping with data mining, but without a data scientist fluent in the programming languages of SQL and Python to write the algorithms, the machine learning system won’t know what to do. Even in deep reinforcement learning, algorithms need to be written to initiate a computer’s self-learning. When it comes to data science projects, sometimes data scientists are presented with volumes of data and no business problem attached. This is when a data scientist needs to draw on all their experience to hypothesise on what the data might show and how to attain evidence through statistical analysis that supports this (or what it means if it nullifies it).

Data can come in many forms and may solely be numerical. Python was not originally designed for numerical computing and so NumPy was developed to support array data structures and matrices as well as offering high-level mathematical functions that can be used on these arrays. Using NumPy rather than pure Python can result in computations that are at least five times faster. Pandas is also a popular software library written for Python that is particularly useful for tabular data and time series. It’s built upon NumPy and another core Python library, matplotlib, which is used for data visualisation. Because of this, pandas eliminates the need to switch tools, and offers access to matplotlib and NumPy methods using significantly less code.

Data science involves analysing data and making sense of it in a meaningful way that can be shared and explained, something that machines can’t currently do. Data is often used for forecasting and predictive analysis, but when an anomaly appears, how do we explain it? Re-running and refining the algorithm may smooth out the data, but we may also need to use human intuition or experiential insight to explain the results. Informatics is a word that is sometimes used interchangeably with computer science but, arguably, it may more aptly describe the human element that is required in decoding and translating data.

To present data, data scientists need to create clear and engaging visualisations. Data visualisation is not new, but it has become more advanced with the rise of data science. Linear regression is one of the most popular machine learning algorithms that provides predictive analysis based on the relationship between a dependent variable and one or more independent variables. This is usually presented as a graph with an upward sloping diagonal line, beautiful in its simplicity.  

Is data science a good career?

Data scientists can hold quite varying roles and duties depending on the size of the organisation that they work for. There may be data analysts on a team who support with data visualisation and presentation, or a data scientist may be expected to translate what the data analytics mean for the business. Either way, it’s a good idea to learn more about the particular area or sector in which you may like to specialise and deepen your knowledge of the relevant skills required. But having an understanding of the entire process of data capture and analysis is a good foundation to start from.

The data science lifecycle is made of stages: collect, clean, analyse, share, and act. With automation increasingly easing the amount of time and effort spent at the start of the lifecycle collecting and cleaning data, the analysis, sharing and actioning of data with potentially nontechnical stakeholders is a large part of the job. Of course, collecting and cleaning is still reliant on clarity in understanding the problem and acuity in creating the problem statement. Data scientists are required to understand how to handle unstructured data and make sense of it by writing the machine learning algorithms from which patterns emerge. Sometimes the title “data engineer” is used for the role which directly manages the big data infrastructure and makes the raw data accessible for data scientists. A data engineer would have expertise in using Hadoop for example, which stores and processes large datasets. Again, this depends on the size of an organisation.

Data scientists are valuable hires in almost all sectors, whether its finance, government, security, healthcare, or tech to name just a few. In conservation, Microsoft has recently launched Project SEEKER, which uses Azure-based technology to help combat illegal wildlife trafficking. Meanwhile, in fashion, brands like Levi’s and Moncler are piloting machine learning bootcamps for their employees. With these kinds of advances and innovations in diverse sectors, knowledge of AI and machine learning in data science continues to be highly desirable across the entire jobs market.

Meaningful data, meaningful career

A career in data science helps bring meaning to the vast amount of data that’s available to analyse. Understanding the world around us and being able to predict possible outcomes helps businesses to continue functioning in times of unpredictability and to prepare in times of instability. 

If you’re ready to take the next step on your career path and specialise with a master’s degree, the University of Sunderland’s 100% online MSc Computer Science is taught part-time so you can study around your current commitments and apply your learning to your current role.