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200 10$aLearn Data Science Using Python $eA Quick-Start Guide /$fby Engy Fouda
205   $a1st ed. 2024.
210  1$aBerkeley, CA :$cApress :$cImprint: Apress,$d2024.
215   $a1 online resource (190 pages)
300   $aIncludes index.
311 08$a9798868809347 
327   $aChapter 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.
330   $aHarness 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.
606   $aArtificial intelligence$xData processing
606   $aArtificial intelligence
606   $aMachine learning
606   $aPython (Computer program language)
606   $aData Science
606   $aArtificial Intelligence
606   $aMachine Learning
606   $aPython
615  0$aArtificial intelligence$xData processing.
615  0$aArtificial intelligence.
615  0$aMachine learning.
615  0$aPython (Computer program language)
615 14$aData Science.
615 24$aArtificial Intelligence.
615 24$aMachine Learning.
615 24$aPython.
676   $a005.7
700   $aFouda$b Engy$0859871
801  0$bMiAaPQ
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996   $aLearn Data Science Using Python$94289958
997   $aUNINA