Understanding Popular Types Of Data Science Jobs

Posted by amit axh on July 7th, 2022

Due to the rising demand for data science, the position of a data scientist is emerging as the most demanding job of the twenty-first century. It's been dubbed "the hottest job title of the twenty-first century" by some. A data scientist is a person who is skilled in the use of statistical and machine learning techniques to understand and analyze large amounts of data.

The average income for a data scientist is between the range of ,000 and $ 165,000 per year, and according to several studies, by the year 2026, 11.5 million new jobs will have been generated.

Popular Data Science Jobs:

Finding various intriguing job opportunities in this industry is possible if you understand data science.  

• Data Scientist

• Data Analyst

• Machine learning expert

• Data engineer

1. Data Scientist

A data scientist is a specialist who uses a vast quantity of data and various tools, approaches, methodologies, algorithms, etc., to produce compelling business insights.

Data scientists must be proficient in technical languages like R, SAS, SQL, Python, Hive, Pig, Apache Spark, and MATLAB. Data scientists must be proficient in mathematics, statistics, visualization, and communication.

2. Data Analyst 

An individual who performs data mining models the data, and searches for patterns, relationships, trends, and other things, is known as a data analyst. Ultimately, he develops visualization and data analysis reporting to aid decision-making and problem-solving.

The following skills are necessary for becoming a data analyst: solid training in mathematics, business intelligence, data mining, and fundamental statistics. Additionally, you must be knowledgeable about a few computer languages and applications, including MATLAB, Python, SQL, Hive, Pig, Excel, SAS, R, JS, Spark, etc.

3. Machine Learning (ML)Expert:

The person who works with machine learning techniques in data science, such as regression, clustering, classification, decision trees, random forests, etc., is a machine learning specialist.

Computer programming languages like Python, C++, R, Java, and Hadoop are required. Additionally, you should be familiar with different methods, analytical problem-solving abilities, probability, and statistics.

4. Data Engineer:

A data engineer is in charge of creating and managing the data architecture of a data science project and works with enormous amounts of data. The construction of data set methods for modeling, mining, acquisition, and verification is another task performed by data engineers.

Data engineers must possess an in-depth understanding of SQL, MongoDB, Cassandra, HBase, Apache Spark, Hive, MapReduce, as well as Python, C/C++, Java, Perl, and other programming languages.

                 Hope this article was informative enough. If you're interested in pursuing a career in data science, do check out Lernbay's data science course, which is co-powered by IBM. Learnbay has emplaced several data science aspirants to land their dream job.