What is data science? What does a Data Scientist do? And how is big data effecting and influencing our world?
In this blog, we’ll answer those questions and let you know why now is a great time to become a Data Scientist.
What does a Data Scientist do?
To understand what a Data Scientist does, you need to first understand what big data is.
Big data is any large data set that has been collected in order to reveal trends and provide insight into a specific user group.
The name ‘big data’ would have you believe that the value of big data lies in its size. But it isn’t so much about the quantity of data, but how you use it.
Big data can be used by any business or organisation (from not-for-profits to multi-national corporations to small start-ups) to discover trends and gain valuable insights. This could help drive business decisions that lead to higher profitability, a better user experience or greater customer engagement. Whatever the goal is, big data can be utilised to help inform decisions that lead to better results.
And that’s where Data Scientists come in.
Data Scientists use their specialised skills and knowledge to collate and analyse big data sets to provide answers and predict outcomes. This might sound straightforward enough, but there’s a lot of work involved.
It’s a job that involves a knowledge of programming languages, machine learning, deep learning and communicating with stakeholders at all levels.
What’s the difference between a Data Scientist and a Data Analyst?
The simplest answer to this question is that Data Scientists use data science to predict future outcomes. A Data Analyst uses data to gain insight into existing scenarios.
The difference between a Data Analyst and a Data Scientist is how they use data. A Data Analyst examine data sets to identify patterns which can be used to better understand the whys of a business. For example, a Data Analyst might be briefed with the task of discovering why profits were up or down during a particular quarter; or to find out why customer awareness had improved or worsened over a certain period.
Data Scientists work towards forecasting the future. With their skills in machine learning and programming, they develop machine learning models that can examine large data sets to gain insights and predict future outcomes to better inform business decisions. Their job is not so much about answering the ‘whys’ but the ‘what ifs’.
A day in the life of a Data Scientist
According to Seek, the average yearly salary for a Data Scientist in Australia is around $130,000.
Because almost every business in every industry relies on data to operate successfully, you can see why demand for skilled professionals in this field would be high. It also means that if you follow a career as a Data Scientist, you have a choice when it comes to finding the industry that suits you. From global corporations to research institutions, these organisations all rely on data science to drive their end goals.
While most people who work in data science will tell you there’s no such thing as a ‘typical day’, here are some of the common tasks and responsibilities of a Data Scientist:
- Identifying problem areas for improvement, even if the problem is not immediately obvious. This usually involves asking the right questions and doing a little bit of investigating.
- Using a variety of tools such as Excel, Hadoop, SAS and MATLAB, among others.
- Using programming languages such as Python, R and SQL, among others.
- Determining which data sets will be the most valuable in helping to solve the problem.
- Collecting data through various techniques.
- Validating data and cleaning it up so that it’s viable.
- Securely storing data.
- Developing machine learning models to extract data from data sources.
- Analysing data and identifying patterns and trends, then interpreting them to discover opportunities and predict future outcomes.
- Visualising data so that it can be more easily understood.
- Liaising with different stakeholders to communicate findings.
What skills do you need to become a Data Scientist?
Here are the top 8 hard and soft skills needed to become a successful Data Scientist.
1. Programming knowledge
Being fluent in programming languages is necessary to working in data science. Knowing at least one programming language is crucial, but knowing more than one is a bonus. Python is one of the most popular programming languages, but there are others like R, SQL, Java, Scala and C/C++.
2. Machine learning
Machine learning is at the heart of data science, and is used to build models that can help analyse data, forecast trends and automate time-intensive tasks that would otherwise take up a lot of the Data Scientist’s valuable time.
3. Deep learning
Deep learning is a subset of machine learning. It allows computers to learn as they progress. It’s the same technology behind driverless cars and voice control. Deep learning models are trained through the use of large data sets with many deep layers.
4. Data storage
Do you know how much data we’re creating every day? About 2.5 quintillion bytes. And all that data needs to be stored somewhere securely. 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
By using visual elements (such as graphs, maps and charts), Data Scientists can 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.
Because Data Scientists need to relay findings to other people who are not Data Scientists, they need to be able to translate data science jargon into digestible, understandable terms. Communicating findings in a succinct way means that the data is understood properly, and clear steps forward can be taken.
So, how much maths do you need to know to be a successful Data Scientist? While you don’t have to be a maths genius, a knack for numbers will definitely help. In the world of data science, linear algebra and statistics will come into play when it comes to creating models and analysing and interpreting data.
8. Problem solving
To be a successful Data Scientist, it helps if you have a naturally inquisitive nature and get a kick out of solving tricky problems. Problem solving is all about fully understanding the problem at hand, defining the requirements in order to find a solution to the problem, and then deciding how to solve the problem (and in the Data Scientist’s case, this would be working out which machine learning models to apply and how).
What qualifications do I need to become 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.
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 start is with the Certified Data 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.
There’s never been a better time to launch your career in data science.
Enrol now in the Certified Data Science Professional course and see where a career in data science could take you.