M.Tech in Big Data Analytics

Python is one of the most popular and efficient programming languages applied in Artificial Intelligence. Python is recognised as a simple yet high-level general-purpose programming language.
The first module of the Big Data Analytics Course will let you get easy with the Python programming language.
Below are the various concepts you will be taught in this module

In the first submodule, learners would master the basics that assist them in learning Python.

A data structure is a data management component that enables efficient access and modification. In this submodule, learners will study Data Structures & handling.

A function is a collection of statements that receive inputs, execute specific computations and render output. In this submodule, you will learn about Functions.

Numpy is a Python library used for Python programming. Numpy provides an easy yet robust data structure. In this submodule, you will learn about Numpy.

Pandas are applied to execute effective data analysis. Pandas perform diverse data manipulation processes like merging, cleaning, reshaping, selecting, and more. In this submodule, you will learn about Pandas.

SciPy is another free and open-source Python library employed for executing scientific computing and technical computing. In this submodule, you will learn about Scipy.

Scikit-learn is a free machine learning library applied for executing the Python programming language. In this submodule of the Big Data Analytics program, you will learn about Scikit-learn.

Exploratory data analysis is a concept employed to analyze data sets to compile their main characteristics, often utilizing statistical graphics and many other data visualization methods.
Below are the various concepts of Exploratory Data Analysis

Data understanding is one of the most crucial functions to be executed in Data Analytics. It refers to the process of obtaining knowledge from the given data. In this submodule of the M.Tech in Big Data Analytics course, you will learn about Data Understanding.

Data preprocessing can refer to the optimisation of the given data to enhance its performance. In this submodule of the Big Data Analytics training, you will learn about Data Pre-processing.

Structuring analytical problems refers to a group of techniques used to model or to map the character or design of a problem. In this submodule of the Big Data Analytics course, you will learn about Structuring Analytic Problems.

Data Storytelling refers to the process of communicating through a compelling narrative and sharing the needful information with a specific set of people. In this submodule of the M.Tech course in Big Data Analytics, you will learn about Structuring Analytic Problems.

Data Visualization is the process of displaying insights derived from the data in a visual format to facilitate a quick and easy understanding of the information while a Dashboard refers to the summary of a large set of information. In this submodule of the Big Data Analytics course, you will learn about Visualization and Dashboard.

Tableau is one of the most popular data visualization tools used in Data Science and Big Data Analytics. In this submodule, you would learn to help people understand the data using tableau.

SQL stands for Structured Query Language which is used to communicate with a database. SQL is used to manage the data in a relational database management system.
Below are the various concepts of the Unsupervised Model you would master in this module of the M.Tech in Big Data Analytics course.

A database refers to a collection of data that is organized and stored electronically in a computer system. In this submodule, you would learn several concepts of DataBase.

A data model is a conceptual model that manages elements of data and normalises how they are connected to one another. SQL stands for Standard Query Language used in DataBase Management Systems. In this submodule, you will learn about Data models and SQL.

A Subquery is a query within another SQL query and infused within the WHERE clause. A join combines rows from two or more tables, considering a related column between them. In this sub module, you will learn about Subqueries and Joins.

A window function performs an analysis across a set of table rows that are related to the current row. In this sub module, you will learn about Analytical / Window Functions.

Transaction processing is a pattern of computing, commonly executed by large server computers, that promotes interactive applications. In this sub module, you will learn about Transaction Processing - ACID properties.

Statistics is an important component of Big Data Analytics. Therefore, you would learn several statistical applications in this module. Take up the best online Big Data Analytics programs to learn the statistical methods.
Below are the various concepts of the Unsupervised Model you would master in this module of the M.Tech in Big Data Analytics course.

Descriptive statistics is the process of investigating the given data. A descriptive statistic is a core statistic that quantitatively describes or collects the attributes from a set of information. In this submodule of the Big Data Analytics course, you will learn about Descriptive Statistics.

A sampling distribution is a probability distribution technique employed to derive the statistics from a larger number of samples drawn from a specific population. In this submodule of the Data Analytics training, you will learn about Sampling Distribution.

The Central Limit Theorem is one of the most important probability theorems that enables us to assume that the sampling distribution of the mean will be normal in most cases. In this submodule of the Big Data Analytics course, you will learn about Central Limit Theorem.

Hypothesis testing is a statistical test where we test an assumption considering a group parameter. This technique is used to evaluate the plausibility of a hypothesis by using sample data. In this submodule, you will learn about Hypothesis Testing.

Multivariate analysis refers to the statistical analysis of data gathered through multiple dependent variables. This module of the Data analytics program concentrates on teaching the several concepts of Multivariate Analysis.
Below are the various concepts of the Unsupervised Model you would master in this module of the M.Tech in Big Data Analytics course.

Factor analysis is a statistical process used to explain variability among several observed and correlated variables in terms of a potentially lower number of unobserved variables usually referred to as factors. In this submodule, you will learn about Factor Analysis.

Clustering is an important technique of Machine learning which refers to the process of dividing the number of data points into several groups. In this submodule, you will learn several techniques of Cluster analysis.

Discriminant analysis is a technique that is employed by the researcher to understand and evaluate the research data when the dependent variable is categorical while the independent variable is the interval in nature. In this submodule, you will learn about Discriminant Analysis

Linear programming is the process applied for deriving the most optimal solution for a problem with given limitations. In this submodule, you will learn about Linear Programming Problem.

Machine learning is an important subset of Artificial Intelligence technology that concentrates on training the machines to learn and act as humans do. This module focuses on teaching several tools and techniques involved in machine learning.

Regression is a supervised learning technique that aids in understanding the relationship between variables and facilitates us to predict the continuous output variable based on one or more predictor variables. In this module, you will learn about Supervised Regression.
Below are the various concepts of the Deep Neural Networks architecture you would master in this module of the Big Data Analytics course.

• Exploratory data analysis

The concept of Exploratory Data Analysis or EDA spins around investigating data. Data Exploration is of three types. They are Data Summarization, Data Visualization, and Data Normalization. In this sub-module, you would learn several techniques of Exploratory Data Analysis.

• Linear Regression (LR)

Linear Regression is used to predict the outcome that is continuous and has a constant slope within a continuous range. In this submodule, you would learn several techniques of Linear Regression.

• Statistical Significance of LR

The likelihood ratio refers to the probability of accurately predicting a condition in ratio to the probability of inaccurately predicting a condition. In this submodule, you will learn about the Statistical Significance of LR.

• Feature engineering

Feature engineering refers to the process of applying domain knowledge to retrieve traits such as features, properties, characteristics and more from the given raw data. In this submodule, you will learn about Feature Engineering.

• Regularization Techniques

Regularization is a process that makes inadequate adjustments to the learning algorithm such that the given model generates better results. In this sub module, you will learn about Regularization Techniques.

• Model deployment

Model deployment is an engineering task employed for putting a Machine Learning model to use. In this sub module, you will learn about Model deployment.

Ensemble learning refers to the process by which multiple models are strategically developed and combined to solve a certain computational intelligence problem. Ensemble classification refers to a set of classifiers combining their predictions for the classification of unrecognised samples through some form of voting.
Below are the various concepts of the Ensemble Learning and Classification you would master in this module of the Big Data Analytics course.

• Logistic Regression

Logistic Regression is used to forecast the categorical dependent variable in a given set of independent variables. In this submodule, you will learn about Logistic Regression.

• K-Nearest Neighbours

The k-nearest neighbours (KNN) algorithm is a supervised machine learning algorithm that is applied to solve both classification and regression problems. In this submodule, you will learn about K-Nearest Neighbors.

• Decision Trees

Decision tree is the most popular and powerful tool used for executing classification and prediction. In this submodule of the Big Data Analytics course, you will learn about Decision Trees.

• Ensemble Techniques

Ensemble techniques develop multiple models and then consolidate them to produce improved results. Ensemble techniques usually render the most accurate outcomes. In this submodule of the Big Data Analytics training, you will learn about Ensemble Techniques.

• Naive Bayes

Naive Bayes is a classification technique executed based on Bayes' Theorem which uses an assumption of independence among predictors. In this submodule, you will also learn the several techniques of Naive Bayes

• Model Improvement

In this sub module, you will learn about the several techniques employed in Model Improvement.

An unsupervised model is a type of machine learning model in which the model is not provided with any pre-assigned tags for the training data. In this submodule, you will learn about the Unsupervised Model.
Below are the various concepts of the Unsupervised Model you would master in this module of the M.Tech in Big Data Analytics course.

• Clustering Techniques

In this submodule, you will learn several techniques of Clustering.

• K-means algorithm

K-Means Clustering is an Unsupervised Learning algorithm, which collects the unlabeled dataset into various clusters. In this submodule, you will learn about the K-means algorithm.

• Hierarchical Clustering

Hierarchical clustering is an algorithm that gathers identical elements into groups called clusters. In this submodule, you will learn about Hierarchical Clustering.

• Dimensionality Reduction

Dimensionality reduction refers to the tools and techniques applied for the transformation of a given data from a high-dimensional space into a low-dimensional space. In this submodule, you will learn about Dimensionality Reduction.

• Recommender system

Recommender system is an important application of AIML in which the systems try to recommend products and services to the user based on many different factors such as past purchases, search history and more. In this submodule, you will learn about the Recommender System.

• Market Basket Analysis

Market basket analysis is a data mining technique used to understand a user's behaviour and boost sales. In this submodule, you will learn about Market Basket Analysis.

Deep learning is an important subset of machine learning that concentrates on imitating the way the human brain enacts to obtain certain types of knowledge.
Below are the various concepts of the Deep Neural Networks architecture you would master in this module of the Big Data Analytics course.

A neural network is a series of algorithms that tries to recognise underlying correlations in a set of data through a process that imitates the way the human brain works. In this submodule, you will learn about Neural Networks.

Keras is a neural network library and TensorFlow is the open-source library for running several tasks in machine learning technology. In this submodule, you will learn about Tensorflow and Keras.

CNN stands for Convolutional Neural Network which is an approach to artificial neural networks. CNN is widely applied in image recognition and classification. In this submodule, you will learn about CNN and its applications.

Transfer learning is a machine learning technique in which a model produced for executing one task is reused as the opening point for a model on a second task. In this submodule, you will learn about Transfer learning and Applications.

Object detection refers to the potential of machine systems to locate objects in a given image and categorise each object. In this submodule, you will learn about Object detection.

Natural language processing is a process where human language is split into several fragments to analyse and understand the actual context of the words in a given search. In this submodule, you will learn about Natural Language Processing.

• Text Analytics - Basics

Text Analytics is the process of retrieving knowledge out of written communication. In this submodule, you will learn about Text Analytics - Basics.

• Document summarization and classification

Document Summarization refers to the task of optimising a document into a more compressed fashion while still maintaining its content. In this submodule, you will understand the several techniques involved in Document Summarization and classification.

• Web Scraping

Web scraping is the process of applying bots to obtain content and data from a specific website. In this submodule, you will master the techniques of Web Scraping.

• Sequential Modelling

Sequence Modelling is the potential of a computer program to develop, assess, make predictions about or produce any type of sequential data, like audio, text etc. In this submodule of the Big Data Analytics course, you will learn about Sequential Modelling.

• Transformers

A transformer is a deep learning application that applies the mechanism of attention, differentially evaluating the importance of each element of the input data. In this submodule, you will learn about Transformers.

This module concentrates on teaching the concepts of Big Data for Machine Learning.
Below are the various concepts you would learn in this module.

Hadoop is a tool for storing and processing big data. Hadoop uses inexpensive commodity servers to store data that run as clusters. Hadoop is a distributed file system that enables simultaneous processing and fault tolerance. In this submodule of the Big Data Analytics course, you will master Hadoop.

HDFS stands for Hadoop Distributed File System which is a Distributed File System that securely stores huge files across machines in a cluster. MapReduce is a software framework for smoothly writing applications that process extensive amounts of data. HDFS and MapReduce are employed together to conceal all the complexities for Big Data analytics. In this submodule of the Big Data Analytics course, you will learn about HDFS and Map Reduce.

MongoDB is an open-source NoSQL database. MongoDB is a document-based database while NoSQL refers to a type of non-relational database which is efficient to process structured as well as semi-structured and unstructured data. In this sub module, you will work on NoSQL and MongoDB.

Batch processing refers to the set of techniques applied for automating and processing multiple SQL Statements as a single group.In this sub module, you will master the batch processing using Hive and HBase.

Real-time data processing is the execution of a given data in a short period, delivering instant output. Spark is a fantastic tool used for processing huge amounts of data, including real-time and near-real-time streams of events.Kafka renders durable and scalable ingestion of streams of events from numerous producers to multiple clients.In this sub module, you will master the real time processing using Spark and Kafka.

The learners will be offered six exciting electives to choose from.
They are:

Operations research is defined as the scientific approach of transforming data into insights for obtaining better decisions.

Industrial safety refers to the management of all processes and functions within an enterprise to secure its employees and assets by minimizing hazards, identifying risks, and more.

Business Analytics applies quantitative methods to interpret data and derive better management decisions.

Cost management is the process of outlining and managing the costs associated with a project or a business. Cost management is one of the most crucial parts of every industry today.

Composite materials have got increasing significance, especially in high-performance engineering domains. Hence, this domain has got immense demand.

Upon taking up this specialisation you would learn the skills desired to develop innovative ways for waste handling and minimization.