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Data Analysis with Python
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DataAnalysiswithPythonoffersamodernapproachtodataanalysissothatyoucanworkwiththelatestandmostpowerfulPythontools,AItechniques,andopensourcelibraries.IndustryexpertDavidTaiebshowsyouhowtobridgedatasciencewiththepowerofprogrammingandalgorithmsinPython.You'llbeworkingwithcomplexalgorithms,andcutting-edgeAIinyourdataanalysis.Learnhowtoanalyzedatawithhands-onexamplesusingPython-basedtoolsandJupyterNotebook.You'llfindtherightbalanceoftheoryandpractice,withextensivecodefilesthatyoucanintegraterightintoyourowndataprojects.Explorethepowerofthisapproachtodataanalysisbythenworkingwithitacrosskeyindustrycasestudies.Fourfascinatingandfullprojectsconnectyoutothemostcriticaldataanalysischallengesyou’relikelytomeetintoday.ThefirstoftheseisanimagerecognitionapplicationwithTensorFlow–embracingtheimportancetodayofAIinyourdataanalysis.Thesecondindustryprojectanalysessocialmediatrends,exploringbigdataissuesandAIapproachestonaturallanguageprocessing.Thethirdcasestudyisafinancialportfolioanalysisapplicationthatengagesyouwithtimeseriesanalysis-pivotaltomanydatascienceapplicationstoday.Thefourthindustryusecasedivesyouintographalgorithmsandthepowerofprogramminginmoderndatascience.You'llwrapupwithathoughtfullookatthefutureofdatascienceandhowitwillharnessthepowerofalgorithmsandartificialintelligence.
目錄(87章)
倒序
- 封面
- 版權頁
- Why subscribe?
- PacktPub.com
- Contributors
- About the author
- About the reviewers
- Packt is searching for authors like you
- Preface
- Why am I writing this book?
- Who this book is for
- What this book covers
- To get the most out of this book
- Get in touch
- Chapter 1. Programming and Data Science – A New Toolset
- 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
- Summary
- Chapter 2. Python and Jupyter Notebooks to Power your Data Analysis
- 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. Accelerate your Data Analysis with Python Libraries
- Anatomy of a PixieApp
- Summary
- Chapter 4. Publish your Data Analysis to the Web - the PixieApp Tool
- Overview of Kubernetes
- Installing and configuring the PixieGateway server
- Summary
- Chapter 5. Python and PixieDust Best Practices and Advanced Concepts
- Use @captureOutput decorator to integrate the output of third-party Python libraries
- Increase modularity and code reuse
- Run Node.js inside a Python Notebook
- Summary
- Chapter 6. Analytics Study: AI and Image Recognition with TensorFlow
- What is machine learning?
- What is deep learning?
- Getting started with TensorFlow
- Image recognition sample application
- Summary
- Chapter 7. Analytics Study: NLP and Big Data with Twitter Sentiment Analysis
- Getting started with Apache Spark
- Twitter sentiment analysis application
- Part 1 – Acquiring the data with Spark Structured Streaming
- Part 2 – Enriching the data with sentiment and most relevant extracted entity
- Part 3 – Creating a real-time dashboard PixieApp
- Part 4 – Adding scalability with Apache Kafka and IBM Streams Designer
- Summary
- Chapter 8. Analytics Study: Prediction - Financial Time Series Analysis and Forecasting
- Getting started with NumPy
- Statistical exploration of time series
- Putting it all together with the StockExplorer PixieApp
- Time series forecasting using the ARIMA model
- Summary
- Chapter 9. Analytics Study: Graph Algorithms - US Domestic Flight Data Analysis
- Introduction to graphs
- Getting started with the networkx graph library
- Part 1 – Loading the US domestic flight data into a graph
- Part 2 – Creating the USFlightsAnalysis PixieApp
- Part 3 – Adding data exploration to the USFlightsAnalysis PixieApp
- Part 4 – Creating an ARIMA model for predicting flight delays
- Summary
- Chapter 10. The Future of Data Analysis and Where to Develop your Skills
- Forward thinking – what to expect for AI and data science
- References
- Appendix A. PixieApp Quick-Reference
- Annotations
- Custom HTML attributes
- Methods
- Index 更新時間：2021-06-11 13:32:11
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