Data Science bridges multiple fields including statistics, scientific methods and data analysis to understand data. Essentially it uncovers hidden patterns from raw data using mathematical tools, algorithms and strong interpersonal skills (problem-solving, critical thinking, lots of patience). Every life examples where this field is used include conducting market research to increase customer loyalty, personalising music user playlists, detecting online fraud.
Data Science, by analysing the continuous produced data (million browser searchers and photos uploaded on social media) not only helps train machine learning models but also informs businesses of better decision making processes to maximise profit.
5 main domains make up Data Science:
- Advanced Computing: enhances computing performance and functionality in areas such as semiconductor technologies, programs, computer hardware and software.
- Domain Expertise: encompasses domain knowledge learnt through undergraduate degrees, job experiences but also the ability of data scientist to analyse the industry. Often, they will need to find the right business problem and propose innovative solutions.
- Data Engineering: develops data pipelines that transform collated data so that data scientists and analysts can interpret them. Data engineers create interfaces and mechanisms to make data more accessible.
- Statistics: refers to the mathematical branch which collects, analyses, interprets and presents large numerical data. Statisticians play key roles in scientific discoveries (significance of data), inform decisions based on data analysis and make predictions.
- Visualisation: focuses on presenting the interpreted data and conclusions in the most comprehensive way as possible. Tools such as colour usage, maps and graphs make it easier to identify trends, patterns and outliers in large datasets.
Most common jobs associated to Data Science include:
- Business Analyst: produce detailed business analysis to identify areas which can be improved to benefit customer outreach, addressing market demands and identifying any caveats. They constantly use both their IT and business skills to enhance a business’ efficiency.
The CPD accredited courses are carefully crafted to help you gain in-depth knowledge on a topic of your interest.
- Data Scientist: apply and develop methods to analyse as well as visualise data using programming languages (Python, R, C etc). Some tasks to expect include making recommendations to improve current business strategies, identify further opportunities and using acquired skills (modelling, statistics and analytics) for problem solving.
- Data Analyst: understand, interpret and present data to many different entities including pharmaceuticals, banking, private/public businesses and governments. As such they require strong critical analysis, problem solving, numeracy and communication skills.
- Statistician: collects, interprets and displays numerical data to aid companies identify any trends, make any predictions for businesses and validate significance of scientific data. Typical tasks include applying statistical treatments to complex data, convey resulting to non-specialists and designing data acquisition trials.