Data Science

Complete Test Bank for Data Science for All 1st Edition by Hunter Glanz and Brennan Davis, covering Chapters 1–10 with comprehensive MCQs and accurate answers. Ideal for exam preparation, quick revision, and strengthening your understanding of essential data science concepts. Includes chapter-by-chapter questions and answers for effective study and test preparation.

This document will assist the students in revision on questions of differential equations, it has question, and well worked answer

Data Structure using C is a fundamental concept in computer science that deals with organizing, storing, and managing data efficiently so that it can be accessed and modified easily. In the C programming language, data structures are implemented using basic data types, arrays, pointers, structures, and dynamic memory allocation, which gives programmers low-level control over memory. Common data structures in C include arrays, linked lists, stacks, queues, trees, graphs, and hash tables. Arrays s...

This Document has notes about the very first Machine learning Algorithm, the linear regression algorithm.

This Document has notes about NLP and steps involved in Text Data Processing.

This document provides steps in unsupervised learning and explains when to use one.

This Document helps understand how to balance an imbalanced data set for better prediction.

This document covers Ensemble learning, bagging, bootsrapping, boosting, random forest and XGB in detail, along with their pros and cons. Ensemble learning means combination of available ML algorithm.

This document explains decision tree algorithm used in regression and classification problems.

This Document is about K-Nearest Neighbour algorithm, a supervised learning technique, used for regression as well as classification. This notes will help one to navigate the significance of KNN model. Also including how to use and when to use this model in real time scenarios.

What if the target problem is categorical? in that case we must know that the task is classification and Classification comes under supervised learning. This Document explains Logistic regression, which is a classification algorithm.

This Document gives a clear introduction about Machine Learning and this is the transition point from understanding the logics and Math behind the problems and implementing the knowledge to Business questions.

This is a series of decks overwritten with notes about processing image and text data using python libraries.

This document has series of decks overwritten with notes about how to handle missing data in Pandas.

This document will help creating an html web page. If all the steps is followed, then you can create your very first html page. Also, the same goal can be achieved by using dash. This document also includes the steps in creating a webpage using python dash.

This document will guide through Data visualization in python using matplotlib and seaborn libraries. The data set used in this model is "iris dataset"

This document is all about probability mathematics, statistics, theorems in understanding real world problems that may arise while analyzing huge data. All the basic mathematics covered in this module are essential for approaching data problems.

This document helps to understand basics in Pandas and how to use it.

This Document is all about algorithms, programs, notations, binary search, stack, heap, type of heap, binary search tree, memory management in python and garbage collector. These topics will help understand basic logics used in solving problems.

This Document is about MySQL setting in your local computer and executing it from terminal