Learn Data Science Using Python: A Quick-Start Guide
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 pro...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Berkeley, CA
Apress
2024
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| Ausgabe: | First Edition |
| Schlagworte: | |
| Zusammenfassung: | 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.What You’ll Learn- 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 predictionWho This Book Is ForData Analysts, data scientists, Python programmers, and software developers new to data science |
| Beschreibung: | Approx. 250 p. - 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 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. |
| Beschreibung: | 250 Seiten 235 mm |
Internformat
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| 500 | |a Approx. 250 p. - 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 | ||
| 500 | |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.What You’ll Learn- 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 predictionWho This Book Is ForData Analysts, data scientists, Python programmers, and software developers new to data science | ||
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| 650 | 4 | |a Python (Computer program language) | |
| 650 | 4 | |a Artificial intelligence—Data processing | |
| 653 | |a Hardcover, Softcover / Informatik, EDV/Informatik | ||
| 943 | 1 | |a oai:aleph.bib-bvb.de:BVB01-035483341 | |
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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. 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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. 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| spelling | Fouda, Engy Verfasser aut Learn Data Science Using Python A Quick-Start Guide First Edition Berkeley, CA Apress 2024 250 Seiten 235 mm txt rdacontent n rdamedia nc rdacarrier Approx. 250 p. - 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 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.What You’ll Learn- 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 predictionWho This Book Is ForData Analysts, data scientists, Python programmers, and software developers new to data science bicssc bisacsh Artificial intelligence Machine learning Python (Computer program language) Artificial intelligence—Data processing Hardcover, Softcover / Informatik, EDV/Informatik |
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| title | Learn Data Science Using Python A Quick-Start Guide |
| title_auth | Learn Data Science Using Python A Quick-Start Guide |
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| title_full_unstemmed | Learn Data Science Using Python A Quick-Start Guide |
| title_short | Learn Data Science Using Python |
| title_sort | learn data science using python a quick start guide |
| title_sub | A Quick-Start Guide |
| topic | bicssc bisacsh Artificial intelligence Machine learning Python (Computer program language) Artificial intelligence—Data processing |
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