Deborah Oliver has 3 audiobooks on Listento.it, narrated by 2 narrators. The most-rated is Statistics for Beginners.
Python for Data Analysis
Summary
This audiobook offers a smart introduction to data analysis using Python and is one of the best books available in the market. It covers basic and advanced data analysis concepts in detail using several Python libraries that are well-known for their data analytical features. The book explains everything in detail and, sometimes, enhances the listeners' knowledge by touching on some advanced topics that need strict adherence and sincerity to master. In this audiobook, we start with an introduction to data analysis using the catering system as an example. We always strive to give Python code for the concepts discussed because it gives a practical understanding of the topic. Below are some the most important topics we have discussed in this book, in no particular order: Data analysis with a universal catering system that helps us understand different data mining models available Introduction to Python, Numpy, and Pandas and their installation Numpy and advanced array functions it offers Pandas and functions it offers Basic and advanced data analysis skills Plotting features in data analysis Time series functions in data analysis E-commerce example to understand the real-world application of data analysis So, what are you waiting for? Go on a drive to conquer data analysis skills by using this audiobook.
©2019 Deborah Oliver (P)2019 Deborah Oliver
Introduction to Machine Learning with Python
Summary
This book gives a layman explanation for machine learning using Python. We will explain a lot of basic machine-learning topics using Python code. There are a lot of examples that we can use to master the skill of data science. This book will help you understand the basic algorithms that machine learning deals with. There are a lot of concepts that can be used to acquire advanced skills in data science and its subsequent subfields. In the first chapter, we will discuss very basics and introduce the Python environment for the users. There are certain basic principles that can be learned using the book. We will then discuss data-processing techniques, which are very important for a good machine-learning model. We will introduce pandas and NumPy models to the listener, along with their use cases. We will also try to expand our knowledge using machine-learning algorithms that are described in the book. In the next sections, we will learn about machine-learning models. The last two chapters will give a practical point of view to what we have discussed. Below, we explain the most important concepts we discussed in this book in no particular order. Introduction to machine learning and Python environment Introduction to NumPy, Python, and other machine-learning Python modules Introduction to data-processing techniques in detail Introduction to data visualization in detail - we will learn about histogram and pie in detail We will learn about a lot of machine-learning algorithms, like regression analysis, decision trees, support vector machine, and others in detail We will also discuss other algorithms in brief We will learn about ensemble modeling in detailed in the chapters inside We will give a few use-cases to it We will also discuss hyperparameter-turning in detail We will next learn about machine-learning project structure, pipelines, and other advanced topics in the last chapter So why are you still waiting? Go buy it!
©2020 Deborah Oliver (P)2020 Deborah Oliver
Statistics for Beginners
Summary
This is a book that explains all statistical concepts in layman terms. We will discuss concepts like ratio, average, the proportion to very basic concepts like median, mode, and mean. We will then start discussing scatter plot and box plots along with concepts like variance analysis and a chi-square test that are important to understand statistics better. We will also discuss various statistical teaching methods along with a lot of examples and equations. Statistics is a tough topic so we explain them in simple words which can make us understand the things better. We also discuss charts that visualizing statistics with a lot of examples like pie chart, histogram, line chart, etc. First of all, we will start with a basic introduction to statistics and all the topics we are going to cover The first chapter deals with all basic concepts. We will discuss something that deals with everything in statistics The next chapters deal with the visualization concepts of statistics. We will discuss histograms, line graphs, and a lot of other topics. We will discuss analysis concepts like median, mode, and all other stuff that will help us understand advanced concepts. The next chapter deals with testing procedures which is a basic concept in statistics for testing purposes. The next chapter deals with variance analysis and chi-square test in detail. The last chapter deals with a lot of statistical concepts that coincide with data science like Linear regression and other topics. So why are you waiting? Let us dive into the topic.
©2020 Deborah Oliver (P)2020 Deborah Oliver