DeepQA (IBM): https://researcher.watson.ibm.com/researcher/view_group_subpage.php?id=2159
Deep parsing in Watson, McCord, Murdock, Boguraev: http://brenocon.com/watson_special_issue/03%20Deep%20parsing.pdf
Jupyter for Data Science, Dan Toomey, Packt Publishing: https://www.packtpub.com/big-data-and-business-intelligence/jupyter-data-science
PixieDust documentation: https://pixiedust.github.io/pixiedust/
The Visual Python Debugger for Jupyter Notebooks You've Always Wanted, David Taieb: https://medium.com/ibm-watson-data-lab/the-visual-python-debugger-for-jupyter-notebooks-youve-always-wanted-761713babc62
Share Your Jupyter Notebook Charts on the Web, David Taieb: https://medium.com/ibm-watson-data-lab/share-your-jupyter-notebook-charts-on-the-web-43e190df4adb
Deploy Your Analytics as Web Apps Using PixieDust's 1.1 Release, David Taieb: https://medium.com/ibm-watson-data-lab/deploy-your-analytics-as-web-apps-using-pixiedusts-1-1-release-d08067584a14
Kubernetes: https://kubernetes...
Data Analysis with Python
By :
Data Analysis with Python
By:
Overview of this book
Data Analysis with Python offers a modern approach to data analysis so that you can work with the latest and most powerful Python tools, AI techniques, and open source libraries. Industry expert David Taieb shows you how to bridge data science with the power of programming and algorithms in Python. You'll be working with complex algorithms, and cutting-edge AI in your data analysis. Learn how to analyze data with hands-on examples using Python-based tools and Jupyter Notebook. You'll find the right balance of theory and practice, with extensive code files that you can integrate right into your own data projects.
Explore the power of this approach to data analysis by then working with it across key industry case studies. Four fascinating and full projects connect you to the most critical data analysis challenges you’re likely to meet in today. The first of these is an image recognition application with TensorFlow – embracing the importance today of AI in your data analysis. The second industry project analyses social media trends, exploring big data issues and AI approaches to natural language processing. The third case study is a financial portfolio analysis application that engages you with time series analysis - pivotal to many data science applications today. The fourth industry use case dives you into graph algorithms and the power of programming in modern data science. You'll wrap up with a thoughtful look at the future of data science and how it will harness the power of algorithms and artificial intelligence.
Table of Contents (16 chapters)
Data Analysis with Python
Contributors
Preface
Other Books You May Enjoy
Free Chapter
Programming and Data Science – A New Toolset
Python and Jupyter Notebooks to Power your Data Analysis
Accelerate your Data Analysis with Python Libraries
Publish your Data Analysis to the Web - the PixieApp Tool
Python and PixieDust Best Practices and Advanced Concepts
Analytics Study: AI and Image Recognition with TensorFlow
Analytics Study: NLP and Big Data with Twitter Sentiment Analysis
Analytics Study: Prediction - Financial Time Series Analysis and Forecasting
Analytics Study: Graph Algorithms - US Domestic Flight Data Analysis
The Future of Data Analysis and Where to Develop your Skills
PixieApp Quick-Reference
Index
Customer Reviews