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Ever noticed how recommendations and suggestions pop up on your social media accounts and other digital platforms based on your internet search history? This is all made possible through Data Science.
Globally, businesses are waking up to the power of their data, and Data Science is the key to unlocking its potential. Data is the currency of the digital world now, and highly qualified experts are needed to make sense of it, harness it, and bring it to life. This creates a high demand for data scientists and engineers in the global job market - roles that are both intellectually stimulating and business-critical of organisation’s looking to stay ahead of the curve.
The data industry encompasses many job titles across various industries and fields. Regardless of title, all roles are united in extracting data from data sets – big or small - to provide actionable insights helping organisations improve their operations and performance.
Data Science, Artificial Learning, and Machine Learning are today’s most exciting technical disciplines, and it comes as no surprise that Harvard Business Review named Data Science as the “sexiest job of the twenty-first century”. With the mass-adoption of Machine Learning, Artificial Intelligence, and the Internet of Things, along with the accessibility of Big Data, demand for Data Science jobs has grown tremendously. In fact, Tech Nation, People and Skills Report 2022, highlights that tech roles requiring data skills are becoming increasingly important. Over the past three years, “data” has moved from third in demand to the most in demand skill across all tech jobs in the UK.
In this blog, we’ll answer all your burning questions. By the end, you’ll be equipped with everything you need to decide if a career in Data Science is for you. Read on, or skip to section:
In-Demand skills in Data Science
Python has emerged as the go-to programming language for Data Science. Tiobe’s January 2023 index ranked Python as the most popular programming language, surpassing longstanding Java and JavaScript in popularity.
Python is a general-purpose and high-level dynamic programming language that focuses on code readability. Its syntax allows programmers to write codes in fewer steps as compared to Java or C++. Some of the other reasons behind Python's popularity include its versatility, effectiveness, ease of understanding, and robust libraries.
The second place is held by SQL, short for Structured Query Language. SQL is perfect for data extraction and relational database management.
Below we outline some of the key hard and soft skills that employers look for and skills that frequently come up in job descriptions.
Data Science soft skills:
- Problem-solving
- Attention to detail
- An analytical mindset
- A methodical and logical approach
Data Science hard skills:
- Proficiency in Microsoft Excel
- Knowledge of programming and querying languages such as Python and SQL
- The ability to work with large, complex datasets
- Expertise in data visualisation
Data Science career path
Graduates might have the upper hand due to their exposure in the field, yet there are a few ways you can start a career in Data Science if you have no experience. Through the availability of open-source resources and materials that are freely available, it is possible to enter this industry through self-preparation as well.
How to prepare for your Data Science interview
Congratulations, you’ve managed to stand out from the crowd to get invited to a face-to-face or virtual interview, now the hard work begins.
Below are some tips to make sure you’re ready for your upcoming Data Science interview.
Research the role: From a small sample of related job titles below, you can see how varied and wide-ranging the role and responsibilities may be:
- Data Scientist
- Business Analyst
- Data Architect
- Data Engineer
- Data Visualization
The more you know about the job you are applying for, the better you will be able to prepare for the interview. Make sure you understand the type of Data Science job you’re applying to (so you can confidently speak about your fit in that role and save your time applying to jobs that don’t fit your interests or experience).
STAR Methodology – Results: In a data interview, the most important part of the STAR methodology is “R” for Results. Hiring managers are looking to see the work a candidate has done which led to a measurable outcome. Hiring managers want to see that a candidate can derive actionable results from data sets, and that they aren’t just analysing for the sake of it.
Characteristics – Solve Problems: Hiring managers are accessing a candidate’s ability to solve problem during a data interview. Most of the time, they do not ask “are you a problem solver” directly. Instead, they assess this through a candidates’ responses.
Know what you have on your CV: When you list something on your CV, you must be able to confidently explain the thought process, problem-solving approach, the datasets you worked with, and introduce the tools you used.
Earn a high salary
Salaries in the Data Science field vary depending on a range of factors including experience, the location, and the sector. According to Adzuna, Entry level positions in the UK start at £35k, rising to £55k per year depending on the experience. Most experienced workers can make up to £80k per year. Glassdoor data highlights that large tech companies tend to pay higher compensation compared to other firms offering jobs in the field.
Gender Diversity - Woman in data science
It’s no secret that there is societal pressure to increase the number of women and minorities in the field of Data Science and technology in general. According to the latest research by PwC UK, 3% of females say a career in technology is their first choice. That lack of diversity in Data Science is a serious issue. The gender split is still very unbalanced with only 26% of the global workforce in the Data Science. As few as 22% of Data Scientists today are women in the UK, with the percentage only getting smaller in senior roles. Harnham found that pay gaps and seniority gaps persist in the field of Data Science in the UK, US, and Europe.
Bright Purple Data Science career advice
One of the stand-out trends over recent years has been the huge spike in demand for cloud-literate data professionals. In fact, almost all Data roles we have at Bright Purple require some element of cloud-literacy. The market is dominated by the “Big 3” – Amazon, Azure, and Google Cloud, with Amazon Web Services the largest by far, taking up 34% of the global cloud market in Q3 (Oct-Dec) of 2022. All these providers are incorporating ever more Big Data processing, Machine Learning, and data warehousing tools into their ecosystems to make it easier than ever for people to build advanced, cloud-first data solutions.
Our advice is, get all the experience of cloud computing you possibly can to make you as valuable and as in-demand as possible.
Start building your data science career
Data is more important than ever in a world full of uncertainty. As businesses continue to transform, they’ll be looking for employees with Data Science and analytical skills to help them optimize resources and make data-driven decisions.
With the increasing demands around the world, working as a data scientist can be a lucrative and rewarding career choice. Whether you want to explore Data Science for the first time or if you’re already working in the field, there’s a path at Bright Purple for you.
Check our latest Data Science jobs here or register your CV with us today and be notified when relevant jobs come up!
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