Data Analytics Unit 2 Notes AKTU | B.Tech 3rd Year | Coreconcepts:engineering

Mastering Data Analytics Unit 2: A Comprehensive Guide for AKTU B.Tech 3rd Year Students Data Analytics Unit 2 is a crucial component of the B.Tech Computer Science & Engineering (CSE) curriculum at Dr. A.P.J. Abdul Kalam Technical University (AKTU). This unit delves into the core concepts of engineering, including regression modeling, Bayesian modeling, support vector machines, and multivariate analysis. To excel in this unit, students must grasp the theoretical foundations and apply them to real-world problems. Study Highlights: * Regression modeling: Understand the step-by-step logic for linear, multiple, and logistic regression with clear derivations and solved examples. * Bayesian modeling: Simplified breakdowns of Bayesian inference and the construction of Bayesian networks. * Support vector machines: A visual and mathematical guide to hyperplanes, margins, and the "Kernel Trick" for non-linear data. * Multivariate analysis: Clean notes on principal component analysis (PCA) and factor analysis for dimensionality reduction. * Unit 2 PYQ solutions: Solved previous year questions specifically targeting the patterns featured on the Coreconcepts engineering channel. * Prerequisites: Linear algebra, calculus, and probability theory. * Follow-up units: Supervised and unsupervised learning, deep learning, and natural language processing. Detailed Educational Overview: Data Analytics Unit 2 is a critical component of the B.Tech CSE curriculum at AKTU, as it provides students with a comprehensive understanding of the core concepts of engineering. This unit is designed to equip students with the skills and knowledge required to analyze and interpret complex data, make informed decisions, and develop predictive models. The unit begins with regression modeling, which is a fundamental concept in data analytics. Students learn the step-by-step logic for linear, multiple, and logistic regression, including clear derivations and solved examples. They also learn how to identify and interpret the relationships between variables, as well as how to select the most appropriate regression model for a given problem. Next, students are introduced to Bayesian modeling, which is a probabilistic approach to machine learning. They learn how to simplify complex models using Bayesian inference and how to construct Bayesian networks, which are graphical representations of probabilistic relationships between variables. Support vector machines (SVMs) are another key concept in data analytics, and students learn how to use them to classify and regress data. They also learn about the "Kernel Trick," which allows SVMs to handle non-linear data. Multivariate analysis is another critical component of data analytics, and students learn how to use principal component analysis (PCA) and factor analysis to reduce the dimensionality of complex data. Throughout the unit, students are provided with solved previous year questions, which are specifically designed to target the patterns featured on the Coreconcepts engineering channel. These questions help students to practice and reinforce their understanding of the material, as well as to develop their problem-solving skills. In terms of practical exam-focused strategy, students should focus on developing a deep understanding of the theoretical foundations of data analytics, as well as the ability to apply these concepts to real-world problems. They should also practice solving problems and questions, and develop a strong foundation in linear algebra, calculus, and probability theory. Overall, Data Analytics Unit 2 is a critical component of the B.Tech CSE curriculum at AKTU, and it provides students with a comprehensive understanding of the core concepts of engineering. By mastering this unit, students will be well-equipped to analyze and interpret complex data, make informed decisions, and develop predictive models. Context Coverage: Data Analytics Unit 2 Notes AKTU | B.Tech 3rd Year | Coreconcepts:engineering, Data Analytics (DA) are core context signals for this material.