What is a Data Scientist?

A data scientist is someone who can use data to draw out key insights for better problem solving and decision making. Combining skills from statistics, computer science and specific domain knowledge they derive patterns and understanding from raw data.

What do Data Scientists Do?

What most people think Data Scientists Do

What Data Scientists Actually Do

How Do Data Scientists Work Their Magic?

  • Maths and Stats

    Data Scientists make use of a number of tools from mathematics/statistics to computer programming to those considered softer skills. Some skills from the world of statistics include:

    • Linear Algebra

    • Probability Theory

    • Regression

    • Feature Engineering

    • Artificial Intelligence/Machine Learning

    Want to learn more? We explain about all of these topics over on the blog.

  • Computer Science

    Some skills from the world of computer science include:

    • Programming, usually in Python or R

    • Distributed programming for big data

    • Data Science packages such as pandas, polars, sk-learn, keras, numpy

    • Data Structures

    • Databases

  • Softer Skills

    Finally, an underestimated set of skills are the so-called “soft skills” needed for a data scientist to become a valuable asset to the team, instead of just a nerd in the corner. These skills include:

    • Critical Thinking

    • Problem Solving

    • Communication, technical and non-technical

    • Project Management

Where Do Data Scientists Work?

  • Data Scientists work in a variety of industries

    And with the recent AI boom, data scientists are becoming more and more in demand, with industries that would never have considered hiring them before taking steps to branch out. Some of the more common places where you would find data scientists include:

  • Finance

    From the classical Quantitative Analysts (who are basically data scientists with a more probability and statistics lean) to modern day machine learning engineers, the finance industry is full of technical people using advanced data science to manage risk, inform trading decisions and understand the financial world better.

  • Tech

    From algorithm wizards working on content recommendation systems to those pushing the frontier of Artificial Intelligence, the tech industry is ripe with opportunities for Data Scientists to flex their technical skills and drive the profession further forward.

  • Healthcare

    The healthcare industry has two things that make it ripe for data science innovation: the mass of patient data and meaningful real-world problems to solve. Analysis of both organisational and symptom data can drive better outcome for patients and the use of Artificial Intelligence to help in the diagnosis process is only increasing.

  • Retail

    How do big retailers know when strawberries will be most popular? Or the latest toy trend that will drive purchases this Christmas? The retail industry employs hundreds of forecasters using data science techniques to optimise supply for predicted demand to ensure that customers can get what they want, when they want it, whilst minimising waste.

  • Logistics

    When it comes to logistics and supply chain management, there are a huge number of moving parts. The more moving parts, the more opportunities for inefficiencies to sneak in. In addition, the complexity means a huge data challenge, giving data scientists the opportunity to optimise this process and remove inefficiencies within the supply chain.

  • Oil and Gas

    From improving exploration of potential sources to more efficient methods of extraction, to optimising delivery to receive the best price for extracted oil and gas, there are many points in the oil and gas pipeline in which data science involvement can add value.

  • And many more

    And this is by no means an exhaustive list. We see more data science openings in governments, marketing, e-commerce and sport analysis. As the utility of data science becomes more prevalent, we see it making inroads to more and more industries, such as gaming, mass media, education, culture and many more.