How To Become A Data Scientist
Data science as a career and practice continues to gain prominence and popularity throughout a wide and varied demographic. The imperativeness of effective data science for the world’s largest companies through to the smallest of start-ups guarantees that a move into a role as a data scientist is one that is based on solid foundations of demand and necessity. As recently suggested in an article by Forbes
“We are in the midst of the fourth industrial revolution, a transformation revolving around intelligent machines”,
…and pioneers and experts in this field are in greater demand than the available supply currently.
That said, whether you are new to the field or already have the appropriate skills, there is a host of misinformation about how to become a data scientist and so we want to try and set the record straight, clarify the work, routes and requirements to enter this career field.
What is data science?
Data science is a multidisciplinary approach to applying techniques to extract information, often using computerised methods, to gain value from data. Data science offers mammoth benefits to economic and social developments and is rapidly becoming acknowledged as an essential practice for the successful development of businesses across the globe.
There are different types of data science and when you decide to embark on a career in this field, it will be necessary to determine which branch of data science you want to practice. The job title of ‘data scientist’ is a broad one and the work undertaken by the scientist will depend upon the industry, company and requirements of their project. This means that one data scientist could have specialisations in areas completely different to another data scientist.
Arguably the greatest influence in determining what type of data scientist you want to be will be the purpose of the data science, which is classified by two types – A and B, as follows:
Type A: Data science for people. This type of data science is used to support evidence or fact-based decisions through the use of analytics.
Type B: Data science for software. This form of data science makes use of recommender systems such as those on Amazon Prime or Netflix.
There are many skills that data science of both types calls for, and these may be pre-existing or developed through training and practice. Data scientists in both fields will need skillsets which include:
- Analysing and evaluation
- Problem solving
- Statistics
- Communication (in order to take data extractions and convert to actionable insight)
- Commercial and Business acumen
- Technological understanding
- Data wrangling
- Computer programming
- For Type B data scientists, software engineering skills will be required in order to transfer machine learning algorithms into practical applications.
As highlighted by the Harvard Business Review, it is vital that aspiring data scientists understand that the job entails much more than data harvesting and rather,
“They recognise that there are nuances and quality issues in the data that they can’t understand while sitting at their desks”.
It is vital that data scientists in both types of data science are able to use multiple skills in order to obtain and process information in the most effectual and beneficial way. A data scientist must be able to understand the bigger picture, assess problems and potentials, understand how decisions are made and which data is relevant.
What You Need to Become a Data Scientist
Some of the skills outlined above may pre-exist in those entering a career in data science, others may need to learn or develop these abilities. Courses, such as our S2DS Programme, aim to take aspiring data scientists through an intensive five week, project-based training programme, which evolves academic understanding into practical, commercial data science skills.
Applicants are required to hold a PhD in analytical Science or be an MScs graduate on entry. To be able to offer this exciting pathway into working in data science projects for real companies and to ensure that five weeks will suffice for the intense learning and practice offered by the course, the prerequisites are important. Applicants need to have a sound understanding of the field and the knowledge and skills gained through university study, such as maths, computer engineering, statistical evaluation and other aptitudes, ensure that course participants can gain the most from the opportunity and can shine in real working environments.
The S2DS course has been established to help you unlock the skills and knowledge required to become a successful data scientist. For even the most talented graduates, getting your foot on the first rung of the career ladder can be difficult without experience and so our project based course ensures that the transition from academic to data scientist is much smoother and swifter. Following on from a week of lectures which enhance understanding of industry insights and academic principles, course participants spend four weeks on company-led projects. Alternatively, applicants can choose our online course, which offers 40 hours of lectures online, followed by the four-week project conducted through video-links, email and chat tools.
A recent research project by Forbes highlighted that there is an imbalance in the demand and supply of data scientists throughout the world and that although the gap is closing, competition is rife amongst scientists:
“But with a younger generation of data scientists, freshly minted from more than 100 graduate programs worldwide, the median years of experience dropped from 9 in 2014 to 6 in 2015.”
A McKinsey study recently determined that
“by 2018, the U.S. alone may face a 50 percent to 60 percent gap between supply and requisite demand of deep analytic talent.”
Courses such as S2DS not only focus the knowledge, skills and practice of the data scientist but also allow participants the advantage of networking, gaining valuable hands-on experience and making connections through company leads and lifelong membership to the S2DS alumni.
Applications for the next course officially closed on Sunday 2nd April, however applicants are encouraged to submit their applications now to ensure they meet the eligibility criteria and reserve any potential space.
