Python® Data Science Full Throttle with Paul Deitel: Introductory Artificial Intelligence (AI), Big Data and Cloud Case Studies

Paul will present programming case studies introducing the following data science, AI, big data, cloud and visualization technologies, libraries and tools:

• Natural Language Processing—TextBlob, Textatistic, spaCy and word_cloud
• Data Mining Twitter (updated for the Twitter v2 APIs) - Sentiment analysis, Tweepy, JSON, getting user account info, searching for tweets, streaming live tweets, word_cloud, obtaining additional metadata in Twitter API responses with Twitter v2 API expansions and fields
• IBM Watson and Cognitive Computing—Building an inter-language speech-to-speech translator
• Supervised Machine Learning—Computer vision with classification using scikit-learn, Seaborn and Matplotlib visualizations
• Unsupervised Machine Learning—Using scikit-learn dimensionality reduction to help visualize multidimensional data; clustering with scikit-learn
• Deep Learning for Computer Vision—Convolutional neural network in Keras running over TensorFlow
• Deep Learning for Sentiment Analysis—Recurrent neural network in Keras running over TensorFlow
• MongoDB NoSQL Document Database—Storing streaming tweets as JSON documents and visualizing with an interactive folium map
• Hadoop—MapReduce with Hadoop Streaming running on a Microsoft Azure cluster
• Spark—Spark and Spark Streaming running on a juypyter/pyspark-notebook Docker container
• Internet of Things (IoT) Streaming Data—Simulated streaming sensors with dweet.io, dweepy and PubNub; simulated streaming stock prices with PubNub; and visualizing streaming data with freeboard.io and Seaborn

• You’re a Python developer and you see exciting AI, big data and data science technologies popping up everywhere and you want a one-day, code-based introduction to them
• You’re a Python developer looking to enhance your career opportunities with these current technologies
• You’re a manager contemplating Python projects using AI, big data and data science technologies and want a one-day, code-based introduction to them
• You’re an R developer whose organization is considering Python and you want a one-day, code-based introduction to Python’s AI, big data and data science capabilities

• Python 3 programming experience
• Attend: Python® Full Throttle with Paul Deitel by Paul Deitel
• Watch: Lessons Lessons 1-10 of Python® Fundamentals LiveLessons by Paul Deitel
• Read: Chapters 1-10 of Intro to Python® for Computer Science and Data Science by Paul Deitel
• Read: Python® for Programmers by Paul Deitel

Note: Python code is easy to read, so even if you’re an experienced developer who does not know Python, you can still get a lot out of this course.

Recommended preparation

• No setup is required—this is a lecture-only presentation
• After the training, if you'd like to run the code, install the free Anaconda Python distribution (for macOS, Windows and Linux)
• For examples that require additional setup, the steps are described in each of the following:
• Read: Chapters 12-17 of Intro to Python® for Computer Science and Data Science by Paul Deitel
• Read: Chapter 11-16 of Python® for Programmers by Paul Deitel
• Watch: Lessons 11-16 of Python® Fundamentals LiveLessons by Paul Deitel
• After the course, we recommend that you run the code examples using JupyterLab with the Jupyter Notebooks we provide. For a quick introduction to Jupyter Notebooks and JupyterLab, see any of the following:
• Watch: Before You Begin lesson and Lesson 1 of Python® Fundamentals LiveLessons by Paul Deitel
• Read: Before You Begin section and Section 1.10 of Intro to Python® for Computer Science and Data Science by Paul Deitel
• Read: Before You Begin section and Section 1.5 of Python® for Programmers by Paul Deitel

Additional materials, downloads, supplemental content, or resources needed in advance:

• Paul will continue to answer your questions after the course at paul@deitel.com. On the day of the course, Paul will provide links to download the slides and the code (in standard Python .py files and in Jupyter Notebooks .ipynb files).
• If you’re an instructor teaching college or professional Python courses, you may want to check out Paul’s full-color textbook, Intro to Python® for Computer Science and Data Science: Learning to Program with AI, Big Data and the Cloud. The textbook includes 240 pages of additional content with programming fundamentals for novices, 557 self-check exercises and 471 exercises and projects.