Data Analytics For Professionals syllabus : Latest Updated Syllabus for syllabus

Machine Learning  

It is a branch of computer science that enables computers to learn without being explicitly programmed.  

The subject involves:  

• Data and Processing  
• Natural Language Processing  
• ML Deployment  
• ML Applications  
• Reinforcement Learning  
• Supervised and Unsupervised Learning  

Data Visualisations

The subject helps in understanding the graphical depiction of information and data is known as data visualisation. The subject involves: Data visualisation tools to examine and comprehend trends, outliers, and patterns in data by employing visual components like charts, graphs, and maps. 

Python Programming,  

This course will be of great interest to anybody who wants to fully understand the fundamental components of computer programming using the Python programming language. 

The subject involves:  

• How to construct simple programs and high-level applications in Python utilising concepts such as Class, Python's BIF, functions, and variables. 

Operating Systems 

The subject helps students to get familiar with the identification of operating systems. The students will learn about the major functionalities of operating systems, Resource management, Process management, storage, and memory. 

Business Intelligence 

Students learn about data mining, process analysis, and performance benchmarking.  

Subject Involves:  

• Retrieve, and analyse data sets into actionable insights  
• Learning different tools  
• Leverage software  
• Transform data  

Internet of Things  

This course covers the basics of the Internet of Things and teaches students how to program IoT boards. Students learn to program a variety of IoT development programs.  

1 

Principles of Data Analytics  

To lay a solid basis for data analytics and associated application areas, as well as to comprehend the underlying key principles and new technologies in the field. 

The subject involves  

• the Fundamentals of data analytics. 
• use data analysis techniques in applications  
• Recognize the different machine learning algorithms  
• Ethical implications of privacy, data sharing, and algorithmic decision-making. 

Statistical Methods using R 

This course involves how to use R to display and analyse data, as well as how to convey statistical results correctly. 

Python for Data Analytics 

The goal of this course is to offer a thorough understanding of the Python programming concepts needed for Data Analytics. 

2  

Mathematical Foundation for Data Analytics  

The theory of Data Science relies heavily on linear algebra. The goal of this course is to introduce the fundamental concepts of vector spaces, linear algebra, and the usage of linear algebra in Data Science applications. 

Database Technologies 

The primary goal of this subject is to provide students with a foundational understanding of database principles as well as practical experience with them. 

The subject involves  

• The principles and terminology that make building database tables and writing successful queries easier.  
• Comprehend the operations of a data warehouse. 

Data Mining  

The subject involves preprocessing and analysing data, selecting appropriate models and algorithms for specific applications, and fostering research interest in data mining advancements. 

3 

Artificial Intelligence 

This subject will; help gain a better understanding of the issues involved in defining and simulating perception, identify problems where AI is needed and the various methods available, compare and contrast different AI techniques, define and explain learning algorithms, and gain additional experience in the analysis and evaluation of complex systems. 

Regression Modelling

Students will learn how to evaluate the relationship between variables in a data set and a continuous response variable in this course. Students will learn how to use the R software to fit basic and multiple linear regression models. 

Big Data Analytics 

The goal of the course is to provide students with an understanding of how Big Data is expanding in real-time applications and how it is manipulated utilising emerging technologies. 

The subject involves:  

• Processing complexity by demonstrating how to build Java apps on top of the Hadoop infrastructure.  
• It explains the Hadoop architecture as well as how to use the Hadoop Distributed File System (HDFS) and HBase on Ubuntu. 

4 

Machine Learning 

The subject involves dealing with various Modelling Processes. Understanding of Training Model, validating model, predicting New observations, supervised learning Algorithms.  

Natural Language Processing 

The subject involves: Application of NLP, Syntax processing, Semantic Analysis, Pragmatic and Discourse Processing: Analysis, Perception 

5 

Data Visualization 

The subject helps in the understanding the graphical depiction of information and data is known as data visualisation. The subject involves: Data visualisation tools to examine and comprehend trends, outliers, and patterns in data by employing visual components like charts, graphs, and maps. 

Neural Networks and Deep Learning 

The subject familiarises the students with the neural networks and deep learning as fundamental concepts. 

The subject involves  

• Design, training, and usefully connected deep neural networks 
• How to implement efficient (vectorized) neural networks 
• How to identify essential parameters in a neural network's architecture 
• How to apply deep learning to your own applications. 

6 

Project Work  

The project will teach you about deep learning's capabilities, problems, and repercussions, as well as equip you to contribute to the creation of cutting-edge technology. It paves the road for the students to get the information and abilities they will need to apply data analytics in their work, to advance their technical career, and take the first step into the data analytics world.  

MTech in Data Science 

• 
  Data Science  
• Data Mining 
• Big Data  
• Data Analytics  
• Natural Language Processing  
• Data Warehousing  
• Data Visualization  

The MTech Data Science program is a two-year postgraduate program separated into four semesters. The course is structured to teach an academic and practical understanding of Data Science and analytics in depth. 

MBA in Data Analytics  

• 
  Python For Data Analysis  
• R for Data Science  
• Big Data  
• Hr Analytics  
• SPSS  
• Power BI 

MBA Data Analytics teaches students to turn datasets into actionable insights that aid in operational and strategic decision-making.  

An MBA in data analytics is a two-year postgraduate degree program that covers all of the important areas in data analytics. 

Msc (Data Analytics) 

• 
  Fundamental of Data Analytics  
• Machine learning  
• Artificial Intelligence  
• Regression Modelling  
• Python  
• Natural Language Processing  

It is a six-trimester interdisciplinary postgraduate degree program in data analytics.  

• This program is for working people and recent graduates who wish to get into the lucrative industry of data analytics.  
• This program aims to provide a solid understanding of data analytics so that one may become a top practitioner in the area. 
•  This program caters to a diverse group of students with a variety of data analytics interests, both technical and business-related. 

Python  

Python Data Science Handbook 

Jake VanderPlas 

This book includes an array for efficient storage and manipulation of dense data arrays in Python. 

It includes the DataFrame for efficient storage and manipulation of labelled & columnar data in  

• Python Matplotlib 
• PythonScikit-Learn, Familiarity with NumPy.  

Data Science  

The Art of Statistics Learning from Data  

David Spiegelhalter  

Without using mathematical equations, this book provides a solid introduction to statistics. The Art of Statistics illuminates how we may better comprehend the world by utilising the ever-increasing tsunami of data. The book involves approaches to research and reporting with a healthy amount of scepticism. 

Machine Learning  

Introduction to Machine Learning with Python 

Andreas C Muller & Sarah Guido 

This book will show you practical techniques to develop your own machine learning solutions if you use Python, even if you are a newbie. This book will teach how to use Python to build a successful machine-learning application. 

1 

IIT Bangalore  

2 

IIM Kozhikode  

3 

IIM Calcutta  

4 

IIM Amritsar  

5 

IIM Kashipur  

6 

VIT Chennai 

7 

Christ University  

8 

Goa Institute of Management  

9 

IIT Dhanbad 

10 

Shaheed Sukhdev College of BUsiness studies