國立虎尾科技大學 |

A friendly guide to data science = everything you should know about the hottest field in tech /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	A friendly guide to data science/ by Kelly P. Vincent.
其他題名:	everything you should know about the hottest field in tech /
作者:	Vincent, Kelly P.
出版者:	Berkeley, CA :Apress : : 2025.,
面頁冊數:	xxxvi, 884 p. :ill. (chiefly color), digital ; : 24 cm.;
Contained By:	Springer Nature eBook
標題:	Data Storage Representation. -
電子資源:	https://doi.org/10.1007/979-8-8688-1169-2
ISBN:	9798868811692

A friendly guide to data science = everything you should know about the hottest field in tech /
Vincent, Kelly P.

A friendly guide to data scienceeverything you should know about the hottest field in tech /[electronic resource] :by Kelly P. Vincent. - Berkeley, CA :Apress :2025. - xxxvi, 884 p. :ill. (chiefly color), digital ;24 cm. - Friendly guides to technology,2731-9369. - Friendly guides to technology..

Part I: Foundations -- Chapter 1: Working with Numbers: What Is Data, Really? -- Chapter 2: Figuring Out What's Going on in the Data: Descriptive Statistics -- Chapter 3: Setting Us Up for Success: The Inferential Statistics Framework and Experiments -- Chapter 4: Coming to Complex Conclusions: Inferential Statistics and Statistical Testing -- Chapter 5: Figuring Stuff Out: Data Analysis -- Chapter 6: Bringing It into the 21st Century: Data Science -- Chapter 7: A Fresh Perspective: The New Data Analytics -- Chapter 8: Keeping Everyone Safe: Data Security and Privacy -- Chapter 9: What's Fair and Right: Ethical Considerations -- Part II: Doing Data Science -- Chapter 10: Grasping the Big Picture: Domain Knowledge -- Chapter 11: Tools of the Trade: Python and R -- Chapter 12: Trying Not to Make a Mess: Data Collection and Storage -- Chapter 13: For the Preppers: Data Gathering and Preprocessing -- Chapter 14: Ready for the Main Event: Feature Engineering, Selection, and Reduction -- Chapter 15: Not a Crystal Ball: Machine Learning -- Chapter 16: How'd We Do? Measuring the Performance of ML Techniques -- Chapter 17: Making the Computer Literate: Text and Speech Processing -- Chapter 18: A New Kind of Storytelling: Data Visualization and Presentation -- Chapter 19: This Ain't Our First Rodeo: ML Applications -- Chapter 20: When Size Matters: Scalability and the Cloud -- Chapter 21: Putting It All Together: Data Science Solution Management -- Chapter 22: Errors in Judgment: Biases, Fallacies, and Paradoxes -- Part III: The Future -- Chapter 23: Getting Your Hands Dirty: How to Get Involved in Data Science -- Chapter 24: Learning and Growing: Expanding Your Skillset and Knowledge -- Chapter 25: Is It Your Future?: Pursuing a Career in Data Science -- Appendix A.

Curious about data science but not sure where to start? This book is a beginner-friendly guide to what data science is and how people use it. It walks you through the essential topics-what data analysis involves, which skills are useful, and how terms like "data analytics" and "machine learning" connect-without getting too technical too fast. Data science isn't just about crunching numbers, pulling data from a database, or running fancy algorithms. It's about asking the right questions, understanding the process from start to finish, and knowing what's possible (and what's not). This book teaches you all of that, while also introducing important topics like ethics, privacy, and security-because working with data means thinking about people, too. Whether you're a student exploring new skills, a professional navigating data-driven decisions, or someone considering a career change, this book is your friendly gateway into the world of data science, one of today's most exciting fields. No coding or programming experience? No problem. You'll build a solid foundation and gain the confidence to engage with data science concepts- just as AI and data become increasingly central to everyday life. What You Will Learn Know what foundational statistics is and how it matters in data analysis and data science Understand the data science project life cycle and how to manage a data science project Examine the ethics of working with data and its use in data analysis and data science Understand the foundations of data security and privacy Collect, store, prepare, visualize, and present data Identify the many types of machine learning and know how to gauge performance Prepare for and find a career in data science.

ISBN: 9798868811692

Standard No.: 10.1007/979-8-8688-1169-2doiSubjects--Topical Terms:

669777
Data Storage Representation.

LC Class. No.: QA76.9.B45

Dewey Class. No.: 005.7

A friendly guide to data science = everything you should know about the hottest field in tech /
LDR:04665nam a2200349 a 4500 001 1159758
003 DE-He213
005 20250626131757.0
006 m d
007 cr nn 008maaau
008 251029s2025 cau s 0 eng d
020 $a 9798868811692 $q (electronic bk.)
020 $a 9798868811685 $q (paper)
024 7 $a 10.1007/979-8-8688-1169-2 $2 doi
035 $a 979-8-8688-1169-2
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.9.B45
072 7 $a UN $2 bicssc
072 7 $a COM021000 $2 bisacsh
072 7 $a UN $2 thema
082 0 4 $a 005.7 $2 23
090 $a QA76.9.B45 $b V772 2025
100 1 $a Vincent, Kelly P. $3 1487050
245 1 2 $a A friendly guide to data science $h [electronic resource] : $b everything you should know about the hottest field in tech / $c by Kelly P. Vincent.
260 $a Berkeley, CA : $b Apress : $b Imprint: Apress, $c 2025.
300 $a xxxvi, 884 p. : $b ill. (chiefly color), digital ; $c 24 cm.
338 $a online resource $b cr $2 rdacarrier
490 1 $a Friendly guides to technology, $x 2731-9369
505 0 $a Part I: Foundations -- Chapter 1: Working with Numbers: What Is Data, Really? -- Chapter 2: Figuring Out What's Going on in the Data: Descriptive Statistics -- Chapter 3: Setting Us Up for Success: The Inferential Statistics Framework and Experiments -- Chapter 4: Coming to Complex Conclusions: Inferential Statistics and Statistical Testing -- Chapter 5: Figuring Stuff Out: Data Analysis -- Chapter 6: Bringing It into the 21st Century: Data Science -- Chapter 7: A Fresh Perspective: The New Data Analytics -- Chapter 8: Keeping Everyone Safe: Data Security and Privacy -- Chapter 9: What's Fair and Right: Ethical Considerations -- Part II: Doing Data Science -- Chapter 10: Grasping the Big Picture: Domain Knowledge -- Chapter 11: Tools of the Trade: Python and R -- Chapter 12: Trying Not to Make a Mess: Data Collection and Storage -- Chapter 13: For the Preppers: Data Gathering and Preprocessing -- Chapter 14: Ready for the Main Event: Feature Engineering, Selection, and Reduction -- Chapter 15: Not a Crystal Ball: Machine Learning -- Chapter 16: How'd We Do? Measuring the Performance of ML Techniques -- Chapter 17: Making the Computer Literate: Text and Speech Processing -- Chapter 18: A New Kind of Storytelling: Data Visualization and Presentation -- Chapter 19: This Ain't Our First Rodeo: ML Applications -- Chapter 20: When Size Matters: Scalability and the Cloud -- Chapter 21: Putting It All Together: Data Science Solution Management -- Chapter 22: Errors in Judgment: Biases, Fallacies, and Paradoxes -- Part III: The Future -- Chapter 23: Getting Your Hands Dirty: How to Get Involved in Data Science -- Chapter 24: Learning and Growing: Expanding Your Skillset and Knowledge -- Chapter 25: Is It Your Future?: Pursuing a Career in Data Science -- Appendix A.
520 $a Curious about data science but not sure where to start? This book is a beginner-friendly guide to what data science is and how people use it. It walks you through the essential topics-what data analysis involves, which skills are useful, and how terms like "data analytics" and "machine learning" connect-without getting too technical too fast. Data science isn't just about crunching numbers, pulling data from a database, or running fancy algorithms. It's about asking the right questions, understanding the process from start to finish, and knowing what's possible (and what's not). This book teaches you all of that, while also introducing important topics like ethics, privacy, and security-because working with data means thinking about people, too. Whether you're a student exploring new skills, a professional navigating data-driven decisions, or someone considering a career change, this book is your friendly gateway into the world of data science, one of today's most exciting fields. No coding or programming experience? No problem. You'll build a solid foundation and gain the confidence to engage with data science concepts- just as AI and data become increasingly central to everyday life. What You Will Learn Know what foundational statistics is and how it matters in data analysis and data science Understand the data science project life cycle and how to manage a data science project Examine the ethics of working with data and its use in data analysis and data science Understand the foundations of data security and privacy Collect, store, prepare, visualize, and present data Identify the many types of machine learning and know how to gauge performance Prepare for and find a career in data science.
650 2 4 $a Data Storage Representation. $3 669777
650 2 4 $a Machine Learning. $3 1137723
650 2 4 $a Big Data. $3 1017136
650 2 4 $a Information Storage and Retrieval. $3 593926
650 2 4 $a Database Management. $3 669820
650 1 4 $a Data Science. $3 1174436
650 0 $a Database management. $3 557799
650 0 $a Big data. $3 981821
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
830 0 $a Friendly guides to technology. $3 1487051
856 4 0 $u https://doi.org/10.1007/979-8-8688-1169-2
950 $a Professional and Applied Computing (SpringerNature-12059)