Index
A
- access tokens
- reference link / Display – a simple interactive API for data visualization
- adjacency list / Graph representations
- adjacency matrix / Graph representations
- AlphaZero
- reference link / Forward thinking – what to expect for AI and data science
- Anaconda
- reference link / Introducing PixieDust
- Apache Hadoop
- Apache Kafka
- reference link / Back to our sentiment analysis of Twitter hashtags project
- Apache Spark
- reference link / Back to our sentiment analysis of Twitter hashtags project
- about / Getting started with Apache Spark
- URL / Getting started with Apache Spark
- architecture / Apache Spark architecture
- notebooks, configuring / Configuring Notebooks to work with Spark
- Apache Spark, components
- Spark SQL / Apache Spark architecture
- Spark Streaming / Apache Spark architecture
- MLlib / Apache Spark architecture
- GraphX / Apache Spark architecture
- Apache Spark DataFrame
- reference link / Introducing PixieDust
- Apache Spark Structured Streaming
- ARIMA model
- used, for forecasting time series / Time series forecasting using the ARIMA model, StockExplorer PixieApp Part 2 – add time series forecasting using the ARIMA model
- building, for MSFT stock time series / Build an ARIMA model for the MSFT stock time series
- creating, for predicting flight delays / Part 4 – Creating an ARIMA model for predicting flight delays
- Artificial Intelligence (AI) / Is data science here to stay?
- autocorrelation function (ACF) / Autocorrelation function (ACF) and partial autocorrelation function (PACF)
- AutoCorrelationSubApp / AutoCorrelationSubApp – third child PixieApp
- Autoregressive Integrated Moving Average (ARIMA) / AutoCorrelationSubApp – third child PixieApp
B
- backpropagation
- URL / What is deep learning?
- BaseSubApp / BaseSubApp – base class for all the child PixieApps
- BI (Business Intelligence) / Introducing PixieDust
- big data / Graph and big data
- Bokeh
- reference link / Display – a simple interactive API for data visualization
- Bootstrap CSS framework
- reference link / A GitHub project tracking sample application
- Breath First Search (BFS) / Graph algorithms
- broadcasting / Broadcasting
- Brunel
- reference link / Display – a simple interactive API for data visualization
- bulk synchronous parallel (BSP) / Graph and big data
- Business Intelligence (BI) / Is data science here to stay?
C
- @captureOutput decorator
- used, for integrating third-party Python libraries output / Use @captureOutput decorator to integrate the output of third-party Python libraries
- used, for creating word cloud image / Create a word cloud image with @captureOutput
- client-side debugging / Client-side debugging
- cognitive computing / What is deep learning?
- collaborative filtering (CF)
- reference link / Data pipeline blueprint
- connected components / Graph algorithms
- convolutional neural network (CNN) / Image recognition sample application
- cost function / What is deep learning?
- Coursera
- reference link / Forward thinking – what to expect for AI and data science
- cross-entropy
- reference link / What is deep learning?
D
- D3
- Dask
- reference link / Jupyter Notebooks at the center of our strategy
- URL / Getting started with Apache Spark
- data
- loading, into Notebook with SampleData / SampleData – a simple API for loading data
- wrangling, with pixiedust_rosie / Wrangling data with pixiedust_rosie
- enriching, with sentiment / Part 2 – Enriching the data with sentiment and most relevant extracted entity
- enriching, with extracted entity / Part 2 – Enriching the data with sentiment and most relevant extracted entity
- DataBricks
- reference link / Why are Notebooks so popular?
- data exploration
- adding, to USFlightsAnalysis PixieApp / Part 3 – Adding data exploration to the USFlightsAnalysis PixieApp
- data pipeline
- data science
- about / What is data science, Is data science here to stay?
- advantages / Why is data science on the rise?
- using / What does that have to do with developers?
- implementing / Putting these concepts into practice
- example / Deep diving into a concrete example, Data pipeline blueprint
- strategy / Data science strategy
- future / Forward thinking – what to expect for AI and data science
- data science analytics
- architecture, operationalizing / Architecture for operationalizing data science analytics
- data scientist
- data visualization / Display – a simple interactive API for data visualization
- data wrangling / SampleData – a simple API for loading data
- debugging
- about / Debugging
- deep learning / What is deep learning?
- Depth First Search (DFS) / Graph algorithms
- Docker command line
- reference link / Installing and configuring the PixieGateway server
- Docker containers
- URL / Overview of Kubernetes
F
- feature, NumPy
- broadcasting / Broadcasting
- features / What is machine learning?
- feed-forward / What is deep learning?
- filtering types, visualized data
- filters
- reference link / A GitHub project tracking sample application
- font icons
- reference link / A GitHub project tracking sample application
G
- Giraph
- reference link / Graph and big data
- Google Map
- reference link / Display – a simple interactive API for data visualization
- graph
- about / Introduction to graphs, Graph and big data
- representing / Graph representations
- US domestic flight data, loading / Part 1 – Loading the US domestic flight data into a graph
- graph, representing / Graph representations
- adjacency matrix / Graph representations
- adjacency list / Graph representations
- graph algorithms
- about / Graph algorithms
- search / Graph algorithms
- connected component / Graph algorithms
- strongly connected component / Graph algorithms
- centrality / Graph algorithms
- graph algorithms, centrality
- degree / Graph algorithms
- PageRank / Graph algorithms
- closeness / Graph algorithms
- shortest path betweenness / Graph algorithms
- graph algorithms, search
- Breath First Search (BFS) / Graph algorithms
- Depth First Search (DFS) / Graph algorithms
- graph centrality / Graph centrality
- GraphX
- reference link / Graph and big data
H
- hand-off pattern / Bridging the gap between developers and data scientists with PixieApps
- hidden layers / What is deep learning?
- hypothetical investment / Hypothetical investment
I
- IBM Watson DeepQA / IBM Watson DeepQA
- architecture / IBM Watson DeepQA
- IBM Watson Natural Language Understanding (NLU)
- IBM Watson Natural Language Understanding (NLU)
- IEEE Spectrum
- reference link / Why choose Python?
- ImageNet Large Scale Visual Recognition Challenge (ILSVRC) / Part 1 – Load the pretrained MobileNet model
- image recognition sample application / Image recognition sample application
- PixieApp, creating / Part 2 – Create a PixieApp for our image recognition sample application
- TensorBoard graph visualization, integrating / Part 3 – Integrate the TensorBoard graph visualization
- model, retraining with custom training data / Part 4 – Retrain the model with custom training data
- image recognition sample application, requisites
- pretrained MobileNet model, loading / Part 1 – Load the pretrained MobileNet model
- Indri
- reference link / IBM Watson DeepQA
- interactive API
- using, for data visualization / Display – a simple interactive API for data visualization
- International Air Transport Association (IATA) / Part 1 – Loading the US domestic flight data into a graph
J
- Java Authentication and Authorization Service (JAAS) / Creating a Spark Streaming DataFrame with a Kafka input source
- JavaScript Date Object
- reference link / Run Node.js inside a Python Notebook
- JinJa2
- jQuery JS framework
- reference link / A GitHub project tracking sample application
- jQuery UI
- reference link / MovingAverageSubApp – second child PixieApp
- Jupyter
- reference link / Back to our sentiment analysis of Twitter hashtags project
- messaging, reference link / Architecture for operationalizing data science analytics
- JupyterHub
- reference link / Why are Notebooks so popular?
- Jupyter Kernel Gateway options
- reference link / PixieGateway server configuration
- Jupyter Notebook
- warmup code / PixieGateway architecture
- run code / PixieGateway architecture
- debugging, with pdb / Debugging on the Jupyter Notebook using pdb
- Jupyter Notebooks
- reference link / Run Node.js inside a Python Notebook
K
- Key Performance Indicators (KPIs) / Data pipeline blueprint
- kube API Server / Overview of Kubernetes
- kubelet / Overview of Kubernetes
- Kubernetes
- URL / Overview of Kubernetes
- overview / Overview of Kubernetes
- PixieGateway server, installing / Installing and configuring the PixieGateway server
- PixieGateway server, configuring / Installing and configuring the PixieGateway server, PixieGateway server configuration
- Kubernetes cluster
- URL / Overview of Kubernetes
- Kubernetes Master Node / Overview of Kubernetes
L
- LaTeX project
- reference link / Jupyter Notebooks at the center of our strategy
- linear classifiers
- reference link / Getting started with TensorFlow
- line magics, magic commands
- reference link / Debugging on the Jupyter Notebook using pdb
M
- machine learning
- about / What is machine learning?
- supervised / What is machine learning?
- unsupervised / What is machine learning?
- magic commands
- reference link / Debugging on the Jupyter Notebook using pdb
- about / Debugging on the Jupyter Notebook using pdb
- Mapbox
- reference link / Display – a simple interactive API for data visualization
- Markdown
- reference link / Create a word cloud image with @captureOutput
- Matplotlib
- reference link / Display – a simple interactive API for data visualization
- mean-square error
- reference link / What is deep learning?
- mean square error (MSE) / Getting started with TensorFlow
- Minimum Viable Product (MVP) / Image recognition sample application
- model / What is machine learning?
- MOOC (Massive Open Online Courses) / Forward thinking – what to expect for AI and data science
- MOOCs (Massive Open Online Courses)
- advantages / Why are Notebooks so popular?
- MovingAverageSubApp / MovingAverageSubApp – second child PixieApp
- MSFT stock time series
- ARIMA model, building / Build an ARIMA model for the MSFT stock time series
N
- natural language processing (NLP) / Why is data science on the rise?, Part 2 – Enriching the data with sentiment and most relevant extracted entity
- ndarray
- about / Getting started with NumPy
- operations / Operations on ndarray
- networkx graph library
- initiating / Getting started with the networkx graph library
- graph, creating / Creating a graph
- graph, visualizing / Visualizing a graph
- neural networks / What is deep learning?
- neurons / What is deep learning?
- New York Stock Exchange (NYSE) / Statistical exploration of time series
- Node.js
- reference link / Bridging the gap between developers and data scientists with PixieApps
- executing, in Python Notebook / Run Node.js inside a Python Notebook
- Notebooks
- about / Jupyter Notebooks at the center of our strategy
- advantages / Why are Notebooks so popular?
- NumPy
- initiating / Getting started with NumPy
- key capabilities / Getting started with NumPy
- NumPy array
- creating / Creating a NumPy array
- selections / Selections on NumPy arrays
O
- OAuth 2.0 protocol
- URL / Authentication with Twitter
- consumer key / Authentication with Twitter
- consumer secret / Authentication with Twitter
- access token / Authentication with Twitter
- access token secret / Authentication with Twitter
- OpenData
- reference link / SampleData – a simple API for loading data
P
- pandas
- reference link / Introducing PixieDust
- Parquet format
- reference link / Creating and running a structured query
- partial autocorrelation function (PACF) / Autocorrelation function (ACF) and partial autocorrelation function (PACF)
- pdb
- used, for debugging on Jupyter Notebook / Debugging on the Jupyter Notebook using pdb
- pd_app attribute
- used, for invoking PixieApp / Increase modularity and code reuse
- about / Increase modularity and code reuse
- pd_widget
- used, for creating widget / Creating a widget with pd_widget
- PixieApp
- modularity, increasing / Increase modularity and code reuse
- code, reusing / Increase modularity and code reuse
- invoking, with pd_app attribute / Increase modularity and code reuse
- widget, creating with pd_widget / Creating a widget with pd_widget
- streaming capabilities, adding / Adding streaming capabilities to your PixieApp
- real-time dashboard, creating / Part 3 – Creating a real-time dashboard PixieApp
- analytics, refactoring / Refactoring the analytics into their own methods
- creating / Creating the PixieApp
- PixieApp events
- used, for adding dashboard drill-downs / Adding dashboard drill-downs with PixieApp events
- Pixie Application (PixieApp)
- about / Anatomy of a PixieApp
- anatomy / Anatomy of a PixieApp
- routes / Routes
- class variables and methods / Routes
- method arguments / Routes
- local variables, to method / Routes
- requests, generating to routes / Generating requests to routes
- GitHub project, sample application / A GitHub project tracking sample application
- search results, displaying in table / Displaying the search results in a table
- PixieDust display() API invoking, pd_entity attribute used / Invoking the PixieDust display() API using pd_entity attribute
- arbitrary Python code, invoking with pd_script / Invoking arbitrary Python code with pd_script
- improving, with pd_refresh / Making the application more responsive with pd_refresh
- reusable widgets, creating / Creating reusable widgets
- PixieApp routes
- debugging, with PixieDebugger / Debugging PixieApp routes with PixieDebugger
- PixieApps
- PixieDebugger
- used, for visual debugging / Visual debugging with PixieDebugger
- used, for debugging PixieApp routes / Debugging PixieApp routes with PixieDebugger
- PixieDust / Introducing PixieDust
- reference link / Why are Notebooks so popular?
- data streaming / PixieDust support of streaming data
- PixieDust logging
- issues, troubleshooting / Troubleshooting issues using PixieDust logging
- PixieDust visualizations
- extending / Extending PixieDust visualizations
- pixiedust_rosie
- used, for data wrangling / Wrangling data with pixiedust_rosie
- PixieGateway
- reference link / Introducing PixieDust
- PixieGateway Docker image
- reference link / Installing and configuring the PixieGateway server
- PixieGateway server
- configuring / PixieGateway server configuration
- admin console credentials / PixieGateway server configuration
- storage connector / PixieGateway server configuration
- remote kernels / PixieGateway server configuration
- architecture / PixieGateway architecture
- application, publishing / Publishing an application
- state, encoding in PixieApp URL / Encoding state in the PixieApp URL
- charts sharing, by publishing as web pages / Sharing charts by publishing them as web pages
- admin console / PixieGateway admin console
- Python console / Python Console
- warmup and run code, displaying for PixieApp / Displaying warmup and run code for a PixieApp
- PixieGateway server, REST interface
- Jupyter Notebook Server / PixieGateway architecture
- browser client, used for executing PixieApp / PixieGateway architecture
- browser client, used for executing admin console / PixieGateway architecture
- Platform as a Service (PaaS) / Why is data science on the rise?
- Python
- advantages / Why choose Python?
- magic commands / Debugging on the Jupyter Notebook using pdb
- Python, logging facility
- reference link / Troubleshooting issues using PixieDust logging
- Python Debugger
- reference link / Debugging on the Jupyter Notebook using pdb
- Python decorators
- reference link / Anatomy of a PixieApp, BaseSubApp – base class for all the child PixieApps
- Python Notebook
- Node.js, executing / Run Node.js inside a Python Notebook
Q
- Question Answering (QA) / IBM Watson DeepQA
R
- RDD (Resilient Distributed Dataset) / Introducing PixieDust
- React
- reference link / Architecture for operationalizing data science analytics
- Rectified Linear Unit (ReLu) / What is deep learning?
- reduction functions / Operations on ndarray
- REPL (Read-Eval-Print-Loop) / Jupyter Notebooks at the center of our strategy
- REPL (Read-Eval-Print Loop)
- reference link / Run Node.js inside a Python Notebook
- Representational State Transfer (REST)
- Resilient Distributed Dataset (RDD) / Creating a Spark Streaming DataFrame
- Rosie Pattern Language (RPL) / Wrangling data with pixiedust_rosie
S
- SampleData
- used, for loading data into Notebook / SampleData – a simple API for loading data
- scalability
- adding, with Apache Kafka / Part 4 – Adding scalability with Apache Kafka and IBM Streams Designer
- adding, with IBM Streams Designer / Part 4 – Adding scalability with Apache Kafka and IBM Streams Designer
- scikit learn package
- reference link / Why choose Python?
- scopes, data science
- descriptive analytics / What is data science
- predictive analytics / What is data science
- prescriptive analytics / What is data science
- Seaborn
- reference link / Display – a simple interactive API for data visualization
- single-page application (SPA)
- about / Anatomy of a PixieApp
- reference link / Anatomy of a PixieApp
- Solr
- reference link / IBM Watson DeepQA
- Spark cluster
- spark-submit / Apache Spark architecture
- notebooks / Apache Spark architecture
- Spark Streaming DataFrame
- creating, with Kafka input source / Creating a Spark Streaming DataFrame with a Kafka input source
- Spark Structured Streaming
- data, acquiring / Part 1 – Acquiring the data with Spark Structured Streaming
- data pipeline, architecture diagram / Architecture diagram for the data pipeline
- Twitter, authentication / Authentication with Twitter
- Twitter stream, creating / Creating the Twitter stream
- Spark Streaming DataFrame, creating / Creating a Spark Streaming DataFrame
- structured query, creating / Creating and running a structured query
- structured query, executing / Creating and running a structured query
- active streaming queries, monitoring / Monitoring active streaming queries
- batch DataFrame, creating from Parquet files / Creating a batch DataFrame from the Parquet files
- statistical exploration
- of time series / Statistical exploration of time series
- StockExplorer PixieApp
- UI layout / Putting it all together with the StockExplorer PixieApp
- BaseSubApp / BaseSubApp – base class for all the child PixieApps
- StockExploreSubApp / StockExploreSubApp – first child PixieApp
- MovingAverageSubApp / MovingAverageSubApp – second child PixieApp
- AutoCorrelationSubApp / AutoCorrelationSubApp – third child PixieApp
- StockExploreSubApp / StockExploreSubApp – first child PixieApp
- strongly connected component / Graph algorithms
T
- TensorFlow
- initiating / Getting started with TensorFlow
- references / Getting started with TensorFlow
- classification, with DNNClassifier / Simple classification with DNNClassifier
- time series
- statistical exploration / Statistical exploration of time series
- forecasting, with ARIMA model / Time series forecasting using the ARIMA model, StockExplorer PixieApp Part 2 – add time series forecasting using the ARIMA model
- time series analysis / Autocorrelation function (ACF) and partial autocorrelation function (PACF)
- Tone Analyzer
- reference link / Back to our sentiment analysis of Twitter hashtags project
- Tornado
- reference link / Why choose Python?
- TurboGears
- reference link / Why choose Python?
- Tweepy
- tweets
- streaming, to Kafka / Streaming the raw tweets to Kafka
- tweets data
- enriching, with Streaming Analytics service / Enriching the tweets data with the Streaming Analytics service
- Twitter
- sentiment analysis / Back to our sentiment analysis of Twitter hashtags project
- Twitter sentiment analysis application / Twitter sentiment analysis application
- types, graph
- directed graphs / Introduction to graphs
- undirected graphs / Introduction to graphs
- types, magic commands
- line magics / Debugging on the Jupyter Notebook using pdb
- cell magics / Debugging on the Jupyter Notebook using pdb
U
- US domestic flight data
- loading, into graph / Part 1 – Loading the US domestic flight data into a graph
- user story / Part 3 – Creating a real-time dashboard PixieApp
- USFlightsAnalysis PixieApp
- creating / Part 2 – Creating the USFlightsAnalysis PixieApp
- data exploration, adding / Part 3 – Adding data exploration to the USFlightsAnalysis PixieApp
- utilities, PixieDust
- PackageManager / Introducing PixieDust
- Scala Bridge / Introducing PixieDust
- Spark Job Progress Monitor / Introducing PixieDust
V
- visual debugging
- with PixieDebugger / Visual debugging with PixieDebugger
- visualized data
- filtering / Filtering
W
- Watson Developer Cloud Python SDK
- Watson Studio
- reference link / Why are Notebooks so popular?
- word cloud image
- creating, with @captureOutput decorator / Create a word cloud image with @captureOutput