Advance your Career with Data Science
20 Jun 2022 2 mins read By Andrea Amato

With the demands of an increasingly technological workforce, business professionals from diverse jobs in Malta or elsewhere look to upskill their current knowledge in new areas, in the hopes of expanding their skill sets and overall work qualities. As we employ hardware and software to manage high volumes of data, we are left to comprehend and interpret these results to help encourage business growth. Data science is just one area in the IT space that can assist us in knowing what to do with all this data.

To put it simply, data science is a discipline that adopts multiple tools and techniques to manage data and observe any patterns, trends, and ultimately help deduce meaningful information. Such information can then be analysed and used as a data-driven insight to draw important business decisions. Like much of IT jobs today, data science relies on machine learning principles to conduct their tasks purposefully and to construct models.

Further, data can arise from many sources and be presented in diverse formats. It is for this reason that disciplines like data science are invaluable to organisations today in understanding all this data, no matter its medium or origins. Generally, data science comprises knowledge in programming, mathematics, and statistics to be able to fully derive meaningful conclusions from data. 

 

Fields Related to Data Science

 

As mentioned above, data science is a mathematic discipline at its core. This is because it combines technical, computer science knowledge, with statistical analysis and interpretation. Nowadays, learning about data science has grown more accessible, whereby many IT or finance jobs can benefit from gaining knowledge in this area without strict prerequisite qualifications. Nevertheless, as the field can become quite technical, it’s worth understanding the following related disciplines to data science, including:

  • Machine learning (ML): ML forms the basic and most critical structure of data science. Data scientists must be able to understand ML principles alongside a proficiency in statistics.
  • Algorithms and modelling: as part of ML, data scientists use mathematical models to inform constructive algorithms that can quickly calculate data, including perform predictive analysis that can help you make sense of insightful material. Data scientists can train these models to solve complex business problems and highlight similar pressing matters over time.
  • Statistics: understanding data comprehensively requires some knowledge in statistics. If you want to ensure you draw the most meaningful conclusions from data, it’s worth brushing up on your statistical skills first.
  • Programming: for software developer jobs, programming is an essential skill. So too is programming important for data scientists, who employ various programming languages such as Python to complete projects. 
  • Database management: collecting and managing high volumes of data necessitates a data scientist who knows how to make use of databases and how to extract valuable data from them.

If you spend some time to explore and learn the above, you will receive a better understanding of what it takes to become a successful data scientist. The above serve as the fundamental skills of a data scientist and also demonstrates its core technical and mathematical background.

 

Responsibilities of a Data Scientist

 

With their great mathematical and technical knowledge, data scientists are able to solve diverse business problems at various levels of complexity. They use data and produce meaningful insights, share these with relevant stakeholders, and recommends ways in which these results can drive business decisions. To do this, data scientists follow the below responsibilities to arrive at analysing business data:

  • Understand business circumstances and determines the issues they are wanting to solve,
  • Set the right scope to explore data using particular variables and datasets,
  • Collect data from diverse sources and mediums,
  • Process raw data and transforms it in a format where it can be observed properly for analysis,
  • Combine the clean data with a ML system or another statistical model to analyse patterns and trends,
  • Interpret the data to understand results and prepare recommendations based off these findings, and
  • Produce valuable results and insights in a way that is understandable for stakeholders across communications.

The above illustrates a simplified approach as to how data scientists arrive at providing business owners with valuable reports that can help promote business growth through thoughtful and strategic decision-making. With an analytic eye and a love for data-driven results, data science can be a highly dynamic and rewarding on-site or remote job for anyone interested in expanding their skill sets in such a domain.

 

The Diversity of Data Science

 

Throughout this article, we’ve learned that data science incorporates numerous disciplines and domains to manage and interpret data. Whilst data science represents its own area of expertise, you can enter similar fields that may be more accessible to you as you work your way up to fully comprehending data science. Other interesting and worthwhile fields that are relevant to data science includes:

  • Data analysis: as a data analyst, you will explore ways to organise data and learn to interpret results according to multiple organisational queries. The skills of a data analyst are similar to data scientists in that individuals learn mathematical and statistical reasoning, programming skills, and similar data techniques.
  • Data engineering: closer to software engineer jobs, data engineers develop, deploy, and optimise data in terms of building a high-performing infrastructure and pipeline. Data engineers support data scientists in this way to help transform data. This role would necessitate learning of programming languages and database management.

Lastly, the diverse nature of data science expands beyond particular job titles and areas of expertise. Data science can be applied across industries, meaning no matter your current on-site or remote job, learning data science can prove fruitful in solving organisational matters. 

Be it in healthcare, gaming, image recognition, fraud detection, etcetera, whatever area you’re interested in specialising in is available to apply data science. Once you’ve decided that data science is right for you to learn, you can then begin to explore how you can apply your newly earned skills to the workplace.

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Data Scientist
Data Analysis
Data Engineer
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