Data Mining Syllabus: Get Latest Syllabus and List of Subjects

Data Science

Big Data, Machine Learning and Data Science Modelling are the three primary components of the Data Science curriculum. Statistics, Coding, Business Intelligence, Data Structures, Mathematics, Machine Learning and Algorithms are the primary topics covered in the Data Science curriculum.

Statistics

According to a data science educational platform, data scientists need to comprehend the essential principles of descriptive statistics and probability theory, which include the key concepts of probability distribution, statistical significance, hypothesis testing and regression.

Information Visualisation

The discipline of portraying data in a meaningful, visual way that people can perceive and understand is known as information visualisation. This includes dashboards and data visualisations. Information visualisation is a powerful tool for communicating findings so non-experts can understand.

Algorithms used in Machine Learning

At its most basic level, machine learning employs pre-programmed algorithms to receive and analyse input data to anticipate output values within an acceptable range. As new data is fed into these algorithms, they learn and optimise their processes to increase performance over time, gaining 'intelligence' in the process.

Data Structures and Data Manipulation

Data structure is a method of storing and organising data to make it more useful. Data Structure and Manipulation covers Arrays, Pointers, Structures, Linked Lists, Stacks, Queues, Graphs, Searching, Sorting and Programs, among other topics.

Applied Mathematics and Informatics

Applied arithmetic is a valuable major or master's specialisation for data scientists, despite lacking SQL expertise. Students develop a strong foundation in numerical analysis, making them well-suited for data science jobs such as data mining, analysis, and modelling.

Big Data Fundamentals and Hadoop Integration with R

R analytics is data analysis using the R programming language, open-source statistical computing and graphics language. In statistical analysis and data mining, this programming language is frequently employed. It can be used for data analytics to find trends and create useful models.

Data Warehouse and OLAP

These topics could include products, customers, suppliers, sales, revenue, etc. Online Analytical Processing Server (OLAP) provides managers and analysts with fast, consistent, and interactive access to information, allowing them to understand the data better. Data warehousing technology aids students in collecting large amounts of historical data from various databases and their unification under a single scheme for use by online analytical processing (OLAP) to assist lecturers and decision-makers. Studying this subject will benefit students as it will save them a lot of time, improve data quality, and increase data security.

Data pre-processing

Data Pre-processing is the step in any Machine Learning process in which the data is changed, or encoded, to make it easier for the machine to parse it. In other words, the algorithm can now easily interpret the data's features.

Clustering

Cluster grouping is a way to provide full-time gifted programmes without breaking the bank, and it has the potential to improve outcomes for all students. All students are purposefully assigned to classrooms based on their abilities and potential in the cluster grouping concept. Clustering increases the performance and scalability.

Core: Applied Electronics

Applied Electronics

Applied Electronics & Instrumentation is a field of applied electronics and instrumentation. Engineering is a branch that deals with applying scientific knowledge in electronics, instrumentation, measurements, and control for any process, practical calibration of equipment, process automation and so on.

Core: Data handling and visualisation

Data handling and visualisation

The graphical display of information and data is known as data visualisation. Data visualisation tools make it easy to observe and comprehend data trends, outliers, and patterns by employing visual elements like charts, graphs, and maps.

Core: Introduction to Data structures and Algorithms

Introduction to Data structures and Algorithms

A data structure is a naming convention for storing and organising data. An algorithm, on the other hand, is a set of instructions for solving a certain problem. We can develop an efficient and optimised computer programme by learning data structures and algorithms.

Core: Mathematical Foundations of Data Science

Mathematical Foundations of Data Science

The mathematical foundations of data science and machine learning are covered in this course. The course's major theme is linear algebra and optimisation in posing and solving modern problems using data, emphasising ECE applications.

Core: Computing for Data Science

Computing for Data Science

Concentrating on subjects that can help with data processing and analysis using modern computing techniques (such as Machine Learning), this subject adds to the digital and data literacy abilities required for various Data Economy jobs.

Core: Introduction to Statistical Learning

Introduction to Statistical Learning

Statistical learning theory is a machine learning framework derived from statistics and functional analysis. The statistical inference problem of finding a prediction function based on data is addressed by statistical learning theory.

Core: Optimisation for Data Science

Optimisation for Data Science

Data science is a field that analyses large amounts of data using various methodologies to make it understandable. We require a thorough understanding of these three principles to understand data science: statistics, linear algebra, and optimisation.

Data engineering

Introduction to Data Engineering

Python

Operating Systems

SQL and NoSQL

Data Warehousing

Basic Machine Learning

Data engineering entails planning and constructing the data infrastructure required to collect, clean, and format data to be accessible and usable to end-users. It's frequently referred to as a cousin of data science or a continuation of software engineering.

Database management and architecture

Basic Concept

ER Model

Relational Model

Transactions and Concurrency Control

Indexing, B, and B+ trees

File Organisation

SQL and NoSQL

SQL Queries

Data is at the core of today's applications; modern businesses cannot function without it. This is because the database maintains all of the company's relevant information, such as personnel records, transactional records, salary information, and so on. Similarly, Data architecture is critical for various reasons, including assisting you in better comprehending the material. Provides principles for handling data from the time it is first captured in source systems until it is consumed by business personnel and provides a framework for developing and implementing data governance.

Machine learning

Artificial intelligence

Data science

Computer science

Mathematics

Statistics

Data mining

Deep learning

Natural Language Processing

Machine learning (ML) is revolutionising Education and altering teaching, learning, and research. Educators are using ML to identify difficult students earlier and take steps to increase their achievement and retention. Researchers use machine learning to speed up research and uncover new findings and insights.

Data visualisation

Business Intelligence

Tableau

Data Modelling

Data Mining

R (programming language)

Machine Learning

SQL

Statistics

Students will learn about interactive visual tools like graphs, charts and infographics to communicate data. Data science teams can use visual tools better to understand trends, anomalies, and patterns in data, allowing them to gain useful insights. Visual tools can also be utilised to communicate information to corporate stakeholders effectively. Data visualisation as a subject will benefit students to gain insight into vast amounts of data they’re working on.

Data Mining

Introduction to Data Mining

Tan, Steinbach & Kumar

All data mining issues are covered theoretically and practically in this book. It also includes several well-integrated examples and images. Every key topic is divided into two chapters. The first covers basic principles that serve as a foundation for understanding each data mining technique, and the second covers more advanced concepts and algorithms.

 Statistics

An Introduction to Statistical Learning: with Applications in R

Gareth James & Daniela Witten

A brief overview of statistical learning based on big data sets. The R programming language is used to explore data exploration approaches. Important prediction and modelling techniques, as well as related applications, are covered in this book. The topics covered are linear regression, classification, clustering, shrinkage approaches, resampling methods, tree-based algorithms, and support vector machines. The methodologies are illustrated with colour graphics and real-world situations.

 Data Science

Data Science for Business: What you need to know about data mining and data-analytic thinking

Foster Provost & Tom Fawcett

A general overview of data science principles and theory. It also teaches how to address a topic like this using analytical thinking. It also goes through several data mining strategies for exploring data. You will learn how to use the data-mining process to obtain good data in the right way to visualise business challenges in data analytics. The book will assist you in grasping the fundamental concepts of data analysis.

 Data

Modelling With Data

Ben Klemens

This book focuses on several methods for resolving data-related analytical difficulties. It discusses the logic of creating tools. This is for examining large information datasets. Although it directly emphasises that this book is deficient compared to all encyclopaedic publications, it also provides a narrative of the vital elements concerning statistics.

Machine Learning

Big Data, Data Mining, and Machine Learning

Jared Dean

The realities of big data and its benefits from a marketing standpoint are addressed in this resource. It also discusses how to store this type of data and process it using algorithms. It was built on the foundation of data mining and machine learning. The book covers big data and its characteristics, high-performance computing architectures for analytics, huge parallel processing (MPP) and in-memory databases, data mining, machine learning algorithms, text analytics, and a brief overview of data mining, machine learning algorithms, and text analytics.

1

IIT Madras

2

NIT Trichy

3

IIT Guwahati

4

Manipal Academy of Higher Education, Manipal

5

IIT Indore

6

VIT University, Vellore

7

Christ University, Bangalore

8

Loyola College, Chennai

9

BIT Mesra, Ranchi

10

Chandigarh University, Chandigarh