M.Tech (Big Data Analytics) with additional specialization in Data Science and Machine Learning

The first module teaches students all the Multivariate techniques involved in Data Analytics, such as exploratory data analysis (EDA), feature engineering, descriptive statistics, hypothesis testing, and more.

Exploratory data analysis, shortened to EDA, lets you uncover patterns and insights regularly using visual methods within sample data.

Feature engineering is transforming data from the raw state to a state appropriate for modeling. It assists in converting the data columns into features that adequately represent a given situation in terms of clarity.

This chapter teaches students about the study of data analysis by describing and summarising different data sets, and this method is called descriptive statistics.

A statistical function that states all the probable values that a random variable takes within a particular range is a Probability Distribution. This chapter teaches students about Probability Distributions and several types like Binomial, Poisson, and Normal Distribution.

This module teaches students about hypothesis testing in statistics, which is an essential procedure in applied statistics for performing experiments based on the observed/surveyed data.

ANOVA, known as Analysis of Variance, is a statistical technique implemented in Data Analytics, which splits observed variance data into several components for additional analysis and tests. ChiSquare is a hypothesis testing method implemented in statistics, which measures how a model compares to actual observed data.This chapter teaches students the procedure to identify the significant differences between two or more groups.

This chapter teaches students how to work with various Statistical Simulation Techniques in Data Analytics.

The second module teaches students about Computing techniques for Data Analytics, including Linear Regression, Regularization Techniques, and Model deployment.

Linear Regression is a widely implemented algorithm used for predictive analysis in Machine Learning, which assists in producing ideal results. It is a technique that assumes a linear relationship between the independent and dependent variables.This chapter will guide you through all the Linear Regression fundamentals and familiarize you with which model best suits architecture by evaluating every model based on the requirements using the Model building and Fine-tuning techniques.

Regularization is used for modifying Machine Learning algorithms to enhance the performance of Machine Learning models. This chapter will familiarize you with all regularization techniques used in Machine Learning.

This chapter will familiarize you with the process of deploying your Machine Learning models using various model deployment techniques.

The next module teaches students all the essentials in Big Data technology, including Hadoop, HDFS, Map Reduce, NoSQL and MongoDB, batch processing, and real-time processing.

Hadoop is a framework that enables the distributed processing of enormous data sets across a network of multiple computers with simple programming models.

HDFS, known as Hadoop Distributed File System, handles massive data sets running on commodity hardware. It assists in storing vast amounts of data sets and provides easier access.Map Reduce, a software framework in Hadoop, enables you to write applications quickly and process vast amounts of data parallelly on large clusters (nearly thousands of nodes) of commodity hardware in a reliable and fault-tolerant manner.

NoSQL typically indicates a non-relational database with no SQL, which provides a mechanism to store data in a database in any format apart from relational tables. This chapter teaches students to implement a NoSQL database using MongoDB, a document-oriented NoSQL database that provides flexibility and scalability with querying and indexing.

Batch processing is a technique to process massive amounts of data within a particular time period. Hive is SQL-based; in contrast, HBase is NoSQL-based Hadoop technologies.

Real-time processing is a technique to process data within a short time period that provides automated outcomes. Spark is an open-source, unified analytics engine to process vast amounts of data.Kafka is an open-source distributed event streaming platform, which several companies implement for high-performance data pipelines, data integration, streaming analytics, and mission-critical applications.

In the next module of this M.Tech in Big Data Analytics program, students will learn all the critical concepts of Machine Learning used for Data Analytics, such as Logistic Regression, Decision Trees, Random Forest, Clustering techniques, Dimensionality reduction, and several other essentials.

Logistic Regression, one of the most popular algorithms in Machine Learning, is a simple classification algorithm that predicts the categorical dependent variables with the support of independent variables.

K-Nearest Neighbours, shortened to KNN, is one of the most straightforward algorithms in Machine Learning, used for solving regression and classification problems.

A Decision Tree is a Supervised Learning algorithm used for both classification and regression problems. The Decision Tree is a hierarchical structure wherein internal nodes represent the dataset features, branches indicate the decision rules, and each leaf node represents the result.

This chapter discusses the shortcomings of your supervised standalone models and learns a few techniques, like Ensemble techniques, to overcome these shortcomings.

Random Forest is another popular Supervised Learning algorithm, which incorporates several Decision Trees on various subsets of a given dataset. Then, it calculates the average for improving the predictive accuracy of that dataset.

Clustering is an Unsupervised Machine Learning technique that involves the grouping of data. This chapter will familiarize you with various Clustering techniques, like K-means clustering and hierarchical clustering.

K-means algorithm is a popular Unsupervised Learning Clustering technique that resolves the Clustering problems in Machine Learning or Data Science and Analytics.

This chapter will make you understand the procedure to work with several Dimensionality Reduction techniques in Machine Learning. These techniques assist in transforming data from a high-dimensional to a low-dimensional space without losing any essential information.

As the name implies, recommender systems assist in predicting future preferences of any product and recommend the best-suited items or products to people.

This module will teach students how to implement a few advanced Machine Learning algorithms for Data Analysis, including Graph Algorithms, Network Algorithms, Distributed Algorithms, and Document Processing.

In this chapter, students will learn the process of implementing various Graph Algorithms used for Data Science or Big Data Analytics.

In this chapter, students will learn the process of implementing various Network Algorithms used for Data Science or Big Data Analytics.

In this chapter, students will learn the process of implementing various Distributed Algorithms used for Data Science or Big Data Analytics.

Document Processing is a technique to convert manual forms and analog data into a digital format in order to integrate these documents into day-to-day business processes.

This chapter will introduce you to Python programming and all the essential topics used for Data Analytics, such as Data Structures, Numpy, and Pandas.

Python is a widely implemented high-level programming language with a simple, easy-to-learn syntax, highlighting readability. This chapter will guide you through all the principles of programming in Python, like syntax and semantics.

Data Structure is a particular method to organize and store data for efficient data usage. In this chapter, students will learn all the different data structures available in Python and learn about data handling.

Functions can be used for code reusability, and this chapter will aid you in understanding and implementing Functions in Python.

This chapter will provide students with a deep understanding of exploring data sets using NumPy, one of the most widely implemented Python libraries. NumPy is a package for scientific computing, for instance, working with arrays.

Pandas is another widely implemented Python library that analyzes and manipulates data. This chapter will provide students with a deep understanding of exploring data sets using Pandas.

The next module will familiarize you with several Natural Language Processing (NLP) techniques. NLP is used for applying computational linguistics to develop real-world applications that work with languages consisting of varying structures. In Layman's terms, NLP is something where we attempt to teach the computer to learn languages and later expect the computer to understand these languages using suitable, efficient algorithms.

In this chapter, students will gain an understanding of how to convert an unstructured text into a structured text in order to discover meaningful insights. This technique is called Text Analytics, also knowns as Text Mining.

In this chapter, students will gain an understanding of Text Classification and how to implement Document summarization.

Web scraping is the technique to extract data from the web. This chapter will teach students the process of collecting and parsing data using web scraping.

As the name implies, a sequence is an ordered collection of numerous items. In this chapter, students will understand how to predict what letter or word appears with the help of sequential modeling in NLP.

The principal objective of Transformers in NLP is to solve sequence-to-sequence tasks while concurrently managing the long-range dependencies conveniently.

Deep Learning is an application of Machine Learning and Artificial Intelligence. Deep Learning involves Artificial Neural Networks (ANNs) that have emerged from the human brain’s inspiration in order to extract deep/high-level information or data from the raw input.

This chapter will familiarize students with all the applications of a neural network, a computing system in Deep Learning similar to the biological neural network that makes up the human brain.

TensorFlow is a widely implemented Python library created by Google. It is an open-source library used for numerical computation and wide-ranging machine learning.Keras is another widely implemented Python library and is a powerful, open-source API created to develop and evaluate deep learning models.

In this chapter, students will understand the procedure to process and work with images for image classification with the help of Neural Networks. Going beyond just Neural Networks, students will also understand a more advanced architecture - Convolutional Neural Networks (CNNs).

Transfer learning in Deep Learning is a research problem that focuses on storing knowledge obtained while training one model and applying it to another model. In this chapter, students will understand all the fundamentals and applications of transfer learning.

Object detection is a Computer Vision (CV) technique wherein a software system can detect, locate, and trace objects from a provided image or video. Face detection is one of the primary examples of object detection. Siamese Networks (sometimes called Twin Neural Networks) are Artificial Neural Networks that contain two or more identical subnetworks, implying that they have the same configuration with similar parameters and weights.GANs, known as Generative Adversarial Networks, are deep generative models. Similar to most generative models, they utilize a differential function defined by a neural network called a Generator network. GANs also comprise another neural network known as the Discriminator network.

Heading to the final module of the M.Tech in Big Data Analytics program, we will cover all the essentials you need to know about SQL programming and DBMS, such as Subqueries, Normalization, Joins, ACID properties, and others.

This chapter will teach students all the database concepts, which include DBMS, ER model, Normalization, Joins, etc.

SQL, known as Structured Query Language, is a set of statements/instructions in which users can access and manipulate data in a database.In this chapter, students will get familiarized with all the SQL concepts and data models.

As the name indicates, a join is an operation combining or joining data or rows from other tables on the basis of the common fields amongst them.In this chapter, students will go through several types of joins and understand the procedure to combine data. In addition, they will learn how to implement subqueries/commands that involve joins.

An Analytic/Window Function is a function that specifies values in a group of rows and generates a single result for each row.

In this chapter, you will gain a deep understanding of everything you need to know concerning ACID (Atomicity, Consistency, Isolation, and Durability) properties of Transaction Processing.