Introduction to Programming and Data Science with Python I and II

Textbook

Intro to Python for Computer Science and Data Science: Learning to Program with AI, Big Data and The Cloud by Paul J. Deitel, and Harvey Deitel

@book{deitel2020intro,
  title={Intro to Python for Computer Science and Data Science},
  author={Deitel, Paul and Deitel, Harvey},
  year={2020},
  publisher={Pearson Education}
}

Learning Outcomes

After successful completion of this course the student will be able to:

• Effectively employ Python as a scientific computation tool, utilizing several built-in functions and import libraries;
• Apply relational and logical operators;
• Apply programming constructs such as decision structures and repetition structures to solve scientific problems;
• Write reusable code using subroutines and functions;
• Import, export, and display data from external files;
• Acquire, analyze, manipulate, and evaluate numerical information;
• Identify programming best practices including programming guidelines, conventions, code optimization (vectorization), and knowledge of common pitfalls.

Topics

• Introduction to Computers and Python
• Varibles, assignments, arithmetic, strings, and comments
• Flow Control Statements (conditionals)
• Flow Control Statements (loops)
• Functions
• Sequences: Lists and Tuples
• Lists (including sorting) and List comprehensions
• Dictionaries and Sets (Set comprehensions)
• Arrays with NumPy
• Strings
• Files and Exceptions
• Files and Exceptions
• Object-oriented programming (OOP)

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Learning Outcomes

After successful completion of this course the student will be able to:

• Employ Python as a tool for data science, utilizing several built-in functions and import libraries;
• Implement multiple sorting algorithms;
• Develop text mining techniques;
• Perform natural language processing (NLP) tasks;
• Detect the language of text and apply APIs to translate;
• Recognize Twitter’s impact on business and society and consider ethical issues; Identify IBM Watson’s range of services;
• Apply machine learning models and measure performance;
• Create Keras neural networks for deep learning applications;
• Access database and execute SQL statements via python.

Topics

• Sorting algorithms and Big O
• Natural Language Processing (NLP)
• Twitter Mining (tweepy)
• IBM Watson services
• Machine Learning: classification, regression, clustering (sklearn)
• Deep Learning (Keras and TensorFlow)
• SQL (sqlite3), Hadoop, Spark