Python Developer

Python's popularity has steadily increased over the years due to its simplicity, readability, and extensive libraries. According to the TIOBE Index, Python consistently ranks among the top programming languages, making it a valuable skill in today's job market.

Its clean syntax and intuitive nature make it an excellent choice for beginners, while its powerful features and vast ecosystem of libraries cater to the needs of experienced developers. Our Python course is perfect for both beginners and...

6 Months / 155 Course Hrs

Open Enrollment

Offered in partnership with your preferred school

Butler Community College

Change School

Learning method

Instructor-led

$995

Self-paced. Study on your own schedule

Enroll Now

for additional information

Details + Objectives

Course code: GES340

What you will learn

Understand Python's significance and versatility among programming languages and its unique strengths.
Learn advanced sorting techniques for efficient data organization
Understand how to write modular and reusable code with object-oriented programming in Python
Discover how to ensure the reliability of your Python code through testing and debugging
Harness the power of regular expressions for advanced pattern matching
Master mathematical operations and data manipulation using Python.
Work seamlessly with databases, CSV files, JSON, and XML using Python.
Perform complex numerical computations with NumPy in Python.
Analyze and manipulate data effortlessly using the pandas library in Python

How you will benefit

Prepare for an entry-level job as a Python programmer
Elevate your programming abilities and expand your existing skill set by mastering new Python fundamentals
Boost your confidence and resume with your new Python skills
Stand out for a promotion or to future employers

How the course is taught

Self-paced, online course
6 Months to complete
Open enrollment, begin anytime
155 course hours

Outline

Introduction to Python
1. Python Basics
  1. Getting Familiar with the Terminal
  2. Running Python
  3. Running a Python File
  4. Exercise: Hello, world!
  5. Literals
  6. Exercise: Exploring Types
  7. Variables
  8. Exercise: A Simple Python Script
  9. Constants and Deleting Variables
  10. Writing a Python Module
  11. print() Function
  12. Collecting User Input
  13. Exercise: Hello, You!
  14. Reading from and Writing to Files
  15. Exercise: Working with Files
2. Functions and Modules
  1. Defining Functions
  2. Variable Scope
  3. Global Variables
  4. Function Parameters
  5. Exercise: A Function with Parameters
  6. Returning Values
  7. Exercise: Parameters with Default Values
  8. Returning Values
  9. Importing Modules
  10. Methods vs. Functions
3. Math
  1. Arithmetic Operators
  2. Exercise: Floor and Modulus
  3. Assignment Operators
  4. Precedence of Operations
  5. Built-in Math Functions
  6. The math Module
  7. The random Module
  8. Exercise: How Many Pizzas Do We Need?
  9. Exercise: Dice Rolling
4. Python Strings
  1. Quotation Marks and Special Characters
  2. String Indexing
  3. Exercise: Indexing Strings
  4. Slicing Strings
  5. Exercise: Slicing Strings
  6. Concatenation and Repetition
  7. Exercise: Repetition
  8. Combining Concatenation and Repetition
  9. Python Strings are Immutable
  10. Common String Methods
  11. String Formatting
  12. Exercise: Playing with Formatting
  13. Formatted String Literals (f-strings) (introduced in Python 3.6)
  14. Built-in String Functions
  15. Exercise: Outputting Tab-delimited Text
5. Iterables: Sequences, Dictionaries, and Sets
  1. Definitions
  2. Sequences
  3. Lists
  4. Sequences and Random
  5. Exercise: Remove and Return Random Element
  6. Tuples
  7. Ranges
  8. Converting Sequences to Lists
  9. Indexing
  10. Exercise: Simple Rock, Paper, Scissors Game
  11. Slicing
  12. Exercise: Slicing Sequences
  13. min(), max(), and sum()
  14. Converting between Sequences and Strings
  15. Unpacking Sequences
  16. Dictionaries
  17. The len() Function
  18. Exercise: Creating a Dictionary from User Input
  19. Sets
  20. *args and **kwargs
6. Virtual Environments, Packages, and pip
  1. Exercise: Creating, Activiting, Deactivating, and Deleting a Virtual Environment
  2. Packages with pip
  3. Exercise: Working with a Virtual Environment
7. Flow Control
  1. Conditional Statements
  2. Compound Conditions
  3. The is and is not Operators
  4. all() and any() and the Ternary Operator
  5. In Between
  6. Loops in Python
  7. Exercise: All True and Any True
  8. break and continue
  9. Looping through Lines in a File
  10. Exercise: Word Guessing Game
  11. The else Clause in Loops
  12. Exercise: for...else
  13. The enumerate() Function
  14. Generators
  15. List Comprehensions
8. Exception Handling
  1. Exception Basics
  2. Generic Exceptions
  3. Exercise: Raising Exceptions
  4. The else and finally Clauses
  5. Using Exceptions for Flow Control
  6. Exercise: Running Sum
  7. Raising Your Own Exceptions
9. Python Dates and Times
  1. Understanding Time
  2. The time Module
  3. Time Structures
  4. Times as Strings
  5. Time and Formatted Strings
  6. Pausing Execution with time.sleep()
  7. The datetime Module
  8. datetime.datetime Objects
  9. Exercise: What Color Pants Should I Wear?
  10. datetime.timedelta Objects
  11. Exercise: Report on Departure Times
10. File Processing
  1. Opening Files
  2. Exercise: Finding Text in a File
  3. Writing to Files
  4. Exercise: Writing to Files
  5. Exercise: List Creator
  6. The os Module
  7. os.walk()
  8. The os.path Module
  9. A Better Way to Open Files
  10. Exercise: Comparing Lists
11. PEP8 and Pylint
  1. PEP8
  2. Pylint
Advanced Python
1. Advanced Python Concepts
  1. Lambda Functions
  2. Advanced List Comprehensions
  3. Exercise: Rolling Five Dice
  4. Collections Module
  5. Exercise: Creating a defaultdict
  6. Counters
  7. Exercise: Creating a Counter
  8. Mapping and Filtering
  9. Mutable and Immutable Built-in Objects
  10. Sorting
  11. Exercise: Converting list.sort() to sorted(iterable)
  12. Sorting Sequences of Sequences
  13. Creating a Dictionary from Two Sequences
  14. Unpacking Sequences in Function Calls
  15. Exercise: Converting a String to a datetime.date Object
  16. Modules and Packages
2. Regular Expressions
  1. Regular Expression Tester
  2. Regular Expression Syntax
  3. Python's Handling of Regular Expressions
  4. Exercise: Green Glass Door
3. Working with Data
  1. Virtual Environment
  2. Relational Databases
  3. Passing Parameters
  4. SQLite
  5. Exercise: Querying a SQLite Database
  6. SQLite Database in Memory
  7. Exercise: Inserting File Data into a Database
  8. Drivers for Other Databases
  9. CSV
  10. Exercise: Finding Data in a CSV File
  11. Creating a New CSV File
  12. Exercise: Creating a CSV with DictWriter
  13. Getting Data from the Web
  14. Exercise: HTML Scraping
  15. XML
  16. JSON
  17. Exercise: JSON Home Runs
4. Testing and Debugging
  1. Testing for Performance
  2. Exercise: Comparing Times to Execute
  3. The unittest Module
  4. Exercise: Fixing Functions
  5. Special unittest.TestCase Methods
5. Classes and Objects
  1. Attributes
  2. Behaviors
  3. Classes vs. Objects
  4. Attributes and Methods
  5. Exercise: Adding a roll() Method to Die
  6. Private Attributes
  7. Properties
  8. Exercise: Properties
  9. Objects that Track their Own History
  10. Documenting Classes
  11. Exercise: Documenting the Die Class
  12. Inheritance
  13. Exercise: Extending the Die Class
  14. Extending a Class Method
  15. Exercise: Extending the roll() Method
  16. Static Methods
  17. Class Attributes and Methods
  18. Abstract Classes and Methods
  19. Understanding Decorators
Python Data Analysis with JupyterLab
1. JupyterLab
  1. Exercise: Creating a Virtual Environment
  2. Exercise: Getting Started with JupyterLab
  3. Jupyter Notebook Modes
  4. Exercise: More Experimenting with Jupyter Notebooks
  5. Markdown
  6. Exercise: Playing with Markdown
  7. Magic Commands
  8. Exercise: Playing with Magic Commands
  9. Getting Help
2. NumPy
  1. Exercise: Demonstrating Efficiency of NumPy
  2. NumPy Arrays
  3. Exercise: Multiplying Array Elements
  4. Multi-dimensional Arrays
  5. Exercise: Retrieving Data from an Array
  6. More on Arrays
  7. Using Boolean Arrays to Get New Arrays
  8. Random Number Generation
  9. Exploring NumPy Further
3. pandas
  1. Getting Started with pandas
  2. Introduction to Series
  3. np.nan
  4. Accessing Elements in a Series
  5. Exercise: Retrieving Data from a Series
  6. Series Alignment
  7. Exercise: Using Boolean Series to Get New Series
  8. Comparing One Series with Another
  9. Element-wise Operations and the apply() Method
  10. Series: A More Practical Example
  11. Introduction to DataFrames
  12. Creating a DataFrame using Existing Series as Rows
  13. Creating a DataFrame using Existing Series as Columns
  14. Creating a DataFrame from a CSV
  15. Exploring a DataFrame
  16. Exercise: Practice Exploring a DataFrame
  17. Changing Values
  18. Getting Rows
  19. Combining Row and Column Selection
  20. Boolean Selection
  21. Pivoting DataFrames
  22. Be careful using properties!
  23. Exercise: Series and DataFrames
  24. Plotting with matplotlib
  25. Exercise: Plotting a DataFrame
  26. Other Kinds of Plots

Instructors & Support

Nat Dunn

Nat Dunn founded Webucator in 2003 to combine his passion for technical training with his business expertise and to help companies benefit from both. His previous experience was in sales, business and technical training, and management. Nat has an MBA from Harvard Business School and a BA in International Relations from Pomona College.

Requirements

Prerequisites:

There are no prerequisites for this course, however prior knowledge of any programming language is helpful.

Requirements:

Hardware Requirements:

This course can be taken on a PC or a Mac.
Dual monitors are helpful but not required.

Software Requirements:

PC: Windows 10 or later.
Mac: macOS 11.0 or later.
Browser: The latest version of Google Chrome or Mozilla Firefox are preferred. Microsoft Edge and Safari are also compatible.
Python (download and installation instructions are provided in course).
Adobe Acrobat Reader.

Other:

Email capabilities and access to a personal email account.

Instructional Material Requirements:

The instructional materials required for this course are included in enrollment and will be available online.

FAQ

According to Indeed, the average Python developer salary is $122,009 annually in the U.S. as of October 13, 2023.

According to Indeed, a Python developer is responsible for utilizing the Python language to develop, debug, and implement application projects. They possess a deep understanding of Python and can accomplish various tasks using this versatile language. Python developers are also proficient in academic research, scientific computing, machine learning, AI, and web development. Their duties can include:

Developing and implementing application projects
Debugging code
Connecting programs to external web services

Additionally, they collaborate with front-end developers to seamlessly integrate their work into Python applications. By fulfilling these responsibilities, Python developers contribute to creating robust and efficient applications while enhancing their skills in server-side web application logic.

This course does not prepare you for a certification, but it will equip you with the skills needed to enter the job market as an entry-level Python programmer or to enhance your programming skills.

This course is open enrollment, so you can register and start the course as soon as you are ready. Access to your course can take 24-48 business hours.

This course is self-paced and open enrollment, so you can start when you want and finish at your own pace. When you register, you'll receive six (6) months to complete the course.

The time allotted for course completion has been calculated based on the number of course hours. However, if you are unable to complete the course, contact the student advising team to see what options you may have available to work out a suitable completion date. Please note that an extension fee may be charged.

Our courses are designed to accommodate various learning preferences. Depending on the course structure, you may get different forms of support. Self-paced courses are designed to be user-friendly and independent, minimizing the need for external support. In courses with instructors, our direct platform support feature includes opportunities for questions, discussions, and remediation, with email and phone support available for select courses. Our student advising team is available to guide you on program requirements and administrative requests, but they do not provide assistance with course content. Our goal is to provide you with the necessary support for success, whether it's through self-paced exploration, direct communication, or guidance from our advising team.

Upon successful completion of the course, you will be awarded a Certificate of Completion.

This course will provide you with the skills you need to obtain an entry-level position in most cases. Potential students should always do research on the job market in their area before registering.

This course is non-credit, so it does not qualify for federal aid, FAFSA and Pell Grant. In some states, vocational rehab or workforce development boards will pay for qualified students to take our courses. Additionally, some students may qualify for financial assistance when they enroll, if they meet certain requirements. Financing is available from select schools. Learn more about financial assistance.

If you have questions that are not answered on our website, representatives are available via LIVE chat. You can also call us at 1-877-221-5151 during regular business hours to have your questions promptly answered. If you are visiting us during non-business hours, please send us a question using the "Contact Us" form.

Reviews

"A great class! I look forward to using these new skills in my job." -E.G., Rutgers, The State University of New Jersey

This program was great: well-paced, and the content was top-notch. I liked all the lessons. They didn't have any unnecessary filler, and they included great explanations of all concepts. - Connor D.

I learned a lot of useful skills and really liked the quiz-style coding activities. - Lucy K.

Overall, the class was excellent! - John S.

This program exceeded my expectations in that it taught me a variety of methods for solving a problem. It also helped me get familiar with the Jupyter Notebook - something that I'd never heard of before. I liked the fact that the videos followed the lessons and explained the code step-by-step. Some of the things were completely new to me, so having it explained by an instructor made it possible for me to comprehend the more complex concepts. - Jerry C.

Virtual learning is a perfect fit for me. Videos were a nice addition to the traditional "book" education. Everything was up (or down))) to my level. Can't wait to use in in my work! - Veronica L.

The class covered a wide array of workforce skills, and the final course project was challenging, but with the skills learned in the course, I was able to do it. - Stan S.

Python is a wonderful language. Learning to write less lines of code to achieve my requirements is spectacular! Ed2Go was easy to follow and allowed me time to learn a new skill while working! - Tressa W.