/Learn-PY

Primary LanguageJupyter NotebookMIT LicenseMIT

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 a person 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)

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