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Scientific Data: A 50 Steps Guide using Python

Scientific Data: A 50 Steps Guide using Python
Author: Matthias Hofmann
Publisher: Walter de Gruyter GmbH & Co KG
Total Pages: 304
Release: 2024-10-07
Genre: Science
ISBN: 3111334708
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This guide offers a comprehensive understanding of experimental data analysis in the natural sciences while ensuring sustainable processing routines from a programmer's perspective. It applies a concise problem-solution-discussion format, supported by Python code snippets, catering to practitioners.

Data Science Using Python and R

Data Science Using Python and R
Author: Chantal D. Larose
Publisher: John Wiley & Sons
Total Pages: 256
Release: 2019-04-09
Genre: Computers
ISBN: 1119526817
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Learn data science by doing data science! Data Science Using Python and R will get you plugged into the world’s two most widespread open-source platforms for data science: Python and R. Data science is hot. Bloomberg called data scientist “the hottest job in America.” Python and R are the top two open-source data science tools in the world. In Data Science Using Python and R, you will learn step-by-step how to produce hands-on solutions to real-world business problems, using state-of-the-art techniques. Data Science Using Python and R is written for the general reader with no previous analytics or programming experience. An entire chapter is dedicated to learning the basics of Python and R. Then, each chapter presents step-by-step instructions and walkthroughs for solving data science problems using Python and R. Those with analytics experience will appreciate having a one-stop shop for learning how to do data science using Python and R. Topics covered include data preparation, exploratory data analysis, preparing to model the data, decision trees, model evaluation, misclassification costs, naïve Bayes classification, neural networks, clustering, regression modeling, dimension reduction, and association rules mining. Further, exciting new topics such as random forests and general linear models are also included. The book emphasizes data-driven error costs to enhance profitability, which avoids the common pitfalls that may cost a company millions of dollars. Data Science Using Python and R provides exercises at the end of every chapter, totaling over 500 exercises in the book. Readers will therefore have plenty of opportunity to test their newfound data science skills and expertise. In the Hands-on Analysis exercises, readers are challenged to solve interesting business problems using real-world data sets.

Unlock Your Data Superpowers: Master Data Science & ML with ChatGPT

Unlock Your Data Superpowers: Master Data Science & ML with ChatGPT
Author: Fredric Cardin
Publisher: DigitalSphere Creations
Total Pages: 66
Release:
Genre: Computers
ISBN:
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Ready to transform data into actionable insights? This comprehensive ebook equips you with the fundamentals of Data Science and Machine Learning, along with the game-changing power of ChatGPT. Inside, you'll discover: Core principles of Data Science and Machine Learning - the foundation for building powerful models. How to leverage ChatGPT's capabilities to streamline tasks and unlock new possibilities. Mastering Matplotlib and Seaborn - essential Python libraries for creating clear and impactful data visualizations. Building 3 complete Data Science and Machine Learning projects - get hands-on experience applying your newfound knowledge efficiently, guided by ChatGPT. This ebook is your roadmap to becoming a data pro, empowering you to: Make data-driven decisions with confidence Extract valuable insights from complex datasets Craft compelling data visualizations to communicate your findings Gain a competitive edge in the job market Don't wait - unlock your data superpowers today!

Practical Statistics for Data Scientists

Practical Statistics for Data Scientists
Author: Peter Bruce
Publisher: "O'Reilly Media, Inc."
Total Pages: 322
Release: 2017-05-10
Genre: Computers
ISBN: 1491952911
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Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not. Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format. With this book, you’ll learn: Why exploratory data analysis is a key preliminary step in data science How random sampling can reduce bias and yield a higher quality dataset, even with big data How the principles of experimental design yield definitive answers to questions How to use regression to estimate outcomes and detect anomalies Key classification techniques for predicting which categories a record belongs to Statistical machine learning methods that “learn” from data Unsupervised learning methods for extracting meaning from unlabeled data

Data Science from Scratch with Python

Data Science from Scratch with Python
Author: Peters Morgan
Publisher: Createspace Independent Publishing Platform
Total Pages: 174
Release: 2018-07-03
Genre:
ISBN: 9781722365288
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***** BUY NOW (Will soon return to 35.59) ******Free eBook for customers who purchase the print book from Amazon****** Are you thinking of learning data science from scratch using Python? If you are looking for a complete step by step guide to data science using Python from scratch, this book is for you.After his great success with his first book "Data Analysis from Scratch with Python", Peters Morgan publish his second book focusing now in data science and machine learning. It is considered by practitioners as the easiest guide ever written in this domain. From AI Sciences Publisher Our books may be the best one for beginners; it's a step-by-step guide for any person who wants to start learning Artificial Intelligence and Data Science from scratch. If you need to see the quality of our job, AI Sciences Company offering you a free eBook in Machine Learning with Python written by Alain Kaufmann at https://aisciences.lpages.co/ai-science-l1/.To get the most out of the concepts that would be covered, readers are advised to adopt hands on approach, which would lead to better mental representations. Step By Step Guide and Visual Illustrations and Examples The Book give complete instructions for manipulating, processing, cleaning, modeling and crunching datasets in Python. This is a hands-on guide with practical case studies of data analysis problems effectively. You will learn pandas, NumPy, IPython, and Jupiter in the Process. Target Users Target UsersThe most suitable users would include: Beginners who want to approach data science, but are too afraid of complex math to start Newbies in computer science techniques and data science Professionals in data science and social sciences Professors, lecturers or tutors who are looking to find better ways to explain the content to their students in the simplest and easiest way Students and academicians, especially those focusing on data science What's Inside This Book? Part 1: Data Science Fundamentals, Concepts and Algorithms Introduction Statistics Probability Bayes' Theorem and Naïve Bayes Algorithm Asking the Right Question Data Acquisition Data Preparation Data Exploration Data Modelling Data Presentation Supervised Learning Algorithms Unsupervised Learning Algorithms Semi-supervised Learning Algorithms Reinforcement Learning Algorithms Overfitting and Underfitting Correctness The Bias-Variance Trade-off Feature Extraction and Selection Part 2: Data Science in Practice Overview of Python Programming Language Python Data Science Tools Jupyter Notebook Numerical Python (Numpy) Pandas Scientific Python (Scipy) Matplotlib Scikit-Learn K-Nearest Neighbors Naive Bayes Simple and Multiple Linear Regression Logistic Regression GLM models Decision Trees and Random forest Perceptrons Backpropagation Clustering Natural Language Processing Frequently Asked Questions Q: Is this book for me and do I need programming experience?A: if you want to smash Python for data science and machine learning, this book is for you. Little programming experience is required. If you already wrote a few lines of code and recognize basic programming statements, you'll be OK.Q: Can I have a refund if this book is not fitted for me?A: Yes, Amazon refund you if you aren't satisfied, for more information about the amazon refund service please go to the amazon help platform. We will also be happy to help you if you send us an email at [email protected].

Data Science Projects with Python

Data Science Projects with Python
Author: Stephen Klosterman
Publisher: Packt Publishing Ltd
Total Pages: 374
Release: 2019-04-30
Genre: Computers
ISBN: 183855260X
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Gain hands-on experience with industry-standard data analysis and machine learning tools in Python Key FeaturesTackle data science problems by identifying the problem to be solvedIllustrate patterns in data using appropriate visualizationsImplement suitable machine learning algorithms to gain insights from dataBook Description Data Science Projects with Python is designed to give you practical guidance on industry-standard data analysis and machine learning tools, by applying them to realistic data problems. You will learn how to use pandas and Matplotlib to critically examine datasets with summary statistics and graphs, and extract the insights you seek to derive. You will build your knowledge as you prepare data using the scikit-learn package and feed it to machine learning algorithms such as regularized logistic regression and random forest. You’ll discover how to tune algorithms to provide the most accurate predictions on new and unseen data. As you progress, you’ll gain insights into the working and output of these algorithms, building your understanding of both the predictive capabilities of the models and why they make these predictions. By then end of this book, you will have the necessary skills to confidently use machine learning algorithms to perform detailed data analysis and extract meaningful insights from unstructured data. What you will learnInstall the required packages to set up a data science coding environmentLoad data into a Jupyter notebook running PythonUse Matplotlib to create data visualizationsFit machine learning models using scikit-learnUse lasso and ridge regression to regularize your modelsCompare performance between models to find the best outcomesUse k-fold cross-validation to select model hyperparametersWho this book is for If you are a data analyst, data scientist, or business analyst who wants to get started using Python and machine learning techniques to analyze data and predict outcomes, this book is for you. Basic knowledge of Python and data analytics will help you get the most from this book. Familiarity with mathematical concepts such as algebra and basic statistics will also be useful.

Exploratory Data Analysis with Python Cookbook

Exploratory Data Analysis with Python Cookbook
Author: Ayodele Oluleye
Publisher: Packt Publishing Ltd
Total Pages: 383
Release: 2023-06-30
Genre: Computers
ISBN: 1803246138
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Extract valuable insights from data by leveraging various analysis and visualization techniques with this comprehensive guide Purchase of the print or Kindle book includes a free PDF eBook Key Features Gain practical experience in conducting EDA on a single variable of interest in Python Learn the different techniques for analyzing and exploring tabular, time series, and textual data in Python Get well versed in data visualization using leading Python libraries like Matplotlib and seaborn Book DescriptionIn today's data-centric world, the ability to extract meaningful insights from vast amounts of data has become a valuable skill across industries. Exploratory Data Analysis (EDA) lies at the heart of this process, enabling us to comprehend, visualize, and derive valuable insights from various forms of data. This book is a comprehensive guide to Exploratory Data Analysis using the Python programming language. It provides practical steps needed to effectively explore, analyze, and visualize structured and unstructured data. It offers hands-on guidance and code for concepts such as generating summary statistics, analyzing single and multiple variables, visualizing data, analyzing text data, handling outliers, handling missing values and automating the EDA process. It is suited for data scientists, data analysts, researchers or curious learners looking to gain essential knowledge and practical steps for analyzing vast amounts of data to uncover insights. Python is an open-source general purpose programming language which is used widely for data science and data analysis given its simplicity and versatility. It offers several libraries which can be used to clean, analyze, and visualize data. In this book, we will explore popular Python libraries such as Pandas, Matplotlib, and Seaborn and provide workable code for analyzing data in Python using these libraries. By the end of this book, you will have gained comprehensive knowledge about EDA and mastered the powerful set of EDA techniques and tools required for analyzing both structured and unstructured data to derive valuable insights.What you will learn Perform EDA with leading python data visualization libraries Execute univariate, bivariate and multivariate analysis on tabular data Uncover patterns and relationships within time series data Identify hidden patterns within textual data Learn different techniques to prepare data for analysis Overcome challenge of outliers and missing values during data analysis Leverage automated EDA for fast and efficient analysis Who this book is forWhether you are a data analyst, data scientist, researcher or a curious learner looking to analyze structured and unstructured data, this book will appeal to you. It aims to empower you with essential knowledge and practical skills for analyzing and visualizing data to uncover insights. It covers several EDA concepts and provides hands-on instructions on how these can be applied using various Python libraries. Familiarity with basic statistical concepts and foundational knowledge of python programming will help you understand the content better and maximize your learning experience.

Practical Statistics for Data Scientists

Practical Statistics for Data Scientists
Author: Peter Bruce
Publisher: O'Reilly Media
Total Pages: 363
Release: 2020-04-10
Genre: Computers
ISBN: 1492072915
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Statistical methods are a key part of data science, yet few data scientists have formal statistical training. Courses and books on basic statistics rarely cover the topic from a data science perspective. The second edition of this popular guide adds comprehensive examples in Python, provides practical guidance on applying statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what’s important and what’s not. Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R or Python programming languages and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format. With this book, you’ll learn: Why exploratory data analysis is a key preliminary step in data science How random sampling can reduce bias and yield a higher-quality dataset, even with big data How the principles of experimental design yield definitive answers to questions How to use regression to estimate outcomes and detect anomalies Key classification techniques for predicting which categories a record belongs to Statistical machine learning methods that "learn" from data Unsupervised learning methods for extracting meaning from unlabeled data

Python for Data Analysis

Python for Data Analysis
Author: Brady Ellison
Publisher:
Total Pages:
Release:
Genre: Computers
ISBN:
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Ready to learn Data Science through Python language? Python for Data Analysis is a step-by-step guide for beginners and dabblers-alike. This book is designed to offer working knowledge of Python and data science and some of the tools required to apply that knowledge. It’s possible that you have little experience with or knowledge of data analysis and are interested in it. You might have some experience in coding. You may have worked with data before and want to use Python. We have made this book in a way that will be helpful to all these groups and more besides in varying ways. This can serve as an introduction to the most current tools and functions of those tools used by data scientists. In this book You will learn: Data Science/Analysis and its applications IPython and Jupyter - an introduction to the basic tools and how to navigate and use them. You will also learn about its importance in a data scientist’s ecosystem. Pandas - a powerful data management Python library that lets you do interesting things with data. You will learn all the basics you need to get started. NumPy - a powerful numerical library for Python. You will learn more about its advantages. Get your copy now

Data Science with Python

Data Science with Python
Author: Julian James McKinnon
Publisher: DM Publishing
Total Pages: 172
Release: 2020-11-08
Genre:
ISBN: 9781801235068
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Data analysis is just getting started. There's no limit to the amount of data available and more companies are now interested in data analysis. For you, it's important to understand the concepts of data analysis and then, through practice, build a good command on working with different datasets. If you are feeling confident enough after finishing this book, you can move towards data science. It's much more complex, contains more abstract concepts, there's more mathematics involved, and it's easier to get lost. The more difficult the field, the higher the rewards. That's why data science is one of the most promising careers today. Data science is a role that is taking up a lot of space for many businesses. There is a wealth of information out there that they are able to use for their own advantage, but they just need to know where to gather it, and how to analyze all of that data for their own needs. Sometimes, this is going to be a process that takes a lot of time and effort and can be hard to keep up with and ensure that we are doing it in the right manner. Data science is the process of gathering, organizing and cleaning, analyzing, and then visualizing data so that we can use that information to make smart business decisions. It is becoming more and more important to a lot of businesses, and it is likely that this will take over as one of the main forms of making big decisions in the future. With that in mind, let's take some time to look more in-depth at data science and how businesses are using it for their own needs. Many businesses, no matter what kind of industry they conduct business in, will find that working with data science is one of the best options for them. Data science can help them to really learn about their industry, and even gain a leg up on the competition. Many of the companies out there are going to already collect a lot of data and information about things like the competition, the industry, and their customers, and data science is going to help them to see what insights and information are inside of that data and use it for their advantage. There are many times when bringing out data science is going to be beneficial, and it will be able to propel your business forward more than anything else can do. When we can focus on the data and the process of analyzing it and seeing what good insights and predictions are inside, we will be able to make accurate decisions that will help us to make a big difference. Companies who have been able to implement a successful data science project from beginning to end are the ones who are doing the best overall in their respective industries. This book gives a comprehensive guide on the following: What is data science? Basics of python The best python libraries for data science Data science and applications The lifecycle of data science Probability, statistics and data types Most common data science problems Comparison of python with other languages Data cleaning and preparation Data visualization ... AND MORE!!!