What is a data scientist?

In today’s data-driven world, data scientists are crucial. They make discoveries and extract insights from large amounts of raw data.  They use software, artificial intelligence, and machine learning to create mathematical models to solve business problems.  Their insights are used to inform decision making and innovation across various industries.  This could lead to new markets and opportunities, increased profits, and better outcomes for the business. 

The title of data scientist has only been around since 2008.  However, the field is now growing fast. Data science jobs are expected to increase by 30 to 35 percent by 2027, according to the 2023 World Economic Forum Future of Jobs Report. 

What does a data scientist do?

As a data scientist, you will gather data from various sources, including databases, APIs, and external datasets. Data scientists typically go through the data wrangling process before any data analysis is conducted, to ensure the data is reliable. Data needs to be pre-processed and cleaned, to address any missing values, outliers, and inconsistencies. 

A typical day for a data scientist involves a variety of tasks. These might include:

Exploratory data analysis (EDA): Analysing and visualising data to identify patterns, trends, and relationships.

Model building: Developing and fine-tuning machine learning models for tasks such as classification, regression, clustering, or recommendation. A lot of coding is involved – you might work with programming languages such as R and Python. 

Regression testing and documenting: it is important to make sure you don’t break someone else’s work while you are bug fixing or developing. 

Model evaluation: Assessing the performance of your models using metrics like accuracy, precision, recall, or Root Mean Squared Error (RMSE).

Deployment: Integrating the models you’ve developed into production systems or applications for real-time predictions.

Collaboration: Working closely with other teams, including engineers, product managers, and business analysts, to address business challenges.

What skills do you need to become a data scientist?

As the name suggests, you’ll need skills and attributes that are familiar to scientists, such as analytical thinking and a natural curiosity. You need to go beneath the surface of a problem in order to make discoveries from big data. 

Because the field is evolving so quickly, you will also need to be prepared to upskill on new techniques. You'll have to stay on top of analytical methods such as machine learning and text analytics.

Programming

Data scientists are proficient in programming languages like Python, R, and  SQL for data manipulation, analysis, and modelling tasks.

Statistics

You’ll need a solid foundation in statistics so that you can apply statistical techniques for hypothesis testing, predictive modelling, and interpreting results accurately.

Machine learning

You will need to understand machine learning algorithms and techniques in order to build predictive models and extract insights from data. Having a strong background in maths will help here.

Data wrangling

As a data scientist, you’ll have to clean data, and transform and structure it from one raw format into your desired format so that it is usable.

Data visualisation

You will need to create meaningful visualisations using tools like Matplotlib, Seaborn, or Tableau. It will help if you are a strong communicator to present these insights to stakeholders.

Domain knowledge

Knowledge of the area that you are interested in working in can be invaluable. As you develop your career you can specialise in a particular industry or domain. You’ll need to be able to explain to people why they should adopt your solution. 

Popular industries for data scientists

Data scientists are in demand across various industries, including:

Technology: Companies like Google, Facebook, and Amazon use data science for product recommendations, user segmentation, and ad targeting.

Finance: Banks and financial institutions leverage data science for risk modelling, fraud detection, and algorithmic trading.

Healthcare: Data scientists analyse medical data to improve patient outcomes, personalise treatment plans, and predict disease outbreaks.

Retail: Retailers use data science for demand forecasting, customer segmentation, and inventory optimisation.

Manufacturing: Data science helps optimise production processes, predict equipment failures, and improve supply chain efficiency.

What is the difference between a data scientist and a data analyst?

Both work with data, but in different ways. Although there can be some overlap, data scientists tend to focus more on developing new automation systems and methods to extract data. They will use machine learning and large scale frameworks. They build their own predictive models and data visualisation tools.

Analysts work only on analysing the data available to them rather than needing to wrangle it into a usable format. They might also conduct consumer data research.  They will look at the data to identify trends, create graphs and charts to visually represent datasets, and show their findings. They also perform routine statistical analyses to help companies answer specific questions or solve problems. A data analyst is likely to have advanced Excel skills, as well as being proficient in tools such as Tableau and Power BI which allow them to visualise the data.

People with more of an interest in computer science often lean towards data science. Those who prefer statistics and research lean more towards data analysis.