Practical data analysis using Jupyter Notebook: learn how to speak the language of data by extracting useful and actionable insights using Python
Data literacy is the ability to read, analyze, work with, and argue using data. Data analysis is the process of cleaning and modeling your data to discover useful information. This book combines these two concepts by sharing proven techniques and hands-on examples so that you can learn how to commun...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Birmingham ; Mumbai
Packt
2020
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| Ausgabe: | 1.Auflage |
| Schlagworte: | |
| Zusammenfassung: | Data literacy is the ability to read, analyze, work with, and argue using data. Data analysis is the process of cleaning and modeling your data to discover useful information. This book combines these two concepts by sharing proven techniques and hands-on examples so that you can learn how to communicate effectively using data. After introducing you to the basics of data analysis using Jupyter Notebook and Python, the book will take you through the fundamentals of data. Packed with practical examples, this guide will teach you how to clean, wrangle, analyze, and visualize data to gain useful insights, and you'll discover how to answer questions using data with easy-to-follow steps. Later chapters teach you about storytelling with data using charts, such as histograms and scatter plots. As you advance, you'll understand how to work with unstructured data using natural language processing (NLP) techniques to perform sentiment analysis. All the knowledge you gain will help you discover key patterns and trends in data using real-world examples. In addition to this, you will learn how to handle data of varying complexity to perform efficient data analysis using modern Python libraries. |
| Beschreibung: | V, 296 Seiten Illustrationen, Diagramme (teilweise farbig) |
| ISBN: | 9781838825096 9781838826031 |
Internformat
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| 505 | 8 | |a Fundamentals of data analysis Overview of Python and Installation of Jupyter notebook Getting Started with NumPy Creating your first Pandas DataFrame Gathering and Loading Data in Python Visualizing and working with time series data Exploring Cleaning, Refining and Blending Datasets Understanding Joins, Relationships and Data Aggregates Plotting, Visualization and Storytelling Exploring Text Data and Unstructured Data Practical Sentiment Analysis Discovering Patterns in Data and providing insights | |
| 520 | 3 | |a Data literacy is the ability to read, analyze, work with, and argue using data. Data analysis is the process of cleaning and modeling your data to discover useful information. This book combines these two concepts by sharing proven techniques and hands-on examples so that you can learn how to communicate effectively using data. After introducing you to the basics of data analysis using Jupyter Notebook and Python, the book will take you through the fundamentals of data. Packed with practical examples, this guide will teach you how to clean, wrangle, analyze, and visualize data to gain useful insights, and you'll discover how to answer questions using data with easy-to-follow steps. Later chapters teach you about storytelling with data using charts, such as histograms and scatter plots. As you advance, you'll understand how to work with unstructured data using natural language processing (NLP) techniques to perform sentiment analysis. All the knowledge you gain will help you discover key patterns and trends in data using real-world examples. In addition to this, you will learn how to handle data of varying complexity to perform efficient data analysis using modern Python libraries. | |
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Datensatz im Suchindex
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| author | Wintjen, Marc |
| author_GND | (DE-588)1225432626 |
| author_facet | Wintjen, Marc |
| author_role | aut |
| author_sort | Wintjen, Marc |
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| bvnumber | BV048599507 |
| classification_rvk | ST 250 |
| contents | Fundamentals of data analysis Overview of Python and Installation of Jupyter notebook Getting Started with NumPy Creating your first Pandas DataFrame Gathering and Loading Data in Python Visualizing and working with time series data Exploring Cleaning, Refining and Blending Datasets Understanding Joins, Relationships and Data Aggregates Plotting, Visualization and Storytelling Exploring Text Data and Unstructured Data Practical Sentiment Analysis Discovering Patterns in Data and providing insights |
| ctrlnum | (OCoLC)1354308817 (DE-599)BVBBV048599507 |
| discipline | Informatik |
| discipline_str_mv | Informatik |
| edition | 1.Auflage |
| format | Book |
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| id | DE-604.BV048599507 |
| illustrated | Illustrated |
| index_date | 2024-07-03T21:09:34Z |
| indexdate | 2024-07-10T09:42:36Z |
| institution | BVB |
| isbn | 9781838825096 9781838826031 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-033975064 |
| oclc_num | 1354308817 |
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| owner_facet | DE-188 |
| physical | V, 296 Seiten Illustrationen, Diagramme (teilweise farbig) |
| publishDate | 2020 |
| publishDateSearch | 2020 |
| publishDateSort | 2020 |
| publisher | Packt |
| record_format | marc |
| spelling | Wintjen, Marc Verfasser (DE-588)1225432626 aut Practical data analysis using Jupyter Notebook learn how to speak the language of data by extracting useful and actionable insights using Python Marc Wintjen 1.Auflage Birmingham ; Mumbai Packt 2020 V, 296 Seiten Illustrationen, Diagramme (teilweise farbig) txt rdacontent n rdamedia nc rdacarrier Fundamentals of data analysis Overview of Python and Installation of Jupyter notebook Getting Started with NumPy Creating your first Pandas DataFrame Gathering and Loading Data in Python Visualizing and working with time series data Exploring Cleaning, Refining and Blending Datasets Understanding Joins, Relationships and Data Aggregates Plotting, Visualization and Storytelling Exploring Text Data and Unstructured Data Practical Sentiment Analysis Discovering Patterns in Data and providing insights Data literacy is the ability to read, analyze, work with, and argue using data. Data analysis is the process of cleaning and modeling your data to discover useful information. This book combines these two concepts by sharing proven techniques and hands-on examples so that you can learn how to communicate effectively using data. After introducing you to the basics of data analysis using Jupyter Notebook and Python, the book will take you through the fundamentals of data. Packed with practical examples, this guide will teach you how to clean, wrangle, analyze, and visualize data to gain useful insights, and you'll discover how to answer questions using data with easy-to-follow steps. Later chapters teach you about storytelling with data using charts, such as histograms and scatter plots. As you advance, you'll understand how to work with unstructured data using natural language processing (NLP) techniques to perform sentiment analysis. All the knowledge you gain will help you discover key patterns and trends in data using real-world examples. In addition to this, you will learn how to handle data of varying complexity to perform efficient data analysis using modern Python libraries. Python Programmiersprache (DE-588)4434275-5 gnd rswk-swf Datenanalyse (DE-588)4123037-1 gnd rswk-swf Jupyter Notebook Datenanalyse (DE-588)4123037-1 s Python Programmiersprache (DE-588)4434275-5 s DE-604 Erscheint auch als Druck-Ausgabe 978-1-83882-603-1 |
| spellingShingle | Wintjen, Marc Practical data analysis using Jupyter Notebook learn how to speak the language of data by extracting useful and actionable insights using Python Fundamentals of data analysis Overview of Python and Installation of Jupyter notebook Getting Started with NumPy Creating your first Pandas DataFrame Gathering and Loading Data in Python Visualizing and working with time series data Exploring Cleaning, Refining and Blending Datasets Understanding Joins, Relationships and Data Aggregates Plotting, Visualization and Storytelling Exploring Text Data and Unstructured Data Practical Sentiment Analysis Discovering Patterns in Data and providing insights Python Programmiersprache (DE-588)4434275-5 gnd Datenanalyse (DE-588)4123037-1 gnd |
| subject_GND | (DE-588)4434275-5 (DE-588)4123037-1 |
| title | Practical data analysis using Jupyter Notebook learn how to speak the language of data by extracting useful and actionable insights using Python |
| title_auth | Practical data analysis using Jupyter Notebook learn how to speak the language of data by extracting useful and actionable insights using Python |
| title_exact_search | Practical data analysis using Jupyter Notebook learn how to speak the language of data by extracting useful and actionable insights using Python |
| title_exact_search_txtP | Practical data analysis using Jupyter Notebook learn how to speak the language of data by extracting useful and actionable insights using Python |
| title_full | Practical data analysis using Jupyter Notebook learn how to speak the language of data by extracting useful and actionable insights using Python Marc Wintjen |
| title_fullStr | Practical data analysis using Jupyter Notebook learn how to speak the language of data by extracting useful and actionable insights using Python Marc Wintjen |
| title_full_unstemmed | Practical data analysis using Jupyter Notebook learn how to speak the language of data by extracting useful and actionable insights using Python Marc Wintjen |
| title_short | Practical data analysis using Jupyter Notebook |
| title_sort | practical data analysis using jupyter notebook learn how to speak the language of data by extracting useful and actionable insights using python |
| title_sub | learn how to speak the language of data by extracting useful and actionable insights using Python |
| topic | Python Programmiersprache (DE-588)4434275-5 gnd Datenanalyse (DE-588)4123037-1 gnd |
| topic_facet | Python Programmiersprache Datenanalyse |
| work_keys_str_mv | AT wintjenmarc practicaldataanalysisusingjupyternotebooklearnhowtospeakthelanguageofdatabyextractingusefulandactionableinsightsusingpython |