Python Developer

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                                CODE
                                    GES340
                            
                                6 Months
                            
                                155 Course Hrs

OVERVIEW

DETAILS

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

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