Intro to Python Programming May registration closes on June 10, 2022

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Python and Statistical modelling

Class description

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.

Uses of the Python Programming Language

  • 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


Course objectives

  • 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

Course Format

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.

Class Syllabus

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Class Syllabus

Module 1 - Introduction to Python Crash Course

  •  System set up
  • Data Structures, Numbers, Strings and Loops
  • Input / Output Operations
  • Algorithms and conditional functions
  • Data Analysis  Modules (Numpy, Pandas)
  • Pandas Dataframes and Series
  • Pandas Operations

Module 1 - Introduction to Descriptive Statistics

  • Measures of concentration
  • Measures of the spread of data
  • Measure of skewness, orientation and representation of data
  • standardization and transformation of data towards assessing volubility  between characteristic features (variables) in the data

Module 2 - Inferential statistics

  • Confidence Interval
  • Correlation test and the introduction tp p-value
  • Using and understanding Command line parameters with Python
  • One sample T-test
  • Wilcox signed rank test
  • Two sample t-Test and Wilcox rank sum test
  • Shapiro test
  • Chi-Squared Test

Module 3 - Advanced Modelling

  • Linear Regression
  • Generalized Linear Models
  • Flow controls Loops and Iterations
  • ANOVA
  • Time series analysis
  • Scikit-Learn for statistical learning
  • Perform various types of encoding of / transformaion on the input data

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