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Mayank Mishra

Updated on 05th April, 2023 , 10 min read

Data Science Syllabus

Course

 

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

Data Science Subjects

Data Science Course Content

 

Introduction to Data Science

Definition of data science, importance, and basic applications.

 

Machine Learning Algorithms

Using mathematical models or algorithms to recognize patterns, classifications, or predictions about a dataset.

 

Artificial Intelligence

Creation of algorithms to create a machine capable of problem-solving capabilities like a human.

 

Data Analysis

Formatting or modeling data to discover insights using algorithms.

 

Coding (Python, SQL, Java)

Basic coding to organize unstructured data.

 
 
 

Statistics

Statistics are used to draw insights from data and apply appropriate mathematical models to variables.

 

Big Data

Managing enormous data sets to make extraction and data analysis easier.

 

Data Visualization

Data representation in form of a chart, diagram, plot, etc.

 

Optimization Techniques

Optimization of software that is used in data extraction to gain the maximum output with limited resources.

 

Predictive Analysis

Use of data, algorithms, and models to predict outcomes based on historical data.

 

 

 

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:

  1. Data handling and Visualization
  2. Information Security and Privacy
  3. Statistical Foundations of Data Science
  4. Optimization for Data Science
  5. Mathematical Foundations of Data Science
  6. Introduction to Data structures and Algorithms
  7. Matrix Computations for Data Science
  8. Computing for Data Science
  9. Introduction to Statistical Learning

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

  1. Statistical Foundations for Data Science
  2. Data Structures & Algorithms
  3. Stochastic Models
  4. Machine Learning
  5. Scientific Computing
  6. Optimization Techniques
  7. Matrix Computations
  8. Python Programming Lab
  9. 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:

  1. Probability and Inferential Statistics
  2. Discrete Mathematics
  3. Data Warehousing and Multidimensional Modelling
  4. Object-Oriented Programming in Java Machine Learning
  5. Operations Research and Optimization Techniques
  6. Introduction to Artificial Intelligence
  7. Cloud Computing
  8. Machine Learning
  9. Operating Systems
  10. Data Structures and Program Design in C
  11. 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:

  1. Introduction to Artificial Intelligence and Machine Learning
  2. Principles of Electrical and Electronics Engineering
  3. CAD Design
  4. Engineering Physics
  5. Engineering Chemistry
  6. Application Based Programming in Python
  7. Data Structures Using C
  8. Applied Statistical Analysis
  9. Computer Networks
  10. Software Engineering and Testing Methodologies
  11. Data Mining
  12. 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.

Introduction to Machine Learning

Deep Learning

Machine Learning Techniques and Algorithms

Artificial Neural Networks and their Application

Machine Learning and Artificial Intelligence

Natural Language Processing

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:

Basics of Programming

Integration and Testing

Agile Methodology

Object-Oriented Design

Testing and Version Control

Big Data Fundamentals 

Large-scale Data Processing

ETL and Data Ingestion

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.

Quantitative Methods

Financial Management

Managerial Economics

Operations Management

Management Information Systems

Human Resource Management

Financial Accounting

Financial Analytics

Marketing Management

Optimization Analytics

Organizational Behaviour

Stochastic Modeling

Statistical Analysis

Business Intelligence

Data Modelling

Research Methods

Managerial Communication

Computational Methods

Predictive Analytics

Strategic Management

Risk Management

Operations & Supply Chain Analytics

Marketing Analysis

HR Analytics

Data Mining

Big Data Analytics

Simulation Modeling

Ethical & Legal Aspects of Analytics

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.

Probability and Probability Distribution

Vector and Matrices

Descriptive Statistics

Statistical Methods

Stochastic Processes

Calculus

Statistical Quality Control

Multivariate Analysis and Nonparametric Methods

C/C++ Programming

R Programming

Analytical tools for Statistics

Numerical Analysis

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.

Data Structures and Algorithms

Supply Chain Analytics

Probability and Statistics

Customer Analytics

Relational Database Management Systems

Retail Analytics

Business Fundamentals

Social Network Analysis

Text Analytics

Pricing Analytics

Data Collection

Marketing Analytics

Data Visualization

Optimization

Statistical Analysis

Machine Learning

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

  1. Data Mining
  2. Cloud Computing
  3. Data Analysis
  4. Data Visualization
  5. Data Model Selection and Evaluation
  6. Machine Learning
  7. Business Intelligence
  8. Data Warehousing
  9. 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

  1. BSc Data Science duration is 3 years.
  2. 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. 

Semester I

Semester II

Linear Algebra

Probability and Inferential Statistics

Basic Statistics

Discrete Mathematics

Programming in C

Data Structures and Program Design in C

Communication Skills in English

Computer Organization and Architecture

Programming in C Lab

Data Warehousing and Multidimensional Modelling

Microsoft Excel Lab

Data Structure Lab

-

Programming in R Lab

Semester III

Semester IV

Object-Oriented Programming in Java

Machine Learning I

Database Management Systems

Cloud Computing

Operating Systems

Data Warehousing and Multidimensional Modelling

Design and Analysis of Algorithms

Operations Research and Optimization Techniques

Database Management Systems Lab

Time Series Analysis

Object-Oriented Programming in Java Lab

Machine Learning I Lab

-

Data Warehousing and Multidimensional Modelling Lab

Semester V

Semester VI

Machine Learning II

Elective I

Introduction to Artificial Intelligence

Elective II

Big Data Analytics

Grand Viva

Data Visualizations

Major Project

Programming in Python Lab

-

Big Data Lab

-

Minor Project

-

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

Semester I

Semester II

Introduction to Artificial Intelligence and Machine Learning

Application-based Programming in Python

Programming for Problem Solving

Maths II

Maths I

Advanced Physics

Engineering Physics

Engineering Chemistry

Soft Skills 1

Mechanical Workshop

Computer-Aided Design and Drafting

Multimedia Application Lab

Principles of Electrical and Electronics Engineering

Soft Skill 2

Semester III

Semester IV

Introduction to biology for Engineers

CTS-2 Communicate to conquer

Discrete Structures

Data Acquisition

Computer Organization and Architecture

Advance Java Lab

Applied Statistical Analysis

Environmental Science

Industrial Internship

Computer Networks

OOPS using JAVA

Principles of Operating System

Data Structures using C

Database Management System

Project-Based Learning- 1

Management Course

Industrial Internship I

Project-Based Learning- 2

Semester V

Semester VI

CTS-3 Impress 2 Impact

Data Mining

Data Warehouse

Compiler Design

Theory of Computation

Ace the Interview

Design and Analysis of the Algorithm

Artificial Intelligence

Project-Based Learning- 3

Statistical Analysis Lab

Industrial Internship II

Program Elective-2

Software Engineering and testing methodologies

Program Elective-3

Program Elective-1

Project-Based Learning- 4

Open Elective-1

Open Elective-2

Linux Programming Lab

-

Semester VII

Semester VIII

Web Technologies

Major Project-2

Industrial Internship

Open Elective-4

Business Intelligence

Program Elective-5

Program Elective-4

Program Elective-6

Major Project-1

Big Data Analytics

Comprehensive Examination

Universal Human Values and Ethics

Professional Ethics and Values

-

Open Elective-3

-

Campus to Corporate

-

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

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.

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