Data Science for Business Professionals: A Practical Guide for Beginners (English Edition)
暫譯: 商業專業人士的資料科學：初學者實用指南（英文版）
Data Science and Consulting Pvt Ltd, P.
- 出版商: BPB Publications
- 出版日期: 2020-05-06
- 售價: $1,380
- 貴賓價: 9.5 折 $1,311
- 語言: 英文
- 頁數: 368
- 裝訂: Quality Paper - also called trade paper
- ISBN: 9389423287
- ISBN-13: 9789389423280
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相關分類:
Data Science
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相關主題
商品描述
Primer into the multidisciplinary world of Data Science
Key FeaturesExplore and use the key concepts of Statistics required to solve data science problems Use Docker, Jenkins, and Git for Continuous Development and Continuous Integration of your web app Learn how to build Data Science solutions with GCP and AWS
Description
The book will initially explain the What-Why of Data Science and the process of solving a Data Science problem. The fundamental concepts of Data Science, such as Statistics, Machine Learning, Business Intelligence, Data pipeline, and Cloud Computing, will also be discussed. All the topics will be explained with an example problem and will show how the industry approaches to solve such a problem. The book will pose questions to the learners to solve the problems and build the problem-solving aptitude and effectively learn. The book uses Mathematics wherever necessary and will show you how it is implemented using Python with the help of an example dataset.
What will you learn
Understand the multi-disciplinary nature of Data Science Get familiar with the key concepts in Mathematics and Statistics Explore a few key ML algorithms and their use cases Learn how to implement the basics of Data Pipelines Get an overview of Cloud Computing & DevOps Learn how to create visualizations using Tableau
Who this book is for
This book is ideal for Data Science enthusiasts who want to explore various aspects of Data Science. Useful for Academicians, Business owners, and Researchers for a quick reference on industrial practices in Data Science. Table of Contents
1. Data Science in Practice
2. Mathematics Essentials
3. Statistics Essentials
4. Exploratory Data Analysis
5. Data preprocessing
6. Feature Engineering
7. Machine learning algorithms
8. Productionizing ML models
9. Data Flows in Enterprises
10. Introduction to Databases
11. Introduction to Big Data
12. DevOps for Data Science
13. Introduction to Cloud Computing
14. Deploy Model to Cloud
15. Introduction to Business Intelligence
16. Data Visualization Tools
17. Industry Use Case 1 - FormAssist
18. Industry Use Case 2 - PeopleReporter
19. Data Science Learning Resources
20. Do It Your Self Challenges
21. MCQs for Assessments
About the Author
The book has been written by collective experience of many of Probyto past client projects, academic collaborations and team members for last 5 years. The collective work is represented by different experts in data driven decision making and portion they deal with in creating value for the clients at Probyto. The team has experienced professionals and freshers who have gained from the approach as mentioned in the book as well. Two key contributions for the book goes to Parvej Reja Saleh (Manager) and Namachivayam Dharmalingam (Senior Analyst).
Your Blog links: https: //probyto/resources/blogs
Your LinkedIn Profile: https: //www.linkedin.com/company/probyto
Key Features
Description
The book will initially explain the What-Why of Data Science and the process of solving a Data Science problem. The fundamental concepts of Data Science, such as Statistics, Machine Learning, Business Intelligence, Data pipeline, and Cloud Computing, will also be discussed. All the topics will be explained with an example problem and will show how the industry approaches to solve such a problem. The book will pose questions to the learners to solve the problems and build the problem-solving aptitude and effectively learn. The book uses Mathematics wherever necessary and will show you how it is implemented using Python with the help of an example dataset.
What will you learn
Who this book is for
This book is ideal for Data Science enthusiasts who want to explore various aspects of Data Science. Useful for Academicians, Business owners, and Researchers for a quick reference on industrial practices in Data Science. Table of Contents
1. Data Science in Practice
2. Mathematics Essentials
3. Statistics Essentials
4. Exploratory Data Analysis
5. Data preprocessing
6. Feature Engineering
7. Machine learning algorithms
8. Productionizing ML models
9. Data Flows in Enterprises
10. Introduction to Databases
11. Introduction to Big Data
12. DevOps for Data Science
13. Introduction to Cloud Computing
14. Deploy Model to Cloud
15. Introduction to Business Intelligence
16. Data Visualization Tools
17. Industry Use Case 1 - FormAssist
18. Industry Use Case 2 - PeopleReporter
19. Data Science Learning Resources
20. Do It Your Self Challenges
21. MCQs for Assessments
About the Author
The book has been written by collective experience of many of Probyto past client projects, academic collaborations and team members for last 5 years. The collective work is represented by different experts in data driven decision making and portion they deal with in creating value for the clients at Probyto. The team has experienced professionals and freshers who have gained from the approach as mentioned in the book as well. Two key contributions for the book goes to Parvej Reja Saleh (Manager) and Namachivayam Dharmalingam (Senior Analyst).
Your Blog links: https: //probyto/resources/blogs
Your LinkedIn Profile: https: //www.linkedin.com/company/probyto
商品描述(中文翻譯)
數據科學多學科世界的入門
主要特點
描述
本書將首先解釋數據科學的「什麼」和「為什麼」，以及解決數據科學問題的過程。將討論數據科學的基本概念，如統計學、機器學習、商業智慧、數據管道和雲計算。所有主題將通過示例問題進行解釋，並展示行業如何解決此類問題。本書將向學習者提出問題，以解決問題並培養解決問題的能力，並有效學習。本書在必要時使用數學，並將展示如何使用 Python 及示例數據集來實現這些概念。
您將學到什麼
本書適合誰
本書非常適合希望探索數據科學各個方面的數據科學愛好者。對於學術界、企業主和研究人員來說，這本書是快速參考數據科學行業實踐的有用資源。目錄
1. 實踐中的數據科學
2. 數學基礎
3. 統計學基礎
4. 探索性數據分析
5. 數據預處理
6. 特徵工程
7. 機器學習算法
8. 生產化機器學習模型
9. 企業中的數據流
10. 數據庫簡介
11. 大數據簡介
12. 數據科學的 DevOps
13. 雲計算簡介
14. 將模型部署到雲端
15. 商業智慧簡介
16. 數據可視化工具
17. 行業案例 1 - FormAssist
18. 行業案例 2 - PeopleReporter
19. 數據科學學習資源
20. 自我挑戰
21. 評估的選擇題
關於作者
本書是基於 Probyto 過去五年多個客戶項目、學術合作和團隊成員的集體經驗而撰寫的。這些集體工作由不同的數據驅動決策專家代表，並展示他們在為 Probyto 客戶創造價值方面的貢獻。團隊中有經驗豐富的專業人士和新手，他們也從書中提到的方法中獲益。對於本書的兩個主要貢獻者是 Parvej Reja Saleh（經理）和 Namachivayam Dharmalingam（高級分析師）。
您的部落格連結： https://probyto/resources/blogs
您的 LinkedIn 個人檔案： https://www.linkedin.com/company/probyto
