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020 _a9798868809354
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024 7 _a10.1007/979-8-8688-0935-4
_2doi
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072 7 _aCOM021000
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100 1 _aFouda, Engy.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_966209
245 1 0 _aLearn Data Science Using Python
_h[electronic resource] :
_bA Quick-Start Guide /
_cby Engy Fouda.
250 _a1st ed. 2024.
264 1 _aBerkeley, CA :
_bApress :
_bImprint: Apress,
_c2024.
300 _aXIV, 180 p. 94 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _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.
520 _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.
650 0 _aArtificial intelligence
_xData processing.
_966210
650 0 _aArtificial intelligence.
_958441
650 0 _aMachine learning.
_955059
650 0 _aPython (Computer program language).
_964608
650 1 4 _aData Science.
_966211
650 2 4 _aArtificial Intelligence.
_958441
650 2 4 _aMachine Learning.
_955059
650 2 4 _aPython.
_947553
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9798868809347
776 0 8 _iPrinted edition:
_z9798868809361
856 4 0 _uhttps://doi.org/10.1007/979-8-8688-0935-4
912 _aZDB-2-CWD
912 _aZDB-2-SXPC
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