What are the different components of a data science course?

November 26, 2022


Data science is one of the most dynamic disciplines and is regarded as the most sought-after modern discipline. As such, it becomes important to understand the different dimensions of this discipline in much more detail. There are numerous components of data science like Big Data Analytics, machine learning, artificial intelligence, data modeling, and the like. The data scientist course consists of numerous components that range from mathematical analysis, statistics, machine learning, coding, business analytics, artificial intelligence, data analysis, etc. Let us take a look at the components of data science in much more detail.

Components of data science course

The syllabus of data science has been structured in such a manner that it fits the requirements of students from various disciplines. 

  • Although most of the coding in data science happens with the help of a programming language called python, it is not a prerequisite to learn the language. Since data science is a very vast discipline, there are different sub-branches associated with it. This means that the students who are not well at programming can learn other components of data science that are much more generic in nature. 
  • The knowledge of advanced algorithms is extremely important in data science. These algorithms help the machine to function on its own and execute specific tasks. Statistical methods provide a quantitative foundation for data science. 
  • In addition to this, data structures are also taught at the basic level of the foundation course on data science. Soft computing is also a part and parcel of the course on data science. In some of the courses of data science, a choice is provided between soft computing and scientific computing depending upon the interest of the student. 
  • A course in data science also features different types of optimization techniques. Matrix computation and advanced data models are also necessary to master while acquiring knowledge of data science. 
  • It is also essential to learn about different data validation as well as data visualization techniques because these methods help to present the data in a lucid and easy-to-understand format. 
  • At an advanced level of the data science course, knowledge about project deployment tools is provided and students are taught to handle various types of basic projects. In these projects, they can utilize techniques like classification, clustering, predictive analytics, regression, and other types of machine learning models for executing specific tasks. 
  • After a student develops familiarity with basic machine learning algorithms, he slowly begins to advance into many complex projects that can be handled with the knowledge of deep learning, applied mathematics, and artificial intelligence. 
  • Consequently, advanced data science courses also feature exploratory data analysis and data informatics. They provide professional and business acumen to the students and allow them to excel in different fields. 
  • The knowledge of interrelated fields like cloud computing and data mining is also essential for venturing into different domains of data science. Some of the elective subjects in the domain of data science include storytelling with data and the art of communication and presentation.


There are a large number of books that can be referred to for acquiring knowledge of data science. 

  • One of the most popular books is the python data science handbook that has been authored by Jake Vanderplas. 
  • Practical statistics for data scientists is another important book for Data Science students that has been written by Peter Bruce. 
  • At the beginner's level, students can also use a book called introducing data science that is written by Davy Cielen. 
  • If one is desirous of learning data science in a very simplified manner, he can make use of a book called Data Science from scratch by Joel Grus. 
  • In addition to this, there is another book called Introduction to machine learning with Python that has been written by Sarah Guido.

The way ahead 

There are different types of short-term and long-term courses that provide detailed knowledge about data science and are available online. If a person is desirous of pursuing a professional qualification in data science, then the best program is to pursue a career in engineering and opt for data science at the bachelor's level. 

Usually, data science is offered as a specialization along with computer science engineering in most of the institutes. Professionals can also pursue a post-graduate degree in data science that lasts for about 2 years. For working professionals, the best way to acquire knowledge about data science is to enroll in a short-term data science course that provides precise knowledge about the subject.

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