Data Science is the art of creating and analyzing data. The Data Science course is designed to help students understand the concepts, techniques and methods used in the analysis of data. It also helps them to develop the ability to solve problems using mathematical methods and statistical techniques. Data Science syllabus covers topics such as data cleaning and preparation, data visualization, statistics, machine learning and predictive analytics.
The data science syllabus is a set of courses that you can take to become a data scientist. It includes courses in computer science, mathematics and statistics, as well as courses in programming languages and software engineering.
The biggest difference between the data science syllabus and other types of training is that it focuses on using data science techniques to solve real problems. In most cases, this means learning how to build machine learning models from scratch. The course will also cover software development techniques like Agile software development and traditional software development.
The syllabus is designed so that students can start working on their own projects after completing the first few weeks of classes. This gives them more time to practice what they've learned rather than having to wait for their instructor to give them feedback on their work.
Data Science Subjects: Key Highlights
Subjects: Statistics, Machine Learning, Algorithms, Programming for Data Analysis and Visualization
Age: Students should be at least 18 years old in order to enrol in this course.
Prerequisites: Before enrolling in this course, students must have completed at least one year of high school or equivalent.
Data Science Course Syllabus in 2022
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Data Science Subjects |
Data Science Course Content |
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Introduction to Data Science |
Definition of data science, importance, and basic applications. |
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Machine Learning Algorithms |
Using mathematical models or algorithms to recognize patterns, classifications, or predictions about a dataset. |
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Artificial Intelligence |
Creation of algorithms to create a machine capable of problem-solving capabilities like a human. |
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Data Analysis |
Formatting or modeling data to discover insights using algorithms. |
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Coding (Python, SQL, Java) |
Basic coding to organize unstructured data. |
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Statistics |
Statistics are used to draw insights from data and apply appropriate mathematical models to variables. |
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Big Data |
Managing enormous data sets to make extraction and data analysis easier. |
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Data Visualization |
Data representation in form of a chart, diagram, plot, etc. |
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Optimization Techniques |
Optimization of software that is used in data extraction to gain the maximum output with limited resources. |
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Predictive Analysis |
Use of data, algorithms, and models to predict outcomes based on historical data. |
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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
Machine Learning Syllabus for Data Science
Machine learning is the execution of algorithms and mathematical models that are used to create machines capable of solving problems like a human. In data science, machine learning can be used to predict the outcomes for the future months or years based on past data using machines.
Check out the list of machine learning subjects included in any data science course syllabus.
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Introduction to Machine Learning |
Deep Learning |
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Machine Learning Techniques and Algorithms |
Artificial Neural Networks and their Application |
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Machine Learning and Artificial Intelligence |
Natural Language Processing |
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Programming Languages (Python, Java, C++, R, etc) |
Reinforcement Learning |
Big Data Syllabus for Data Science
Data produced as snaps, orders, recordings, pictures, remarks, articles, and so on is mostly unstructured and is termed Big Data.
Big Data instruments and procedures play a crucial role in changing over this unstructured data into a structured structure.
For instance, when somebody needs to follow the costs of various items on web-based business destinations, they can access the data of similar items from various websites utilizing Web APIs and RSS Feeds. At that point, the data is converted into structured data.
The list of Big Data topics included in the Data Science syllabus are:
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Basics of Programming |
Integration and Testing |
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Agile Methodology |
Object-Oriented Design |
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Testing and Version Control |
Big Data Fundamentals |
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Large-scale Data Processing |
ETL and Data Ingestion |
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NoSQL Databases |
Hive and Querying |
Business Intelligence Syllabus for Data Science
The data delivered consistently by businesses when broken down cautiously and afterward introduced in visual reports such as graphs can rejuvenate great dynamics. This process can help the administration in making the best choice and cautiously diving into examples and subtleties the reports rejuvenate.
Check out the list of business analytics/intelligence subjects included in any data science course syllabus.
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Quantitative Methods |
Financial Management |
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Managerial Economics |
Operations Management |
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Management Information Systems |
Human Resource Management |
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Financial Accounting |
Financial Analytics |
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Marketing Management |
Optimization Analytics |
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Organizational Behaviour |
Stochastic Modeling |
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Statistical Analysis |
Business Intelligence |
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Data Modelling |
Research Methods |
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Managerial Communication |
Computational Methods |
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Predictive Analytics |
Strategic Management |
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Risk Management |
Operations & Supply Chain Analytics |
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Marketing Analysis |
HR Analytics |
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Data Mining |
Big Data Analytics |
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Simulation Modeling |
Ethical & Legal Aspects of Analytics |
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Analytics, Systems Analysis & Design |
Project Management |
Statistics Syllabus for Data Science
The syllabus for Data Science includes a ton of Probability and Linear Algebra. The applicants must build up their abilities for conditional probability as a great deal of machine learning algorithms relies upon it.
- Naive Bayes Classification manages Conditional Probability while Linear and Logistic Regression algorithms cover both probability and the idea of types from Algebra.
- Mathematical abilities are needed not only for Machine Learning algorithms but also not to lose the idea of Neural Networks even if you don't comprehend Linear Algebra.
- Neural Networks is the science behind Machines Learning and improving cycles appropriately.
- The investigation of Neural Networks includes Matrices as they utilize Linear Equations spoken through a matrix/matrices.
- One must also know the need to realize Euclidean distance for K-implies, Entropy for Decision trees, and other Machine Learning algorithms.
Check out the list of statistics subjects included in any data science course syllabus.
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Probability and Probability Distribution |
Vector and Matrices |
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Descriptive Statistics |
Statistical Methods |
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Stochastic Processes |
Calculus |
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Statistical Quality Control |
Multivariate Analysis and Nonparametric Methods |
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C/C++ Programming |
R Programming |
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Analytical tools for Statistics |
Numerical Analysis |
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Elementary Inference |
Parametric and Non-Parametric Tests |
Data Analytics Syllabus for Data Science
Data Analysis can be both quantitative as well as qualitative.
- The whole cycle begins with data assorting, sifting, and dissecting.
- Acquainting recently prepared algorithms using cleaned data.
- These algorithms are tried on the cleaned data set afterward.
Check out the list of data analytics subjects included in any data science course syllabus.
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Data Structures and Algorithms |
Supply Chain Analytics |
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Probability and Statistics |
Customer Analytics |
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Relational Database Management Systems |
Retail Analytics |
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Business Fundamentals |
Social Network Analysis |
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Text Analytics |
Pricing Analytics |
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Data Collection |
Marketing Analytics |
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Data Visualization |
Optimization |
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Statistical Analysis |
Machine Learning |
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Forecasting Analytics |
Simulation |
Data Science Syllabus for Beginners
Data science beginners or those who want to check out data science courses after the 12th can pursue data science courses online. There are plenty of data science courses for beginners online by udemy, Coursera, Google, Microsoft, and IBM. Check out the data science course syllabus for beginners in the section below:
Introduction to Data Science
- Data Mining
- Cloud Computing
- Data Analysis
- Data Visualization
- Data Model Selection and Evaluation
- Machine Learning
- Business Intelligence
- Data Warehousing
- Data Dashboards and Storytelling
Data Science Syllabus: Course-wise
BSc Data Science, MSc Data Science, BTech Data Science, MTech Data Science, etc. are top Data Science Courses After the 12th. The sections below discuss the data science course syllabus of these top data science courses after the 12th in detail.
BSc Data Science Course Syllabus
- BSc Data Science duration is 3 years.
- BSc Data Science Syllabus is divided into 6 semesters.
The syllabus for each semester is different and includes Artificial Intelligence, Applied Statistics, and Cloud Computing, along with elective subjects.
The table below summarises the BSc Data Science Syllabus semester-wise.
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Semester I |
Semester II |
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Linear Algebra |
Probability and Inferential Statistics |
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Basic Statistics |
Discrete Mathematics |
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Programming in C |
Data Structures and Program Design in C |
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Communication Skills in English |
Computer Organization and Architecture |
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Programming in C Lab |
Data Warehousing and Multidimensional Modelling |
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Microsoft Excel Lab |
Data Structure Lab |
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Programming in R Lab |
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Semester III |
Semester IV |
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Object-Oriented Programming in Java |
Machine Learning I |
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Database Management Systems |
Cloud Computing |
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Operating Systems |
Data Warehousing and Multidimensional Modelling |
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Design and Analysis of Algorithms |
Operations Research and Optimization Techniques |
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Database Management Systems Lab |
Time Series Analysis |
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Object-Oriented Programming in Java Lab |
Machine Learning I Lab |
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Data Warehousing and Multidimensional Modelling Lab |
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Semester V |
Semester VI |
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Machine Learning II |
Elective I |
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Introduction to Artificial Intelligence |
Elective II |
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Big Data Analytics |
Grand Viva |
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Data Visualizations |
Major Project |
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Programming in Python Lab |
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Big Data Lab |
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Minor Project |
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BTech Data Science Course Syllabus
BTech Data Science is a 4 years bachelor's course with an 8-semester system and 6 Program Electives. The BTech Data Science syllabus is mentioned below
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Semester I |
Semester II |
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Introduction to Artificial Intelligence and Machine Learning |
Application-based Programming in Python |
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Programming for Problem Solving |
Maths II |
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Maths I |
Advanced Physics |
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Engineering Physics |
Engineering Chemistry |
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Soft Skills 1 |
Mechanical Workshop |
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Computer-Aided Design and Drafting |
Multimedia Application Lab |
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Principles of Electrical and Electronics Engineering |
Soft Skill 2 |
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Semester III |
Semester IV |
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Introduction to biology for Engineers |
CTS-2 Communicate to conquer |
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Discrete Structures |
Data Acquisition |
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Computer Organization and Architecture |
Advance Java Lab |
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Applied Statistical Analysis |
Environmental Science |
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Industrial Internship |
Computer Networks |
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OOPS using JAVA |
Principles of Operating System |
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Data Structures using C |
Database Management System |
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Project-Based Learning- 1 |
Management Course |
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Industrial Internship I |
Project-Based Learning- 2 |
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Semester V |
Semester VI |
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CTS-3 Impress 2 Impact |
Data Mining |
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Data Warehouse |
Compiler Design |
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Theory of Computation |
Ace the Interview |
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Design and Analysis of the Algorithm |
Artificial Intelligence |
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Project-Based Learning- 3 |
Statistical Analysis Lab |
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Industrial Internship II |
Program Elective-2 |
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Software Engineering and testing methodologies |
Program Elective-3 |
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Program Elective-1 |
Project-Based Learning- 4 |
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Open Elective-1 |
Open Elective-2 |
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Linux Programming Lab |
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Semester VII |
Semester VIII |
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Web Technologies |
Major Project-2 |
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Industrial Internship |
Open Elective-4 |
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Business Intelligence |
Program Elective-5 |
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Program Elective-4 |
Program Elective-6 |
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Major Project-1 |
Big Data Analytics |
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Comprehensive Examination |
Universal Human Values and Ethics |
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Professional Ethics and Values |
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Open Elective-3 |
- |
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Campus to Corporate |
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Best Data Science Books
Following are the best books for Data Science:
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Name of the Book |
Author |
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Python Data Science Handbook |
Jake VanderPlas |
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Practical Statistics for Data Scientists |
Peter Bruce, Andrew Bruce & Peter Gedeck |
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Introducing Data Science |
Davy Cielen, Anro DB Meysman, Mohamed Ali |
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The Art of Statistics Learning from Data |
David Spiegelhalter |
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Data Science from Scratch |
Joel Grus |
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R for Data Science |
Hadley Wickham & Garrett Grolemund |
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Think Stats |
Allen B Downey |
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Introduction to Machine Learning with Python |
Andreas C Muller & Sarah Guido |
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Data Science Job: How to Become a Data Scientist |
Przemek Chojecki |
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Hands-on Machine Learning with Scikit-Learn and TensorFlow |
Aurelien Geron |
FAQs
What are the eligibility criteria to pursue Data Science?
To pursue a degree in Data Science, it is necessary to have a background in a related field and an understanding of the basic concepts that are covered in the field.
What is the duration of Data Science courses?
The duration of a data science course can differ considerably based on the level of qualification. The course can be 20 weeks long for a diploma degree and go on for many years if an established program like a bachelor's degree or master's is pursued in Data Science or a related field.
Is Maths required for Data Science?
Knowledge of certain basic concepts of Maths like Algebra, Calculus, and Statistics might be required for Data Science but having a background in maths is not mandatory.
Does Data Science require coding?
A prospective student needs to have an idea of the programming languages like C++, Java, and Python as coding is an important aspect of data science.
Should I learn R or Python if I intend to be a Data Scientist?
Both programming languages are useful in Data Science. While Python is a general-purpose programming language, R is a platform for statistical analysis. R should be used for computational statistics and machine learning whereas Python should be used for programming and building applications.
What major should I choose if I want to become a Data Scientist?
Statistics and Computer Science are the top majors for an aspiring Data Scientist. A degree in Statistics would focus on the applications of Data Science as well as Data Analysis. A degree in Computer Science will help in understanding Machine Learning in the future.
What do Data Science subjects consist of?
The basic Data Science subjects are Python, R, Statistics, and Data Engineering.
What kind of math is required for data science?
Linear Algebra, Statistics, Calculus, Discrete Maths, and Probability are the mathematical concepts that are included in Data Science.