Practitioner’s Guide to Data Science: Streamlining Data Science Solutions using Python, Scikit-Learn, and Azure ML Service Platform
- Length: 242 pages
- Edition: 1
- Language: English
- Publisher: BPB Publications
- Publication Date: 2022-01-17
- ISBN-10: 9391392873
- ISBN-13: 9789391392871
- Sales Rank: #4639285 (See Top 100 Books)
Covers Data Science concepts, processes, and the real-world hands-on use cases.
Key Features
- Covers the journey from a basic programmer to an effective Data Science developer.
- Applied use of Data Science native processes like CRISP-DM and Microsoft TDSP.
- Implementation of MLOps using Microsoft Azure DevOps.
Description
“How is the Data Science project to be implemented?” has never been more conceptually sounding, thanks to the work presented in this book. This book provides an in-depth look at the current state of the world’s data and how Data Science plays a pivotal role in everything we do.
This book explains and implements the entire Data Science lifecycle using well-known data science processes like CRISP-DM and Microsoft TDSP. The book explains the significance of these processes in connection with the high failure rate of Data Science projects.
The book helps build a solid foundation in Data Science concepts and related frameworks. It teaches how to implement real-world use cases using data from the HMDA dataset. It explains Azure ML Service architecture, its capabilities, and implementation to the DS team, who will then be prepared to implement MLOps. The book also explains how to use Azure DevOps to make the process repeatable while we’re at it.
By the end of this book, you will learn strong Python coding skills, gain a firm grasp of concepts such as feature engineering, create insightful visualizations and become acquainted with techniques for building machine learning models.
What you will learn
- Organize Data Science projects using CRISP-DM and Microsoft TDSP.
- Learn to acquire and explore data using Python visualizations.
- Get well versed with the implementation of data pre-processing and Feature Engineering.
- Understand algorithm selection, model development, and model evaluation.
- Hands-on with Azure ML Service, its architecture, and capabilities.
- Learn to use Azure ML SDK and MLOps for implementing real-world use cases.
Who this book is for
This book is intended for programmers who wish to pursue AI/ML development and build a solid conceptual foundation and familiarity with related processes and frameworks. Additionally, this book is an excellent resource for Software Architects and Managers involved in the design and delivery of Data Science-based solutions.
Cover Page Title Page Copyright Page Foreword Dedication Page About the Author About the Reviewer Acknowledgement Preface Errata Table of Contents 1. Data Science for Business Structure Objectives Application programmer to Data Science professional What is Data Science? The unprecedented scope of Data Science Data Science application Big Data, DM, ML, DL, AI, and Data Science Legal, ethical, and security aspects of Data Science Methodology used in organizing this book Conclusion Points to remember Multiple choice questions Answers Questions Key terms 2. Data Science Project Methodologies and Team Processes Structure Objectives What is a process and its importance? Data Science from a process perspective Software engineering and Data Science Data Science project methodologies and processes Knowledge Discovery in Databases CCC Big Data pipeline CRoss-Industry Standard Process for Data Mining Domino’s Data Science Life Cycle Microsoft’s Team Data Science Process Data Science lifecycle Standardized project structure Infrastructure and resources Tools and utilities Sample, Explore, Modify, Model, and Assess Data-Driven Scrum (DDS) Conclusion Points to remember Multiple choice questions Answers Questions Key terms 3. Business Understanding and Its Data Landscape Structure Objectives What is involved in business understanding? CRISP-DM guidelines Microsoft TDSP guidelines Business problem types and Data Science solutions Reliability and validity of business data Hands-on use case Project charter Business background Project scope Project team Evaluation metrics Project plan Solution architecture Communication plan Data sources Data dictionary Conclusion Points to remember Multiple choice questions Answers Questions Key terms 4. Acquire, Explore, and Analyze Data Structure Objectives Development environment options Guidelines for data acquisition and understanding CRISP-DM Microsoft TDSP Data acquisition and sampling Essential considerations Use case data Down-sampling the use case data Down-sampling for rate spread use case Data exploration and visualization Essential considerations Explore and visualize HMDA use case data HMDA use case data distribution Data relations (bivariate) Categorical variables Data relations (multivariate) Data quality report and decision checkpoint Data quality Decision checkpoint Conclusion Points to remember Multiple choice question Answers Questions Key terms 5. Pre-processing and Preparing Data Structure Objectives Guidelines for data preparation CRISP-DM for data preparation Selection of data Cleaning of data Construction of data Integration of data Data formatting Microsoft TDSP for data preparation Data pre-processing concept Data health screening Data pre-processing major tasks Feature engineering Data pre-processing and cleaning Feature engineering Conclusion Points to remember Multiple choice questions Answers Questions Key terms 6. Developing a Machine Learning Model Structure Objectives Guidelines for model development CRISP-DM Selection of modeling technique Generation of test design Model building Model assessment Microsoft TDSP Goals Tasks Deliverables Modeling algorithms and evaluation What is a model? How to choose an algorithm? Metrics for model evaluation Classification metrics Regression metrics Model development procedure Modeling for HMDA use case Choosing an algorithm Modeling scenario-1 Modeling scenario-2 Model tuning Feature selection Dimensionality reduction Cross-validation Regularization Bagging and boosting Conclusion Points to remember Multiple choice questions Answers Questions Key terms 7. Lap Around Azure ML Service Structure Objectives Azure ML Service overview Architecture and key concepts Workspace Compute Managed compute Un-managed compute Datasets and datastores Environments Experiments Runs Run configurations Snapshots Pipelines Models Model registry Deployment Endpoints Web service endpoint IoT module endpoints Getting started: signup and provisioning AutoML in Azure ML Service Model development with Azure ML Service Azure ML Designer AutoML using ML Studio UI AutoML using Python SDK Conclusion Points to remember Multiple choice questions Answers Questions Key terms 8. Deploying and Managing Models Structure Objectives Guidelines for deployment and evaluation CRISP-DM Microsoft TDSP Model lifecycle management Model lifecycle using Azure ML SDK Training the model Registering model Deploying the model Testing/consuming deployed model Retraining a model Model lifecycle using Azure ML Studio UI MLOps with Azure Pipelines Pre-requisites Azure DevOps project Project repository Azure Subscription Azure Service connection Creating a build pipeline Creating a release pipeline Conclusion Points to remember Multiple choice questions Answers Questions Key terms Index
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