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Post a JobIn 2009, Hal Varian, Google Chief Economist, was quoted as saying to the McKinsey Quarterly: “The sexiest job in the next 10 years will be statisticians.”
Well, that certainly has an odd ring to it, doesn’t it? “Statisticians” and “sexy” in the same sentence is something you rarely hear, but Varian was right. In a news piece from October 2012, Harvard Business Review authors Thomas H, Davenport and DJ Patil called the role of a data scientist “the sexiest job of the 21st century.”
Anyone who works in data science, or knows someone who does already understands that it is rapidly evolving into one of the most exciting and promising professions of the modern century.
So, is the role of a data scientists overhyped? Is it a dull and drab job in reality? Well, not according to Godefroy Clair, CTO at Flylab, who said that being a data scientist gave him a chance to step into his detective shoes. He added that each new case he took on, he had to understand a new field of data science to understand how it works, how to extract meaningful insights from unstructured data, and how to acquire knowledge without being a ‘field specialist’.
Vishnu Subramanian, CEO and founder at Jarvislabs.AI said that data science excites him because of its unmatched ubiquity – it’s everywhere and derived merely from computer science and math skills, which can be universally applied to not just learn from the past but especially improve future performance in just about any discipline. In his view, this is what makes data science so relevant in the modern century: its gigantic potential to improve ‘quality of life’ across a broad range of sectors. He believes data-driven decisions are the way forward and will become more commonplace among businesses around the world.
Data science is the fastest emerging field around the globe. It analyses data extraction, preparation, visualisation and maintenance to help businesses make better informed decisions and drive future growth. Data scientists must be experts at using machine learning (ML), natural language processing (NLP), artificial intelligence (AI) and algorithms to uncover probable future occurrences – although how much knowledge you require in each discipline largely depends on each organisation’s respective goals.
This field where massive volumes of structured and unstructured data is studied through modern tools and AI helps data scientists uncover specific patterns and trends – in many cases, these can hold the key to gaining a competitive edge in the market or making more fruitful growth-boosting decisions, for example.
So, to quickly sum it up: data science is the study of data to ascertain specific patterns – patterns which aid in making better, and less risky decisions. This isn’t something new although the application of data science is something we’ve seen spike noticeably in the last few years. Data science often combines business knowledge with mathematics and statistics by integrating a complex algorithm at the core with a business’s knowledge base. Does the Tom Cruise movie Minority Report ring a bell? Hair-raising, bone-chilling stuff indeed!
The end result is a prediction model for businesses which can be accessed simply through a dash with a bunch of statistics. We must admit we’re oversimplifying it a little but what business wouldn’t want to take advantage of data science, and more importantly, work with a skilled data scientist?
After all, data analysis has always been paramount to success in fields like weather forecasting, healthcare recommendations and breakthroughs, disease outbreak predictions, fraud detection, etc.
Still think the role of a data scientist is overhyped? We’ll leave that up to you then!
We now have a basic understanding of what data science is, what the role of a typical data scientist involves, and how businesses might benefit from it. We can also derive from the above that rather than being overhyped, data science is something that may soon become a necessity for growth and competitiveness, rather than a ‘nice to have’.
The only logical question at this stage would be: How viable is it moving forward?
Extremely, as it turns out. In fact, when Bill Gates once said “Content is King”, data can be seen as the Queen! Think about this for a moment:
Around 25-years ago, when the world was about to witness the first dial-up modem and the internet was still a thing from a distant sci-fi movie, grocers in your local area were unknowingly using data science to understand which products would sell more and which would sell less. Based on the findings from that data, they would then order the next batch of supplies. This was data analysis at work, granted at a very crude level, but data analysis nevertheless.
So, then came the internet and knocked everyone off their feet – it was one of the best inventions of the last three decades put together and with the internet evolving to ridiculous levels over the years, data analysis has become very, very sophisticated and chock full of ‘hidden treasures’, thanks to advances in AI and ML.
Moreover, as global economies mature and evolve, understanding the core mechanics of customer behaviour will be one of the most sought after business skills as it will undeniably be a chief marketing tool. We are pretty much at the cusp of the data collection and analysis explosion when we talk about using data science to better understand customer behaviour and predict future trends, for example – although there is still a shortage of data scientists, as the role hasn’t reached the same level of “commonplace” as, say, doctors, accountants, or lawyers.
Is data science an exciting field to be in? You bet your statistics and algorithms, it is! The world has become largely data driven – even flight training schools and videogame developers, for example, are tapping into the power of AI to create better products for the end user.
With time moving forward, the demand for qualified data scientists will only grow. Also, it’s ironic that people are so conscious today about the importance of keeping their data private on these so-called “free apps” – yet industry giants like Amazon or Facebook are collecting data each day at an alarmingly high rate.
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Data on the internet is growing at an exponential rate as we know it – we believe that contribution to the field of data science will grow at a much faster rate. So will data science jobs in the coming years.
Whether it’s determining a specific country’s happiness index or fraud detection in banks, data science is going to be around for a long, long time, and organisations will likely find it hard to operate without it – just like the internet.
We can certainly think of a few industries that can benefit from data science from the outset:
As patient databases continue to grow, data-driven healthcare systems will be able to identify any deficiencies, shortcomings or upcoming trends quickly, which can help local governments mitigate any emerging health crises.
If specific AI tools and algorithms are in place, it can be much easier to prevent fraudulent transactions from occurring and even if attackers or cyber-criminals manage to bypass firewalls or security systems, the damage can be contained bettered and rectified almost instantly. Specific operations or activities can be shut down automatically by the AI to ‘contain the situation’.
With previous years’ data and innovative analysis tools, accurately predicting changing weather patterns such as storms, hurricanes and tsunamis (and the potential damage they could do) will be a requirement for weather stations and experts, potentially saving hundreds to thousands of lives, not to mention property loss.
AI systems are already helping people with navigation, telling them which route is the best one to take or how to avoid traffic, thanks to Google Maps. Systems like Google Maps will become more advanced in time, helping authorities make roads safer by preventing situations that cause accidents or responding quickly to them through real-time data and reporting.
With time, these systems are only going to grow and become more potent, helping drivers avoid different kind of problems, such as a damaged road, or roads which are prone to natural disasters like landslides, for example.
The videogames industry is a multi-billion dollar industry, even exceeding revenues previously made by Hollywood Box Office hits only. The user or ‘gamer’ experience is now more personalised than ever, thanks to the huge amount of data being collected. Game console manufacturers, for example, are collecting user data to constantly improve online services as well as the performance and functionality of their console hardware; while software giants like Microsoft are using AI to provide aviation enthusiasts with photo-realistic satellite imagery using Bing Maps, as well as ‘live weather’ and ‘live aerial traffic’ in products like Flight Simulator 2020.
The entertainment industry is already tapping into the power of data collection, which is evident with apps and websites like Disney, Amazon, Netflix, and a number of other OTT platforms. User watch history, for example, is a data-rich bank for such companies, which means the more someone consumes content on a particular platform, the more refined their suggestions get. The same applies to YouTube which also relies on data collection and analysis to make videos more relevant to your interests and preferences over time.
If you’ve read this far, then you probably have your sights set on pursuing data science as a career. Good move! In the not too distant future, nearly every decision businesses and even people make at the individual level is going to be governed by data.
Every industry today requires a data scientist because businesses now fully understand the value of self-analysis to grow, stand out, and effectively outpace the competition. You, the data scientist, are the star of the show as you’ll be doing the data analysis to identify trends and patterns, allowing the organisation you work for to make the most fruitful decisions which drive the business forward.
When it comes to pursuing a career in data science, we need to talk about the variety of skills required. Programming languages, for example, are a must, so being proficient with R, SQL, SAS, Java, Python, etc. is a good starting point. Additionally, a data scientist must be well-versed in popular Big Data frameworks like Spark, Hadoop, and Pig. He/she must also be familiar with machine learning (ML), deep learning (DP), and artificial intelligence (AI) as well as natural language processing (NLP), to reach new heights in their career. However, you probably won’t need to acquire all these specialised skills at once, as different organisations have different data collection and analysis needs.
A data scientist could handpick certain skills, like NLP and AI, or ML and programming languages, for example, and become a specialist. So, as it appears, you could wear many hats within the world of data science, such as that of a data engineer, quantitative analyst, AI engineer, ML engineer, data architect, statistician, and so on.
Certifications in data science courses and practicing your skills across a variety of projects will definitely help to build your portfolio – the more hands-on experience you have, the higher your chances of getting hired as a data scientist.
Let’s shift our attention to academic qualifications. While a Bachelor’s degree is required by most organisations, a higher qualification or advanced degree is almost always preferred and gives candidates an edge. For example, an advanced degree in Statistics or Mathematics will always increase your chances of securing a well-paying job as you’d likely have the problem-solving skills businesses require from data scientists. Also, since data science revolves around multiple programming languages, a degree in Computer Science will only boost your chances of landing a dream role.
With the above said, perhaps, the most important aspect of any job, particularly one in the field of data science, is knowledge. Therefore, knowledge on the technical aspects of programming and business are vital. All in all, data scientists must focus on acquiring the following skills, just to name a few:
Hive, R, Python, SQL, and C++ are commonly used languages in data science. However, we’d recommend learning Python first as it is one of the most popular and widely used languages for implementing data science methodologies. It’s also very versatile, easy to understand, and contains a wide array of libraries.
MATLAB and SAS are two common pieces of software in data science – the former is used to analyse, clean, and gain insight from complex data while the latter is a statistical analytics software used to manage information, analysis, and reporting.
Statistics is considered one of the most essential components of data science, used for analysing data in one of the two represented forms: inferential or descriptive.
The more mathematics you know, the better, although topics like linear algebra, probability, calculus, etc. play a particularly important role in studying and practicing data science.
It’s a no-brainer that good communication skills are vital to success in any line of work but it is especially important in data science. Data scientists are expected to communicate their findings in a succinct, effective, and easy-to-digest way. Their data findings help businesses make better decisions, after all, so adequate soft skills are a must.
Understanding the business they work for is crucial for data scientists, as their work is what fuels business growth and takes it to the next level. Mitigating an organisation’s pain points and business challenges should be every data scientist’s primary concern.
Businesses need a viable solution to complex problems, and for a data scientist, this is ‘all in a day’s work’. Therefore, you need to train your mind to think logically and learn the art of analytical reasoning.
LinkedIn published a report in 2022 titled “Jobs on the Rise”, which listed Data Engineer as among the most in-demand jobs in the UK, in fact, listing ‘data skills’ as requirements in many other ‘on the rise’ jobs too.
Not only that, but LinkedIn also called Data Scientist “the most promising career”, while Glassdoor called it the “best job in America”.
Clearly, data science offers promising careers with some amazing advancement opportunities to those who have the skills, qualifications, and acumen for it.
Did you know that MSc graduates in Data Science in the UK are earning anywhere between £30,000 and £40,000 a year? Meanwhile, the average salary of a data scientist in the UK, as reported by Glassdoor, is £45,000 a year, although large tech companies pay up to £70,000 a year. The deeper you go into data science and acquire the necessary skills, knowledge, and experience, the more you can earn. It is indeed an exciting job role to take and one where you can make a very lucrative living.
Oh, yes – absolutely! Named by both LinkedIn and Glassdoor as “the best job” and a “very promising career”, the demand for data scientist roles will only grow with time, just like the internet grew ‘out of control’, and now we can’t imagine our lives without it. Data is the future and as technology progresses and evolves, so will the roles of data scientists.
Not necessarily, but that also depends on your general outlook. Most jobs do involve a certain degree of stress but that’s only to keep you on your toes! As for data science, if you love working with numbers and data to solve real business challenges, you’ll barely feel the stress.
Yes, it is – for the next few decades at least, data science prospects are at no risk of being replaced by robots, machines or automation. However, you may have to confront some ethical considerations about which job you take on or what you build.
CareerExplorer does regular surveys to determine how satisfied people are with their careers – data scientists rated their career happiness 3.3/5 starts, which actually puts them at the top 43% of careers. But to put this into perspective, CareerExplorer also compared ‘Data Scientist’ to the satisfaction and happiness levels of people in similar careers – where most averaged 3.3 or 3.4 starts out of 5.
Suffice it to say, data scientists are pretty happy with their careers, especially those who love what they do!
Let’s be honest: working with numbers and analysing data isn’t everyone’s cup of tea. However, if you enjoy playing around with numbers and extracting insights from data to help businesses overcome challenges and grow, then data science offers the perfect career opportunity, and yes, it can be a fun career!
The short answer is yes. Data Science is a growing field where the role of a data scientist is in hot demand. Data scientists can make anywhere between £30,000 and £45,000 a year although experienced ones working with larger firms make up to £70,000 or more a year.
Absolutely! Data science has applications across nearly every industry, so there are endless career opportunities in the field, especially if you’re eager to learn and take on new challenges.
Still think data science is overhyped? Well, the experts responsible for extensively researching a variety of factors in data science don’t seem to think so, and neither do the people who are pursuing it as a career.
Do you have what it takes to become a data scientist? Drop us your resume to explore career prospects in one of the UK’s fastest growing data science specialists: Fast Data Science.
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Above: video of the AICamp meetup in London on 10 December 2024. Harmony starts at 40:00 - the first talk is by Connor Leahy of Conjecture
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