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Data Analysis with Python

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  • A new toolset that has been carefully crafted to meet for your data analysis challenges
  • Full and detailed case studies of the toolset across several of today’s key industry contexts
  • Become super productive with a new toolset across Python and Jupyter Notebook
  • Look into the future of data science and which directions to develop your skills next

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.

  • Bridge your data analysis with the power of programming, complex algorithms, and AI
  • Use Python and its extensive libraries to power your way to new levels of data insight
  • Work with AI algorithms, TensorFlow, graph algorithms, NLP, and financial time series
  • Explore this modern approach across with key industry case studies and hands-on projects
Page Count 490
Course Length 14 hours 42 minutes
ISBN 9781789950069
Date Of Publication 31 Dec 2018
Anatomy of a PixieApp
Use @captureOutput decorator to integrate the output of third-party Python libraries
Increase modularity and code reuse
Run Node.js inside a Python Notebook
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
Getting started with NumPy
Statistical exploration of time series
Putting it all together with the StockExplorer PixieApp
Time series forecasting using the ARIMA model
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
Forward thinking – what to expect for AI and data science


David Taieb

David Taieb is the Distinguished Engineer for the Watson and Cloud Platform Developer Advocacy team at IBM, leading a team of avid technologists on a mission to educate developers on the art of the possible with data science, AI and cloud technologies. He's passionate about building open source tools, such as the PixieDust Python Library for Jupyter Notebooks, which help improve developer productivity and democratize data science. David enjoys sharing his experience by speaking at conferences and meetups, where he likes to meet as many people as possible.