M.Tech in Data Engineering

In the first module of the Data Engineering course, we will cover all the fundamental concepts you must learn about Python programming for Data Science, such as Data Structures, Functions, NumPy, Pandas, SciPy, and Scikit-learn.

Python programming is an extensively implemented high-level programming language with a simple, easy-to-learn syntax. This chapter teaches you all the Python programming fundamentals, like syntax and semantics, etc.

Data Structure is a process of organizing and storing data for efficient data usage. In this chapter, you will master the skills to handle different data structures using Python.

This chapter will help you in understanding and implementing Functions in Python, which are generally used for code reusability.

This chapter will give you an extensive understanding of exploring data sets with the help of NumPy, which is one of the most widely implemented Python libraries. NumPy is a package used for scientific computing, such as working with arrays.

Like NumPy, Pandas is also another widely implemented Python library. Pandas is used for analyzing and manipulating data. This chapter will give you an in-depth understanding of exploring data sets with the help of Pandas.

In this chapter, you will gain an extensive understanding of the implementation of SciPy, a free and open-source library in Python used for technical and scientific computing.

Scikit-learn is an open-source library for Machine Learning in Python, which is simple and provides efficient tools for various Machine Learning techniques like Classification, Regression, Clustering, Predictive Data Analysis, and many more.

In the next module, after Python for Data Science, we will cover fundamental concepts of DBMS, SQL programming, Subqueries, Joins, ACID properties, and many more.

A database is something where one can store, edit, and organize data in their databases. This chapter will teach you several fundamentals of database concepts, such as DBMS, Normalization, ER model, and more.

SQL or Structured Query Language is a set of statements/instructions where one can access and manipulate data in a database. Here, you will gain an understanding of several SQL concepts and data models in DBMS.

A join, as the name suggests, is an operation that joins or combines data or rows from other tables on the basis of the common fields amongst them. This chapter will take you through various types of joins and teach you how to combine data. In addition, you will understand how to work with subqueries/commands that involve joins.

Here, you will gain an understanding of determining values in a group of rows and generating a single result for every row. This function is called an Analytic/Window Function.

This chapter will teach you about ACID (Atomicity, Consistency, Isolation, and Durability) properties of Transaction Processing.

The next module in this Data Engineering course will teach you several fundamentals of Mathematical Foundations, such as Linear Algebra, Vectors and Matrices, Eigen Decomposition, and many more.

This chapter will take you through several crucial concepts like Linear Algebra to implement Mathematics for Data Science and Data Engineering.

A vector is simply a list of numbers that can be represented in one row or column. In contrast, a matrix is an array of numbers that can be represented in one or more rows or columns.

This chapter will teach you the process of implementing all the functions and derivatives for your Machine Learning algorithms.

Eigen Decomposition in PCA is the method to decompose a matrix into its eigenvalues and eigenvectors.

Singular Value Decomposition or SVD is another method to decompose a matrix into single values and single vectors.

Loss Function is a technique for predicting errors in neural networks. The optimization method tries to minimize a loss function.

In the next module, we will discuss Data Analysis and Visualization. Here, we will talk about several fundamentals like Data Understanding, Data Pre-processing, Tableau, and much more.

Data Understanding is the process of collecting data, assessing it, and deriving insights from the discovered data with the help of tables and graphs.

Data Preprocessing is the process of cleaning and preparing data. This chapter will teach you how to work with data using all the methods involved in preprocessing the data.

This chapter will teach you the process of structuring and solving analytic problems using Data Analysis and Visualization techniques.

Data Storytelling is the technique to read data in detail, identify crucial messages, and communicate these messages most effectively.

In this chapter, you will understand the method to create an interactive dashboard and charts to model and organize data using Visualization principles.

Tableau is the most widely implemented Data Visualization tool that is used for solving problems. This chapter will teach you about Tableau and how to work with it.

Heading to the next module, we will discuss Machine Learning and its various essential concepts, such as Supervised Regression, Supervised Classification, and Unsupervised Models.

In this section, you will understand the different concepts of Supervised Regression, such as EDA, Linear Regression, Feature Engineering, and much more.

• Exploratory data analysis

Exploratory Data Analysis, shortened to EDA, enables you to uncover patterns and insights frequently with visual methods within some data.

• Linear Regression (LR)

Linear Regression is a widely implemented algorithm that is used for predictive analysis in Machine Learning, assisting in producing the best outcomes. LR is a technique that assumes a linear relationship between the independent variable and dependent variable.

• Statistical Significance of LR

In this chapter, you will gain an extensive understanding of the statistical significance of LR.

• Feature engineering

Feature engineering is the technique to transform data from the raw state to a state that becomes appropriate for modelling. Feature engineering assists in converting the data columns into features that better represent a provided situation in terms of clarity.

• Regularization Techniques

Regularization is a method to tune the function by adding extra information to it to prevent overfitting. In this chapter, you will understand Regularization, followed by various techniques.

• Model deployment

This chapter will teach you the process of deploying your Machine Learning models with the help of several available tools.

In this section, you will understand the different concepts of Supervised Classification, such as Logistic Regression, k-NN, Decision Trees, and much more.

• Logistic Regression

Logistic Regression is a vastly implemented algorithm, which is a simple classification algorithm for predicting the categorical dependent variables with the aid of independent variables.

• K-Nearest Neighbours

k-Nearest Neighbours, also called the k-NN algorithm, is one of the simple ML algorithms that is used for solving classification and regression problems.

• Decision Trees

A Decision Tree is a vastly implemented 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 denotes the result.

• Ensemble Techniques

This chapter will discuss the shortcomings of your supervised standalone models, where you will learn a few techniques, such as Ensemble techniques, to overcome these shortcomings.

• Naive Bayes

This chapter will teach you how to solve classification problems using the Naive Bayes algorithm.

• Model Improvement

This chapter will discuss the process of improving and optimizing your Machine Learning algorithms or models.

In this section, you will understand the different concepts of Unsupervised Models, such as Clustering Techniques, Dimensionality Reduction, Recommender systems, and much more.

• Clustering Techniques

Clustering is a technique that entails the grouping of data. In this chapter, you will understand various Clustering techniques, like K-means clustering and hierarchical clustering.

• K-means algorithm

K-means Algorithm is a vastly implemented algorithm for resolving the clustering problems in Machine Learning or Data Science and Engineering.

• Hierarchical Clustering

Hierarchical Clustering is used for building a hierarchy or tree-like structure of clusters. To give an example, Hierarchical Clustering is used for combining a list of unlabeled datasets into a cluster in the hierarchical format.

• Dimensionality Reduction

Dimensionality Reduction is used to transform data from a high dimensional to a low dimensional space without losing any vital information.

• Recommender system

As the name implies, a recommender system assists in predicting some products’ future preferences and recommends the best-suited items to the user.

• Market Basket Analysis

Market Basket Analysis, also called Affinity Analysis, is a modelling technique that is based on the theory—if you buy a particular set of items, then you are more likely to purchase another set of items.

Time Series Forecasting Moving forward to the next module, we will cover some essential concepts of Time Series Forecasting, such as Moving Average, Auto-correlation, ARIMA, and much more.

Decomposition in Time Series is a statistical task where the Time Series data is decomposed into numerous components or extracts trends, seasonality from the Time Series data.

Moving Average is a method for predicting the future value based on past values. In this chapter, you will understand how to work with the Moving Average approach.

In this chapter, you will understand the process of collecting data and predicting the future value of data by focusing on its unique trends. This process is called Forecasting data.

This chapter will teach you the method to solve the problems of correlation.

ARIMA, abbreviated as Auto Regression Integrated Moving Average, is used for forecasting time series following a seasonal trend and pattern.

SARIMA, abbreviated as Seasonal Auto Regression Integrated Moving Average, is an extension to ARIMA that is used for supporting the direct modelling advanced is-Data of the seasonal component of the time series.

Moving further ahead, this module will teach you how to work with Advanced Data Visualization techniques, such as Structuring Analytic Problems, Analytical Framework, Interactive Dashboard, and much more.

This chapter will teach you the procedure to structure and solve analytic problems with the aid of various Data Visualization techniques.

The Analytical Framework is used to collect customers’ issues to solve them subsequently. This chapter will teach you how to solve these problems.

This chapter will teach you the process of representing data and information in a graphical format using Tableau. This process is known as Data Visualization.

An interactive dashboard lets you analyze and filter data and information so that they can be viewed thoroughly from different perspectives.

As discussed earlier, Storytelling is the technique to read data in detail, identify crucial messages, and communicate these messages most effectively.

LODs, abbreviated as Level of Detail expressions, enable you to compute values at the data source and the visualization level. Yet, LODs offer you more precise control over the level of granularity you want to compute.

Deep Learning is an application of Machine Learning and Artificial Intelligence. Deep Learning is a study where Artificial Neural Networks (ANNs) have emerged from the inspiration of the human brain to extract deep/high-level information from the raw input. This chapter will teach you all the Deep Learning concepts.

Neural Networks are computing systems in Deep Learning based on the biological neural network making up the human brain.

TensorFlow is a vastly implemented Python library created by Google. It is an open-source library used for numerical computation and wide-ranging Deep Learning and Machine Learning.
Keras is a robust, open-source API that is designed to develop and evaluate Deep Learning models.

This chapter will teach you how to process and work with images using Neural Networks for image recognition and classification. Heading into much more profound than plain Neural Networks, you will also understand a more advanced architecture - Convolutional Neural Networks (CNNs).

Transfer Learning is a research technique in Deep Learning that provides emphasis on storing knowledge that is gained while training one model and applying it to another model.

Object detection is a computer vision method where 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.

Natural Language Processing, shortened to NLP, applies computational linguistics for building real-world applications that work with languages incorporating varying structures. Using NLP, we attempt to teach the computer to learn languages and then anticipate the computer to understand them with appropriate, efficient algorithms.

• Text Analytics - Basics

In this chapter, you will understand the process of converting unstructured text into a structured text to discover relevant insights. This process is called Text Analytics, also called Text Mining.

• Document summarization and classification

In this chapter, you will understand various fundamentals of Text Classification and the implementation of Document summarization.

• Web Scraping

Web Scraping is the technique to extract data from the web. This chapter will take you through the process of collecting and parsing data using Web Scraping.

• Sequential Modelling

A Sequence, as the name implies, is an ordered collection of different items. In this chapter, you will understand the process of predicting what letter or word appears with the help of Sequential Modelling.

• Transformers

Transformers in NLP are used for solving sequence-to-sequence tasks while simultaneously handling the wide-range dependencies conveniently.

The next module of the Data Science and Engineering course discusses Big Data technology. Big Data is used for determining large and complex data sets, which can be both structured and unstructured. Big Data is generally used for Machine Learning, Product Development, and Fraud Protection.

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

HDFS, abbreviated as Hadoop Distributed File System, is used for handling massive data sets that run on commodity hardware. It aids in storing enormous amounts of data sets and provides easier access.
MapReduce is a framework in Hadoop that lets you write applications quickly and process vast amounts of data parallelly on large clusters (thousands of nodes) of commodity hardware in a fault-tolerant and reliable means.

NoSQL generally means a non-relational database with no SQL. NoSQL provides a mechanism to store data in any format in the database other than relational tables.

Batch Processing is a technique to process enormous amounts of data within a particular time period. Hive is SQL-based, while HBase is NoSQL-based Hadoop technologies.

Real-time processing is a technique to process data within a short period and provides automated results. Spark is an open-source, unified analytics engine, which is used for processing massive amounts of data. Several companies use Kafka as it is an open-source distributed event streaming platform used for high-performance data pipelines, data integration, streaming analytics, and mission-critical applications.

Approaching the end of the Data Engineering course, here, the learners can choose any elective they are interested in pursuing. Following is the list of open elective courses offered:

Operations Research is the method to apply advanced analytical techniques to assist in making better decisions.

Industrial Safety is the discipline of managing and maintaining safety precautions in the industry.

Entrepreneurship is the act of commencing a business, bearing the majority of risks, and leveraging most of the profits. IPR (Intellectual Property Rights) enable innovative entrepreneurs to protect their discoveries or inventions.

Business Analysis and Communication assist organizations in improving their systems, processes, and operations by evaluating existing practices, defining business requirements, and proposing solutions.

Professional Ethics is a set of principles that govern the behaviour modelling Advanced-Data individual or a group in a business environment.