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How to become a data scientist

Posted on: December 6, 2021
Woman in a suit sat at a desk looking at data on a computer screen

Data science is a rapidly expanding area of interest with many specialist roles available in tech, but also in practically every other sector you can think of. This is because businesses in every sector rely on data analysis and predictive modelling in some form and so have a requirement for data science skills across the board. Data science in the business world primarily deals with understanding customer and market behaviours and aggregating data around these behaviours to create data sets which can be analysed. Data scientists are increasingly involved in data analysis as a natural progression of their role in defining problem statements and querying data, which requires knowledge of algorithms and how to write them. Once the data is collected and cleaned it can start to be visualised through graphs and provide analysis.

Machine learning is key to the success of some of the biggest names in business, from Netflix to Amazon and Google to Tesla, they all use artificial intelligence to help predict behaviours and personalise service. Big data collected over many years contains valuable insights that machine learning can help us find in a fraction of the time it would take for humans to come to the same conclusions. Through data visualisation and modelling, informed predictions can be made which help with problem-solving and decision-making.     

So how do you become a data scientist? What are the demands of a data scientist job? Let’s find out.

Start a degree in data science

Data scientists generally require at least a bachelor’s degree in data science or a related field of computer science. Even better for data science roles is to acquire a master’s degree with more focus on the specific area you want to work in. 

So for example, an MSc Computer Science with Data Analytics will set you up for collecting and extracting data using machine learning as well as analysing it and using the data analytics to improve systems. You don’t necessarily have to have a BSc in Data Science though to pursue a master’s in the field of data science. Students who have backgrounds in the social sciences and many other subjects are able to enter the field by taking on a master’s regardless of what they studied in their undergraduate degree or their previous career path.

Learn the required skills to become a data scientist

What will you learn on a data science master’s course? The fundamentals include gaining a good grasp on the maths needed to work with machine learning algorithms and artificial intelligence. 

Data mining uncovers the patterns that are so valuable to solving business problems. It involves methods at the intersection of machine learning, statistics, and database systems, all of which will be covered in a master’s course. Learning programming languages is fundamental to understanding data science and depending on your course, you may learn Python, R, Java, SQL, but probably a combination of them. Other areas your course may cover include user interaction (UI) design, software engineering, web technologies and security, research skills, and consultancy skills.

Consider specialisation

There are various different roles in the world of data science that require slightly different skill sets. The job title “data scientist” can cover a broad spectrum of skills and mean different things to different recruiters. 

In some jobs, the skills of software engineering overlap with those of a data scientist, in others a software engineer will work side by side with a data scientist. A software engineer may also be referred to as a data engineer, which can be confusing. Generally, data engineers tend to be concerned purely with integrating, consolidating, and cleansing data. Even as a qualified data scientist, you may be drawn more to the role of data analyst, which is concerned with creating reports and dashboards, identifying trends, and translating key business information for stakeholders to take away and utilise.

Specialisation areas you might consider include artificial intelligence, machine learning, research, database management, or reporting and visualisation. The sector you would like to work in will also have an influence on which skills you’d like to specialise in. In the finance sector, hedge fund jobs increasingly favour those with Python coding skills. In healthcare, NHSX is driving forward the digital transformation of the NHS by implementing artificial intelligence and deep learning to automate tasks and improve healthcare exponentially.

What might a typical day of work look like for a data scientist?

Data science projects are generally started because a problem or query has been identified and defined so an algorithm needs to be written for the initial data collection. But in the case of large datasets that already exist, the raw data may require data cleaning and further refining to make it usable. Just how those large amounts of data needs refining is encapsulated in the coding of an algorithm and can turn previously unstructured data into structured data ready for visualisation and analysis. From this, predictive models can be created. Machine learning models train the computer to recognise patterns and create its own algorithms through deep learning. However, the data scientist needs to initiate and track the training before the computer can be left to its own devices.

While you’re working on these problems, it helps to have good communication skills as you’ll need to work with stakeholders and keep them up to date on progress through meetings and documentation. Sharing data visualisation through graphs, you’ll need to be able to walk people through what they mean and what the implications for business are. Regression analysis is commonly used and recognised because it clearly shows your dependent variable (the main factor you’re trying to understand or predict) and your independent variables (the factors that you hypothesise have an impact on your dependent variable).

Learning tools and professional development

Even once you’ve entered into a data scientist career, it’s important to keep up with shifts and developments in the sphere of data and analytics. 

GitHub is a code hosting platform that allows you to collaborate on open-source projects. Although it can be used for work projects, it is also a way to contribute to projects outside of work and has a social networking aspect too. Kaggle is another online community used by data scientists and machine learning engineers, which was created by Google. It’s a space where you can publish data sets, build models, work with peers across the world, follow tutorials, and enter competitions to solve data science challenges.

Can a non-technical person learn data science?

Technical skills are required if you’re seriously thinking about a data science career. If you come from a non-technical background, there’s nothing to stop you learning these skills, but it may be more challenging, and you need to be honest with yourself about your abilities so you can stay the course. 

Even data analysts who may have not been initially involved in machine learning techniques or advanced coding are increasingly conversant in SQL and Python as it helps them to understand the entire process of data collection and be involved further up the pipeline. Soft skills that may be expected include use of Microsoft Office (particularly Excel), use of analytics and visualisation software like Tableau, and data management software like Hadoop.

Choose a career in data science with a data science master’s

Data science is the fastest growing job area on LinkedIn and the World Economic Forum predicts that by 2022, data scientist and analyst will become the number one role globally. The high demand for data scientists is reflected in the high salaries often offered. A forecast by Randstad estimates that the salaries of leading scientists could hit as high as £100,000 in 2022.

The financial rewards may be great but the sense of achievement that comes from solving complex problems and questioning the way things are done to create more efficient systems is even greater. An MSc Computer Science with Data Analytics could offer you the chance to change your career and be part of a community intent on making things better. Study 100% online and part-time around your current commitments with Keele University.

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