Data has become a priceless resource in the digital age. But to mine its full potential, you need the right tools for the job.
That’s why we need Data Scientists – skilled, experienced, professionals who are experts in collating and analysing data.
The role of a Data Scientist involves collecting data and mining it for information. From the collected data, a Data Scientist can forecast trends and patterns, hypothesise future outcomes and discover new solutions to problems. This information can be used by organisations to inform decisions and make improvements.
As Data Scientists can work for any number of organisations – from charities to international corporations – each organisation will have its own idea of what success looks like.
For an agency that’s focused on environmental issues, success might involve discovering new solutions to the climate crisis. For a multinational business, success could look like improving customer satisfaction and boosting profitability.
Read on to discover what hard and soft skills you need to become a Data Scientist.
What skills do you need to be a Data Scientist?
These are some of the technical skills you’ll need as a Data Scientist.
1. Machine learning
What is machine learning? Machine learning involves applications that, like humans, learn from experience and can improve their decision-making skills with time and experience. This is a branch of artificial intelligence.
You will often hear ‘machine learning’ in the same sentence as data science. That’s because machine learning is at the heart of data science, and is used to build models that can help analyse data and forecast trends. Machine learning can also help automate time-intensive tasks that would otherwise take up a lot of the Data Scientist’s valuable time.?
2. Deep learning
Deep learning is a subset of machine learning. It’s what allows computers to learn as they progress. Deep learning models are trained through the use of large data sets with many deep layers, hence the name. Some real-life examples of deep learning are voice and image recognition, self-driving cars and recommendation engines like Netflix and Amazon.
3. Programming knowledge
To work as a Data Scientist you need to know at least one programming language. Knowing more than one will make you stand out to employers. Python is one of the most popular programming languages, but there are others like R, SQL, Java, Scala and C/C++.
4. Data storage
We are creating an astonishing amount of data every single day. About 2.5 quintillion bytes. Yep, that’s right: quintillions. And all that data needs to be stored somewhere securely. Because as we’re all very aware, cyber security has become a global focus and there are plenty of cyber criminals out there who are looking to extort, corrupt, steal or spy on peoples’ data. Part of a Data Scientist’s job is knowing how to store data safely so that it can be used when needed.
5. Data visualisation
Communicating the meaning behind a big dataset can be difficult. That’s why Data Scientists need to know how to use visual elements (such as graphs, maps and charts) to create a graphical representation of information and data. Data visualisation tools transform data into an accessible format that makes it easier to understand trends and patterns in data.?
There seem to be conflicting opinions on how much maths you need to know to be a Data Scientist, so you may feel a bit overwhelmed if you’ve begun researching this particular role. An interest in and a knack for numbers will definitely help, but the good news is you don’t need to be a maths prodigy. You don’t need to worry about performing complex, in-depth equations (that’s why we have computers); but you will need to be familiar with statistics, calculus and linear algebra, which are used to create and train models and analyse and interpret data.
As well as hard skills, Data Scientists need a specific range of soft skills, too.
Data Scientists often work with people in an organisation who don’t have much experience in this field. They could be people in the IT team who have some knowledge, or those in management who only have a vague idea. This means that Data Scientists need to be able to clearly relate their findings to other people, minus the jargon and technical terms. Communicating findings in a succinct way means that the data is understood properly, and clear steps forward can be taken.?
2. Critical thinking
Critical thinking is used to make sound, unbiased judgements that are based on evidence and fact.
Critical thinking is an important soft skill to have in many professions, not just data science. But it’s important for Data Scientists as it allows them to objectively frame questions and analyse hypotheses and results, while also looking at problems from different angles. Analysing data objectively is critical before establishing your own opinion – it encourages you to take a second look, to examine things more closely and to remove any bias before you present the results. By examining things more closely, you may also gain particular insights from the data that others haven’t perceived.
Data Scientists need to constantly be asking the questions “Why?” and “How?” Sometimes, you’ll need to go beyond the face-value of initial results to discover the answer to a question no-one has asked yet. To be a successful Data Scientist, you need an innate inquisitiveness that constantly drives you to ask more and more questions to the get to the heart of an issue.
What qualification do I need to be a Data Scientist?
If you’re thinking of beginning a career in data science, then now’s the perfect time. This is an area that is growing rapidly, and demand will only continue to increase.
Experience is highly valued by employers, and while there is no prerequisite qualification needed for becoming a Data Scientist, a degree in data science or a related field is recommended. So if you’re looking to enter the data science arena, a great place to begin is with the Certified Data Science Professional course.
This course will teach you the basic core skills and knowledge needed to take the first steps towards a rewarding career in data science.
What are you waiting for? Take the first step towards an exciting career in data science and enrol today.