Data has clearly become the 21st century equivalent to oil. It remains an untapped asset and those learning to extract the value will see huge rewards. In our digital economy, data is more valuable than ever and it underpins everything from government, enterprises to consumers. It is no surprise that many companies are now in a race to transition their data infrastructure from a cost centre to a profit centre.
In order to make that transition work, companies hire people in key data-related roles such as data analyst, data scientist, business analyst, data engineer, machine learning engineer, and so on. Each of these job roles come with different job descriptions and for young graduates, it might look like a daunting task to understand the difference between them.
While each of these data-related roles has its own significance, the two roles dominating most conversations are data scientist and data analyst. In fact, it is widely believed that data scientist will be the most in-demand profession in the next five years. If you are confused between these roles, we will focus on the difference and how these roles have evolved.
The key differences highlighted in this article have been sourced from an article originally published by Nate Rosidi on .cult by Honeypot.
Data Scientists: Key responsibilities
The primary job of a data scientist is to estimate the unknowns using algorithms and statistical models. Their job often revolves around turning disparate data into clean, actionable insights. They often work on answering a particular business question that requires data-driven insight. Data scientists can also further extrapolate and share insights derived to solve some of the major problems facing an organisation.
In terms of responsibilities, most employers require data scientists to identify key areas of improvement within an organisation. They are also expected to scope out problems using a data science lens and deliver multiple key initiatives using advanced techniques. Data scientists are required by organisations to drive business performance and revenue.
Data scientists are also expected to communicate with technical and non-technical folks within their own organisations as well as those outside. They are further expected to make recommendations to adapt existing business strategies. One of the key areas of success for data scientists is to extract data from multiple sources.
Data science is considered to be challenging since it requires candidates to have both technical skills as well as an understanding of business problems. The fundamental difference between data scientists and data analysts in terms of responsibility boils down to product and consulting. Data scientists are more product-oriented while data analysts act as consultants. In its current state, data scientists will often find themselves cleaning and processing raw data from multiple sources and making them actionable and ready for actual deployment.
Data Analysts: Key responsibilities
The Data Analyst role can be described as that of being a close ally to the data scientist. They are often referred to as close cousins within data-related roles. A data analyst primarily scrutinises information using various analytical tools at their disposal. Their responsibility is to identify facts and trends that will help organisations to make informed goals and business targets.
While data scientists play with the data to make inferences, data analysts are often involved in the process of acquiring those data from primary and secondary sources. They are even required at times to clean the data from less structured datasets. Data analysts are also required to design and maintain data systems and databases. They are heavily involved in the A/B testing phase of products and services.
There is definitely a blurred line between the data analyst and data scientist roles. However, it is a fact that data analysts eventually get promoted to the role of a data scientist. A data analyst can be found performing analysis to determine what the data presented means for their organisation.
They are further expected to prepare reports based on their analysis and report the same to various stakeholders. They analyse the quality of data and act as the first line of defence against corrupted data. Harpreet Sahota’s description best distinguishes these roles. “Data Scientist discovers and Data analyst analyse”.
Data Scientist: Technical knowledge and coding skills required
Data scientists are required to be proficient in SQL and Python or R but they are also expected to be comfortable working in the cloud environment. This means they are expected to have knowledge and experience using software or languages such as Scala, Spark, Hadoop, AWS, Databricks, to name a few.
Another key technical requirement for data scientists is to be familiar with a major tech stack. This includes understanding OOP, machine learning libraries, software development and ability to work with legacy scripts and algorithms. In a nutshell, data scientists are required to have a solid foundation in mathematics and statistics and they are also expected to have comprehensive skills in data collection, processing and visualisation.
Data science is still a domain that is evolving and the roles associated with it keep evolving too. In some organisations, data scientists can be found getting exposure to a large suite of algorithms and front seat to AI technologies. They gain exposure to Natural Language Processing (NLP), Computer Vision, and Deep Learning.
Simply put, data scientists are required to have a broad skill set combining computer science skills with statistics and probability, maths, analytics and modelling. They are also expected to have business acumen since they are responsible for uncovering answers to important questions.
Data Analyst: Technical knowledge and coding skills required
A data analyst is also required to know SQL, Python or R but they are also expected to know Excel, SAS and BI software. Their job requires them to focus on statistical analysis, data modelling and data visualisation. Some of the skills required for data analysts include data mining, data warehousing and database management.
It is crucial for a data analyst to be well versed in the field of SQL and database management. They are also expected to establish data collection structures that are essential for future analysis. Since data analysts don’t work on advanced data modelling techniques, they are expected to only know basic supervised learning models like regression.
The commonality between data scientists and data analysts in terms of technical knowledge is that they are required to have a good foundation in mathematics and statistics.