What is the Syllabus of Data Science?

What is the syllabus of Data Science? Data Science is simply defined as an interdisciplinary field of study which uses scientific processes, approaches, methods, systems and algorithms to extract requisite insights and information from structured and unstructured data. The syllabus of Data Science is constituted of three main components: Big Data, Machine Learning and Modelling in Data Science. The major topics in the Data Science syllabus are Statistics, Coding, Business Intelligence, Data Structures, Mathematics, Machine Learning, and Algorithms, amongst others. Read this blog to know all about the syllabus of data sciences, for beginners, course subjects as well as the IIT syllabus for data science courses.

What is Data Science Course?

Ever thought about how Netflix recommend videos based on the genre of your choice? How does Facebook automatically tag the faces of recognised individuals? Or how do banks identify the potentially loyal customers and which are most likely to leave for a competitor and how has the drug discovery process simplified? You will study these phenomena and how they work through a data science course. A blend of business acumen, machine learning techniques, algorithms and mathematics, Data Science helps to find out the hidden patterns from raw data. This skill becomes instrumental because this information will help the organization make informed and big decisions relating to their business.

Data Science Syllabus

Want to know about the syllabus of Data Science? The Data Science course syllabus comprises three main components, i.e. Big Data, Machine Learning and Modelling in Data Science. Across these three main components, the subjects cover varied areas of this sought-after discipline. Here is the complete Data Science Syllabus:

• Introduction to Data Science
• Mathematical & Statistical Skills
• Machine Learning
• Coding
• Algorithms used in Machine Learning
• Statistical Foundations for Data Science
• Data Structures & Algorithms
• Scientific Computing
• Optimization Techniques
• Data Visualization
• Matrix Computations
• Scholastic Models
• Experimentation, Evaluation and Project Deployment Tools
• Predictive Analytics and Segmentation using Clustering 
• Applied Mathematics and Informatics
• Exploratory Data Analysis
• Business Acumen & Artificial Intelligence

Note: The aforementioned data science course syllabus summarizes the major offerings under this specialisation and can vary as per the course and university.

Data Science Syllabus for Beginners

If you are a beginner in data science, there are various introductory courses available online that you can take to familiarise yourself with the basics. Here is an overview of the Data Science syllabus 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

Some of the top online Introduction to Data Science courses are:

• Introduction to Data Science by Canvas WPI Hub
• Introduction to Data Science in Python by Coursera
• Introduction to Data Science by Paul G. Allen School of Computer Science & Engineering, University of Washington
• Introduction to Data Science by High School of Economics, National Research University

Components of Data Science Syllabus

The Data Science curriculum is designed in a way to help students gather knowledge in the field of business, besides applying the tools and statistics to meet organizational challenges shortly. Therefore, the skills acquired during the trajectory of Data Science and Data Analytic courses are indispensable to becoming an asset in the field of Data Science. Whether you are looking for Data Science Syllabus for beginners or experts, here we have curated a general syllabus of Data Science. Following are the 3 most important components of Data Science which are followed by most the universities to help you adapt to both the theoretical and practical aspects of the subject:

• Big Data
• Machine Learning
• Business Acumen & Artificial Intelligence
• Modelling in Data Science

Big Data 

This part of the Syllabus of Data Science focuses on engaging students with Big Data methods and strategies so that unstructured data can be transformed into organised data. Big Data is initially made up of unstructured data gathered in the form of clicks, videos, orders, messages, images, RSS fields, posts, etc. When comparing different products with web API and RSS feeds, you can access data from different websites for that product.

Machine Learning

This part of the syllabus of Data Science comprises mathematical models and algorithms that are employed to code machines so that they can adapt to everyday developments and face the challenges of an organization. Machine Learning is also used for predictive analysis and for time series forecasting, as it can be very useful in financial systems. It employs historical data patterns to predict future outcomes over a few months or a year. If you want to gain more knowledge about the topic then do go through some of the best books for Machine Learning!

Business Acumen or Intelligence

After an enterprise assimilates and gathers loads of data regularly, it is important that it has experts who can carefully interpret and display this data in the form of visual presentations and graphs so that the data can be used efficiently to make smart business decisions. The easiest way to achieve this is by Artificial Intelligence. Not only can it improve your understanding of the market side of the process, but it will also help you make patterns and bring about progress.

Modelling Process in Data Science

Data Science Subjects

If you plan to pursue a course in Data Science, you must know know what some subjects will be essential to your learning experience and fundamental for your understanding of the course. So, if you want to know what are the topics under Data Science then here is a list that elucidates the same. Here are the Data Science subjects:

• 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

Data Science Syllabus IIT

IITs offer BTech in Data Science and Engineering as well as MTech Data Science for those aiming to pursue a successful career in this field in India.

Here are the core subjects under the syllabus of BTech in Data Science and Engineering by IIT Mandi:

• Data handling and Visualization
• Information Security and Privacy
• Statistical Foundations of Data Science
• Optimization for Data Science
• Mathematical Foundations of Data Science
• Introduction to Data structures and Algorithms
• Matrix Computations for Data Science
• Computing for Data Science
• Introduction to Statistical Learning

Here are the core subjects under the MTech Data Science syllabus by IIT Guwahati:

• Statistical Foundations for Data Science
• Data Structures & Algorithms
• Stochastic Models
• Machine Learning
• Scientific Computing
• Optimization Techniques
• Matrix Computations
• Python Programming Lab
• Machine Learning Lab

BSc Data Science Syllabus

BSc Data Science is a 3-year undergraduate program which familiarises students with the basic foundational concepts of data algorithms, structures, python programming, statistical foundations, machine learning and more. Here is the BSc Data Science syllabus and subjects:

• Probability and Inferential Statistics
• Discrete Mathematics
• Data Warehousing and Multidimensional Modelling
• Object-Oriented Programming in Java Machine Learning
• Operations Research and Optimization Techniques
• Introduction to Artificial Intelligence
• Cloud Computing
• Machine Learning
• Operating Systems
• Data Structures and Program Design in C
• Basic Statistics

BTech Data Science Syllabus

BTech Data Science is a 4-year undergraduate course that familiarises learners with the core components of Data Science such as business analytics, data analysis, machine learning, and algorithms, to name a few. Here is the BTech Data Science syllabus:

• Introduction to Artificial Intelligence and Machine Learning
• Principles of Electrical and Electronics Engineering
• CAD Design
• Engineering Physics
• Engineering Chemistry
• Application Based Programming in Python
• Data Structures Using C
• Applied Statistical Analysis
• Computer Networks
• Software Engineering and Testing Methodologies
• Data Mining
• Artificial Intelligence

How does Predictive Analysis Work?

The syllabus of Data Science is not just limited to the structuring of data comprehensively but can also be extended to analyzing unstructured data. The algorithms and tools taught through the course will help you in understanding the predictive analysis aspect of Data Science, which is used in modelling the business structure. Predictive analysis uses historical data to analyze and predict the upcoming trends in the market. This information can be used to influence the present way of handling business as well as help them make future decisions.

Is Coding Needed in Data Science?

Yes. to establish a successful career in this field, you need to have sound knowledge of programming languages like C, C++, Java, SQL, Python, etc. But why so? Coding/programming languages help you identify, analyse, and efficiently organise unstructured data. These languages thus constitute an integral part of the syllabus of Data Science.

Best Data Science Books

Following are the best books for Data Science:

Name of the Book	Author
Python Data Science Handbook	Jake VanderPlas
Practical Statistics for Data Scientists	Peter Bruce, Andrew Bruce & Peter Gedeck
Introducing Data Science	Davy Cielen, Anro DB Meysman, Mohamed Ali
The Art of Statistics Learning from Data	David Spiegelhalter
Data Science from Scratch	Joel Grus
R for Data Science	Hadley Wickham & Garrett Grolemund
Think Stats	Allen B Downey
Introduction to Machine Learning with Python	Andreas C Muller & Sarah Guido
Data Science Job: How to Become a Data Scientist	Przemek Chojecki
Hands-on Machine Learning with Scikit-Learn and TensorFlow	Aurelien Geron

FAQs

The field of Data Science is growing at an unprecedented rate and has a lot of scope for further growth if you decide to dive into it.  While we have given you an insight into what the field holds for you, the syllabus of Data Science can vary in different colleges even if the core subjects stay the same. So if you wish to pursue Data Science courses and are confused about how to go about it, let the counsellors at Leverage Edu help you make the right decision and shortlist the best colleges for you. Call us immediately at 1800 57 2000 for a free 30-minute counselling session.