東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

FindBook

Google Book

Amazon

博客來

Learn data science using Python = a quick-start guide /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Learn data science using Python/ by Engy Fouda.
其他題名:	a quick-start guide /
作者:	Fouda, Engy.
出版者:	Berkeley, CA :Apress : : 2024.,
面頁冊數:	xiv, 180 p. :ill., digital ;24 cm.
內容註:	Chapter 1: Data Science in Action -- Chapter 2: Getting Started -- Chapter 3: Data Visualization -- Chapter 4: Statistical Analysis and Linear Models -- Chapter 5: Advanced Data Pre-processing and Feature Engineering -- Chapter 6: Preparing Data for Analysis -- Chapter 7: Regression.
Contained By:	Springer Nature eBook
標題:	Electronic data processing. -
電子資源:	https://doi.org/10.1007/979-8-8688-0935-4
ISBN:	9798868809354

Learn data science using Python = a quick-start guide /
Fouda, Engy.

Learn data science using Pythona quick-start guide /[electronic resource] :by Engy Fouda. - Berkeley, CA :Apress :2024. - xiv, 180 p. :ill., digital ;24 cm.

Chapter 1: Data Science in Action -- Chapter 2: Getting Started -- Chapter 3: Data Visualization -- Chapter 4: Statistical Analysis and Linear Models -- Chapter 5: Advanced Data Pre-processing and Feature Engineering -- Chapter 6: Preparing Data for Analysis -- Chapter 7: Regression.

Harness the capabilities of Python and gain the expertise need to master data science techniques. This step-by-step book guides you through using Python to achieve tasks related to data cleaning, statistics, and visualization. You'll start by reviewing the foundational aspects of the data science process. This includes an extensive overview of research points and practical applications, such as the insightful analysis of presidential elections. The journey continues by navigating through installation procedures and providing valuable insights into Python, data types, typecasting, and essential libraries like Pandas and NumPy. You'll then delve into the captivating world of data visualization. Concepts such as scatter plots, histograms, and bubble charts come alive through detailed discussions and practical code examples, unraveling the complexities of creating compelling visualizations for enhanced data understanding. Statistical analysis, linear models, and advanced data preprocessing techniques are also discussed before moving on to preparing data for analysis, including renaming variables, variable rearrangement, and conditional statements. Finally, you'll be introduced to regression techniques, demystifying the intricacies of simple and multiple linear regression, as well as logistic regression. You will: Understand installation procedures and valuable insights into Python, data types, typecasting Examine the fundamental statistical analysis required in most data science and analytics reports Clean the most common data set problems Use linear progression for data prediction.

ISBN: 9798868809354

Standard No.: 10.1007/979-8-8688-0935-4doiSubjects--Topical Terms:

520749
Electronic data processing.

LC Class. No.: QA76

Dewey Class. No.: 004

Learn data science using Python = a quick-start guide /
LDR:02862nmm a2200325 a 4500 001 2381324
003 DE-He213
005 20241115115751.0
006 m d
007 cr nn 008maaau
008 250418s2024 cau s 0 eng d
020 $a 9798868809354 $q (electronic bk.)
020 $a 9798868809347 $q (paper)
024 7 $a 10.1007/979-8-8688-0935-4 $2 doi
035 $a 979-8-8688-0935-4
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76
072 7 $a UN $2 bicssc
072 7 $a COM021000 $2 bisacsh
072 7 $a UN $2 thema
082 0 4 $a 004 $2 23
090 $a QA76 $b .F764 2024
100 1 $a Fouda, Engy. $3 3504561
245 1 0 $a Learn data science using Python $h [electronic resource] : $b a quick-start guide / $c by Engy Fouda.
260 $a Berkeley, CA : $b Apress : $b Imprint: Apress, $c 2024.
300 $a xiv, 180 p. : $b ill., digital ; $c 24 cm.
505 0 $a Chapter 1: Data Science in Action -- Chapter 2: Getting Started -- Chapter 3: Data Visualization -- Chapter 4: Statistical Analysis and Linear Models -- Chapter 5: Advanced Data Pre-processing and Feature Engineering -- Chapter 6: Preparing Data for Analysis -- Chapter 7: Regression.
520 $a Harness the capabilities of Python and gain the expertise need to master data science techniques. This step-by-step book guides you through using Python to achieve tasks related to data cleaning, statistics, and visualization. You'll start by reviewing the foundational aspects of the data science process. This includes an extensive overview of research points and practical applications, such as the insightful analysis of presidential elections. The journey continues by navigating through installation procedures and providing valuable insights into Python, data types, typecasting, and essential libraries like Pandas and NumPy. You'll then delve into the captivating world of data visualization. Concepts such as scatter plots, histograms, and bubble charts come alive through detailed discussions and practical code examples, unraveling the complexities of creating compelling visualizations for enhanced data understanding. Statistical analysis, linear models, and advanced data preprocessing techniques are also discussed before moving on to preparing data for analysis, including renaming variables, variable rearrangement, and conditional statements. Finally, you'll be introduced to regression techniques, demystifying the intricacies of simple and multiple linear regression, as well as logistic regression. You will: Understand installation procedures and valuable insights into Python, data types, typecasting Examine the fundamental statistical analysis required in most data science and analytics reports Clean the most common data set problems Use linear progression for data prediction.
650 0 $a Electronic data processing. $3 520749
650 0 $a Python (Computer program language) $3 729789
650 2 4 $a Artificial Intelligence. $3 769149
650 2 4 $a Machine Learning. $3 3382522
650 2 4 $a Python. $3 3201289
650 1 4 $a Data Science. $3 3538937
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/979-8-8688-0935-4
950 $a Professional and Applied Computing (SpringerNature-12059)