SQL for Data Scientists: A Beginner`s Guide for Building Datasets for Analysis, Teate Renee M.
Автор: John M. Stewart Название: Python for Scientists ISBN: 1316641236 ISBN-13(EAN): 9781316641231 Издательство: Cambridge Academ Рейтинг: Цена: 33780.00 T Наличие на складе: Поставка под заказ. Описание: Scientific Python is taught from scratch in this book via copious, downloadable, useful and adaptable code snippets. Everything the working scientist needs to know is covered, quickly providing researchers and research students with the skills to start using Python effectively.
Python is one of the most beloved programming languages in any circle of programmers. Software engineers, hackers, and Data Scientists alike are in love with the versatility that Python has to offer. Besides, the Object-Oriented feature of Python coupled with its flexibility is also one of the major attractions for this language.
That's the reason why Python is a perfect fit with Data Analysis, Machine Learning and Data Science.
Data is the future. The world of technology as we know it is evolving towards an open-source platform where people share ideas freely. This is seen as the first step towards the decentralization of ideas and eliminating unnecessary monopolies. Therefore, the data, tools, and techniques used in the analysis are easily available for anyone to interpret data sets and get relevant explanations.
The goal of this 4-in-1 bundle is simple: explaining everything you need to know to Master Python. With a special emphasis on the main steps that are needed to correctly implement Data Analysis and Machine Learning algorithms,
In manuscript one, Python for Beginners, you will learn:
How to install Python
What are the different Python Data Types and Variables
Basic Operators of Python Language
Data Structures and Functions
Conditional and Loops in Python
And Much More
In manuscript two, Python Advanced Guide, you will master:
Object-Oriented Programming (OOP), Inheritance and Polymorphism
Essential Programming Tools
Exception Handling
Working with Files
And Much More
In manuscript three, Python for Data Analysis, you will learn:
What Data Analysis is all about and why businesses are investing in this sector
The 5 steps of a Data Analysis
The 7 Python libraries that make Python one of the best choices for Data Analysis
Pandas, Jupyter and PyTorch
And Much More
In manuscript four, Applications to Data Science, you will understand:
How Data Visualization and Matplotlib can help you to understand the data you are working with.
Neural Networks
Decision Trees
What industries are using data to improve their business with 14 real-world applications
And So Much More
Where most books about Python programming are theoretical and have few or little practical examples, this book provides lots of simple, step-by-step examples and illustrations that are used to underline key concepts and help improve your understanding.
Furthermore, topics are carefully selected to give you broad exposure to Python, while not overwhelming you with too much information. Also, the outputs of ALL the examples are provided immediately so you do not have to wait till you have access to your computer to test the examples.
Even if you have never coded before, this is the perfect guide because it breaks down complex concepts into simple steps and in a concise and simple way that fits well with beginners. Regardless of your previous experience, you will learn the steps of Data Analysis, how to implement them, and the most important real-world applications.
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Interested in starting Data analysis and not sure which Programming language to use to develop Data Analysis Programs? Are you a beginner and would like to learn Python in order to develop the necessary skills for data analysis and note sure where to start?
Do you have data and want to analyze and provide professional figures for your presentation and communication and don't know which programming language to use or interested in using Python?
This book is intended to address all these questions. In fact, the aim of this book is providing the absolute beginners or other programmers that has no experience with Python programming the basic and fundamental tools of the Python language.
This book provides not only the basic syntax of Python but also an advanced command to evaluate the performance of your code in interactive programming environment (i.e. Jupyter) and a guide to use the fundamentals Python libraries for data analysis which are Pandas, NumPy and Matplotlib libraries. All these libraries are presented with examples with real data so you can practice at the same time when learning the functionalities of this library.
By reading this book, you will learn:
- The characteristics and utilities of using Python over other programming languages
- How to get started and install Python
- The basic syntax of Python programming and different basic data structures in Python
- How to develop modules and functions in Python
- How to install and use magic command in Ipython
- Functionalities of NumPy library for numerical programming
- Functionalities of Pandas library for data analysis
- Creating and customizing figures to visualize data with Matplotlib library
Even if don't have any background or skills in programming, this book will provide you and help you develop the required skills and basic technics to start data analysis with Python programming. It also provides you with code and data so you can practice. By reading this book will easily learn how to use python and Python libraries for easy data analysis and management. By developing the skills provided in this book, you will easily develop more advanced skills and acquire advanced technics that will allow to go from a beginner to an expert in Python.
Автор: Crickard Paul Название: Data Engineering with Python: Work with massive datasets to design data models and automate data pipelines using Python ISBN: 183921418X ISBN-13(EAN): 9781839214189 Издательство: Неизвестно Рейтинг: Цена: 63750.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Racism and white supremacy have survived in the United States for a very long time because from the onset, it was based on sound `engineering.` The book contends that racism is not a belief, practice, or ideology that flared up by accident or through the spur of the moment. Rather, it followed sound engineering stages like planning, design, and construction. Racism was designed to serve a domineering purpose for white people and, therefore, there was a lot of planning that eventually led to its design and construction. Because the architects had a vision of its permanency, they chose the most durable materials for its construction. Metaphorically, they used mortar, bricks, and steel beams. These durable materials, among others, include religion, science, government (including the Supreme Court), the constitution and laws, brutality, and social media. As the centuries rolled by, succeeding architects of this design and construction have done a superb job in maintenance and modifications to elude stumbling blocks. Based on the times and prevailing winds, the racism construct has undergone mutations to evade capture and destruction - even to this day. Like a car, the engineering and engineers have evolved, the models and make are changing, but the underlying engineering remains intact. Today, the overt, brash, and brutal racism has generally ceded to an equally destructive, calculated, politically-correct, less pompous, highly sophisticated, and veiled racism. The book dissects this durable foundational construct and proffers recommendations that will systematically minimize its intensity.
Автор: Natasa Przulj Название: Analyzing Network Data in Biology and Medicine: An Interdisciplinary Textbook for Biological, Medical and Computational Scientists ISBN: 110842175X ISBN-13(EAN): 9781108421751 Издательство: Cambridge Academ Рейтинг: Цена: 95030.00 T Наличие на складе: Поставка под заказ. Описание: The increased and widespread availability of large network data resources in recent years has resulted in the increased need for effective methods for the analysis of these networks. The challenge is to detect patterns that provide a better understanding of the data. However, this is not a straight forward task because of the size of the data sets and the computer power required for the analyses. The solution is to devise methods for approximately answering the questions posed, and these methods will vary depending on the data sets under scrutiny. This cutting-edge text introduces graph and network theory, cluster analysis and machine learning, before discussing the thought processes and creativity involved in the analysis of large-scale biological and medical data sets, using a wide range of real-life examples. Bringing together leading experts, this inter-disciplinary text provides an ideal introduction to and insight into the field of network data analysis.
Are you looking for a complete guide on sql? If yes, keep reading...
As programming is commonly intensive, the same may be said with SQL because it also takes different pathways for the successful development of an operational database system. Some may term SQL as a computer programming language because it takes the form of commands, which consists of instructions for the system to engage in a particular action like most programming tools. On the other hand, others refer to it as a data management system as it involves the creation of multiple tables organized under one or more databases essential for the storage of data.
Therefore, scientists and developers conclude that SQL is a standard computer language which helps in the communication between different database systems.
The first SQL model was developed by Ted Code, who introduced the ability of communication between different storage units. His idea was later acquired by Donald Chamberlin and Raymond Boyce and developed the model in the 1970s.
However, the version at the time was referred to as SEQUEL (Structured English Query Language) specifically designed to help in data storage and retrieval. More modifications were made over the years in IBM, with the first being done in a laboratory in San Jose, California, United States, where the first subscript notation was introduced. After testing the effectiveness of the software in customer test websites, SQL was then developed for use commercially after its usefulness and practicality determined.
Today, nearly all businesses around the world have turned into digital handling of data, which keeps growing daily. Ranging from small online business stores to large organizations such as Fortune 500 utilize databases as a way to store and manage this information. This way, SQL has gained popularity over time with demand for more database administrators increasing each passing day.
There are a lot of times when we will want to work with a database to help our business to grow and to keep all of the data and information that we have that concerns our business in order. And the SQL language is going to ensure that we are able to make all of this happen for our needs.
This book covers:
SQL (What it is and What it is used for)
Operators
Data Definition Language
Data Control Language
Data Manipulation Language
Data Control Language
Data Control Language
Stored Program
Subquery and many more
If you're trying to dive into learning SQL, you may feel frazzled and lost. It may look like a bunch of meaningless words and texts put in random order. Rest assured, there is sense to this language and it is quite easy to navigate through when you are given the necessary tools to understand it. In a lot of ways, SQL can have many similarities to the English language itself. You just need to understand how to formulate commands in order to be successful with the language. In the same way that we formulate sentences every day in order to communicate with individuals around us, the same goes for SQL when it comes to databases.
When you are ready to learn more about SQL and how this language can make the management and all of the other parts of running our database easier, make sure to read through this guidebook to help you get started.
Автор: Pruneau Claude A Название: Data Analysis Techniques for Physical Scientists ISBN: 1108416780 ISBN-13(EAN): 9781108416788 Издательство: Cambridge Academ Рейтинг: Цена: 90810.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: A comprehensive guide to data analysis techniques for the physical sciences including probability, statistics, data reconstruction, data correction and Monte Carlo methods. This book provides a valuable resource for advanced undergraduate and graduate students, as well as practitioners in the fields of experimental particle physics, nuclear physics and astrophysics.
Businesses, governments, and organizations all need data for some reason. Data today is an opportunity to understand their current situation and use it to prepare for the unknown. The techniques used in data analysis today are easily available to anyone to interpret the data and obtain relevant explanations.
Data analysis requires a detailed understanding of the operation of the computers, peripherals, and software in question. The objective is to give the reader the knowledge necessary to familiarize themselves with the Python language by orienting the problem so as to focus on the functioning of these objects. This book was written with the desire to be accessible to everyone and the conviction that a "democratization" of the understanding of the computer tool is now essential.
This book offers a detailed approach: it begins with an introduction to the Python language and then presents how to use it to retrieve and manipulate the data produced by our computers. The authors thus deal with various themes ranging from the inspection of the process RAM, to the internal functioning of mainstream software or to the extraction of web browser history.
Different tools are studied: from the most basic to the most recent technologies such as machine learning with scikit-learn and its ecosystem resulting from scientific computing.ompiles (if there is no updated bytecode on disk), and runs on the Python virtual machine.
With Python for Data Analysis you'll learn step by step how to implement data analysis and procedures to extract data correctly.
In this you also will learning:
what's Data Analysis
Python For Data Analysis
Data Aggregation
Application Of Data Analytic today
Mathematics For data Analysis
Data Wrangling
Scipy, Numpy, Panda
While most books focus on advanced predictive models, this book begins to explain the basic concepts and how to correctly implement Data Analysis and Data Visualization, with practical examples and simple coding scripts.
This guide provides the necessary knowledge in a practical way. You will learn the steps of Data Analysis, how to implement them in Python, and the most important applications in the real world.
Download the eBook, Python For Data Analysis. Scroll to the top of the page and click the "Buy now" button to get your copy now.
Are you looking for a proven career in an uncertain world of business and technology?
Would you like to master and use one of the most powerful automation and data science tools for your business?
Or maybe you are looking to expand your knowledge and expertise in coding and computer programming?
If you answered "Yes" to at least one of these questions, please read on...
It is not a secret that computer programming is one of the most in-demand high-payed professions in the world. It is also one of the most important skillsets for most successful companies in the world, as well as for ambitious start-ups and growth-oriented businesses that want to expand fast and save money in the process.
Here are just a few things you'll discover inside:
How do you get started with Python? A complete computer programming guide for beginners
Why is Python Programming Language considered one of the easiest to learn out there? The most effective one as well
What is Data Science in Python, and how can you use it?
Python for Start-ups and Businesses - how it can help you scale and grow?
Machine Learning and AI(artificial intelligence), what do you need to know about it?
More Advanced Python - for people who really want to master this skill and become good at it
Python Coding Strategies for medium and small businesses
Much much more...
And you don't have to be some great coder to master and use this powerful language. This book will take you by the hand and lead through every single step
Автор: Matt A. Wood Название: Python and Matplotlib Essentials for Scientists and Engineers ISBN: 1681749106 ISBN-13(EAN): 9781681749105 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 41580.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book provides an introduction to the core features of the Python programming language and Matplotlib plotting routings for scientists and engineers (or students of either discipline) who want to use Python™ to analyse data, simulate physical processes, and render publication-quality plots. No previous programming experience is needed before reading the first page.Readers will learn the core features of the Python programming language in under a day. They will be able to immediately use Python to implement codes that solve their own problems and make beautiful plots and animations. Python code is extremely fast to prototype, allowing users to achieve results quickly and accurately. The examples within the book are available for download.Python and Matplotlib Essentials for Scientists and Engineers is accessible for motivated high-school students, but will likely be most useful for undergraduate and graduate students as well as working professionals who have some background with the basic mathematical concepts. This book is intended for technical people who want to get things done.
Автор: Juana Sanchez Название: Probability for Data Scientists ISBN: 1516532694 ISBN-13(EAN): 9781516532698 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 100280.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Probability for Data Scientists provides students with a mathematically sound yet accessible introduction to the theory and applications of probability. Students learn how probability theory supports statistics, data science, and machine learning theory by enabling scientists to move beyond mere descriptions of data to inferences about specific populations. The book is divided into two parts. Part I introduces readers to fundamental definitions, theorems, and methods within the context of discrete sample spaces. It addresses the origin of the mathematical study of probability, main concepts in modern probability theory, univariate and bivariate discrete probability models, and the multinomial distribution. Part II builds upon the knowledge imparted in Part I to present students with corresponding ideas in the context of continuous sample spaces. It examines models for single and multiple continuous random variables and the application of probability theorems in statistics. Probability for Data Scientists effectively introduces students to key concepts in probability and demonstrates how a small set of methodologies can be applied to a plethora of contextually unrelated problems. It is well suited for courses in statistics, data science, machine learning theory, or any course with an emphasis in probability. Numerous exercises, some of which provide R software code to conduct experiments that illustrate the laws of probability, are provided in each chapter.
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