Data science careers are one of the most sought-after professional pathways in technology today, and the demand for skilled data scientists continues to rise. Data scientists analyse, mine, and program different data sets; they also write code, mix it with statistics, and transform data. These insights assist groups in measuring the data’s social effect and help corporations to determine their return on investment (ROI).
The foundation of data science is an interdisciplinary study concerning society’s fundamental operations, ranging from stocking supermarkets, monitoring election campaigns, and maintaining medical records. Being a member of this expanding sector can lead to an exciting and rewarding career, with several roles for a trained expert.
In this guide, we take a deeper look into the world of data science, including descriptions of the field, in-demand skills, career categories, and more.
Fundamental skills for a career in data science
The basic skill set required to establish a career in data science includes programming expertise, good communication, and a preferred degree in mathematics or statistics. Strong candidates would also have an aptitude for working with data presentation, analytics and modelling, to name a few core requirements.
Good programming abilities are a must-have for any data scientist if they wish to transition from theoretical projects to developing practical applications. Most employers will assume you are familiar with R, Python, and other programming languages. This includes libraries, documentation, basic semantics, procedures, and object-oriented programming. Having a degree of comfort when working with flow control statements is also essential.
Since data cannot talk independently, someone must manipulate it into a presentable format. Even then, the specifics of the data might have to be presented in greater detail, which is why a good data scientist must possess outstanding communication abilities. Communication is crucial to a project’s success, whether it is team communication to ascertain the steps you want to take to use the data to go from point A to point B or presenting to corporate leadership.
Since data scientists often work in diverse roles within larger organisations, they need to be able to explain the jargon of their field to colleagues who may not be equipped or skilled enough to understand it otherwise.
Knowledge of statistics and mathematics
Any competent data scientist will have a solid background in both mathematics and statistics. Most companies, particularly if they are data-driven, will look to their data scientists to grasp the many statistical methods, such as maximum likelihood forecasting models, distributors, and statistical measures, to aid in making suggestions and judgements.
Due to its connections to machine learning algorithms, calculus and linear algebra are also crucial. This is why a college or university degree in mathematics and statistics is often quite valuable.
Working with data visualisation
Another core responsibility of a professional data scientist is to convey critical messages and develop practical solutions. When looking to grow in your career as a data scientist, it is essential to understand how to break complex data down into smaller, more manageable chunks and present it using different formats.
To do this effectively, data scientists employ several types of infographics (figures, diagrams, and more), which is why it is essential to have a level of professional comfort when working with visual media.
Large data sets may not necessarily provide valuable information insights. However, a skilled data scientist has intuition and understands when to probe further for valuable data insights. Due to their practical expertise and appropriate training, data scientists develop this competence, which increases their productivity.
It is important to note that this skill is usually found in data scientists trained correctly with a focus on practical approaches. The Institute of Data offers some excellent course options for budding data scientists, focusing on developing these critical skills and getting job ready.
Familiarity with modelling and analytics
A qualified data scientist must possess the most recent data science training and expertise since data is only as good as those conducting the analytics and modelling. A data scientist should be capable of analysing data, running tests, and developing models to get fresh insights and forecast potential consequences. This expertise should be built upon a background in critical thinking and communication.
Data science career options
The career options for a data science expert are varied, including but not limited to the role of a statistician, business IT analyst, data administrator or architect, clinical data manager, data analyst, machine learning engineer and data scientist.
Data is gathered, analysed, and interpreted by statisticians to find patterns and connections that might guide corporate decision-making. Additionally, a statistician’s professional responsibilities to stakeholders include offering organisational planning advice.
Business IT analyst
A business analyst assesses a company’s procedures and studies market and industry trends. They are strategists in mind and analysts at heart. Business analysts search for chances to boost company income and growth while processing vast volumes of data. Business intelligence (BI) developers and business consultants are often interrelated positions.
A BI developer should be skilled and masterful in their command of BI analytical tools and well-versed in multiple coding languages. These abilities ensure the developer can process the data.
Data architect and administrator
Data architects collaborate closely with engineers to visualise the organisation-wide data management system. Their primary focus is figuring out the corporate strategy and the data that must be gathered.
After doing this, they will either develop brand-new database systems or improve the functionality of already-existing ones. Data architects also design the flows and procedures for data management while data engineers build the infrastructure.
Operations research analyst
Operations research analysts find and fix issues in various industries, such as logistics, business, and healthcare. Their workload is concerned with both qualitative and quantitative data, including statistics. Analysts in operations research will then utilise this information to create solutions that assist firms in operating more successfully and effectively.
Since they are primarily concerned with influencing organisational decision-making on a senior level, they collaborate on their results directly with senior management.
Clinical data managers
Clinical data managers combine maths concepts, programming, machine learning (ML), and statistics with healthcare training. These professionals actively absorb, evaluate, and forecast trends in the medical business, just like data scientists in other industries do with data collecting, data governance, and data quality throughout clinical trials and research.
Most data scientists work as data analysts and engineers in their early careers. Raw data acquired through the platforms is immediately available to data analysts. So, they must constantly collaborate with other marketing, sales, customer service, and finance teams to handle data better.
Data analysts use several tools for data visualisation, such as Tableau and Excel, to clean the data, examine it, and produce reports. Once the data is presented in accessible formats, it helps teams develop ideas.
Political analysis, a subfield of political science, uses statistics to spot patterns in topics including national affairs, the global economy, risks to domestic security, and diplomatic affairs. Political analysts compile and analyse economic, societal, and political data.
Experts in this area are significant during political campaigns so that they can devise strategies to locate and sway potential voters.
Machine learning engineer
As an expert in software engineering and data science, a machine learning engineer regularly works with large amounts of data. In a prominent consumer-facing structure, both talents could have autonomous duties yet collaborate in constructing the most effective systems.
Machine learning professionals with superior software programming abilities are highly valuable for any organisation if they switch to being data scientists. ML engineers are responsible for creating software, ML models, and artificial intelligence (AI) systems to power diverse organisational processes. They often work in senior jobs since being a ML engineer needs years of training and experience.
Information research scientist
These researchers create new technology while creatively utilising the tools that already exist. They devise experiments and resolve to challenge technological problems. Information research scientists may process large data sets in novel ways; for example, they may develop artificial intelligence “bots” that can simulate and foresee human behaviour.
These experts are in a prestigious position as they have the potential to further data science through advancements in machine learning, programming languages, software, database administration, and algorithms. Since the role involves advanced technical research, applicants with advanced degrees tend to have the upper hand.
Big data analysis is just one aspect of what data scientists do; they also tackle real-life business challenges. For example, the C-Suite depends on data scientists to provide trends and patterns across data and deliver practical insights and marketing initiatives. In addition, corporate strategy choices are directly impacted by their insights.
Some noteworthy characteristics expected for a data science position are skills as an effective communicator, business theorist, and even better analyst and mathematician.
Why is data science an ideal career option?
Data scientists and analysts are increasingly in demand worldwide to assist in navigating a disruptive market dominated by big data. To connect the dots across terabytes of data and provide trends, forecasts, and insights that will help generate competitive advantage, businesses from the technology industry to the healthcare industry depend on data scientists’ expertise.
Data drives all primary and secondary decision-making in modern business. Most corporate operations relocated to digital platforms during the pandemic, boosting e-commerce and subsequent data volumes. This raw consumer behaviour data is necessary for firms big and small. It is essential to have professional help to develop effective methods for gathering, organising, and analysing it to prepare for emergencies.
Despite the varied career options and increasing demand for data scientists, businesses are still seeking qualified candidates. Data scientists are more likely to get hired for higher-paying roles with profitable projects if they have strong programming language proficiency and sophisticated technical abilities.
Every industry requires data science professionals, from government security to dating applications. In addition, big data is vital for the success and improved customer service of millions of enterprises and government agencies. As a result, careers in data science are in great demand, and unlike a market fad, this demand is only expected to rise.
To establish yourself in a data science role, you must hone your technical skills in several aspects. If you do that right, the tech industry is your oyster, and you will be an excellent candidate for any challenging role.
The Institute of Data’s courses are a great means to help you step into the data science world. Whether it is your first move into the industry or you are looking to upskill and utilise new opportunities, our team are available to discuss your questions and conditions. Take the first step by booking a career consultation call.