Data Science is one of the most promising and in-demand career paths for skilled professionals right now. As an interdisciplinary field of scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data, data science allows professionals to apply knowledge and actionable insights from data across a broad range of application domains.
It has become important right now for successful data professionals to understand that they must advance past the traditional skills of analysing large amounts of data. Data science allows them to look beyond data mining, and programming skills. Data scientists, who are skilled in organising and analysing massive amounts of data, can uncover useful intelligence for their organisations.
With a median yearly salary of €65,000 in Europe, data scientists remain one of the most sought after professionals in the tech industry. The role becomes lucrative since they muster the full spectrum of the data science life cycle and are capable of maximising the return at each phase of the process.
Data Science: why is it important
Traditionally, the data available for analysis was mostly structured and small in size, making simple BI tools sufficient to analyse them. However, data professionals are now dealing with mostly data in the unstructured and semi-structured form. A report shows that more than 80 per cent of the data is unstructured since 2020, meaning they are generated from different sources.
As a result, simple BI tools cannot be used to process this large amount of unstructured data. Data professionals need a complex and advanced analytical tool to process, analyse and draw meaningful insights from the data available to them. This need for a complex tool makes data science important since it can be used in predictive analytics like weather forecasting, decision making like autonomous vehicles and recommendation engines like ones used by e-commerce platforms.
Data Science and its life cycle
The data science life cycle can be classified into five stages: capture, maintain, process, analyse, and communicate. The capture phase of data science life cycle is broken down into data acquisition, data entry, signal reception, and data extraction. In maintenance, the data life cycle consists of data warehousing, data cleansing, data staging, data processing, and data architecture.
The process phase includes data mining, clustering/classification, data modelling, and data summarisation. The analysis phase of the data science life cycle includes exploratory/confirmatory, predictive analysis, regression, text mining, and qualitative analysis. Lastly, the communication part includes data reporting, data visualisation, business intelligence, and decision making.
In other words, the data science lifecycle begins with discovery where important parameters and specifications are defined. It is followed by data preparation where an analytical sandbox can be used to perform analysis for the entire duration of the project. Then comes model planning where a data scientist will determine the methods and techniques necessary to draw the relationship between variables.
The model planning is followed by model building where you will develop datasets for training and testing purposes. The final reports are delivered in the operational phase and results are eventually communicated by determining whether the results or goals were achieved.
Key roles to know
Data is everywhere and there are many roles associated with mining, cleaning, analysing, and interpreting data. Depending on these functions, the roles are often used interchangeably but there are three important roles involving different skillsets and complexity of data.
A data scientist examines which question needs to be answered and where to find the related data. They often have business acumen and analytical skills as well as the ability to mine, clean, and present data. Businesses use data scientists to source, manage, and analyse large amounts of unstructured data. They are responsible for synthesising and communicating the results to key stakeholders.
Skills needed: Programming skills (SAS, R, Python), statistical and mathematical skills, storytelling and data visualisation, Hadoop, SQL, machine learning
A data analyst bridges the gap between data scientists and business analysts. They are often provided with the questions that need to be answered and are responsible for organising and analysing the data to find results that align with high-level business strategy. They are also tasked at times with translating technical analysis to qualitative action items.
Skills needed: Programming skills (SAS, R, Python), statistical and mathematical skills, data wrangling, data visualisation
A data engineer focuses on the development, deployment, management, and optimisation of data pipelines. They manage exponential amounts of rapidly changing data and handle the infrastructure necessary to transform and transfer data to data scientists for querying.
Skills needed: Programming languages (Java, Scala), NoSQL databases (MongoDB, Cassandra DB), frameworks (Apache Hadoop)