Career in Data Science
Many companies report difficulty finding highly skilled workers for data science-related jobs. There is a greater demand for data science professionals than there is supply, which is good news for students and professionals. A data science career can take a variety of directions due to this shortage. There is nothing wrong with having options, but sometimes it can be difficult to understand how these careers differ and what skill sets and educational backgrounds are required for each. For those who are just starting out in data science, this can be a challenge.
We will introduce the different types of careers in data science in this resource and summarize what qualities a good candidate for these roles needs to possess.
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Data Scientist
In comparison with some of the other roles mentioned here, the term "data scientist" is relatively new. The specific job title of "data scientist" can sometimes be misconstrued as an elevated synonym for "data analyst" because all the roles discussed below are part of the wider field of data science. All at once, data scientists have to be mathematicians, computer scientists, and business strategists. As a result of this complex skill set, data scientists need to be able to keep one foot firmly planted in the IT sector, and one firmly planted in the business sector. This is one of the reasons they are in such high demand.
In addition to experience with algorithms and coding, data scientists must possess analytic, machine learning, data mining, and statistical skills. In addition to R, SAS, Python, Matlab, SQL, Hive, Pig, and Spark, data scientists are also proficient in R, SAS, Python, Matlab, and Spark. An important skill for a good data scientist is the ability to explain the significance of data in a way that others can understand. This role requires polished verbal and written communication skills, which may not be as important for other roles listed below.
It is common for data scientists to communicate their findings and analyses to their superiors, colleagues on different teams, and even company stakeholders who may not understand the technical jargon that data scientists likely take for granted (but just as often may not).The Harvard Business Review ranks the lack of easy communication between data teams and non-technical stakeholders as one of the biggest challenges facing the field. HBR says data teams know they are sitting on valuable insights, but they cannot sell them. According to them, decision makers misunderstand or oversimplify their analysis and expect them to do magic, to provide them with all the answers. In data science, a good data scientist will be able to communicate results in a language that executives understand. An easy-to-digest summary of what has been discovered and what needs to be done now that it has been discovered. It's not always easy.
In comparison with some of the other roles mentioned here, the term "data scientist" is relatively new. The specific job title of "data scientist" can sometimes be misconstrued as an elevated synonym for "data analyst" because all the roles discussed below are part of the wider field of data science. All at once, data scientists have to be mathematicians, computer scientists, and business strategists. As a result of this complex skill set, data scientists need to be able to keep one foot firmly planted in the IT sector, and one firmly planted in the business sector. This is one of the reasons they are in such high demand.
In addition to experience with algorithms and coding, data scientists must possess analytic, machine learning, data mining, and statistical skills. In addition to R, SAS, Python, Matlab, SQL, Hive, Pig, and Spark, data scientists are also proficient in R, SAS, Python, Matlab, and Spark. An important skill for a good data scientist is the ability to explain the significance of data in a way that others can understand. This role requires polished verbal and written communication skills, which may not be as important for other roles listed below.
It is common for data scientists to communicate their findings and analyses to their superiors, colleagues on different teams, and even company stakeholders who may not understand the technical jargon that data scientists likely take for granted (but just as often may not).The Harvard Business Review ranks the lack of easy communication between data teams and non-technical stakeholders as one of the biggest challenges facing the field. HBR says data teams know they are sitting on valuable insights, but they cannot sell them. According to them, decision makers misunderstand or oversimplify their analysis and expect them to do magic, to provide them with all the answers. In data science, a good data scientist will be able to communicate results in a language that executives understand. An easy-to-digest summary of what has been discovered and what needs to be done now that it has been discovered. It's not always easy.
Data Analyst
The role of a data analyst is to collect, process, and analyze statistical data in order to help companies make better business decisions. A data analyst's primary responsibility is transforming data sets into usable forms, such as reports and presentations. Consumer data may provide insight, dense financial data may provide strategic recommendations, or messy data may simply be organized into a more user-friendly format. An experienced data analyst will be familiar with R, Python, HTML, C/C++, and SQL. Despite being at the lower end of the organizational chart, these roles are some of the easiest to qualify for and you will have plenty of opportunity to learn and advance. This information is specifically broken down in our Data Analyst Salary Guide
Data Engineer
The data engineer designs, builds, and manages the information or big data infrastructure. They help develop the architecture that helps analyze and process data in a way that is most appropriate for the organization. They must also ensure that those systems are functioning smoothly. Unlike other data science careers, data engineering focuses on the systems and hardware that facilitate a company's data activities rather than on the actual data analysis. Generally, a data engineer has a background in software engineering as well as skills in SQL, HIVe, Pig, R, Matlab, SAS, SPSS, Python, Java, and Ruby. As part of their duties, they also provide data warehousing solutions to the company. As a senior position, this role requires an advanced degree and years of experience.
Business Analyst
Business analysts are often less technically oriented than IT analysts, but they possess a deep understanding of business processes and business intelligence. Business analysts work as liaisons between business and IT with a clear directive to advance strategic business objectives by improving business processes. Data analysts are usually focused on producing usable deliverables, such as reports and presentations, that can be understood by others within the organization who are not data scientists themselves. Although business analysts possess basic skills in data visualization and data modeling, their educational background is in business. Business analysts perform similar duties to data analysts. For someone with a solid foundation in numbers and a keen interest in business management or development, business analysis is an excellent career choice.
Marketing Analyst
The purpose of a marketing analyst is to assist companies in making informed decisions regarding market opportunities by analyzing information. In this process, a company determines which product to produce and how to market it. In order to interpret large data sets, a market analyst uses statistical, math, and analytical skills. A career in data science is more of an entry-level position.
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