Thoughtful data science: a programmer's toolset for data analysis and artificial intelligence with Python, Jupyter Notebook, and PixieDust
Cover -- Copyright -- Packt upsell -- Contributors -- Table of Contents -- Preface -- Chapter 1 - Perspectives on Data Science from a Developer -- What is data science -- Is data science here to stay? -- Why is data science on the rise? -- What does that have to do with developers? -- Putting these...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Birmingham ; Mumbai
Packt
July 2018
|
| Schlagworte: | |
| Zusammenfassung: | Cover -- Copyright -- Packt upsell -- Contributors -- Table of Contents -- Preface -- Chapter 1 - Perspectives on Data Science from a Developer -- What is data science -- Is data science here to stay? -- Why is data science on the rise? -- What does that have to do with developers? -- Putting these concepts into practice -- Deep diving into a concrete example -- Data pipeline blueprint -- What kind of skills are required to become a data scientist? -- IBM Watson DeepQA -- Back to our sentiment analysis of Twitter hashtags project -- Lessons learned from building our first enterprise-ready data pipeline -- Data science strategy -- Jupyter Notebooks at the center of our strategy -- Why are Notebooks so popular? -- Summary -- Chapter 2 - Data Science at Scale with Jupyter Notebooks and PixieDust -- Why choose Python? -- Introducing PixieDust -- SampleData - a simple API for loading data -- Wrangling data with pixiedust_rosie -- Display - a simple interactive API for data visualization -- Filtering -- Bridging the gap between developers and data scientists with PixieApps -- Architecture for operationalizing data science analytics -- Summary -- Chapter 3 - PixieApp under the Hood -- Anatomy of a PixieApp -- Routes -- Generating requests to routes -- A GitHub project tracking sample application -- Displaying the search results in a table -- Invoking the PixieDust display() API using pd_entity attribute -- Invoking arbitrary Python code with pd_script -- Making the application more responsive with pd_refresh -- Creating reusable widgets -- Summary -- Chapter 4 - Deploying PixieApps to the Web with the PixieGateway Server -- Overview of Kubernetes -- Installing and configuring the PixieGateway server -- PixieGateway server configuration -- PixieGateway architecture -- Publishing an application -- Encoding state in the PixieApp URL Sharing charts by publishing them as web pages -- PixieGateway admin console -- Python Console -- Displaying warmup and run code for a PixieApp -- Summary -- Chapter 5 - Best Practices and Advanced PixieDust Concepts -- Use @captureOutput decorator to integrate the output of third-party Python libraries -- Create a word cloud image with @captureOutput -- Increase modularity and code reuse -- Creating a widget with pd_widget -- PixieDust support of streaming data -- Adding streaming capabilities to your PixieApp -- Adding dashboard drill-downs with PixieApp events -- Extending PixieDust visualizations -- Debugging -- Debugging on the Jupyter Notebook using pdb -- Visual debugging with PixieDebugger -- Debugging PixieApp routes with PixieDebugger -- Troubleshooting issues using PixieDust logging -- Client-side debugging -- Run Node.js inside a Python Notebook -- Summary -- Chapter 6 - Image Recognition with TensorFlow -- What is machine learning? -- What is deep learning? -- Getting started with TensorFlow -- Simple classification with DNNClassifier -- Image recognition sample application -- Part 1 - Load the pretrained MobileNet model -- Part 2 - Create a PixieApp for our image recognition sample application -- Part 3 - Integrate the TensorBoard graph visualization -- Part 4 - Retrain the model with custom training data -- Summary -- Chapter 7 - Big Data Twitter Sentiment Analysis -- Getting started with Apache Spark -- Apache Spark architecture -- Configuring Notebooks to work with Spark -- Twitter sentiment analysis application -- Part 1 - Acquiring the data with Spark Structured Streaming -- Architecture diagram for the data pipeline -- Authentication with Twitter -- Creating the Twitter stream -- Creating a Spark Streaming DataFrame -- Creating and running a structured query -- Monitoring active streaming queries Creating a batch DataFrame from the Parquet files -- Part 2 - Enriching the data with sentiment and most relevant extracted entity -- Getting started with the IBM Watson Natural Language Understanding service -- Part 3 - Creating a real-time dashboard PixieApp -- Refactoring the analytics into their own methods -- Creating the PixieApp -- Part 4 - Adding scalability with Apache Kafka and IBM Streams Designer -- Streaming the raw tweets to Kafka -- Enriching the tweets data with the Streaming Analytics service -- Creating a Spark Streaming DataFrame with a Kafka input source -- Summary -- Chapter 8 - Financial Time Series Analysis and Forecasting -- Getting started with NumPy -- Creating a NumPy array -- Operations on ndarray -- Selections on NumPy arrays -- Broadcasting -- Statistical exploration of time series -- Hypothetical investment -- Autocorrelation function (ACF) and partial autocorrelation function (PACF) -- Putting it all together with the StockExplorer PixieApp -- BaseSubApp - base class for all the child PixieApps -- StockExploreSubApp - first child PixieApp -- MovingAverageSubApp - second child PixieApp -- AutoCorrelationSubApp - third child PixieApp -- Time series forecasting using the ARIMA model -- Build an ARIMA model for the MSFT stock time series -- StockExplorer PixieApp Part 2 - add time series forecasting using the ARIMA model -- Summary -- Chapter 9 - US Domestic Flight Data Analysis Using Graphs -- Introduction to graphs -- Graph representations -- Graph algorithms -- Graph and big data -- Getting started with the networkx graph library -- Creating a graph -- Visualizing a graph -- Part 1 - Loading the US domestic flight data into a graph -- Graph centrality -- Part 2 - Creating the USFlightsAnalysis PixieApp -- Part 3 - Adding data exploration to the USFlightsAnalysis PixieApp |
| Beschreibung: | xvi, 467 Seiten Illustrationen, Diagramme |
| ISBN: | 9781788839969 |
Internformat
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| 520 | 3 | |a Cover -- Copyright -- Packt upsell -- Contributors -- Table of Contents -- Preface -- Chapter 1 - Perspectives on Data Science from a Developer -- What is data science -- Is data science here to stay? -- Why is data science on the rise? -- What does that have to do with developers? -- Putting these concepts into practice -- Deep diving into a concrete example -- Data pipeline blueprint -- What kind of skills are required to become a data scientist? -- IBM Watson DeepQA -- Back to our sentiment analysis of Twitter hashtags project -- Lessons learned from building our first enterprise-ready data pipeline -- Data science strategy -- Jupyter Notebooks at the center of our strategy -- Why are Notebooks so popular? -- Summary -- Chapter 2 - Data Science at Scale with Jupyter Notebooks and PixieDust -- Why choose Python? -- Introducing PixieDust -- SampleData - a simple API for loading data -- Wrangling data with pixiedust_rosie -- Display - a simple interactive API for data visualization -- Filtering -- Bridging the gap between developers and data scientists with PixieApps -- Architecture for operationalizing data science analytics -- Summary -- Chapter 3 - PixieApp under the Hood -- Anatomy of a PixieApp -- Routes -- Generating requests to routes -- A GitHub project tracking sample application -- Displaying the search results in a table -- Invoking the PixieDust display() API using pd_entity attribute -- Invoking arbitrary Python code with pd_script -- Making the application more responsive with pd_refresh -- Creating reusable widgets -- Summary -- Chapter 4 - Deploying PixieApps to the Web with the PixieGateway Server -- Overview of Kubernetes -- Installing and configuring the PixieGateway server -- PixieGateway server configuration -- PixieGateway architecture -- Publishing an application -- Encoding state in the PixieApp URL | |
| 520 | 3 | |a Sharing charts by publishing them as web pages -- PixieGateway admin console -- Python Console -- Displaying warmup and run code for a PixieApp -- Summary -- Chapter 5 - Best Practices and Advanced PixieDust Concepts -- Use @captureOutput decorator to integrate the output of third-party Python libraries -- Create a word cloud image with @captureOutput -- Increase modularity and code reuse -- Creating a widget with pd_widget -- PixieDust support of streaming data -- Adding streaming capabilities to your PixieApp -- Adding dashboard drill-downs with PixieApp events -- Extending PixieDust visualizations -- Debugging -- Debugging on the Jupyter Notebook using pdb -- Visual debugging with PixieDebugger -- Debugging PixieApp routes with PixieDebugger -- Troubleshooting issues using PixieDust logging -- Client-side debugging -- Run Node.js inside a Python Notebook -- Summary -- Chapter 6 - Image Recognition with TensorFlow -- What is machine learning? -- What is deep learning? -- Getting started with TensorFlow -- Simple classification with DNNClassifier -- Image recognition sample application -- Part 1 - Load the pretrained MobileNet model -- Part 2 - Create a PixieApp for our image recognition sample application -- Part 3 - Integrate the TensorBoard graph visualization -- Part 4 - Retrain the model with custom training data -- Summary -- Chapter 7 - Big Data Twitter Sentiment Analysis -- Getting started with Apache Spark -- Apache Spark architecture -- Configuring Notebooks to work with Spark -- Twitter sentiment analysis application -- Part 1 - Acquiring the data with Spark Structured Streaming -- Architecture diagram for the data pipeline -- Authentication with Twitter -- Creating the Twitter stream -- Creating a Spark Streaming DataFrame -- Creating and running a structured query -- Monitoring active streaming queries | |
| 520 | 3 | |a Creating a batch DataFrame from the Parquet files -- Part 2 - Enriching the data with sentiment and most relevant extracted entity -- Getting started with the IBM Watson Natural Language Understanding service -- Part 3 - Creating a real-time dashboard PixieApp -- Refactoring the analytics into their own methods -- Creating the PixieApp -- Part 4 - Adding scalability with Apache Kafka and IBM Streams Designer -- Streaming the raw tweets to Kafka -- Enriching the tweets data with the Streaming Analytics service -- Creating a Spark Streaming DataFrame with a Kafka input source -- Summary -- Chapter 8 - Financial Time Series Analysis and Forecasting -- Getting started with NumPy -- Creating a NumPy array -- Operations on ndarray -- Selections on NumPy arrays -- Broadcasting -- Statistical exploration of time series -- Hypothetical investment -- Autocorrelation function (ACF) and partial autocorrelation function (PACF) -- Putting it all together with the StockExplorer PixieApp -- BaseSubApp - base class for all the child PixieApps -- StockExploreSubApp - first child PixieApp -- MovingAverageSubApp - second child PixieApp -- AutoCorrelationSubApp - third child PixieApp -- Time series forecasting using the ARIMA model -- Build an ARIMA model for the MSFT stock time series -- StockExplorer PixieApp Part 2 - add time series forecasting using the ARIMA model -- Summary -- Chapter 9 - US Domestic Flight Data Analysis Using Graphs -- Introduction to graphs -- Graph representations -- Graph algorithms -- Graph and big data -- Getting started with the networkx graph library -- Creating a graph -- Visualizing a graph -- Part 1 - Loading the US domestic flight data into a graph -- Graph centrality -- Part 2 - Creating the USFlightsAnalysis PixieApp -- Part 3 - Adding data exploration to the USFlightsAnalysis PixieApp | |
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| id | DE-604.BV045422162 |
| illustrated | Illustrated |
| indexdate | 2024-07-10T08:17:43Z |
| institution | BVB |
| isbn | 9781788839969 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-030808030 |
| oclc_num | 1088316461 |
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| owner | DE-706 |
| owner_facet | DE-706 |
| physical | xvi, 467 Seiten Illustrationen, Diagramme |
| publishDate | 2018 |
| publishDateSearch | 2018 |
| publishDateSort | 2018 |
| publisher | Packt |
| record_format | marc |
| spelling | Taieb, David ca. 20./21. Jh. Verfasser (DE-588)1178741656 aut Thoughtful data science a programmer's toolset for data analysis and artificial intelligence with Python, Jupyter Notebook, and PixieDust David Taieb Birmingham ; Mumbai Packt July 2018 xvi, 467 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Cover -- Copyright -- Packt upsell -- Contributors -- Table of Contents -- Preface -- Chapter 1 - Perspectives on Data Science from a Developer -- What is data science -- Is data science here to stay? -- Why is data science on the rise? -- What does that have to do with developers? -- Putting these concepts into practice -- Deep diving into a concrete example -- Data pipeline blueprint -- What kind of skills are required to become a data scientist? -- IBM Watson DeepQA -- Back to our sentiment analysis of Twitter hashtags project -- Lessons learned from building our first enterprise-ready data pipeline -- Data science strategy -- Jupyter Notebooks at the center of our strategy -- Why are Notebooks so popular? -- Summary -- Chapter 2 - Data Science at Scale with Jupyter Notebooks and PixieDust -- Why choose Python? -- Introducing PixieDust -- SampleData - a simple API for loading data -- Wrangling data with pixiedust_rosie -- Display - a simple interactive API for data visualization -- Filtering -- Bridging the gap between developers and data scientists with PixieApps -- Architecture for operationalizing data science analytics -- Summary -- Chapter 3 - PixieApp under the Hood -- Anatomy of a PixieApp -- Routes -- Generating requests to routes -- A GitHub project tracking sample application -- Displaying the search results in a table -- Invoking the PixieDust display() API using pd_entity attribute -- Invoking arbitrary Python code with pd_script -- Making the application more responsive with pd_refresh -- Creating reusable widgets -- Summary -- Chapter 4 - Deploying PixieApps to the Web with the PixieGateway Server -- Overview of Kubernetes -- Installing and configuring the PixieGateway server -- PixieGateway server configuration -- PixieGateway architecture -- Publishing an application -- Encoding state in the PixieApp URL Sharing charts by publishing them as web pages -- PixieGateway admin console -- Python Console -- Displaying warmup and run code for a PixieApp -- Summary -- Chapter 5 - Best Practices and Advanced PixieDust Concepts -- Use @captureOutput decorator to integrate the output of third-party Python libraries -- Create a word cloud image with @captureOutput -- Increase modularity and code reuse -- Creating a widget with pd_widget -- PixieDust support of streaming data -- Adding streaming capabilities to your PixieApp -- Adding dashboard drill-downs with PixieApp events -- Extending PixieDust visualizations -- Debugging -- Debugging on the Jupyter Notebook using pdb -- Visual debugging with PixieDebugger -- Debugging PixieApp routes with PixieDebugger -- Troubleshooting issues using PixieDust logging -- Client-side debugging -- Run Node.js inside a Python Notebook -- Summary -- Chapter 6 - Image Recognition with TensorFlow -- What is machine learning? -- What is deep learning? -- Getting started with TensorFlow -- Simple classification with DNNClassifier -- Image recognition sample application -- Part 1 - Load the pretrained MobileNet model -- Part 2 - Create a PixieApp for our image recognition sample application -- Part 3 - Integrate the TensorBoard graph visualization -- Part 4 - Retrain the model with custom training data -- Summary -- Chapter 7 - Big Data Twitter Sentiment Analysis -- Getting started with Apache Spark -- Apache Spark architecture -- Configuring Notebooks to work with Spark -- Twitter sentiment analysis application -- Part 1 - Acquiring the data with Spark Structured Streaming -- Architecture diagram for the data pipeline -- Authentication with Twitter -- Creating the Twitter stream -- Creating a Spark Streaming DataFrame -- Creating and running a structured query -- Monitoring active streaming queries Creating a batch DataFrame from the Parquet files -- Part 2 - Enriching the data with sentiment and most relevant extracted entity -- Getting started with the IBM Watson Natural Language Understanding service -- Part 3 - Creating a real-time dashboard PixieApp -- Refactoring the analytics into their own methods -- Creating the PixieApp -- Part 4 - Adding scalability with Apache Kafka and IBM Streams Designer -- Streaming the raw tweets to Kafka -- Enriching the tweets data with the Streaming Analytics service -- Creating a Spark Streaming DataFrame with a Kafka input source -- Summary -- Chapter 8 - Financial Time Series Analysis and Forecasting -- Getting started with NumPy -- Creating a NumPy array -- Operations on ndarray -- Selections on NumPy arrays -- Broadcasting -- Statistical exploration of time series -- Hypothetical investment -- Autocorrelation function (ACF) and partial autocorrelation function (PACF) -- Putting it all together with the StockExplorer PixieApp -- BaseSubApp - base class for all the child PixieApps -- StockExploreSubApp - first child PixieApp -- MovingAverageSubApp - second child PixieApp -- AutoCorrelationSubApp - third child PixieApp -- Time series forecasting using the ARIMA model -- Build an ARIMA model for the MSFT stock time series -- StockExplorer PixieApp Part 2 - add time series forecasting using the ARIMA model -- Summary -- Chapter 9 - US Domestic Flight Data Analysis Using Graphs -- Introduction to graphs -- Graph representations -- Graph algorithms -- Graph and big data -- Getting started with the networkx graph library -- Creating a graph -- Visualizing a graph -- Part 1 - Loading the US domestic flight data into a graph -- Graph centrality -- Part 2 - Creating the USFlightsAnalysis PixieApp -- Part 3 - Adding data exploration to the USFlightsAnalysis PixieApp Data Science (DE-588)1140936166 gnd rswk-swf Datenanalyse (DE-588)4123037-1 gnd rswk-swf Künstliche Intelligenz (DE-588)4033447-8 gnd rswk-swf Data Science (DE-588)1140936166 s Datenanalyse (DE-588)4123037-1 s Künstliche Intelligenz (DE-588)4033447-8 s DE-604 Erscheint auch als Online-Ausgabe 978-1-78883-043-0 |
| spellingShingle | Taieb, David ca. 20./21. Jh Thoughtful data science a programmer's toolset for data analysis and artificial intelligence with Python, Jupyter Notebook, and PixieDust Data Science (DE-588)1140936166 gnd Datenanalyse (DE-588)4123037-1 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd |
| subject_GND | (DE-588)1140936166 (DE-588)4123037-1 (DE-588)4033447-8 |
| title | Thoughtful data science a programmer's toolset for data analysis and artificial intelligence with Python, Jupyter Notebook, and PixieDust |
| title_auth | Thoughtful data science a programmer's toolset for data analysis and artificial intelligence with Python, Jupyter Notebook, and PixieDust |
| title_exact_search | Thoughtful data science a programmer's toolset for data analysis and artificial intelligence with Python, Jupyter Notebook, and PixieDust |
| title_full | Thoughtful data science a programmer's toolset for data analysis and artificial intelligence with Python, Jupyter Notebook, and PixieDust David Taieb |
| title_fullStr | Thoughtful data science a programmer's toolset for data analysis and artificial intelligence with Python, Jupyter Notebook, and PixieDust David Taieb |
| title_full_unstemmed | Thoughtful data science a programmer's toolset for data analysis and artificial intelligence with Python, Jupyter Notebook, and PixieDust David Taieb |
| title_short | Thoughtful data science |
| title_sort | thoughtful data science a programmer s toolset for data analysis and artificial intelligence with python jupyter notebook and pixiedust |
| title_sub | a programmer's toolset for data analysis and artificial intelligence with Python, Jupyter Notebook, and PixieDust |
| topic | Data Science (DE-588)1140936166 gnd Datenanalyse (DE-588)4123037-1 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd |
| topic_facet | Data Science Datenanalyse Künstliche Intelligenz |
| work_keys_str_mv | AT taiebdavid thoughtfuldatascienceaprogrammerstoolsetfordataanalysisandartificialintelligencewithpythonjupyternotebookandpixiedust |