Python and Statistical modelling

This is an instructor-led course to introduce you to the Python programming language. Python is one of the most popular programming languages used in industries ranging from gaming to finance. Python is an interpreted, object-oriented high-level programming language, which has recently become a popular tool in industry and in academia.

Python is free to use, easy to install and learn and its open source. In this course, you will learn to create programs, functions, complex data structures and to collect user input with Python. Furthermore, you will learn to read and write from external sources such as files and databases By learning these concepts, students will have a starting point to learn a few of the many ways that Python is utilized from basic reporting to complex machine learning solutions and much more.

• The Python programming language is used by business analysts, software developers to analyze data and to create captivating yet insightful data visualizations
• The Python programming language can be used for developing both desktop, web and mobile applications.
• Python can be used for creating Windows, UNIX and Mac applications, from simple console and web-based to
• elaborate graphic interfaces for video games.
• Also, you can use Python for developing complex scientific and numeric applications for Machine Learning, Robotics and Finance

• Introduce students to the basics of Python.
• Provide students with the hands-on Python programming building block skills needed to develop applications
• Create working Python scripts following best practices
• Use python data types appropriately
• Read and write files with both text and binary data
• Search and replace text with regular expressions
• Get familiar with the standard library and its work-saving modules
• Create "real-world", basic level professional Python applications
• Know when to use collections such as lists, dictionaries, and sets
• Understand Pythonic features such as comprehensions and iterators
• Write robust code using exception handling

This course is a 50% hands-on labs to 50% lecture ratio with engaging instruction, demos, group discussions,

labs, and project work including a capstone assignment.

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