What is the Syllabus of a Data Science Course?

Often described as the most lucrative career option of the 21st century, data science has truly become an integral part of the way industries work and provide services. Data science is an interdisciplinary field that uses various tools, scientific processes, approaches, and algorithms to extract and decipher insights from structured and unstructured data. Institutions across India are now trying their best to cater to this new demand in the need for data scientists. But before deciding which data science institute to choose, you must first be familiar with what is the basic data science course syllabus, and this article will help you do just that.

The Major Components of a Data Science Course

A common factor seen across most data science course syllabus is the existence of three major components. It’s these major components that make up the syllabus and provide the students with the foundation that they need to become successful data scientists. Let’s take a look at those components.

Big Data

Introducing students to big data is to primarily familiarize them with the tools and strategies required to transform unstructured data into organized information. Big data is basically unstructured data gathered together in the form of clicks, videos, orders, messages, images, posts, etc. Data Scientists are tasked with the responsibility of deciphering hidden patterns from massive hordes of data and being familiar with big data is a key prerequisite.

Machine Learning

Machine learning is employed in predictive analysis and time series forecasting. Data scientists need to have knowledge of mathematical models and algorithms so that they can fine-tune code machines to help them face and adapt to the ever-changing everyday problems and challenges faced by a company or organization. 

Business Intelligence

As corporations gather and assimilate data in the hordes, it is important that a specialist with the ability to display the data in the form of visual presentations is present to help make smart business decisions. This is why business intelligence is a key part of most data science course syllabus. Having key awareness and decision-making skills, along with knowledge of business intelligence is an ideal formula that can help you become a remarkable data scientist. 

Data Science Course Syllabus

The data science course syllabus for aspirants of data science varies from level to level. There are various forms of bachelor in data science, PGP in Data Science, that you could opt for and each one comes with its own set of varying syllabus and curriculum. Given below is a comprehensive syllabus and list of subjects for a beginner with no prior knowledge of data science.

• Introduction and Importance of Data Science
• Statistics
• Information Visualisation
• Data Mining, Data Structures, and Data Manipulation
• Algorithms used in Machine Learning
• Data Scientist Roles and Responsibilities
• Data Acquisition and Data Science Life Cycle
• Deploying Recommender Systems on Real-World Data Sets 
• Experimentation, Evaluation, and Project Deployment Tools
• Predictive Analytics and Segmentation using Clustering 
• Applied Mathematics and Informatics
• Working on Data Mining, Data Structures, and Data Manipulation
• Big Data Fundamentals and Hadoop Integration with R (as per https://leverageedu.com/)

Data scientists are in high demand and data science has proven itself to be an enticing career option. The journey to becoming a data scientist will surely be hard, but perseverance and persistence will ensure that this journey will indeed be a rewarding one. We at Praxis are proud and delighted to present our state-of-the-art Post-Graduate Program in Data Science with a tried and tested data science course syllabus. This 9-Month long data science program will train you in all the necessary skills and will ensure that you have the right exposure before stepping out and starting your career. Our remarkable placement records are just testimonials to our rich history of success and we take pride in developing the next crop of young and hungry data scientists.