A newbie entering into the world of data science will encounter the two terms ‘data scientist’ and ‘data analyst’ fairly early into their career. With the roles of either being closely parallel and sometimes even overlapping, it is easy to get confused with what you want to direct your energies into becoming.
Therefore, in this blog, we will try to understand the basic difference between the two and what each career path entails.
According to a private educational organization Udacity, “A data analyst is essentially a junior data scientist.” On the other hand a data scientist “is someone who can do undirected research and tackle open-ended problems and questions.” Let’s look at some details.
A data analyst has a solid knowledge of existing algorithms and an understanding of how to use them best. They are familiar with business intelligence and data warehousing concepts, while being exposed to in-depth study of SQL and Hadoop-based analytics including HBase, Hive, Impada and MapReduce functions. They are expected to have a level of expertise in data storage and retrieving skills.
A data scientist has to be familiar with database systems like MySQL and Hive and languages like Java, Python and MapR. To work as a data scientist, one needs to have a strong base of machine learning – Mahout, Clustering, Bayesian, etc. – and statistical insights. They have to have a rock-strong foundation in computer applications, mathematics, and modeling among others.
While a data analyst works under guidelines provided by senior data professionals to acquire, analyze, and summarize data, a data scientist is more concerned with the job of leveraging data to glean insights that will underline important business decisions and strategies. Data analysts are responsible for ensuring quality of data scourging, providing stakeholders information based on data, and bringing solutions for data incongruities. Data scientists tackle a bigger amount and velocity of data and invent algorithms to address open-ended questions like how can they minimize wastage and increase profits.
In a nutshell, a data analyst uses data to summarize the past and a data scientist uses data to prescribe/describe the future. However, regardless of the title, an enduring curiosity about data and how it can shape the world around us is a characteristic that helps every data professional to be more passionate about their work and develop expertise in the process.
Hope this information led you closer to understanding your interests. If you are looking for training or certification courses in any of the related analytics fields, AnalytixLabs can help you find a module that suits you best.