Data Science Workflow For Beginners

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Data Science Workflow For Beginners

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Author : Alejandro Garcia
language : en
Publisher: Alejandro Garcia
Release Date :

Data Science Workflow For Beginners written by Alejandro Garcia and has been published by Alejandro Garcia this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.

This book brings to you a simple yet effective 40 to 60 mins introduction that will clear all your doubts about Data Sience and will answer some important questions like: What is data Science ? The book explores all the initial concepts a person might want to know about the data science workflow. There’s not coding, math or statistics required to successfully understand the goals and end results of this process. This book takes you on an exclusive tour of datasets and sites to download your first datasets. Then jumps into a comprehensive and easy-to-follow data science process letting you go through 3 data visualization projects. (Python Code Understanding is Recommended for the Data Visualization projects) - 40 to 60 mins reading time. - 3 Data Visualization projects. - 10 Datasets sources. - 26 Quality datasets for your first visualizations. - Get the code and reuse in your own projects. The ebook covers: - Intro to Data Science. - The Workflow of Data Science. - Data Science and Machine Learning. - Datasets to start right away. - Data Visualization Projects. (Python Code Understanding Recommended)

Data Science And Big Data Analytics

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Author : EMC Education Services
language : en
Publisher: John Wiley & Sons
Release Date : 2015-01-27

Data Science And Big Data Analytics written by EMC Education Services and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-01-27 with Computers categories.

Data Science and Big Data Analytics is about harnessing the power of data for new insights. The book covers the breadth of activities and methods and tools that Data Scientists use. The content focuses on concepts, principles and practical applications that are applicable to any industry and technology environment, and the learning is supported and explained with examples that you can replicate using open-source software. This book will help you: Become a contributor on a data science team Deploy a structured lifecycle approach to data analytics problems Apply appropriate analytic techniques and tools to analyzing big data Learn how to tell a compelling story with data to drive business action Prepare for EMC Proven Professional Data Science Certification Get started discovering, analyzing, visualizing, and presenting data in a meaningful way today!

Predictive Analytics Using Oracle Data Miner

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Author : Brendan Tierney
language : en
Publisher: McGraw Hill Professional
Release Date : 2014-08-08

Predictive Analytics Using Oracle Data Miner written by Brendan Tierney and has been published by McGraw Hill Professional this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-08-08 with Computers categories.

Build Next-Generation In-Database Predictive Analytics Applications with Oracle Data Miner “If you have an Oracle Database and want to leverage that data to discover new insights, make predictions, and generate actionable insights, this book is a must read for you! In Predictive Analytics Using Oracle Data Miner: Develop & Use Oracle Data Mining Models in Oracle Data Miner, SQL & PL/SQL, Brendan Tierney, Oracle ACE Director and data mining expert, guides you through the basic concepts of data mining and offers step-by-step instructions for solving data-driven problems using SQL Developer’s Oracle Data Mining extension. Brendan takes it full circle by showing you how to deploy advanced analytical methodologies and predictive models immediately into enterprise-wide production environments using the in-database SQL and PL/SQL functionality. Definitely a must read for any Oracle data professional!” --Charlie Berger, Senior Director Product Management, Oracle Data Mining and Advanced Analytics Perform in-database data mining to unlock hidden insights in data. Written by an Oracle ACE Director, Predictive Analytics Using Oracle Data Miner shows you how to use this powerful tool to create and deploy advanced data mining models. Covering topics for the data scientist, Oracle developer, and Oracle database administrator, this Oracle Press guide shows you how to get started with Oracle Data Miner and build Oracle Data Miner models using SQL and PL/SQL packages. You'll get best practices for integrating your Oracle Data Miner models into applications to automate the discovery and distribution of business intelligence predictions throughout the enterprise. Install and configure Oracle Data Miner for Oracle Database 11g Release 11.2 and Oracle Database 12c Create Oracle Data Miner projects and workflows Prepare data for data mining Develop data mining models using association rule analysis, classification, clustering, regression, and anomaly detection Use data dictionary views and prepare your data using in-database transformations Build and use data mining models using SQL and PL/SQL packages Migrate your Oracle Data Miner models, integrate them into dashboards and applications, and run them in parallel Build transient data mining models with the Predictive Queries feature in Oracle Database 12c

Business Data Science Combining Machine Learning And Economics To Optimize Automate And Accelerate Business Decisions

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Author : Matt Taddy
language : en
Publisher: McGraw Hill Professional
Release Date : 2019-08-23

Business Data Science Combining Machine Learning And Economics To Optimize Automate And Accelerate Business Decisions written by Matt Taddy and has been published by McGraw Hill Professional this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-08-23 with Business & Economics categories.

Use machine learning to understand your customers, frame decisions, and drive value The business analytics world has changed, and Data Scientists are taking over. Business Data Science takes you through the steps of using machine learning to implement best-in-class business data science. Whether you are a business leader with a desire to go deep on data, or an engineer who wants to learn how to apply Machine Learning to business problems, you’ll find the information, insight, and tools you need to flourish in today’s data-driven economy. You’ll learn how to: Use the key building blocks of Machine Learning: sparse regularization, out-of-sample validation, and latent factor and topic modeling Understand how use ML tools in real world business problems, where causation matters more that correlation Solve data science programs by scripting in the R programming language Today’s business landscape is driven by data and constantly shifting. Companies live and die on their ability to make and implement the right decisions quickly and effectively. Business Data Science is about doing data science right. It’s about the exciting things being done around Big Data to run a flourishing business. It’s about the precepts, principals, and best practices that you need know for best-in-class business data science.

2009 Joint Assembly Abstracts 24 27 May 2009 Toronto Ontario Canada

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Author : American Geophysical Union. Joint Assembly
language : en
Publisher:
Release Date : 2009

2009 Joint Assembly Abstracts 24 27 May 2009 Toronto Ontario Canada written by American Geophysical Union. Joint Assembly and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009 with Geophysics categories.

The Data Science Workflow From Data Collection To Decision Making

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Author : RENATA. SLOANE
language : en
Publisher: Independently Published
Release Date : 2025-05-20

The Data Science Workflow From Data Collection To Decision Making written by RENATA. SLOANE and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-20 with Computers categories.

Data Science Workflow From Data Collection to Decision Making is your comprehensive guide to navigating the complete journey of a data science project. This book covers every phase of the data science process, from collecting raw data to transforming it into actionable insights that drive strategic decision-making. Whether you're an aspiring data scientist or an experienced analyst looking to refine your workflow, this guide will equip you with the best practices and techniques to manage data science projects effectively. Inside, you'll discover: Data Collection and Acquisition: Learn how to gather data from various sources, including databases, APIs, and web scraping, and understand the importance of sourcing accurate and relevant data for analysis. Data Cleaning and Preprocessing: Dive into the critical step of data cleaning, including handling missing values, outliers, and inconsistencies, as well as data transformation techniques like normalization and encoding. Exploratory Data Analysis (EDA): Explore the process of visualizing and summarizing your data to identify patterns, relationships, and trends that guide the modeling process. Feature Engineering and Selection: Master the art of creating new features and selecting the most important ones that improve model performance and interpretability. Building Data Models: Learn how to build and evaluate machine learning models, including supervised and unsupervised learning techniques, from regression to classification, clustering, and dimensionality reduction. Model Evaluation and Tuning: Understand how to assess the performance of your models using metrics such as accuracy, precision, recall, F1 score, and ROC-AUC, and fine-tune hyperparameters to achieve optimal results. Interpreting Results and Insights: Learn how to interpret the results of your models, understand their limitations, and communicate your findings in a way that supports business decision-making. Data Visualization for Decision Making: Discover the importance of data visualization in conveying insights to non-technical stakeholders and how to present your findings through compelling charts and dashboards. Deploying Models and Continuous Monitoring: Understand how to deploy machine learning models into production, monitor their performance over time, and ensure they remain effective as data changes. Why This Book Is Essential: End-to-End Data Science Process: Provides a complete roadmap for tackling data science projects, ensuring no phase is overlooked. Practical Guidance: Offers actionable tips, techniques, and real-world examples to enhance your understanding of each phase in the workflow. Focus on Business Impact: Emphasizes the importance of turning data insights into business decisions and demonstrates how to communicate findings effectively. Hands-On Examples: Includes practical examples and code snippets to help you implement each stage of the workflow using Python, R, or other data science tools. Whether you're handling small-scale datasets or working on complex, enterprise-level projects, Data Science Workflow From Data Collection to Decision Making will empower you to effectively manage and execute data-driven projects that deliver valuable insights and support business strategies.

Data Science Solutions

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Author : Manav Sehgal
language : en
Publisher:
Release Date : 2017-02-07

Data Science Solutions written by Manav Sehgal and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-02-07 with categories.

The field of data science, big data, machine learning, and artificial intelligence is exciting and complex at the same time. Data science is also rapidly growing with new tools, technologies, algorithms, datasets, and use cases. For a beginner in this field, the learning curve can be fairly daunting. This is where this book helps. The data science solutions book provides a repeatable, robust, and reliable framework to apply the right-fit workflows, strategies, tools, APIs, and domain for your data science projects. This book takes a solutions focused approach to data science. Each chapter meets an end-to-end objective of solving for data science workflow or technology requirements. At the end of each chapter you either complete a data science tools pipeline or write a fully functional coding project meeting your data science workflow requirements. SEVEN STAGES OF DATA SCIENCE SOLUTIONS WORKFLOW Every chapter in this book will go through one or more of these seven stages of data science solutions workflow. STAGE 1: Question. Problem. Solution. Before starting a data science project we must ask relevant questions specific to our project domain and datasets. We may answer or solve these during the course of our project. Think of these questions-solutions as the key requirements for our data science project. Here are some templates that can be used to frame questions for our data science projects. Can we classify an entity based on given features if our data science model is trained on certain number of samples with similar features related to specific classes?Do the samples, in a given dataset, cluster in specific classes based on similar or correlated features?Can our machine learning model recognise and classify new inputs based on prior training on a sample of similar inputs?STAGE 2: Acquire. Search. Create. Catalog.This stage involves data acquisition strategies including searching for datasets on popular data sources or internally within your organisation. We may also create a dataset based on external or internal data sources. The acquire stage may feedback to the question stage, refining our problem and solution definition based on the constraints and characteristics of the acquired datasets. STAGE 3: Wrangle. Prepare. Cleanse.The data wrangle phase prepares and cleanses our datasets for our project goals. This workflow stage starts by importing a dataset, exploring the dataset for its features and available samples, preparing the dataset using appropriate data types and data structures, and optionally cleansing the data set for creating model training and solution testing samples. The wrangle stage may circle back to the acquire stage to identify complementary datasets to combine and complete the existing dataset. STAGE 4: Analyse. Patterns. Explore.The analyse phase explores the given datasets to determine patterns, correlations, classification, and nature of the dataset. This helps determine choice of model algorithms and strategies that may work best on the dataset. The analyse stage may also visualize the dataset to determine such patterns. STAGE 5: Model. Predict. Solve.The model stage uses prediction and solution algorithms to train on a given dataset and apply this training to solve for a given problem. STAGE 6: Visualize. Report. Present.The visualization stage can help data wrangling, analysis, and modeling stages. Data can be visualized using charts and plots suiting the characteristics of the dataset and the desired results.Visualization stage may also provide the inputs for the supply stage.STAGE 7: Supply. Products. Services.Once we are ready to monetize our data science solution or derive further return on investment from our projects, we need to think about distribution and data supply chain. This stage circles back to the acquisition stage. In fact we are acquiring data from someone else's data supply chain.

Process Mining In Practice Development Of A Project Methodology

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Author : Lukas Braun
language : en
Publisher: GRIN Verlag
Release Date : 2022-09-02

Process Mining In Practice Development Of A Project Methodology written by Lukas Braun and has been published by GRIN Verlag this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-09-02 with Business & Economics categories.

Master's Thesis from the year 2020 in the subject Business economics - Business Management, Corporate Governance, grade: 1,3, University of Bayreuth, language: English, abstract: Process Mining uses data to discover, monitor, and improve ongoing processes running inside organizations. Even though the relatively novel research discipline has matured theoretically, its practical application was treated with restraint by companies and lacks methodological thoroughness to this day. In order to diminish these shortcomings, this thesis aims to develop a process mining project methodology, which guides organizations through the implementation of process mining projects in practice. This work was developed using the Design Science Research Methodology (DSRM) and is based on both a theoretical and practical foundation. To this end, preexisting theoretical methodologies and fundamental literature were instrumented to build the methodology’s rough framework. Subsequently, a case study analysis enabled the incorporation of further details about specific tools, techniques, and roles typically employed in practical process mining applications. This way, the methodology obtains a deep level of detail, is geared to practical applicability in several economic sectors, and facilitates the entry into the field for beginners. Ultimately, a significant number of domain experts evaluated the methodology in an online survey format, allowing for further improvement of its design and validity.

Data Science At The Command Line

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Author : Jeroen Janssens
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2021-08-17

Data Science At The Command Line written by Jeroen Janssens and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-08-17 with Computers categories.

This thoroughly revised guide demonstrates how the flexibility of the command line can help you become a more efficient and productive data scientist. You'll learn how to combine small yet powerful command-line tools to quickly obtain, scrub, explore, and model your data. To get you started, author Jeroen Janssens provides a Docker image packed with over 100 Unix power tools--useful whether you work with Windows, macOS, or Linux. You'll quickly discover why the command line is an agile, scalable, and extensible technology. Even if you're comfortable processing data with Python or R, you'll learn how to greatly improve your data science workflow by leveraging the command line's power. This book is ideal for data scientists, analysts, engineers, system administrators, and researchers. Obtain data from websites, APIs, databases, and spreadsheets Perform scrub operations on text, CSV, HTML, XML, and JSON files Explore data, compute descriptive statistics, and create visualizations Manage your data science workflow Create your own tools from one-liners and existing Python or R code Parallelize and distribute data-intensive pipelines Model data with dimensionality reduction, regression, and classification algorithms Leverage the command line from Python, Jupyter, R, RStudio, and Apache Spark

Methods And Techniques In Drug Discovery

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Author : John Sterling
language : en
Publisher:
Release Date : 2005

Methods And Techniques In Drug Discovery written by John Sterling and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005 with Computers categories.