Course Description

The Python Programming for Data Science and Cybersecurity course will introduce cybersecurity professionals to Python, Jupyter notebooks, and basic statistics. The purpose of this course is to prepare the cybersecurity professional with the foundational knowledge required to take the Applied Data Science for AI & Cybersecurity course. This course requires no prerequisites or knowledge of programming or mathematics.

Hands-on labs, lectures, quizzes and sample code will provide an active learning experience for optimal retention. The course will cover the basics of Python programming using Jupyter notebooks (a popular, user-friendly IDE). Basic statistical methods will be reviewed using the python programming knowledge learned in this course. Exercises and quizzes are provided throughout the course to let you check your knowledge along the way.

At the end of the course, an exam is given to provide a formal way to demonstrate what was learned. Participants who earn a grade of 80% or higher on the exam will receive a course completion certificate.

Learning Modules

• Intro to Python & Jupyter

  The definition, history, and current version of python will be described along with the motivation for using python in this course and how it is useful for cybersecurity and data science. A Jupyter notebook is an Integrated Development Environment (IDE). You will learn what a Juypter notebook is and why it is popular in data science and education. The course materials will be accessed/downloaded and you will practice running cells using multiple languages. 

  • Define Python

  • Explain why Python is a popular language in data science and it’s relevance to cybersecurity

  • Clarify the version of Python used for this course

  • Create a jupyter notebook

  • Save (download) a jupyter notebook

  • Compile example code by executing cells containing python and markdown 

  • Explain what a Jupyter notebook is

  • Give examples for cases where Jupyter is useful and where it is not

• Python Basics

  The fundamental concepts in Python programming, including variables, operators, data types, string operations, Python libraries, regular expressions, collections, conditionals, loops, and functions.

  • Variables: Basics of variables, including assignment, comments, valid names, case sensitivity, error interpretation, and simultaneous assignments.

  • Operators & Data Types: Exploring mathematical operators, data types, conversions, pitfalls, and error handling.

  • String Operations: Mastering string manipulation using built-in methods like whitespace stripping, splitting, slicing, replacing, case changing, concatenating, and length determination.

  • Python Libraries: Installing, importing, and executing code with commonly used Python libraries.

  • Regular Expressions: Using regex to parse and extract specific text data parts.

  • Collections: Understanding data type collections like lists, tuples, sets, and dictionaries, and their manipulation.

  • Conditionals and Loops: Differentiating for and while loops, using conditional expressions, and datetime library usage.

  • Functions: Creation, usage, and error correction in functions, covering keywords, docstrings, arguments, and returns.

• Statistics

  This section will cover basic statistical methods commonly used in cleaning, analyzing and visualization of data. The relationship between statistics and data science will be discussed. The python skills learned thus far will be used to practice translating a mathematical equation into code. 

  • Explain the difference between and calculate mean, median, mode, range, standard deviation, variance of a dataset

  • Differentiate when and why it is appropriate to use mean vs median

  • Use a histogram and box plot to understand the distribution of a dataset

  • Identify outliers and decide what actions to take

  • Normalize and scale a feature and describe when and why it is necessary

  • Describe what it means to scale and normalize a set of numbers and how this is relevant to data science

• Pandas

  Pandas is one of the most popular and user-friendly libraries for exploring and processing raw data. You will import the pandas library and use it to import a small set of data to create your first pandas dataframe. 

  • Import a .csv file using Pandas and create a dataframe

  • Print the first 5 rows

  • Get the statistical description of each column

  • List the names of each column

  • List the datatype of each column

  • Calculate the number of NaNs in the dataframe

• Exam

  An exam will be given at the end of the course. You must earn an 80% or better to receive a course completion certificate. The exam will be open-book, online and graded once you submit your answers.