Know The Difference between Data Analyst, Data Engineer and Data Scientist
With the emergence of “Big data”, several new job titles and job roles are also emerging from past few years. Data scientist being the most sought after amongst them. The data which are being generated, are huge that it has actually opened new career options altogether. In fact, the demand is such high that there is a big gap between the requirements and the availability of specialized resources. What exactly they do that there is such great demand for these professionals? Let’s try to understand their roles, job descriptions, and know how are they differentiate from each other.
As the job title suggests, they analyze data. Data Analysts, generally, do not deal with “Big Data”. They run query and process data using (mostly) conventional databases. They try and find a solution to any problem being presented to them through predictive data analysis and present it with the help of charts visualization. The tools required for data analysis are basic Statistics, Microsoft Excel, SPSS, SAS, and Tableau.
Data Engineers develop the infrastructure where “Big Data” can be analyzed. Their job role includes designing, building, and integrating data from various sources. One of the main functions of Data Engineers is optimization of the big data ecosystem. They create data warehouse so that the data is easily accessible whenever needed for analysis. The tools required for data engineering are Hadoop, NoSQL, MapReduce, and MySQL along with programming skills.
As mentioned earlier, Data scientist is the most-sought after role these days. They are an advanced version of a data analyst. Apart from data analysis, they also need to be experts in programming. Generally, data scientists have a deep understanding of statistics and mathematics as they need to develop new machine learning algorithms. Their main job is to find insight from the big sets of data which can be used to improve processes and products.
- Unlike data analysts, a data scientist may not know the problem they need to find a solution for. They explore with very large data sets, come up with the questions and then find answers to those questions.
- They also need to present their findings in a way which is easy to understand by everyone. They also need to know data visualization techniques through which they can use story telling to present their insights.
- Data scientists need more than one skill to be master at being a data scientist. It is challenging but rewarding at the same time.
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