Download or read book The The Applied Data Science Workshop written by Alex Galea and published by Packt Publishing Ltd. This book was released on 2020-07-22 with total page 351 pages. Available in PDF, EPUB and Kindle. Book excerpt: Designed with beginners in mind, this workshop helps you make the most of Python libraries and the Jupyter Notebook’s functionality to understand how data science can be applied to solve real-world data problems. Key FeaturesGain useful insights into data science and machine learningExplore the different functionalities and features of a Jupyter NotebookDiscover how Python libraries are used with Jupyter for data analysisBook Description From banking and manufacturing through to education and entertainment, using data science for business has revolutionized almost every sector in the modern world. It has an important role to play in everything from app development to network security. Taking an interactive approach to learning the fundamentals, this book is ideal for beginners. You’ll learn all the best practices and techniques for applying data science in the context of real-world scenarios and examples. Starting with an introduction to data science and machine learning, you’ll start by getting to grips with Jupyter functionality and features. You’ll use Python libraries like sci-kit learn, pandas, Matplotlib, and Seaborn to perform data analysis and data preprocessing on real-world datasets from within your own Jupyter environment. Progressing through the chapters, you’ll train classification models using sci-kit learn, and assess model performance using advanced validation techniques. Towards the end, you’ll use Jupyter Notebooks to document your research, build stakeholder reports, and even analyze web performance data. By the end of The Applied Data Science Workshop, you’ll be prepared to progress from being a beginner to taking your skills to the next level by confidently applying data science techniques and tools to real-world projects. What you will learnUnderstand the key opportunities and challenges in data scienceUse Jupyter for data science tasks such as data analysis and modelingRun exploratory data analysis within a Jupyter NotebookVisualize data with pairwise scatter plots and segmented distributionAssess model performance with advanced validation techniquesParse HTML responses and analyze HTTP requestsWho this book is for If you are an aspiring data scientist who wants to build a career in data science or a developer who wants to explore the applications of data science from scratch and analyze data in Jupyter using Python libraries, then this book is for you. Although a brief understanding of Python programming and machine learning is recommended to help you grasp the topics covered in the book more quickly, it is not mandatory.

Download or read book The Applied Data Science Workshop Second Edition written by Alex Galea and published by . This book was released on 2020-07-21 with total page 352 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book The The Data Science Workshop written by Anthony So and published by Packt Publishing Ltd. This book was released on 2020-08-28 with total page 823 pages. Available in PDF, EPUB and Kindle. Book excerpt: Gain expert guidance on how to successfully develop machine learning models in Python and build your own unique data platforms Key FeaturesGain a full understanding of the model production and deployment processBuild your first machine learning model in just five minutes and get a hands-on machine learning experienceUnderstand how to deal with common challenges in data science projectsBook Description Where there’s data, there’s insight. With so much data being generated, there is immense scope to extract meaningful information that’ll boost business productivity and profitability. By learning to convert raw data into game-changing insights, you’ll open new career paths and opportunities. The Data Science Workshop begins by introducing different types of projects and showing you how to incorporate machine learning algorithms in them. You’ll learn to select a relevant metric and even assess the performance of your model. To tune the hyperparameters of an algorithm and improve its accuracy, you’ll get hands-on with approaches such as grid search and random search. Next, you’ll learn dimensionality reduction techniques to easily handle many variables at once, before exploring how to use model ensembling techniques and create new features to enhance model performance. In a bid to help you automatically create new features that improve your model, the book demonstrates how to use the automated feature engineering tool. You’ll also understand how to use the orchestration and scheduling workflow to deploy machine learning models in batch. By the end of this book, you’ll have the skills to start working on data science projects confidently. By the end of this book, you’ll have the skills to start working on data science projects confidently. What you will learnExplore the key differences between supervised learning and unsupervised learningManipulate and analyze data using scikit-learn and pandas librariesUnderstand key concepts such as regression, classification, and clusteringDiscover advanced techniques to improve the accuracy of your modelUnderstand how to speed up the process of adding new featuresSimplify your machine learning workflow for productionWho this book is for This is one of the most useful data science books for aspiring data analysts, data scientists, database engineers, and business analysts. It is aimed at those who want to kick-start their careers in data science by quickly learning data science techniques without going through all the mathematics behind machine learning algorithms. Basic knowledge of the Python programming language will help you easily grasp the concepts explained in this book.

Download or read book The Data Science Workshop written by Anthony So and published by Packt Publishing Ltd. This book was released on 2020-01-29 with total page 817 pages. Available in PDF, EPUB and Kindle. Book excerpt: Cut through the noise and get real results with a step-by-step approach to data science Key Features Ideal for the data science beginner who is getting started for the first time A data science tutorial with step-by-step exercises and activities that help build key skills Structured to let you progress at your own pace, on your own terms Use your physical print copy to redeem free access to the online interactive edition Book DescriptionYou already know you want to learn data science, and a smarter way to learn data science is to learn by doing. The Data Science Workshop focuses on building up your practical skills so that you can understand how to develop simple machine learning models in Python or even build an advanced model for detecting potential bank frauds with effective modern data science. You'll learn from real examples that lead to real results. Throughout The Data Science Workshop, you'll take an engaging step-by-step approach to understanding data science. You won't have to sit through any unnecessary theory. If you're short on time you can jump into a single exercise each day or spend an entire weekend training a model using sci-kit learn. It's your choice. Learning on your terms, you'll build up and reinforce key skills in a way that feels rewarding. Every physical print copy of The Data Science Workshop unlocks access to the interactive edition. With videos detailing all exercises and activities, you'll always have a guided solution. You can also benchmark yourself against assessments, track progress, and receive content updates. You'll even earn a secure credential that you can share and verify online upon completion. It's a premium learning experience that's included with your printed copy. To redeem, follow the instructions located at the start of your data science book. Fast-paced and direct, The Data Science Workshop is the ideal companion for data science beginners. You'll learn about machine learning algorithms like a data scientist, learning along the way. This process means that you'll find that your new skills stick, embedded as best practice. A solid foundation for the years ahead.What you will learn Find out the key differences between supervised and unsupervised learning Manipulate and analyze data using scikit-learn and pandas libraries Learn about different algorithms such as regression, classification, and clustering Discover advanced techniques to improve model ensembling and accuracy Speed up the process of creating new features with automated feature tool Simplify machine learning using open source Python packages Who this book is forOur goal at Packt is to help you be successful, in whatever it is you choose to do. The Data Science Workshop is an ideal data science tutorial for the data science beginner who is just getting started. Pick up a Workshop today and let Packt help you develop skills that stick with you for life.

Download or read book The Applied SQL Data Analytics Workshop Second Edition written by Matt Goldwasser and published by . This book was released on 2020-07-30 with total page 484 pages. Available in PDF, EPUB and Kindle. Book excerpt: Take a step-by-step approach to learning SQL data analysis in this interactive workshop that uses fun exercises and activities to make learning data analytics for beginners easy and approachable.Key Features* Explore ways to use SQL for data analytics and gain key insights from your data* Study advanced analytics, such as geospatial and text analytics* Discover ways to integrate your SQL pipelines with other analytics technologiesBook DescriptionEvery day, businesses operate around the clock and a huge amount of data is generated at a rapid pace. Hidden in this data are key patterns and behaviors that can help you and your business understand your customers at a deep, fundamental level. Are you ready to enter the exciting world of data analytics and unlock these useful insights?Written by a team of expert data scientists who have used their data analytics skills to transform businesses of all shapes and sizes, The Applied SQL Data Analytics Workshop is a great way to get started with data analysis, showing you how to effectively sieve and process information from raw data, even without any prior experience.The book begins by showing you how to form hypotheses and generate descriptive statistics that can provide key insights into your existing data. As you progress, you'll learn how to write SQL queries to aggregate, calculate and combine SQL data from sources outside of your current dataset. You'll also discover how to work with different data types, like JSON. By exploring advanced techniques, such as geospatial analysis and text analysis, you'll finally be able to understand your business at a deeper level. Finally, the book lets you in on the secret to getting information faster and more effectively by using advanced techniques like profiling and automation.By the end of The Applied SQL Data Analytics Workshop, you'll have the skills you need to start identifying patterns and unlocking insights in your own data. You will be capable of looking and assessing data with the critical eye of a skilled data analyst.What you will learn* Understand what data analytics is and why it is important* Experiment with data analytics using basic and advanced queries* Interpret data through descriptive statistics and aggregate functions* Export data from external sources using powerful SQL queries* Work with and manipulate data using SQL joins and constraints* Speed up your data analysis workflow by automating tasks and optimizing queriesWho this book is forIf you are a database engineer who is looking to transition into analytics or someone who knows SQL basics but doesn't know how to use it to create business insights, then this book is for you.

Download or read book THE APPLIED DATA SCIENCE WORKSHOP Urinary biomarkers Based Pancreatic Cancer Classification and Prediction Using Machine Learning with Python GUI written by Vivian Siahaan and published by BALIGE PUBLISHING. This book was released on 2023-07-23 with total page 327 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Applied Data Science Workshop on "Urinary Biomarkers-Based Pancreatic Cancer Classification and Prediction Using Machine Learning with Python GUI" embarks on a comprehensive journey, commencing with an in-depth exploration of the dataset. During this initial phase, the structure and size of the dataset are thoroughly examined, and the various features it contains are meticulously studied. The principal objective is to understand the relationship between these features and the target variable, which, in this case, is the diagnosis of pancreatic cancer. The distribution of each feature is analyzed, and potential patterns, trends, or outliers that could significantly impact the model's performance are identified. To ensure the data is in optimal condition for model training, preprocessing steps are undertaken. This involves handling missing values through imputation techniques, such as mean, median, or interpolation, depending on the nature of the data. Additionally, feature engineering is performed to derive new features or transform existing ones, with the aim of enhancing the model's predictive power. In preparation for model building, the dataset is split into training and testing sets. This division is crucial to assess the models' generalization performance on unseen data accurately. To maintain a balanced representation of classes in both sets, stratified sampling is employed, mitigating potential biases in the model evaluation process. The workshop explores an array of machine learning classifiers suitable for pancreatic cancer classification, such as Logistic Regression, K-Nearest Neighbors, Decision Trees, Random Forests, Gradient Boosting, Naive Bayes, Adaboost, Extreme Gradient Boosting, Light Gradient Boosting, Naïve Bayes, and Multi-Layer Perceptron (MLP). For each classifier, three different preprocessing techniques are applied to investigate their impact on model performance: raw (unprocessed data), normalization (scaling data to a similar range), and standardization (scaling data to have zero mean and unit variance). To optimize the classifiers' hyperparameters and boost their predictive capabilities, GridSearchCV, a technique for hyperparameter tuning, is employed. GridSearchCV conducts an exhaustive search over a specified hyperparameter grid, evaluating different combinations to identify the optimal settings for each model and preprocessing technique. During the model evaluation phase, multiple performance metrics are utilized to gauge the efficacy of the classifiers. Commonly used metrics include accuracy, recall, precision, and F1-score. By comprehensively assessing these metrics, the strengths and weaknesses of each model are revealed, enabling a deeper understanding of their performance across different classes of pancreatic cancer. Classification reports are generated to present a detailed breakdown of the models' performance, including precision, recall, F1-score, and support for each class. These reports serve as valuable tools for interpreting model outputs and identifying areas for potential improvement. The workshop highlights the significance of graphical user interfaces (GUIs) in facilitating user interactions with machine learning models. By integrating PyQt, a powerful GUI development library for Python, participants create a user-friendly interface that enables users to interact with the models effortlessly. The GUI provides options to select different preprocessing techniques, visualize model outputs such as confusion matrices and decision boundaries, and gain insights into the models' classification capabilities. One of the primary advantages of the graphical user interface is its ability to offer users a seamless and intuitive experience in predicting and classifying pancreatic cancer based on urinary biomarkers. The GUI empowers users to make informed decisions by allowing them to compare the performance of different classifiers under various preprocessing techniques. Throughout the workshop, a strong emphasis is placed on the significance of proper data preprocessing, hyperparameter tuning, and robust model evaluation. These crucial steps contribute to building accurate and reliable machine learning models for pancreatic cancer prediction. By the culmination of the workshop, participants have gained valuable hands-on experience in data exploration, machine learning model building, hyperparameter tuning, and GUI development, all geared towards addressing the specific challenge of pancreatic cancer classification and prediction. In conclusion, the Applied Data Science Workshop on "Urinary Biomarkers-Based Pancreatic Cancer Classification and Prediction Using Machine Learning with Python GUI" embarks on a comprehensive and transformative journey, bringing together data exploration, preprocessing, machine learning model selection, hyperparameter tuning, model evaluation, and GUI development. The project's focus on pancreatic cancer prediction using urinary biomarkers aligns with the pressing need for early detection and treatment of this deadly disease. As participants delve into the intricacies of machine learning and medical research, they contribute to the broader scientific community's ongoing efforts to combat cancer and improve patient outcomes. Through the integration of data science methodologies and powerful visualization tools, the workshop exemplifies the potential of machine learning in revolutionizing medical diagnostics and healthcare practices.

Download or read book THE APPLIED DATA SCIENCE WORKSHOP Prostate Cancer Classification and Recognition Using Machine Learning and Deep Learning with Python GUI written by Vivian Siahaan and published by BALIGE PUBLISHING. This book was released on 2023-07-19 with total page 357 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Applied Data Science Workshop on Prostate Cancer Classification and Recognition using Machine Learning and Deep Learning with Python GUI involved several steps and components. The project aimed to analyze prostate cancer data, explore the features, develop machine learning models, and create a graphical user interface (GUI) using PyQt5. The project began with data exploration, where the prostate cancer dataset was examined to understand its structure and content. Various statistical techniques were employed to gain insights into the data, such as checking the dimensions, identifying missing values, and examining the distribution of the target variable. The next step involved exploring the distribution of features in the dataset. Visualizations were created to analyze the characteristics and relationships between different features. Histograms, scatter plots, and correlation matrices were used to uncover patterns and identify potential variables that may contribute to the classification of prostate cancer. Machine learning models were then developed to classify prostate cancer based on the available features. Several algorithms, including Logistic Regression, K-Nearest Neighbors, Decision Trees, Random Forests, Gradient Boosting, Naive Bayes, Adaboost, Extreme Gradient Boosting, Light Gradient Boosting, and Multi-Layer Perceptron (MLP), were implemented. Each model was trained and evaluated using appropriate techniques such as cross-validation and grid search for hyperparameter tuning. The performance of each machine learning model was assessed using evaluation metrics such as accuracy, precision, recall, and F1-score. These metrics provided insights into the effectiveness of the models in accurately classifying prostate cancer cases. Model comparison and selection were based on their performance and the specific requirements of the project. In addition to the machine learning models, a deep learning model based on an Artificial Neural Network (ANN) was implemented. The ANN architecture consisted of multiple layers, including input, hidden, and output layers. The ANN model was trained using the dataset, and its performance was evaluated using accuracy and loss metrics. To provide a user-friendly interface for the project, a GUI was designed using PyQt, a Python library for creating desktop applications. The GUI allowed users to interact with the machine learning models and perform tasks such as selecting the prediction method, loading data, training models, and displaying results. The GUI included various graphical components such as buttons, combo boxes, input fields, and plot windows. These components were designed to facilitate data loading, model training, and result visualization. Users could choose the prediction method, view accuracy scores, classification reports, and confusion matrices, and explore the predicted values compared to the actual values. The GUI also incorporated interactive features such as real-time updates of prediction results based on user selections and dynamic plot generation for visualizing model performance. Users could switch between different prediction methods, observe changes in accuracy, and examine the history of training loss and accuracy through plotted graphs. Data preprocessing techniques, such as standardization and normalization, were applied to ensure the consistency and reliability of the machine learning and deep learning models. The dataset was divided into training and testing sets to assess model performance on unseen data and detect overfitting or underfitting. Model persistence was implemented to save the trained machine learning and deep learning models to disk, allowing for easy retrieval and future use. The saved models could be loaded and utilized within the GUI for prediction tasks without the need for retraining. Overall, the Applied Data Science Workshop on Prostate Cancer Classification and Recognition provided a comprehensive framework for analyzing prostate cancer data, developing machine learning and deep learning models, and creating an interactive GUI. The project aimed to assist in the accurate classification and recognition of prostate cancer cases, facilitating informed decision-making and potentially contributing to improved patient outcomes.

Download or read book The Applied SQL Data Analytics Workshop Second Edition written by Upom Malik and published by . This book was released on 2020-02-27 with total page 404 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book The the Data Science Workshop written by Anthony So and published by . This book was released on 2020-08-28 with total page 824 pages. Available in PDF, EPUB and Kindle. Book excerpt: Gain expert guidance on how to successfully develop machine learning models in Python and build your own unique data platforms Key Features Gain a full understanding of the model production and deployment process Build your first machine learning model in just five minutes and get a hands-on machine learning experience Understand how to deal with common challenges in data science projects Book Description Where there's data, there's insight. With so much data being generated, there is immense scope to extract meaningful information that'll boost business productivity and profitability. By learning to convert raw data into game-changing insights, you'll open new career paths and opportunities. The Data Science Workshop begins by introducing different types of projects and showing you how to incorporate machine learning algorithms in them. You'll learn to select a relevant metric and even assess the performance of your model. To tune the hyperparameters of an algorithm and improve its accuracy, you'll get hands-on with approaches such as grid search and random search. Next, you'll learn dimensionality reduction techniques to easily handle many variables at once, before exploring how to use model ensembling techniques and create new features to enhance model performance. In a bid to help you automatically create new features that improve your model, the book demonstrates how to use the automated feature engineering tool. You'll also understand how to use the orchestration and scheduling workflow to deploy machine learning models in batch. By the end of this book, you'll have the skills to start working on data science projects confidently. By the end of this book, you'll have the skills to start working on data science projects confidently. What you will learn Explore the key differences between supervised learning and unsupervised learning Manipulate and analyze data using scikit-learn and pandas libraries Understand key concepts such as regression, classification, and clustering Discover advanced techniques to improve the accuracy of your model Understand how to speed up the process of adding new features Simplify your machine learning workflow for production Who this book is for This is one of the most useful data science books for aspiring data analysts, data scientists, database engineers, and business analysts. It is aimed at those who want to kick-start their careers in data science by quickly learning data science techniques without going through all the mathematics behind machine learning algorithms. Basic knowledge of the Python programming language will help you easily grasp the concepts explained in this book.

Download or read book Data Science written by Vijay Kotu and published by Morgan Kaufmann. This book was released on 2018-11-27 with total page 568 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn the basics of Data Science through an easy to understand conceptual framework and immediately practice using RapidMiner platform. Whether you are brand new to data science or working on your tenth project, this book will show you how to analyze data, uncover hidden patterns and relationships to aid important decisions and predictions. Data Science has become an essential tool to extract value from data for any organization that collects, stores and processes data as part of its operations. This book is ideal for business users, data analysts, business analysts, engineers, and analytics professionals and for anyone who works with data. You’ll be able to: Gain the necessary knowledge of different data science techniques to extract value from data. Master the concepts and inner workings of 30 commonly used powerful data science algorithms. Implement step-by-step data science process using using RapidMiner, an open source GUI based data science platform Data Science techniques covered: Exploratory data analysis, Visualization, Decision trees, Rule induction, k-nearest neighbors, Naïve Bayesian classifiers, Artificial neural networks, Deep learning, Support vector machines, Ensemble models, Random forests, Regression, Recommendation engines, Association analysis, K-Means and Density based clustering, Self organizing maps, Text mining, Time series forecasting, Anomaly detection, Feature selection and more... Contains fully updated content on data science, including tactics on how to mine business data for information Presents simple explanations for over twenty powerful data science techniques Enables the practical use of data science algorithms without the need for programming Demonstrates processes with practical use cases Introduces each algorithm or technique and explains the workings of a data science algorithm in plain language Describes the commonly used setup options for the open source tool RapidMiner

Download or read book The Data Wrangling Workshop written by Brian Lipp and published by Packt Publishing Ltd. This book was released on 2020-07-29 with total page 575 pages. Available in PDF, EPUB and Kindle. Book excerpt: A beginner's guide to simplifying Extract, Transform, Load (ETL) processes with the help of hands-on tips, tricks, and best practices, in a fun and interactive way Key FeaturesExplore data wrangling with the help of real-world examples and business use casesStudy various ways to extract the most value from your data in minimal timeBoost your knowledge with bonus topics, such as random data generation and data integrity checksBook Description While a huge amount of data is readily available to us, it is not useful in its raw form. For data to be meaningful, it must be curated and refined. If you're a beginner, then The Data Wrangling Workshop will help to break down the process for you. You'll start with the basics and build your knowledge, progressing from the core aspects behind data wrangling, to using the most popular tools and techniques. This book starts by showing you how to work with data structures using Python. Through examples and activities, you'll understand why you should stay away from traditional methods of data cleaning used in other languages and take advantage of the specialized pre-built routines in Python. Later, you'll learn how to use the same Python backend to extract and transform data from an array of sources, including the internet, large database vaults, and Excel financial tables. To help you prepare for more challenging scenarios, the book teaches you how to handle missing or incorrect data, and reformat it based on the requirements from your downstream analytics tool. By the end of this book, you will have developed a solid understanding of how to perform data wrangling with Python, and learned several techniques and best practices to extract, clean, transform, and format your data efficiently, from a diverse array of sources. What you will learnGet to grips with the fundamentals of data wranglingUnderstand how to model data with random data generation and data integrity checksDiscover how to examine data with descriptive statistics and plotting techniquesExplore how to search and retrieve information with regular expressionsDelve into commonly-used Python data science librariesBecome well-versed with how to handle and compensate for missing dataWho this book is for The Data Wrangling Workshop is designed for developers, data analysts, and business analysts who are looking to pursue a career as a full-fledged data scientist or analytics expert. Although this book is for beginners who want to start data wrangling, prior working knowledge of the Python programming language is necessary to easily grasp the concepts covered here. It will also help to have a rudimentary knowledge of relational databases and SQL.

Download or read book Data Science For Dummies written by Lillian Pierson and published by John Wiley & Sons. This book was released on 2017-03-06 with total page 384 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discover how data science can help you gain in-depth insight into your business - the easy way! Jobs in data science abound, but few people have the data science skills needed to fill these increasingly important roles. Data Science For Dummies is the perfect starting point for IT professionals and students who want a quick primer on all areas of the expansive data science space. With a focus on business cases, the book explores topics in big data, data science, and data engineering, and how these three areas are combined to produce tremendous value. If you want to pick-up the skills you need to begin a new career or initiate a new project, reading this book will help you understand what technologies, programming languages, and mathematical methods on which to focus. While this book serves as a wildly fantastic guide through the broad, sometimes intimidating field of big data and data science, it is not an instruction manual for hands-on implementation. Here’s what to expect: Provides a background in big data and data engineering before moving on to data science and how it's applied to generate value Includes coverage of big data frameworks like Hadoop, MapReduce, Spark, MPP platforms, and NoSQL Explains machine learning and many of its algorithms as well as artificial intelligence and the evolution of the Internet of Things Details data visualization techniques that can be used to showcase, summarize, and communicate the data insights you generate It's a big, big data world out there—let Data Science For Dummies help you harness its power and gain a competitive edge for your organization.

Download or read book Practical Data Science Cookbook written by Prabhanjan Tattar and published by Packt Publishing Ltd. This book was released on 2017-06-29 with total page 428 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over 85 recipes to help you complete real-world data science projects in R and Python About This Book Tackle every step in the data science pipeline and use it to acquire, clean, analyze, and visualize your data Get beyond the theory and implement real-world projects in data science using R and Python Easy-to-follow recipes will help you understand and implement the numerical computing concepts Who This Book Is For If you are an aspiring data scientist who wants to learn data science and numerical programming concepts through hands-on, real-world project examples, this is the book for you. Whether you are brand new to data science or you are a seasoned expert, you will benefit from learning about the structure of real-world data science projects and the programming examples in R and Python. What You Will Learn Learn and understand the installation procedure and environment required for R and Python on various platforms Prepare data for analysis by implement various data science concepts such as acquisition, cleaning and munging through R and Python Build a predictive model and an exploratory model Analyze the results of your model and create reports on the acquired data Build various tree-based methods and Build random forest In Detail As increasing amounts of data are generated each year, the need to analyze and create value out of it is more important than ever. Companies that know what to do with their data and how to do it well will have a competitive advantage over companies that don't. Because of this, there will be an increasing demand for people that possess both the analytical and technical abilities to extract valuable insights from data and create valuable solutions that put those insights to use. Starting with the basics, this book covers how to set up your numerical programming environment, introduces you to the data science pipeline, and guides you through several data projects in a step-by-step format. By sequentially working through the steps in each chapter, you will quickly familiarize yourself with the process and learn how to apply it to a variety of situations with examples using the two most popular programming languages for data analysis—R and Python. Style and approach This step-by-step guide to data science is full of hands-on examples of real-world data science tasks. Each recipe focuses on a particular task involved in the data science pipeline, ranging from readying the dataset to analytics and visualization

Download or read book Applied Data Science written by Martin Braschler and published by Springer. This book was released on 2019-06-13 with total page 465 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book has two main goals: to define data science through the work of data scientists and their results, namely data products, while simultaneously providing the reader with relevant lessons learned from applied data science projects at the intersection of academia and industry. As such, it is not a replacement for a classical textbook (i.e., it does not elaborate on fundamentals of methods and principles described elsewhere), but systematically highlights the connection between theory, on the one hand, and its application in specific use cases, on the other. With these goals in mind, the book is divided into three parts: Part I pays tribute to the interdisciplinary nature of data science and provides a common understanding of data science terminology for readers with different backgrounds. These six chapters are geared towards drawing a consistent picture of data science and were predominantly written by the editors themselves. Part II then broadens the spectrum by presenting views and insights from diverse authors – some from academia and some from industry, ranging from financial to health and from manufacturing to e-commerce. Each of these chapters describes a fundamental principle, method or tool in data science by analyzing specific use cases and drawing concrete conclusions from them. The case studies presented, and the methods and tools applied, represent the nuts and bolts of data science. Finally, Part III was again written from the perspective of the editors and summarizes the lessons learned that have been distilled from the case studies in Part II. The section can be viewed as a meta-study on data science across a broad range of domains, viewpoints and fields. Moreover, it provides answers to the question of what the mission-critical factors for success in different data science undertakings are. The book targets professionals as well as students of data science: first, practicing data scientists in industry and academia who want to broaden their scope and expand their knowledge by drawing on the authors’ combined experience. Second, decision makers in businesses who face the challenge of creating or implementing a data-driven strategy and who want to learn from success stories spanning a range of industries. Third, students of data science who want to understand both the theoretical and practical aspects of data science, vetted by real-world case studies at the intersection of academia and industry.

Download or read book Data Science in Production written by Ben Weber and published by . This book was released on 2020 with total page 234 pages. Available in PDF, EPUB and Kindle. Book excerpt: Putting predictive models into production is one of the most direct ways that data scientists can add value to an organization. By learning how to build and deploy scalable model pipelines, data scientists can own more of the model production process and more rapidly deliver data products. This book provides a hands-on approach to scaling up Python code to work in distributed environments in order to build robust pipelines. Readers will learn how to set up machine learning models as web endpoints, serverless functions, and streaming pipelines using multiple cloud environments. It is intended for analytics practitioners with hands-on experience with Python libraries such as Pandas and scikit-learn, and will focus on scaling up prototype models to production. From startups to trillion dollar companies, data science is playing an important role in helping organizations maximize the value of their data. This book helps data scientists to level up their careers by taking ownership of data products with applied examples that demonstrate how to: Translate models developed on a laptop to scalable deployments in the cloud Develop end-to-end systems that automate data science workflows Own a data product from conception to production The accompanying Jupyter notebooks provide examples of scalable pipelines across multiple cloud environments, tools, and libraries (github.com/bgweber/DS_Production). Book Contents Here are the topics covered by Data Science in Production: Chapter 1: Introduction - This chapter will motivate the use of Python and discuss the discipline of applied data science, present the data sets, models, and cloud environments used throughout the book, and provide an overview of automated feature engineering. Chapter 2: Models as Web Endpoints - This chapter shows how to use web endpoints for consuming data and hosting machine learning models as endpoints using the Flask and Gunicorn libraries. We'll start with scikit-learn models and also set up a deep learning endpoint with Keras. Chapter 3: Models as Serverless Functions - This chapter will build upon the previous chapter and show how to set up model endpoints as serverless functions using AWS Lambda and GCP Cloud Functions. Chapter 4: Containers for Reproducible Models - This chapter will show how to use containers for deploying models with Docker. We'll also explore scaling up with ECS and Kubernetes, and building web applications with Plotly Dash. Chapter 5: Workflow Tools for Model Pipelines - This chapter focuses on scheduling automated workflows using Apache Airflow. We'll set up a model that pulls data from BigQuery, applies a model, and saves the results. Chapter 6: PySpark for Batch Modeling - This chapter will introduce readers to PySpark using the community edition of Databricks. We'll build a batch model pipeline that pulls data from a data lake, generates features, applies a model, and stores the results to a No SQL database. Chapter 7: Cloud Dataflow for Batch Modeling - This chapter will introduce the core components of Cloud Dataflow and implement a batch model pipeline for reading data from BigQuery, applying an ML model, and saving the results to Cloud Datastore. Chapter 8: Streaming Model Workflows - This chapter will introduce readers to Kafka and PubSub for streaming messages in a cloud environment. After working through this material, readers will learn how to use these message brokers to create streaming model pipelines with PySpark and Dataflow that provide near real-time predictions. Excerpts of these chapters are available on Medium (@bgweber), and a book sample is available on Leanpub.

Download or read book Fundamentals of Data Science written by Sanjeev J. Wagh and published by CRC Press. This book was released on 2021-09-26 with total page 297 pages. Available in PDF, EPUB and Kindle. Book excerpt: Fundamentals of Data Science is designed for students, academicians and practitioners with a complete walkthrough right from the foundational groundwork required to outlining all the concepts, techniques and tools required to understand Data Science. Data Science is an umbrella term for the non-traditional techniques and technologies that are required to collect, aggregate, process, and gain insights from massive datasets. This book offers all the processes, methodologies, various steps like data acquisition, pre-process, mining, prediction, and visualization tools for extracting insights from vast amounts of data by the use of various scientific methods, algorithms, and processes Readers will learn the steps necessary to create the application with SQl, NoSQL, Python, R, Matlab, Octave and Tablue. This book provides a stepwise approach to building solutions to data science applications right from understanding the fundamentals, performing data analytics to writing source code. All the concepts are discussed in simple English to help the community to become Data Scientist without much pre-requisite knowledge. Features : Simple strategies for developing statistical models that analyze data and detect patterns, trends, and relationships in data sets. Complete roadmap to Data Science approach with dedicatedsections which includes Fundamentals, Methodology and Tools. Focussed approach for learning and practice various Data Science Toolswith Sample code and examples for practice. Information is presented in an accessible way for students, researchers and academicians and professionals.

Download or read book New Frontiers in Applied Data Mining written by Longbing Cao and published by Springer Science & Business Media. This book was released on 2012-02-15 with total page 526 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the thoroughly refereed post-conference proceedings of five international workshops held in conjunction with PAKDD 2011 in Shenzhen, China, in May 2011: the International Workshop on Behavior Informatics (BI 2011), the Workshop on Quality Issues, Measures of Interestingness and Evaluation of Data Mining Models (QIMIE 2011), the Workshop on Biologically Inspired Techniques for Data Mining (BDM 2011), the Workshop on Advances and Issues in Traditional Chinese Medicine Clinical Data Mining (AI-TCM 2011), and the Second Workshop on Data Mining for Healthcare Management (DMGHM 2011). The book also includes papers from the First PAKDD Doctoral Symposium on Data Mining (DSDM 2011). The 42 papers were carefully reviewed and selected from numerous submissions. The papers cover a wide range of topics discussing emerging techniques in the field of knowledge discovery in databases and their application domains extending to previously unexplored areas such as data mining based on optimization techniques from biological behavior of animals and applications in Traditional Chinese Medicine clinical research and health care management.