Differences between science, analysis and engineering careers

If a decade or two ago someone told you would be interested in a career in data science, would you have believed them? The data science world has been proving to be a demanding career industry as more and more companies are focusing on generating, collecting, and analysing big data to help them run their businesses. However, who does what? In simple terms, data scientists reveal future insights from raw data, engineers focus on developing and maintaining data pipelines, and analysts take actions that affect the company’s scope. But worry not; this article breaks down the three careers, as well as offering insights on the data career as a whole and how to hone your skills.

Data science, explained

Dubbed the ‘sexiest job of the 21st century’, data science is fast becoming the career in demand. This is because companies need data in order to increase productivity and performance and optimise delivery to customers and target audiences. To gain insight into certain trends and customer behaviours, companies extract data to analyse it. These analyses can’t happen without the right statistical tools and programs, and that is where data scientists come in. They are the makers of these tools and, together with their skills in algorithms, have built data science to be the ocean that includes all the data operations such as extraction, processing, analysis and data prediction to gain necessary insights.

Data analytics, analysed

Data analytics refers to the process of extracting information from a set of data. A data analyst is someone who performs this type of analysis by extracting data using various methods such as data cleansing, data conversion, and data modelling. In data analytics, descriptive or summary statistics and inferential statistics are the two most significant methodologies. A data analyst also knows how to use a variety of visualisation techniques and tools.

Data engineering, defined

A data engineer is more experienced with core programming concepts and algorithms. The role of a data engineer also follows closely that of a software engineer. This is because a data engineer is assigned to develop platforms and architecture that utilise guidelines of software development. Data engineers are well-versed in Structured Query Language (SQL) as well as noSQL because they analyse big data and engage in numerous responsibilities such as data cleaning, management, transformation, and deduplication.

Data engineers usually hail from a software engineering background and are proficient in programming languages like Java, Python, SQL, and Scala. Alternatively, they might have a degree in mathematics or statistics that helps them apply different analytical approaches to solve business problems.

Joining the data world

Some of the industries you can be a part of where data-driven decisions are made include:

• Information Technology (IT): To manage assets, leading IT departments continue to rely on big data operations. Furthermore, many businesses are migrating away from Active Directory and towards cloud directory services that provide 360-degrees visibility across devices, networks, and applications.
• Finance: Finance companies are transforming their data infrastructure into models that will enable them to analyse risks, detect fraud, make quick choices in a crisis, personalise services, and trade using algorithms.
• Transportation: To upgrade and improve the transport industry, as well as provide better solutions regarding transportation and employment in the sector, data-driven decisions must be made.
• Manufacturing: Big data is used by manufacturing organisations to improve product, trend, and cost research, as well as get a better understanding of their markets, competitors, and customers.
• Healthcare: Big data, according to experts, has numerous advantages, including improved diagnosis, medical research, preventive treatment, and a reduction in bad pharmaceutical reactions. Fitbit, which is owned by Google, is one consumer example. Wearers’ physical activity data is sent to cloud servers before being shared with doctors looking to improve health and fitness programmes.
• Retail & Commerce: Established brands, such as Amazon, have access to a wealth of data that they can analyse and use to their advantage. They make use of this access to create new market routes and increase sales.

You will require the following qualifications and educational experience:

• A three- to four-years degree in a quantitative field (Physics, Maths, Statistics, Finance, Economics, Computer Science, etc.).
• 3 to 8 years for higher degree/research (Masters or PhD).
• A four-year Bachelor’s degree in Data Science provides immediate entrance.
• Data science or data analysis short courses in South Africa which are internationally authorised and acknowledged which can be achieved by writing your AWS or Microsoft examinations.

The three careers together

A data analyst is in charge of making decisions that have an impact on the company’s scope. A data engineer is in charge of creating a platform on which data analysts and data scientists can work. A data scientist is also in charge of extracting future insights from existing data and assisting businesses in making data-driven decisions. A data analyst does not take part in the decision-making process directly, but instead assists indirectly by giving static information about the company’s performance. A data engineer is not in charge of making decisions. A data scientist is also involved in the active decision-making process that influences the company’s direction.

Final thoughts

A career in the data industry is not only great for the job market but also for your career development too. There are demands for more C-Suite roles and not much supply meaning there are openings for growth within companies as well as education to train analysts, scientists and engineers to enter C-Suite. Take advantage of those opportunities.