Python for Data Science
Learn the Python basics you need to start doing data science. This is a targeted approach to learning the language for a purpose.
ADS101
Python for Data Science
The Why Behind the Course
This course was created to give you, the cyber security professional, the necessary Python experience required to do basic data science and take our course, Applied Data Science and Machine Learning for Cyber Security. Most courses for introductory Python spend a lot more time on basics and we wanted to accelerate the pace for learning basics. In this course you will ramp up quickly on installing and using Python and end with manipulating data using statistics.
AMA With Summer
Hear the course author for an AMA with the Level Effect Discord community answering anything and all things Data Science. We covered neural nets, ML Ops, the brain versus machine learning, and the future of AI security - and more! check it out.
Author Origin Story
Get to know more about the course author - Summer Rankin, PhD! hear her story before becoming an instructor starting from music to neuroscience and her journey into data science intersecting with cybersecurity.
Course Description
The ADS101: Python for Data Science 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 Outcomes
-
Utilize Jupyter Notebooks to execute python code and manipulate data
-
Invoke conditional statements and loops to process data in a list or dictionary
-
Import and use python libraries
-
Write clean, readable code with comments and docstrings
-
Compose functions that take in multiple arguments and data types
-
Decode a python error message and debug code accordingly
-
Explain why standardization and scaling are important for analysis and modeling
-
Calculate basic statistical analyses from scratch
Prerequisites
-
Level Effect’s Cybersecurity Fundamentals courses starting with IT
-
0-1+ years of professional experience in technology, preferably within Data Science, IT, or Cybersecurity
-
Hobbyists with a solid understanding of Data Science, or Cybersecurity or IT
0
Modules
0
Units
0
Labs
0
Exam
Course Author
Name: Summer Rankin, PhD (LinkedIn)
Bio: Summer is a senior lead data scientist in the CTO at Booz Allen Hamilton in Honolulu (Aloha!), with over 20 years of experience as an instructor and scientist. As a consultant, she has worked with a wide range of commercial and government clients (civil and defense sectors), building products and advising them on technical and management practices. She is an expert in deep learning (natural language processing, geodata, healthcare), digital signal processing, statistical methods for time-series analysis, and fraud detection.
Summer holds a PhD in Complex Systems and Brain Sciences (2010) from Florida Atlantic University. She completed a postdoctoral fellowship at Johns Hopkins School of Medicine analyzing neuroimaging and healthcare data. She is the recipient of multiple National Institutes of Health (NIH) competitive training grants. She has over 30 peer-reviewed publications/proceedings in topics of machine learning, healthcare-AI, and analytic methods in neuroscience data. Selected certifications: PMP (project management professional), Elasticsearch Engineer, Databricks Data Engineer Associate. Summer has taught a wide range of students from high-school to medical residents and graduate students on topics of mathematics, programming, data science, and cognitive neuroscience. She has developed and delivered courses both in-person and online.
Course Features
Built for Expertise
Dr. Rankin has designed this course based on her real world experience as a Senior Data Scientist and Computational Neuroscientist.
Certification
Take a comprehensive exam to certify your skills and knowledge toward utilizing Python for Data Science and not just memorizing syntax.
Explore the Curriculum
Learning Modules
-
Overview
• Course and Python Overview: Introduction to the course objectives, Python’s role in data science, and the tools used throughout the course.
• Module Quiz: Assess the initial understanding of Python and its applications in data science.
-
Jupyter
• Setup and Introduction to Jupyter: Configure Python and Jupyter environments, and familiarize with Jupyter Notebook as an IDE.
• Module Quiz: Test knowledge on Python setup, Jupyter functionalities, and basic Python commands within Jupyter.
-
Variables
• Understanding and Commenting Variables: Learn the basics of variable creation and the importance of code commenting for clarity.
• Best Practices and Error Handling: Focus on variable naming conventions and common error identifications in Python.
• Module Quiz: Evaluate understanding of variable handling and best practices in Python coding.
-
Operators & Data Types
• Using Operators: Explore different operators in Python for performing calculations and data manipulations.
• Exploring Data Types: Deep dive into Python’s fundamental data types including numeric and string types.
• Module Quiz: Test application of operators and understanding of various data types.
-
Strings
• Manipulating Strings: Techniques for indexing, slicing, and applying string methods like lower, strip, and split.
• Advanced String Functions: Learn string formatting and advanced manipulations using replace and other functions.
• Module Quiz: Assess skills in string handling and manipulation techniques.
-
Libraries
• Introduction to Libraries: Learn how to import standard libraries and explore their basic functionalities.
• Using Aliases and Third-Party Libraries: Understand the use of aliases for ease of code writing and incorporating third-party libraries to extend Python’s capabilities.
• Modulequiz: Evaluate the ability to import and utilize libraries effectively in Python projects.
-
Regular Expressions
• Fundamentals of Regular Expressions: Introduction to character sets, repetition commands, and practical applications in Python.
• Regex Exercises: Apply regular expressions to various string processing tasks.
• Module Quiz: Test knowledge and application of regular expressions in data parsing and manipulation.
-
Collections
• Lists, Tuples, and Sets: Learn to create and manipulate list, tuple, and set data structures.
• Working with Dictionaries: Understand dictionaries for storing and accessing data via key-value pairs.
• Module Quiz: Assess understanding of Python collections and their applications.
-
Conditionals & Loops
• Conditional Statements and Loops: Implement conditional logic and looping constructs to control the flow of programs.
• Advanced Data Handling with List Comprehensions: Use list comprehensions for more efficient data processing.
• Module Quiz: Evaluate proficiency in using conditionals, loops, and comprehensions for data operations.
-
Functions
• File Handling and Functions: Learn basic file operations and create custom functions to simplify repetitive tasks.
• Module Quiz: Test ability to handle files and write functional code blocks in Python.
-
Pandas
• Introduction to Pandas Library: Master the basics of the Pandas library for high-level data manipulation.
• Module Quiz: Evaluate skills in using Pandas for data analysis tasks.
-
Statistics
• Basic Statistical Concepts: Understand and calculate key statistical measures like mean, mode, median, and variability.
• Advanced Statistics: Explore data normalization, standardization, and hypothesis testing.
• Module Quiz: Assess understanding and application of statistical methods using Python.
Course Cost
Pay what you can!
Minimum $19. Suggested $29.
This course is included free if you purchase the ADS201 course.
Frequently Asked Questions
-
Do I need experience with writing code?No prior experience is required
-
Will I need to have Python installed already?
No. We will show you how to install everything you need to run Python in Jupyter Notebooks
-
What if I am bad at math?
No problem! The math covered in this course is basic statistics and we will teach you what you need to know.
-
What will I get out of this course?
The ability to manipulate and view data in a jupyter notebook using Python. The confidence and required knowledge to take the Applied Data Science and Machine Learning for Cyber Security course.