Python® Data Science Full Throttle with Paul Deitel: Introductory Artificial Intelligence (AI), Big Data and Cloud Case Studies
Published by Pearson
A One-Day, Presentation-Only, Case-Study-Intensive Seminar
- Leverage your Python skills to dive into some key Python data science, AI, big data and cloud technologies
- Study many Python code examples, from individual snippets to highlights of fully implemented case studies
- See how Python libraries for data science, AI, big data and cloud software technologies enable you to create powerful applications with minimal code quickly
- Live instruction by Paul Deitel, bestselling book and video course author and one of the world’s most experienced programming-language trainers.
In this live training for Python programmers, Paul introduces some of today's most compelling, leading-edge computing technologies with cool examples on natural language processing, data mining Twitter® for sentiment analysis (updated for the Twitter v2 APIs) , cognitive computing with IBM® Watson™, computer vision via supervised machine learning with classification, unsupervised machine learning with dimensionality reduction and clustering, computer vision via deep learning with a convolutional neural network, and big data infrastructure topics using the MongoDB NoSQL database, Spark™ streaming, and the Internet of Things. This is an aggressively paced, presentation-only, code-highlights and discussion seminar. There is no lab component. You’ll receive all the code and Jupyter Notebooks.
What you’ll learn and how you can apply it
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
This live event is for you because...
- 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
Prerequisites
- 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.
Schedule
The time frames are only estimates and may vary according to how the class is progressing.
The timeframes are only estimates and may vary according to how the class is progressing.
- Part 1: Natural Language Processing
Break
- Part 2: Data Mining Twitter
Break
- Part 3: IBM Watson and Cognitive Computing
- Part 4: Supervised Machine Learning with scikit-learn, Part 1
Meal break (45 minutes; approximately 3 hours 15 minutes after class begins)
- Part 4: Supervised Machine Learning with scikit-learn, Part 2
- Part 5: Unsupervised Machine Learning with scikit-learn
Break
- Part 6: Deep Learning
Break
- Part 7: Big Data, NoSQL, Spark and IoT (Internet of Things)
Your Instructor
Paul J. Deitel
Paul J. Deitel, CEO and Chief Technical Officer of Deitel & Associates, Inc., is an MIT graduate with 43 years in computing. Paul is one of the world’s most experienced programming-languages trainers, having taught professional courses to software developers at all levels since 1992. He has delivered hundreds of programming courses to industry clients internationally, including SLB (formerly Schlumberger), Cisco, IBM, Siemens, Sun Microsystems (now Oracle), Dell, Fidelity, NASA at the Kennedy Space Center, the National Severe Storm Laboratory, White Sands Missile Range, Rogue Wave Software, Boeing, Puma, iRobot, UCLA Anderson’s Master of Science in Business Analytics (MSBA) and Master of Financial Engineering (MFE) programs, and many more. He is among the world’s best-selling programming-language textbook, professional book, video and interactive multimedia authors.