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With the age of technology, data has been paramount to all decision making. Many modern services and processes run completely on data and organisations wouldn’t be able to survive a day without the concise, multi-purpose results that data management provides. There are several roles in the industry that deal with data and many people have several misconceptions about them, namely a Data Analyst, Data Engineer and Data Scientist.
A Data Analyst assesses all the numeric and other kinds of data and translate it into the English language so that everyone can understand. This information is passed on to upper management which then use the data to make informed business decisions. The main responsibilities of a data analyst include data collection, correlation and analysis and reporting.
Most companies store their data in variety of formats across databases and text files. This is where Data Engineers come in — they build pipelines that transform that data into formats that data scientists can use. Data engineers are just as important as data scientists but tend to be less visible because they tend to be further from the product of the analysis. A Data Engineer is the one who is involved in creating the data for analytical or operational uses. They develop and construct tests which result in a pool of data – ready to be analysed.
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Data scientist is the one who analyses and interprets complex digital data. They deal with a lot of structured and unstructured data as a result skill in statistics, programming and machine learning are constantly referred to in their day to day operations. Data scientists have a higher proficiency out of the three.
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The journey to becoming a data management expert usually starts from the same position – a data analyst. For this you need a bachelor’s degree and a keen understanding of modelling in statistics.
The transition between a data analyst to a data engineer is rather simple. Most positions require a master’s degree in a related field or that you gather a substantial amount of experience which proves your proficiency as a data analyst. A data engineer needs to have a strong technical background. The ability to create and integrate APIs, have an in-depth ML algorithm and have a proficient understanding of data pipelines and performance optimisations techniques.
The next milestone is becoming a data scientist. To become a data scientist of course you must have developed enough experience within the industry, and your understanding of data must be exponentially profound. This will be evident in your data-driven problem-solving skills which more than likely developed as you progress and gain more experience. Advanced statistical analyses, an advanced understanding of predictive algorithms and data conditioning are all necessary with becoming a data scientist.
Cavendish Professionals specialises in sourcing the most viable candidates for a variety of technology roles, as well as those in the Architecture, Construction & Engineering, Finance and Healthcare industries, if you would like to find out more about the roles we offer, click here.