If you are an aspiring data scientist, you may have come across the terms artificial intelligence (AI), machine learning, deep learning and neural networks. Although these may appear to be futuristic technologies, you might be surprised to find out they are already incorporated in many businesses and industries, and none of it would exist without advances in data science.
This article explains the differences and similarities between data science and machine learning to help you decide which career path is most suitable for you.
What is data science?
Data science is a broad field that analyses large amounts of structured and unstructured datasets – ‘big data’ – to provide organisations with actionable insights. This field combines mathematics, statistics and programming to obtain, analyse, process and manipulate raw data.
As industries and companies are increasingly relying on data mining and data analysis to thrive in today’s competitive world, data science allows you to work in virtually any sector you can think of, including:
- Finance and e-commerce
- Healthcare and medicine
- Entertainment
- Retail
- Academic research
- Government
- Cybersecurity
- Transport
- Utilities
What does a data scientist do?
Each sector will require different data science tools, but every data scientist’s tasks and responsibilities are similar. You might:
- Use data to solve business challenges faced by your company
- Collect and process data from various sources to develop models and algorithms
- Visualise data after analysis using high-level statistical software
- Communicate your findings to diverse audiences
Working in the business sector, you may use your skills to understand customer behaviour and patterns to recommend products to your company. As a data scientist in healthcare, you could model diseases or process data from drug trials. The entertainment industry may require you to study media consumption trends for targeted advertising.
What are the required skills for data scientists?
Data scientists are well versed in maths, statistics and computer science. Coding is a challenging skill to learn, requiring highly analytical thinking, problem-solving, resourcefulness, attention to detail and immense patience; you will be expected to be proficient in widely used programming languages, such as Python, C#, Java, SQL and R, even for junior level positions.
Additionally, data analytics and computer technologies are constantly evolving. Notably, quantum computing has witnessed significant progress over the years, with experts claiming it will revolutionise how computers function. Therefore, you must be willing to continue learning new concepts and skills to thrive in this field.
Other than technical skills, you must be an excellent communicator, both verbally and written, since you will be working as part of an organisation, often advising it on aspects such as marketing and advertising. Hence, you also need to be aware of business management.
What is machine learning?
Machine learning is a branch of AI, which in turn is a field made possible with data science. Through machine learning, algorithms learn patterns from data and make predictions or decisions without explicit programming. It involves creating models and algorithms that improve their performance over time by pattern recognition, adjusting to new data and refining their outcomes.
Spotify recommendations, predictions of new flu strains, targeted ads on your phone, self-driving vehicles – they all utilise machine learning to function, rather than data scientists constantly updating datasets and adjusting algorithms.
In simple terms, machine learning allows data scientists to automate tasks and teach a program to learn how to deal with data rather than having to do it manually each time.
Machine learning can be divided into three main types:
- Supervised: algorithms are trained with labelled data to allow them to make predictions for new, unseen data accurately
- Unsupervised: the algorithm is trained with unlabelled datasets to detect hidden patterns
- Reinforcement: algorithms are trained to make decisions based on feedback to maximise a reward
Neural networks are a crucial concept in machine learning inspired by the human brain. These are organised in nodes – representing neurons in the brain – in a layer. Each node turns an input into an output and passes it on to the next layer. Depending on the number of layers working together, neural networks can be classified as either deep learning (many layers) or shallow learning (few layers).
What kind of job roles use machine learning?
Machine learning is more of a new, exciting skill in the realm of computer science rather than a job per se. As such, you can incorporate machine learning into any role across any sector which deals with data.
Examples of machine learning jobs include:
- Machine learning engineer
- Data analyst/scientist
- Robotics engineer
- Software engineer
- Deep learning engineer
- Network architect
What are the required skills for machine learning engineers?
Since machine learning can be viewed as an extension of data science, the skills and knowledge required to succeed as a machine learning engineer are broadly the same as for data science roles. However, certain concepts are more relevant in machine learning. For example, you should have a good grasp of decision analysis, whereby decision trees visualise a decision-making process.
Use of logistic regression will allow you to classify observations into categories, an algorithm which underpins neural networks. This is particularly useful in natural language processing (NLP) which utilises machine learning to understand text or speech. A similar algorithm, called clustering, is important in unsupervised machine learning.
Problem-solving, as with data science, is arguably one of the most important skills in machine learning too. For instance, a common challenge you encounter in machine learning is the concept of overfitting, where your model is only able to make predictions for datasets you provided, not new data. You will have to detect such issues and develop measures to prevent them.
Differences between data science and machine learning
Whilst data science is the study of data in general, machine learning is a tool to automate tasks and algorithms involved, hence minimising constant human input. In contrast to data science, machine learning doesn’t require you to know several programming languages because the industry primarily runs on Python. Secondly, the role of a data scientist is rather well-defined, whereas machine learning is not a specific job title. As such, working in machine learning is not limited to any particular role.
Educational routes: data scientist
You usually need a BSc degree, typically in maths, computer science, engineering or other numerate subjects that teach high-level statistics. Programming languages you may learn as part of your course or independently. Usual entry requirements for a bachelor’s degree are two or three A levels.
Many data scientists additionally pursue a master’s degree such as an MSc in data science and analytics, business analytics or big data technologies. A relevant postgraduate qualification is particularly useful if your background is in an unrelated field but you wish to enter data science; however, some postgraduate courses do require you to have completed an undergraduate degree in related subjects.
The Office for National Statistics (ONS) also offers data analyst apprenticeships if you choose not to opt for the university route. Entry onto these will require four or five GCSEs at grade 9 to 4 (A* to C) and A levels for a higher or degree apprenticeship.
Whichever route you choose, get involved with various placements to gain work experience. There are several professional and industry bodies which provide training, work experience and certification exams for specific career paths to confirm your skills and aptitude meet global standards. Examples include the Computing Technology Industry Association (CompTIA), the Institute of Analytics (IoA), the Institute of Coding (IoC) and the Royal Statistical Society (RSS).
Although institutes such as the IoA offer accreditation for your training, it is not required. Securing a junior data scientist job and career progression will depend on your skills and knowledge, as well as commitment to the industry or firm.
You may even start your own company or become part of other start-ups. As a data scientist, you have the freedom to work globally, either in an office or remotely, although other countries may require specific certification.
Educational routes: machine learning engineer
As previously mentioned, machine learning is part of data science and therefore not strictly confined to a specific role; you can be a data scientist working with machine learning and AI technologies. All educational routes to becoming a data scientist therefore apply to machine learning.
Nonetheless, job titles such as machine learning engineer are becoming more common. Naturally, to work in a machine learning role for an organisation you must know and be able to apply machine learning and AI concepts.
Many universities now even offer exclusive AI and machine learning courses. More senior roles will probably require a master’s degree too. Certification of your training is not necessary but may be desirable at some companies.
Salary information/differences
Your pay as a data scientist will depend on your location, sector, company, job role and seniority; the average salary for a data scientist is estimated to be £30,000 to £70,000 a year in the UK. London houses the highest paid data scientists in the country, with some earning six-figure salaries.
In the US, the average salary according to the US Bureau of Labor Statistics is $103,500 a year, ranging from $58,510 to $174,790.
There isn’t sufficient data to distinguish between the salaries of data scientists who utilise machine learning as part of their job and those who do not. Machine learning engineers have a similar earning potential to data scientists which can be influenced by various factors, such as seniority and sector. Demand for professionals trained in machine learning, however, is increasing, which can translate to high pay.
Conclusion
If you aspire to become a data scientist, you will probably learn machine learning and AI as part of your training. These technologies are becoming more and more popular across industries.
As machine learning is becoming more prominent, specific job roles and educational routes are emerging too. The focus of both disciplines, however, does remain distinct. Whilst traditional data scientists analyse and study big data, machine learning engineers develop algorithms to teach computers to achieve this themselves.
Resources
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