A Hands On Introduction to Data Science

An introductory textbook offering a low barrier entry to data science; the hands-on approach will appeal to students from a range of disciplines.

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Author: Chirag Shah

Publisher: Cambridge University Press

ISBN: 9781108472449

Category: Business & Economics

Page: 400

View: 421

An introductory textbook offering a low barrier entry to data science; the hands-on approach will appeal to students from a range of disciplines.

Hands On Data Science with R

This book covers the entire data science ecosystem for aspiring data scientists, right from zero to a level where you are confident enough to get hands-on with real-world data science problems.

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Author: Vitor Bianchi Lanzetta

Publisher: Packt Publishing Ltd

ISBN: 9781789135831

Category: Computers

Page: 420

View: 517

A hands-on guide for professionals to perform various data science tasks in R Key Features Explore the popular R packages for data science Use R for efficient data mining, text analytics and feature engineering Become a thorough data science professional with the help of hands-on examples and use-cases in R Book Description R is the most widely used programming language, and when used in association with data science, this powerful combination will solve the complexities involved with unstructured datasets in the real world. This book covers the entire data science ecosystem for aspiring data scientists, right from zero to a level where you are confident enough to get hands-on with real-world data science problems. The book starts with an introduction to data science and introduces readers to popular R libraries for executing data science routine tasks. This book covers all the important processes in data science such as data gathering, cleaning data, and then uncovering patterns from it. You will explore algorithms such as machine learning algorithms, predictive analytical models, and finally deep learning algorithms. You will learn to run the most powerful visualization packages available in R so as to ensure that you can easily derive insights from your data. Towards the end, you will also learn how to integrate R with Spark and Hadoop and perform large-scale data analytics without much complexity. What you will learn Understand the R programming language and its ecosystem of packages for data science Obtain and clean your data before processing Master essential exploratory techniques for summarizing data Examine various machine learning prediction, models Explore the H2O analytics platform in R for deep learning Apply data mining techniques to available datasets Work with interactive visualization packages in R Integrate R with Spark and Hadoop for large-scale data analytics Who this book is for If you are a budding data scientist keen to learn about the popular pandas library, or a Python developer looking to step into the world of data analysis, this book is the ideal resource you need to get started. Some programming experience in Python will be helpful to get the most out of this course

Introduction to Data Science

The work is also eminently suitable for professionals on continuous education short courses, and to researchers following self-study courses.

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Author: Laura Igual

Publisher: Springer

ISBN: 9783319500171

Category: Computers

Page: 218

View: 424

This accessible and classroom-tested textbook/reference presents an introduction to the fundamentals of the emerging and interdisciplinary field of data science. The coverage spans key concepts adopted from statistics and machine learning, useful techniques for graph analysis and parallel programming, and the practical application of data science for such tasks as building recommender systems or performing sentiment analysis. Topics and features: provides numerous practical case studies using real-world data throughout the book; supports understanding through hands-on experience of solving data science problems using Python; describes techniques and tools for statistical analysis, machine learning, graph analysis, and parallel programming; reviews a range of applications of data science, including recommender systems and sentiment analysis of text data; provides supplementary code resources and data at an associated website.

Deep Learning with Python

This book bridges the gap between the academic state-of-the-art and the industry state-of-the-practice by introducing you to deep learning frameworks such as Keras, Theano, and Caffe.

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Author: Nikhil Ketkar

Publisher: Apress

ISBN: 9781484227664

Category: Computers

Page: 226

View: 715

Discover the practical aspects of implementing deep-learning solutions using the rich Python ecosystem. This book bridges the gap between the academic state-of-the-art and the industry state-of-the-practice by introducing you to deep learning frameworks such as Keras, Theano, and Caffe. The practicalities of these frameworks is often acquired by practitioners by reading source code, manuals, and posting questions on community forums, which tends to be a slow and a painful process. Deep Learning with Python allows you to ramp up to such practical know-how in a short period of time and focus more on the domain, models, and algorithms. This book briefly covers the mathematical prerequisites and fundamentals of deep learning, making this book a good starting point for software developers who want to get started in deep learning. A brief survey of deep learning architectures is also included. Deep Learning with Python also introduces you to key concepts of automatic differentiation and GPU computation which, while not central to deep learning, are critical when it comes to conducting large scale experiments. What You Will Learn Leverage deep learning frameworks in Python namely, Keras, Theano, and Caffe Gain the fundamentals of deep learning with mathematical prerequisites Discover the practical considerations of large scale experiments Take deep learning models to production Who This Book Is For Software developers who want to try out deep learning as a practical solution to a particular problem. Software developers in a data science team who want to take deep learning models developed by data scientists to production.

A Hands On Introduction to Forensic Science

Let's say that when you plot your data of hand length versus body height, you get a result similar to that shown below in Figure 10.45.

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Author: Mark Okuda

Publisher: CRC Press

ISBN: 9781482234909

Category: Law

Page: 508

View: 131

One failing of many forensic science textbooks is the isolation of chapters into compartmentalized units. This format prevents students from understanding the connection between material learned in previous chapters with that of the current chapter. Using a unique format, A Hands-On Introduction to Forensic Science: Cracking the Case approaches the topic of forensic science from a real-life perspective in a way that these vital connections are encouraged and established. The book utilizes an ongoing fictional narrative throughout, entertaining students as it provides hands-on learning in order to "crack the case." As two investigators try to solve a missing persons case, each succeeding chapter reveals new characters, new information, and new physical evidence to be processed. A full range of topics are covered, including processing the crime scene, lifting prints, trace and blood evidence, DNA and mtDNA sequencing, ballistics, skeletal remains, and court testimony. Following the storyline, students are introduced to the appropriate science necessary to process the physical evidence, including math, physics, chemistry, and biology. The final element of each chapter includes a series of cost-effective, field-tested lab activities that train students in processing, analyzing, and documenting the physical evidence revealed in the narrative. Practical and realistic in its approach, this book enables students to understand how forensic science operates in the real world.

A Hands On Introduction to Forensic Science

Activity 11.2 Aligning DNA Sequence Data Activity 11.1 demonstrated the principles of chain termination DNA sequencing. As most genes (and chromosomes) are ...

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Author: Mark M. Okuda

Publisher: CRC Press

ISBN: 9781351023801

Category: Law

Page: 511

View: 810

A Hands-On Introduction to Forensic Science, Second Edition continues in the tradition of the first edition taking a wholly unique approach to teaching forensic science. Each chapter begins with a brief, fictional narrative that runs through the entire book; it is a crime fiction narrative that describes the interaction of a veteran homicide detective teamed with a criminalist and the journey they take together to solve a missing persons case. Step-by-step the book progressive reveals pieces of information about the crime, followed by the more traditional presentation of scientific principles and concepts on a given forensic topics. Each chapter concludes with a series of user friendly, cost effective, hands-on lab activities that provide the students the skills necessary to analyze the evidence presented in each chapters. The new edition is completely updated with special focus on new DNA techniques in DNA sequencing, DNA phenotyping, and bioinformatics. Students will engage in solving a missing persons case by documenting the crime scene, analyzing physical evidence in the lab, and presenting findings in a mock trial setting. Within the chapters themselves, students learn about the technical, forensic concepts presented within each of the opening stories segments. The book culminates with having the students playing to role of the main characters in a trial—attorneys, scientific experts, suspect, judge, bailiff, and jury—to present and judge the evidence in a mock trial setting. The mock trial will mimic what takes place in a real courtroom, and the jury of swill be asked to deliberate on the evidence presented to determine the guilt or innocence of the suspect.

An Introduction to Data

This book reflects the author’s years of hands-on experience as an academic and practitioner.

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Author: Francesco Corea

Publisher: Springer

ISBN: 9783030044688

Category: Technology & Engineering

Page: 131

View: 322

This book reflects the author’s years of hands-on experience as an academic and practitioner. It is primarily intended for executives, managers and practitioners who want to redefine the way they think about artificial intelligence (AI) and other exponential technologies. Accordingly the book, which is structured as a collection of largely self-contained articles, includes both general strategic reflections and detailed sector-specific information. More concretely, it shares insights into what it means to work with AI and how to do it more efficiently; what it means to hire a data scientist and what new roles there are in the field; how to use AI in specific industries such as finance or insurance; how AI interacts with other technologies such as blockchain; and, in closing, a review of the use of AI in venture capital, as well as a snapshot of acceleration programs for AI companies.

Hands On Data Analysis with Pandas

Pandas is the powerful and popular library synonymous with data science in Python. This book will give you a hands-on introduction to data analysis using ...

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Author: Stefanie Molin

Publisher: Packt Publishing Ltd

ISBN: 9781789612806

Category: Computers

Page: 716

View: 546

Get to grips with pandas—a versatile and high-performance Python library for data manipulation, analysis, and discovery Key Features Perform efficient data analysis and manipulation tasks using pandas Apply pandas to different real-world domains using step-by-step demonstrations Get accustomed to using pandas as an effective data exploration tool Book Description Data analysis has become a necessary skill in a variety of positions where knowing how to work with data and extract insights can generate significant value. Hands-On Data Analysis with Pandas will show you how to analyze your data, get started with machine learning, and work effectively with Python libraries often used for data science, such as pandas, NumPy, matplotlib, seaborn, and scikit-learn. Using real-world datasets, you will learn how to use the powerful pandas library to perform data wrangling to reshape, clean, and aggregate your data. Then, you will learn how to conduct exploratory data analysis by calculating summary statistics and visualizing the data to find patterns. In the concluding chapters, you will explore some applications of anomaly detection, regression, clustering, and classification, using scikit-learn, to make predictions based on past data. By the end of this book, you will be equipped with the skills you need to use pandas to ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets. What you will learn Understand how data analysts and scientists gather and analyze data Perform data analysis and data wrangling in Python Combine, group, and aggregate data from multiple sources Create data visualizations with pandas, matplotlib, and seaborn Apply machine learning (ML) algorithms to identify patterns and make predictions Use Python data science libraries to analyze real-world datasets Use pandas to solve common data representation and analysis problems Build Python scripts, modules, and packages for reusable analysis code Who this book is for This book is for data analysts, data science beginners, and Python developers who want to explore each stage of data analysis and scientific computing using a wide range of datasets. You will also find this book useful if you are a data scientist who is looking to implement pandas in machine learning. Working knowledge of Python programming language will be beneficial.

Big Data Science Analytics

An accompanying website for this book contains additional support for instruction and learning (www.big-data-analytics-book.com) The book is organized into three main parts, comprising a total of twelve chapters.

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Author: Arshdeep Bahga

Publisher: Vpt

ISBN: 0996025537

Category:

Page: 544

View: 472

We are living in the dawn of what has been termed as the "Fourth Industrial Revolution," which is marked through the emergence of "cyber-physical systems" where software interfaces seamlessly over networks with physical systems, such as sensors, smartphones, vehicles, power grids or buildings, to create a new world of Internet of Things (IoT). Data and information are fuel of this new age where powerful analytics algorithms burn this fuel to generate decisions that are expected to create a smarter and more efficient world for all of us to live in. This new area of technology has been defined as Big Data Science and Analytics, and the industrial and academic communities are realizing this as a competitive technology that can generate significant new wealth and opportunity. Big data is defined as collections of datasets whose volume, velocity or variety is so large that it is difficult to store, manage, process and analyze the data using traditional databases and data processing tools. Big data science and analytics deals with collection, storage, processing and analysis of massive-scale data. Industry surveys, by Gartner and e-Skills, for instance, predict that there will be over 2 million job openings for engineers and scientists trained in the area of data science and analytics alone, and that the job market is in this area is growing at a 150 percent year-over-year growth rate. We have written this textbook, as part of our expanding "A Hands-On Approach"(TM) series, to meet this need at colleges and universities, and also for big data service providers who may be interested in offering a broader perspective of this emerging field to accompany their customer and developer training programs. The typical reader is expected to have completed a couple of courses in programming using traditional high-level languages at the college-level, and is either a senior or a beginning graduate student in one of the science, technology, engineering or mathematics (STEM) fields. An accompanying website for this book contains additional support for instruction and learning (www.big-data-analytics-book.com) The book is organized into three main parts, comprising a total of twelve chapters. Part I provides an introduction to big data, applications of big data, and big data science and analytics patterns and architectures. A novel data science and analytics application system design methodology is proposed and its realization through use of open-source big data frameworks is described. This methodology describes big data analytics applications as realization of the proposed Alpha, Beta, Gamma and Delta models, that comprise tools and frameworks for collecting and ingesting data from various sources into the big data analytics infrastructure, distributed filesystems and non-relational (NoSQL) databases for data storage, and processing frameworks for batch and real-time analytics. This new methodology forms the pedagogical foundation of this book. Part II introduces the reader to various tools and frameworks for big data analytics, and the architectural and programming aspects of these frameworks, with examples in Python. We describe Publish-Subscribe messaging frameworks (Kafka & Kinesis), Source-Sink connectors (Flume), Database Connectors (Sqoop), Messaging Queues (RabbitMQ, ZeroMQ, RestMQ, Amazon SQS) and custom REST, WebSocket and MQTT-based connectors. The reader is introduced to data storage, batch and real-time analysis, and interactive querying frameworks including HDFS, Hadoop, MapReduce, YARN, Pig, Oozie, Spark, Solr, HBase, Storm, Spark Streaming, Spark SQL, Hive, Amazon Redshift and Google BigQuery. Also described are serving databases (MySQL, Amazon DynamoDB, Cassandra, MongoDB) and the Django Python web framework. Part III introduces the reader to various machine learning algorithms with examples using the Spark MLlib and H2O frameworks, and visualizations using frameworks such as Lightning, Pygal and Seaborn.

Introduction to Data Science

Data Analysis and Prediction Algorithms with R Rafael A. Irizarry ... draw two cards without replacements: hand <- sample(deck, 2) hand #> [1] "Queen Clubs" ...

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Author: Rafael A. Irizarry

Publisher: CRC Press

ISBN: 9781000708035

Category: Mathematics

Page: 713

View: 358

Introduction to Data Science: Data Analysis and Prediction Algorithms with R introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression, and machine learning. It also helps you develop skills such as R programming, data wrangling, data visualization, predictive algorithm building, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation. This book is a textbook for a first course in data science. No previous knowledge of R is necessary, although some experience with programming may be helpful. The book is divided into six parts: R, data visualization, statistics with R, data wrangling, machine learning, and productivity tools. Each part has several chapters meant to be presented as one lecture. The author uses motivating case studies that realistically mimic a data scientist’s experience. He starts by asking specific questions and answers these through data analysis so concepts are learned as a means to answering the questions. Examples of the case studies included are: US murder rates by state, self-reported student heights, trends in world health and economics, the impact of vaccines on infectious disease rates, the financial crisis of 2007-2008, election forecasting, building a baseball team, image processing of hand-written digits, and movie recommendation systems. The statistical concepts used to answer the case study questions are only briefly introduced, so complementing with a probability and statistics textbook is highly recommended for in-depth understanding of these concepts. If you read and understand the chapters and complete the exercises, you will be prepared to learn the more advanced concepts and skills needed to become an expert.

Hands on Data Science for Biologists Using Python

This book is a readily accessible resource that can be used as a handbook for data analysis, as well as a platter of standard code templates for building models.

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Author: Yasha Hasija

Publisher: CRC Press

ISBN: 9781000345483

Category: Science

Page: 286

View: 383

Hands-on Data Science for Biologists using Python has been conceptualized to address the massive data handling needs of modern-day biologists. With the advent of high throughput technologies and consequent availability of omics data, biological science has become a data-intensive field. This hands-on textbook has been written with the inception of easing data analysis by providing an interactive, problem-based instructional approach in Python programming language. The book starts with an introduction to Python and steadily delves into scrupulous techniques of data handling, preprocessing, and visualization. The book concludes with machine learning algorithms and their applications in biological data science. Each topic has an intuitive explanation of concepts and is accompanied with biological examples. Features of this book: The book contains standard templates for data analysis using Python, suitable for beginners as well as advanced learners. This book shows working implementations of data handling and machine learning algorithms using real-life biological datasets and problems, such as gene expression analysis; disease prediction; image recognition; SNP association with phenotypes and diseases. Considering the importance of visualization for data interpretation, especially in biological systems, there is a dedicated chapter for the ease of data visualization and plotting. Every chapter is designed to be interactive and is accompanied with Jupyter notebook to prompt readers to practice in their local systems. Other avant-garde component of the book is the inclusion of a machine learning project, wherein various machine learning algorithms are applied for the identification of genes associated with age-related disorders. A systematic understanding of data analysis steps has always been an important element for biological research. This book is a readily accessible resource that can be used as a handbook for data analysis, as well as a platter of standard code templates for building models.

A Hands On Introduction to Using Python in the Atmospheric and Oceanic Sciences

This book is a mini-course for researchers in the atmospheric and oceanic sciences. "We assume readers will already know the basics of programming... in some other language." - Back cover.

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Author: Johnny Wei-Bing Lin

Publisher: Lulu.com

ISBN: 9781300076162

Category: Atmosphere

Page: 186

View: 186

This book is a mini-course for researchers in the atmospheric and oceanic sciences. "We assume readers will already know the basics of programming... in some other language." - Back cover.

Data Science Crash Course for Beginners with Python Fundamentals and Practices with Python

How Is This Book Different? Every book by AI Publishing has been carefully crafted. This book lays equal emphasis on the theoretical sections as well as the practical aspects of data science.

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Author: Ai Publishing

Publisher: AI Publishing LLC

ISBN: 1734790148

Category: Computers

Page: 310

View: 618

Data Science Crash Course for Beginners with Python Data Science is here to stay. The tremendous growth in the volume, velocity, and variety of data has a substantial impact on every aspect of a business. While data continues to grow exponentially, accuracy remains a problem. This is where data scientists play a decisive role. A data scientist analyzes data, discovers new insights, paints a picture, and creates a vision. And a competent data scientist will provide a business with the competitive edge it needs and address pressing business problems. Data Science Crash Course for Beginners with Python presents you with a hands-on approach to learn data science fast. How Is This Book Different? Every book by AI Publishing has been carefully crafted. This book lays equal emphasis on the theoretical sections as well as the practical aspects of data science. Each chapter provides the theoretical background behind the numerous data science techniques, and practical examples explain the working of these techniques. In the Further Reading section of each chapter, you will find the links to informative data science posts. This book presents you with the tools and packages you need to kick-start data science projects to resolve problems of practical nature. Special emphasis is laid on the main stages of a data science pipeline--data acquisition, data preparation, exploratory data analysis, data modeling and evaluation, and interpretation of the results. In the Data Science Resources section, links to data science resources, articles, interviews, and data science newsletters are provided. The author has also put together a list of contests and competitions that you can try on your own. Another added benefit of buying this book is you get instant access to all the learning material presented with this book-- PDFs, Python codes, exercises, and references--on the publisher's website. They will not cost you an extra cent. The datasets used in this book can be downloaded at runtime, or accessed via the Resources/Datasets folder. The author simplifies your learning by holding your hand through everything. The step by step description of the installation of the software you need for implementing the various data science techniques in this book is guaranteed to make your learning easier. So, right from the beginning, you can experiment with the practical aspects of data science. You'll also find the quick course on Python programming in the second and third chapters immensely helpful, especially if you are new to Python. This book gives you access to all the codes and datasets. So, access to a computer with the internet is sufficient to get started. The topics covered include: Introduction to Data Science and Decision Making Python Installation and Libraries for Data Science Review of Python for Data Science Data Acquisition Data Preparation (Preprocessing) Exploratory Data Analysis Data Modeling and Evaluation Using Machine Learning Interpretation and Reporting of Findings Data Science Projects Key Insights and Further Avenues Click the BUY button to start your Data Science journey.

Introduction to Data Science

This book is an introduction to data science without any type of programming. Q: Does this book include everything I need to become a data science expert?A: Unfortunately, no.

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Author: Peters Morgan

Publisher:

ISBN: 1722799579

Category:

Page: 118

View: 680

******Free eBook for customers who purchase the print book from Amazon****** Are you thinking of learning data science with easiest way (For Beginners)? If you are looking for a complete introduction to data science, this book is for you.After his great success with his first book "Data Analysis from Scratch with Python", Peters Morgan publish this book focusing now in data science and machine learning. Practitioners consider it 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. It will help you in preparing a solid foundation and learn any other high-level courses.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 This book is an introduction to the main concepts of data science explained with easiest examples. Peters Morgan focus on the practical aspects of using data science and machine learning algorithms, rather than the math behind them. Target Users Target UsersThe book is designed for a variety of target audiences. The 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? 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 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: No programming experience is required. This book is an introduction to data science without any type of programming.Q: Does this book include everything I need to become a data science expert?A: Unfortunately, no. This book is designed for readers taking their first steps in data science and machine learning and further learning will be required beyond this book to master all aspects.Q: Can I loan this book to friends?A: Yes. Under Amazon's Kindle Book Lending program, you can lend this book to friends and family for a duration of 14 days.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]

Python

Computer Programming Academy structured these guides as a course with seven chapters for seven days with special exercises for each section.This protocol, tested on both beginners and people who were already familiar with coding, takes ...

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Author: Computer Programming Academy

Publisher:

ISBN: 1914185161

Category: Computers

Page: 332

View: 847

Have you always wanted to learn computer programming but you're worried it will take too long? Would you like to automate something simple with your PC but you don't know how to do it? Or maybe you know other programming languages and are interested in learning Python quickly? As a beginner you might think that programming is difficult and the possibility to give up before mastering it could be high... So, if you have a project to develop you could think on hiring a programmer to shorten the time. This may seem like a good idea but it is certainly very expensive. Otherwise you could waste your time pursuing tutorials online. The best solution is to follow a complete programming manual with hands-on projects and practical exercises. What you will find inside and a quick overview of the main topics: ✓ Why Python is considered the best programming language for a beginner ✓ The most common mistakes to avoid when you start programming ✓ BOOK 1: PYTHON PROGRAMMING - The 7 built-in functions to make your life easier while coding a software program - The program you need to develop your first own application ✓ BOOK 2: PYTHON MACHINE LEARNING - The algorithms that will make your life easier - The 2 libraries you need implementing to develop the desired ML models ✓ BOOK 3: PYTHON DATA SCIENCE - 3 actions required to gain insights from big data - A simple method to implement predictive analytics ✓ Some projects to write Python codes in less than a week ✓ Quizzes at the end of every chapter to review immediately what you've learned Why is this book different? Computer Programming Academy structured these guides as a course with seven chapters for seven days with special exercises for each section.This protocol, tested on both beginners and people who were already familiar with coding, takes advantage of the principle of diving, concentrating learning in one week. The result? The content of the course was learned faster and remembered longer. Even if you're completely new to programming in 2020 or you are just looking to widen your skills as programmer this book is perfect for you. Now's the best time to begin learning Python... click the "BUY NOW" button and get started!

An Introduction to Data Analysis in R

The text requires only basic statistics skills, as it strikes a balance between statistical and mathematical understanding and implementation in R, with a special emphasis on reproducible examples and real-world applications.

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Author: Alfonso Zamora Saiz

Publisher: Springer

ISBN: 3030489965

Category: Computers

Page: 276

View: 385

This textbook offers an easy-to-follow, practical guide to modern data analysis using the programming language R. The chapters cover topics such as the fundamentals of programming in R, data collection and preprocessing, including web scraping, data visualization, and statistical methods, including multivariate analysis, and feature exercises at the end of each section. The text requires only basic statistics skills, as it strikes a balance between statistical and mathematical understanding and implementation in R, with a special emphasis on reproducible examples and real-world applications. This textbook is primarily intended for undergraduate students of mathematics, statistics, physics, economics, finance and business who are pursuing a career in data analytics. It will be equally valuable for master students of data science and industry professionals who want to conduct data analyses.

Statistics for Data Science

This is a process that the data scientist uses to discover a set of patterns ... The next chapter will be a hands-on introduction to statistical analysis of ...

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Author: James D. Miller

Publisher: Packt Publishing Ltd

ISBN: 9781788295345

Category: Computers

Page: 286

View: 543

Get your statistics basics right before diving into the world of data science About This Book No need to take a degree in statistics, read this book and get a strong statistics base for data science and real-world programs; Implement statistics in data science tasks such as data cleaning, mining, and analysis Learn all about probability, statistics, numerical computations, and more with the help of R programs Who This Book Is For This book is intended for those developers who are willing to enter the field of data science and are looking for concise information of statistics with the help of insightful programs and simple explanation. Some basic hands on R will be useful. What You Will Learn Analyze the transition from a data developer to a data scientist mindset Get acquainted with the R programs and the logic used for statistical computations Understand mathematical concepts such as variance, standard deviation, probability, matrix calculations, and more Learn to implement statistics in data science tasks such as data cleaning, mining, and analysis Learn the statistical techniques required to perform tasks such as linear regression, regularization, model assessment, boosting, SVMs, and working with neural networks Get comfortable with performing various statistical computations for data science programmatically In Detail Data science is an ever-evolving field, which is growing in popularity at an exponential rate. Data science includes techniques and theories extracted from the fields of statistics; computer science, and, most importantly, machine learning, databases, data visualization, and so on. This book takes you through an entire journey of statistics, from knowing very little to becoming comfortable in using various statistical methods for data science tasks. It starts off with simple statistics and then move on to statistical methods that are used in data science algorithms. The R programs for statistical computation are clearly explained along with logic. You will come across various mathematical concepts, such as variance, standard deviation, probability, matrix calculations, and more. You will learn only what is required to implement statistics in data science tasks such as data cleaning, mining, and analysis. You will learn the statistical techniques required to perform tasks such as linear regression, regularization, model assessment, boosting, SVMs, and working with neural networks. By the end of the book, you will be comfortable with performing various statistical computations for data science programmatically. Style and approach Step by step comprehensive guide with real world examples

Introduction to Data Science for Social and Policy Research

Notice that the command files_download actually returns metadata and the data in a raw format that is not directly readable by pandas.

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Author: Jose Manuel Magallanes Reyes

Publisher: Cambridge University Press

ISBN: 9781108364119

Category: Social Science

Page:

View: 638

Real-world data sets are messy and complicated. Written for students in social science and public management, this authoritative but approachable guide describes all the tools needed to collect data and prepare it for analysis. Offering detailed, step-by-step instructions, it covers collection of many different types of data including web files, APIs, and maps; data cleaning; data formatting; the integration of different sources into a comprehensive data set; and storage using third-party tools to facilitate access and shareability, from Google Docs to GitHub. Assuming no prior knowledge of R and Python, the author introduces programming concepts gradually, using real data sets that provide the reader with practical, functional experience.

Python Data Science

Inside this book you will find all the basic notions to start with Python and all the programming concepts to implement predictive analytics. With our proven strategies you will write efficient Python codes in less than a week!

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Author: Computer Programming Academy

Publisher:

ISBN: 1914185102

Category:

Page: 202

View: 328

Inside this book you will find all the basic notions to start with Python and all the programming concepts to implement predictive analytics. With our proven strategies you will write efficient Python codes in less than a week!

Introduction to Research Methods

Using real, annotated research examples, the text invites readers to see research as a dynamic conversation on timely topics that are relevant to their lives.

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Author: Bora Pajo

Publisher: SAGE Publications

ISBN: 9781483386966

Category: Social Science

Page: 392

View: 293

With clear, engaging, and humorous prose, Introduction to Research Methods: A Hands-on Approach offers readers an applied introduction to the exciting world of social science research. Using real, annotated research examples, the text invites readers to see research as a dynamic conversation on timely topics that are relevant to their lives. Robust pedagogy, practical tips, and FREE instructor and student online resources provide extensive support for a successful hands-on experience with research.