Data science is the application of statistical analysis to extract insights from data to optimise processes and inform business decisions.
As the volume of data collected and stored explodes, so does the number of companies looking to gain a competitive advantage by leveraging that data to operate in a data driven manner.
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Most companies sit on untapped treasure troves of data varying from documentation of internal processes to customer data. A data scientist's role is to tap into that data to deliver business value.
They accomplish this through a variety of approaches. A typical data science job description will have the following responsibilities:
Overall, the responsibility is about writing code to prepare and analyse data to help make decisions, and build statistical and machine learning models to deliver business value.
You might not realise it, but you interact with data science products everyday.
How it is that Netflix always seems to know what you want to watch? Although we don't know the exact recommendation algorithm, they likely use a form of collaborative filtering to accomplish this.
You're probably wondering what that is. It's actually surprisingly simple. Netflix tracks every interaction you have with its website - every show you hover your mouse over, how long you watch any show, what time of the day it is and much more. The algorithm then matches you with other Netflix users who have similar behaviour profiles, and recommends you titles that they've watched which you may not have. The idea is that people with similar behaviours will have similar tastes.
Let's say you run an online bakery selling products ranging from cakes to pastries to cookies. You're wondering "What products should I release next?"
Your data science friend can help you figure this out. They make hypotheses about which factors influence the sales of a product and analyze to find patterns. For example, they may think that product descriptions influence sales so analyse the correlation between sales and words used in the description.
Through this they may realise that sales for products which have the word "soft centre" in the description have been increasing 80% every month.
Seeing this emerging trend, you know you should release more cookies which have a soft centre as they are likely to sell well.
So when do you need a data scientist?
You know you need a data scientist when you have data and questions that need answering but the insights you're looking for can't be found by a simple calculation.
Data science demands a being able to apply a strong quantitative toolkit as well as an understanding of business processes. It's about more than just understanding the maths behind predictive models or being able to implement machine learning models in Python. When working on industry AI applications, data scientists need to interoperate with other technical engineering teams and even non-technical stakeholders. That means that they are required to write production-grade software, and need to be able to communicate well with others.
Here are some of the key tools and skills which we've seen most widely used by data scientists in industry:
Entry level data scientists in the UK can expect a salary of £35,000 and above. With a few years of experience, mid level data scientist salaries are around £70,000. If you stay in the industry, keep building on your skills and gain experience managing teams, senior positions pay upwards of £91,250.
The high demand for data scientists has continued growing steadily for over a decade. The number of open roles citing data science grew by 37% between 2019 and 2021.
The role was labelled the "Sexist Job of the 21st Century" by Harvard. Even though competition has increased compared to when the role first started taking off around 2012, a data science career continues to be a strong, future-proof choice.
The line between where data analysis ends and data science starts differs depending on which company you go to.
Both roles are responsible for analysing data to extract insights, creating visualisations and presenting their findings to stakeholders.
The main difference is in the complexity of the algorithms use to find those insights - a data analyst would typically use simple statistical testing whereas data scientists would build complex predictive machine learning models. As a result, analysts do not need to know any programming languages as there are platforms that help them perform these analyses without code, such as Power BI or Tableau.
Data analysts perform a subset of the tasks that a data scientists would be responsible for so is often seen as a less advanced role. Many data analysts move into data science roles once they have built a solid foundation in coding and machine learning.
On the other hand, data engineers play a completely different role. They are responsible for building infrastructure to collect, process and store the data in a format that is accessible by analysts and scientists.
The foundation of data engineer is coding and building systems on top of cloud services. As a result, lots of software engineers see the growing demand and make the jump into
Data scientists need to have a good foundation in mathematics, scientific thinking and programming so mostly come from STEM backgrounds.
There is no single path to becoming a data scientist. However, the fastest way we know to launch your career in data scientist is through the AiCore programme.
The 18 week programme delivers the most industry-informed, hands-on education in data science. You will learn from established experts how production-grade data science algorithms work then get experience by building systems that are currently deployed at companies such as Uber and Pinterest.
Are you considering becoming a data scientist, trying to figure out if it's right for you? Maybe you are certain you want to become a data scientist and are trying to find the fastest route into this lucrative career. In either case, the AiCore team would be delighted to help you. Book a 15 minute call here.
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