Data Science Syllabus 2024

We now live in a time when the sentence "Information is wealth" means up to its fullest. Everything depends on data from corner shops to stock markets, the strength of passwords to the likes on our social media posts. Today, the data science sector's estimated value is over 1 billion dollars as it is growing at its fastest pace possible, and employment in this sector today means a treasury opened from heaven. This is due to the increased use of gadgets and media that disseminate essential data. Also, the lack of skilled civilians to perform such complex tasks gives further impetus to employment in this field. Recommended content that we get on social media platforms is an excellent example.

So if you're a tech freak and want to make a living out of your knowledge, entering the data science industry can be a wise choice. Because data is the future, and information is the key to it.

What is Data Science?

Data science is nothing but the collection, utilization and application of data on a large scale. It is mainly concentrated on graphs, spreadsheets, codes and stuff like that. This field is an offspring of the IT industry, the most developing sector today. It has a large target audience, and a big quota for employment as the data collected is of huge magnitude, and the Human Resources needed for these jobs are pretty few. An employee in this field gathers data and finds patterns in them. Later the person subtly uses these results to find solutions for day to day problems.

Data Science Subjects

For those considering a Data Science course, it's crucial to understand the key subjects that form the foundation of this field. These subjects not only enhance your learning experience but also provide a deep understanding of the course's core concepts. Below is a list of essential Data Science topics:

• Fundamentals and Significance of Data Science
• Principles of Statistics
• Techniques in Information Visualization
• Comprehensive Overview of Data Mining, Data Structures, and Data Manipulation
• Algorithms Applied in Machine Learning
• The Role and Responsibilities of a Data Scientist
• Data Acquisition Processes and the Data Science Lifecycle
• Implementation of Recommender Systems in Real-World Scenarios
• Methods for Experimentation, Evaluation, and Deployment of Projects
• Predictive Analytics and Customer Segmentation through Clustering
• Applied Mathematics and Informatics in Data Science
• Advanced Study of Data Mining, Data Structures, and Data Manipulation
• Essentials of Big Data and Integrating Hadoop with R Programming

Data Science Course Syllabus

Data Science, a rapidly expanding domain within the IT sector, has pervasive applications across various industries including healthcare and finance. Key areas and trends in Data Science that students should focus on include:

• Artificial Intelligence (AI)
• Automated Machine Learning
• Natural Language Processing (NLP)
• Data Fabric technology
• Transition to Cloud Computing
• Data as a Service (DaaS)
• Robotic Process Automation (RPA)
• Federated Learning concepts
• The concept of Data Democratisation
• Data Regulation and Governance policies

Data Science Syllabus: Course-Wise

BSc Data Science Syllabus

The BSc Data Science curriculum is structured across six semesters, each offering a unique set of subjects. Core areas of study include Artificial Intelligence, Applied Statistics, and Cloud Computing, supplemented by a range of elective courses. A detailed overview of the main subjects within the BSc Data Science program is outlined below.

• Linear Algebra
• Probability and Inferential Statistics
• Basic Statistics
• Discrete Mathematics
• Programming Fundamentals using C
• Advanced-Data Structures and Program Design in C
• Object-Oriented Programming with Java
• Essentials of Machine Learning
• Database Management Systems
• Fundamentals of Cloud Computing
• Big Data Analytics
• Techniques in Data Visualization

The BTech in Data Science is a four-year undergraduate program, structured into eight semesters with six specialized electives. The program covers a comprehensive range of topics, essential for proficiency in the field. Below is a summary of the main subjects included in the BTech Data Science curriculum.

• Introduction to Artificial Intelligence and Machine Learning
• Programming Applications using Python
• Mathematics for Data Science
• Advanced Physics Concepts
• Fundamentals of Engineering Physics
• Basics of Engineering Chemistry
• Discrete Structures in Computing
• Techniques in Data Acquisition
• Object-Oriented Programming (OOP) with Java
• Principles of Operating Systems
• Data Structures with C
• Database Management Systems
• Design and Analysis of Algorithms
• Advanced Studies in Artificial Intelligence

The BCA in Data Science is a three-year undergraduate program, divided into six semesters. This curriculum is crafted to provide comprehensive and in-depth knowledge of Data Science and related software applications. Key subjects included in the BCA Data Science course are listed below.

• Discrete Mathematics
• Statistics and Probability Theory
• Environmental Science and Sustainability Studies
• Fundamentals of Database Management Systems
• Core Concepts in Computer Essentials for Data Science
• Data Structure and Algorithm Development
• Computational Thinking and C Programming
• Basics of Operating Systems
• Object-Oriented Programming with C++
• Introduction to Java and Web Programming
• Principles of Software Engineering
• Python Programming Laboratory
• Data Modelling and Visualization Techniques
• Introduction to Big Data Analytics
• R Programming for Data Science Applications
• Information and Data Security Essentials
• Machine Learning Foundations
• Basics of Natural Language Processing

BTech Artificial Intelligence Data Science Syllabus

The syllabus for the BTech program in Artificial Intelligence and Data Science includes a variety of key subjects, as outlined in the table below. The main subjects covered are:

• Calculus
• Engineering Physics
• Engineering Chemistry
• Python Programming
• Data Structures
• Data Science and Analysis
• Computer Networks
• Machine Learning
• Deep Learning
• Embedded Systems

Syllabus of Data Science for Beginners

For those just starting in data science, numerous beginner-level online courses are available to help you grasp the fundamental concepts. Below is a summary of the syllabus typically found in a Data Science course for beginners:

• Introduction to Data Science
• Understanding Exploratory Data Analysis
• Machine Learning
• Model Selection and Evaluation
• Data Warehousing
• Data Mining
• Data Visualization
• Cloud Computing
• Business Intelligence
• Storytelling with Data
• Communication and Presentation

In conclusion, the syllabus for Data Science offers a comprehensive pathway for those aspiring to delve into this dynamic and ever-evolving field. It encompasses a broad range of subjects, from the foundational aspects of mathematics and programming to advanced topics like machine learning and big data technologies. This curriculum is designed not only to impart theoretical knowledge but also to equip students with practical skills essential for real-world applications. As the demand for data science professionals continues to surge, this syllabus serves as a crucial stepping stone for anyone looking to forge a successful career in this domain. It's an exciting time for learners to dive into Data Science, a field that promises both intellectual challenge and significant career opportunities.