Data Science appears to be one of history's most misunderstood terms. The term data scientist, like stoicism, minimalism, and rationality, has been stripped of its original meaning. So, let's take a look at what becoming a data scientist entails. The word "data science" refers to the collecting, analysis, manipulation, and interpretation of data. Three different vocations can be derived from its definition. Data science, data engineering, and data analysis. Let's talk about the three jobs and how we got them from the definition of data science and get a chance to go into why data science is a profession from the name data science.

1. Data Engineer

To be able to comprehend and handle data, one must first acquire it; this is precisely what a data engineer does. A data engineer's (DE) ultimate purpose is to make information accessible to analysts and scientists. That is to say, data engineering is the first profession that comes to mind when someone hears the term data science. A DE's other duty would be to create mechanisms that would make it easier to obtain pertinent data. What qualifications are required to begin a career in data engineering?

Coding - Expertise in languages such as SQL, Python, NoSQL, and R, which are the most commonly used in this industry, is required.

· Database administration entails becoming familiar with both relational and non-relational databases.

· Machine Learning - this is a burgeoning subject that focuses mostly on data science and may thus be used to create systems that help people comprehend the topic. That is, using machine learning to learn and construct systems to assist you in collecting data sets.

· ETL systems - Extract, Transform, and Load solutions allow you to transport data sets from one location to another. Stitch, Alooma, and other similar technologies are examples.

2. Data Analyst

A data analyst attempts to analyze and alter the acquired data after getting it from an engineer. Cleaning data sets to improve interpretation could be part of the job. It is necessary to establish patterns from prior company data that may be used to assist firms in making better profit decisions. Large data sets are frequently converted into useful formats such as reports or presentations by data analysts. That involves learning skills like:

• Data visualization with Tableau, Numpy, Pandas, and Excel

• Coding - You should know SQL, Python, NoSQL, and R, which are the most common languages in this profession.

• Statistical Programming

• Machine Learning

• Presentation and Communication Skills

3. Data Scientist

A data scientist is supposed to estimate how the result will be in the future based on the thesis developed during the investigation. As a result, a data scientist's primary purpose is to forecast future outcomes based on the data available. They are also expected to develop data models and algorithms to assist the company in resolving complex issues.

Data scientists must have a combination of skills such as;

· Knowledge with algorithms and a combination of analytic capabilities

· Machine learning,

· Data mining

· Statistical skills.

· Coding with languages such as; R, SAS, Python, MatLab, SQL, Hive, Pig, and Spark are examples of coding languages.

That isn't to say that there are only three types of data science career paths; rather, the article focuses on the most primary ones. Business analysts, data architects, machine learning engineers, database administrators, and so on are among the others. I hope this post has helped you understand what data science is all about.