How do they differ in the technical guild
With data science careers booming in recent years, recent graduates or even seasoned IT professionals want to become data science connoisseurs. But what exactly would professional roles be in data science? People looking to start their careers in this field can often be stuck and feel helpless. This article will help enthusiasts choose two main roles, Data Analyst and Data Engineer, which are very popular in the field. The article presents what you need to master before you succeed in these two distinct roles.
Data Analyst: The Analyzer and the Visualizer
The role of a data analyst in an organization involves managing tasks such as data mining, data cleansing, data mining, and data visualization. The analyst is not only limited to performing these tasks, but also researching to find the right data to meet client / client requirements. In addition, the data must be processed using statistical methods and hence, it must analyze a large number of sources relating to the data. In addition to this, he / she must have an eye for detail to go through various data reports in order to refine the reports and audit skills. Not to mention teamwork, which is also an essential factor. It may all seem intimidating at first, but with constant effort and keen interest it will be fun.
A degree (bachelor’s / master’s) in statistics or computer science is generally preferable. The aspects related to these areas are also taken into account. Most importantly, the candidate should have a strong taste for math and statistics, as he / she is regularly engaged in data analysis as part of his / her role.
Technical skills required
When it comes to the technical skills of a data analyst, the options are diverse. The candidate should be well versed in programming skills as well as in data visualization. The best programming languages and data visualization tools making the news in today’s market are listed below.
The software listed above is not limited to data analyst tasks, but also helps in areas such as business intelligence and data mining.
Point : Data analysis is essential for any large-scale business today. It is suggested that the candidates be deepened with the market scenario. In addition to this, he / she needs to consider whether the career matches his / her knowledge and interests.
Data Engineer: The Architect and the Concierge
Data analysts are often confused with data engineers because some skills such as programming almost overlap in their respective fields. But, there is a distinct difference between these two roles.
A data engineer builds the infrastructure or framework necessary to generate data. Engineers work on the architecture aspect of data, such as data collection, data storage, data management, among many other tasks. Their main focus would be database management and big data technologies. Especially, data warehousing is a specific area of interest in data mining.
A degree in computer science or information technology is a must for any data engineer. Certifications from top tech companies such as Google and IBM that provide on-the-job training, will be an added benefit and increase the chances of securing data engineering jobs as well as enhancing career growth in this field.
Technical skills required
As the position primarily focuses on database systems, a thorough knowledge of Structured Query Language (SQL) is required. On the other hand, there is another popular database system called NoSQL, in which database modeling is a complete departure from SQL. Knowledge of both technologies is essential if we want to broaden our horizons in the field of data engineering. At the same time, Big data has also caught up in this area. There are tons of big data tools to learn when dealing with large amounts of data. The most popular are mentioned below.
Popular databases that rely on NoSQL are also listed below.
A beginner can choose to master the tools mentioned above because they offer more features and are still the best in the IT industry. When it comes to choosing big data tools, the options are plentiful. It is recommended that the data engineer consider the scalability and flexibility aspects of a project before choosing a tool of his or her choice.
Point : The role of advertisingengineer ata is quite difficult. Therefore, it is suggested that any newbie in this field has a broad prospect of learning database architecture and is constantly improving with the latest related technologies.
Data analyst vs data engineer at a glance
The article briefly highlights the professional roles of a typical data analyst and a typical data engineer so that the reader has a clear understanding of what the job entails. The jobs are also attractive and also offer better career opportunities. One should do better research before crossing one final frontier in these data science careers.