PYTHON
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Python Courses
Introduction to Python
Course Number:
This course leads the student from the basics of writing and running Python scripts to more advanced features such as file operations, regular expressions, working with binary data, and using the extensive functionality of Python modules. Extra emphasis is placed on features unique to Python, such as tuples, array slices, and output formatting.
This is a hands-on programming class. All concepts are reinforced by informal practice during the lecture followed by graduated lab exercises.
Python Intro is a practical introduction to a working programming language, not an academic overview of syntax and grammar. Students will immediately be able to use Python to complete tasks in the real world.
Note: This course can be taught for either Python 2.x or Python 3.x. Version-specific course materials and exercises will be provided.
Audience:
Prerequisites:
Students should already have a working, user-level knowledge of Unix/Linux, Mac, or Windows. While not required, basic skills with at least one other programming language will be helpful.
- 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
- Use lesser-known but powerful Python data types
- Create “real-world”, professional Python applications
- Work with dates, times, and calendars
- 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
- An overview of Python
- What is python?
- Python Timeline
- Advantages/Disadvantages of Python
- Getting help with pydoc
- The Python Environmens
- Starting Python
- Using the interpreter
- Running a Python script
- Python scripts on Unix/Windows
- Editors and IDEs
- Getting Started
- Using variables
- Built-in function
- Strings
- Numbers
- Converting among types
- Writing to the screen
- Command line parameters
- Flow Control
- About flow control
- White space
- Conditional expressions
- Relational and Boolean operators
- While loops
- Alternate loop exits
- Sequences
- About sequences
- Lists and list methods
- Tuples
- Indexing and slicing
- Iterating through a sequence
- Sequence functions,keywords, and operators
- List comprehensions
- Generator Expressions
- Nested sequences
- Working with files
- File overview
- Opening a text file
- Reading a text file
- Writing to a text file
- Reading and writing raw (binary) data
- Converting binary data with struct
- Dictionaries and Sets
- About dictionaries
- Creating dictionaries
- Iterating through a dictionary
- About sets
- Creating sets
- Working with sets
- Functions
- Defining functions
- Parameters
- Global and local scope
- Nested functions
- Returning values
- Sorting
- The sorted() function
- Alternate keys
- Lambda functions
- Sorting collections
- Using operator.itemgetter()
- Reverse sorting
- Errors and Exception Handling
- Syntax errors
- Exceptions
- Using try/catch/else/finally
- Handling multiple exceptions
- Ignoring exceptions
- Modules and Packages
- The import statement
- Module search path
- Creating Modules
- Using packages
- Function and Module aliases
- Classes
- About OO programming
- Defining classes
- Constructors
- Methods
- Instance data
- Properties
- Class methods and data
- Regular Expressions
- RE syntax overview
- RE Objects
- Searching and matching
- Compilation flags
- Groups and special groups
- Replacing text
- Splitting strings
- The Standard library
- The sys module
- Launching external programs
- Math functions
- Random numbers
- The string module
- Reading CSV data
- Dates and times
- Working with dates and times
- Translating timestamps
- Parsing dates from text
- Formatting dates
- Calendar data
- Working with the file system
- Paths, directories, and filenames
- Checking for existence
- Permissions and other file attributes
- Walking directory trees
- Creating filters with fileinput
- Using shutil for file operations
- Advanced data handling
- Defaultdict and Counter
- Prettyprinting data structures
- Compressed archives (zip, gzip, tar, etc.)
- Persistent data
- Network services
- Grabbing web content
- Sending email
- Using SSH for remote access
- Using FTP
- Writing real-life applications
- Parsing command-line options
- Detecting the current platform
- Trapping signals
- Implementing logging
Python for Scientists
Course Number:
This course provides a ramp-up to using Python for scientific and mathematical computing. Starting with the basics, it progresses to the most important Python modules for working with data, from arrays, to statistics, to plotting results. The material is geared towards scientists and engineers.
This is a hands-on programming class. All concepts are reinforced by informal practice during the lecture followed by lab exercises. Many labs build on earlier labs, which helps students retain the earlier material.
Applied Python is 35 percent hands-on, 65 percent lecture, with the longest lecture segments lasting for around 45 minutes. Students “learn by doing,” with immediate opportunities to apply the material they learn to real-world problems.
Audience:
Prerequisites:
While there are no programming prerequisites, programming experience is helpful. Students should be comfortable working with files and folders, and should not be afraid of the command line.
- Create and run basic programs
- Design and code modules and classes
- Implement and run unit tests
- Use benchmarks and profiling to speed up programs
- Process XML and JSON
- Manipulate arrays with numpy
- Get a grasp of the diversity of subpackages that make up scipy
- Use iPython notebooks for ad hoc calculations, plots, and what-if?
- Manipulate images with PIL
- Solve equations with sympy
- The Python Environment
- About Python
- Starting Python
- Using the interpreter
- Running a Python script
- Python scripts on Unix/Windows
- Using the Spyder editor
- Getting Started
- Using variables
- Built-in functions
- Strings
- Numbers
- Converting among types
- Writing to the screen
- String formatting
- Command line parameters
- Flow Control
- About flow control
- White space
- Conditional expressions (if,else)
- Relational and Boolean operators
- While loops
- Alternate loop exits
- Sequences
- About sequences
- Lists and tuples
- Indexing and slicing
- Iterating through a sequence
- Sequence functions,keywords, and operators
- List comprehensions
- Generator expressions
- Nested sequences
- Working with Files
- File overview
- Opening a text file
- Reading a text file
- Writing to a text file
- Raw (binary) data
- Dictionaries and Sets
- Creating dictionaries
- Iterating through a dictionary
- Creating sets
- Working with sets
- Functions
- Defining functions
- Parameters
- Variable scope
- Returning values
- Lambda functions
- Errors and Exception Handling
- Syntax errors
- Exceptions
- Using try/catch/else/finally
- Handling multiple exceptions
- Ignoring exceptions
- OS Services
- The OS module
- Environment variables
- Launching external commands
- Walking directory trees
- Paths, directories, and filenames
- Working with file systems
- Dates and times
- Pythonic idioms
- Small Pythonisms
- Lambda functions
- Packing and unpacking sequences
- List Comprehensions
- Generator Expressions
- Modules and Packages
- Initialization code
- Namespaces
- Executing modules as scripts
- Documentation
- Packages and name resolution
- Naming conventions
- Using imports
- Classes
- Defining classes
- Constructors
- Instance methods and data
- Attributes
- Inheritance
- Multiple inheritance
- Developer Tools
- Analyzing programs with pylint
- Creating and running unit tests
- Debugging applications
- Benchmarking code
- Profiling applications
- XML and JSON
- Using ElementTree
- Creating a new XML document
- Parsing XML
- Finding by tags and XPath
- Parsing JSON into Python
- Parsing Python into JSON
- iPython
- iPython basics
- Terminal and GUI shells
- Creating and using notebooks
- Saving and loading notebooks
- Ad hoc data visualization
- numpy
- numpy basics
- Creating arrays
- Indexing and slicing
- Large number sets
- Transforming data
- Advanced tricks
- scipy
- What can scipy do?
- Most useful functions
- Curve fitting
- Modeling
- Data visualization
- Statistics
- A Tour of scipy Subpackages
- Clustering
- Physical and mathematical Constants
- FFTs
- Integral and differential solvers
- Interpolation and smoothing
- Input and Output
- Linear Algebra
- Image Processing
- Distance Regression
- Root-finding
- Signal Processing
- Sparse Matrices
- Spatial data and algorithms
- Statistical distributions and functions
- C/C++ Integration
- pandas
- pandas overview
- Dataframes
- Reading and writing data
- Data alignment and reshaping
- Fancy indexing and slicing
- Merging and joining data sets
- matplotlib
- Creating a basic plot
- Commonly used plots
- Ad hoc data visualization
- Advanced usage
- Exporting images
- The Python Imaging Library (PIL)
- PIL overview
- Core image library
- Image processing
- Displaying images
Python I Training
Course Number:
This course leads the student from the basics of writing and running Python scripts to more advanced features such as file operations, regular expressions, working with binary data, and using the extensive functionality of Python modules. Extra emphasis is placed on features unique to Python, such as tuples, array slices, and output formatting.
This is a hands-on programming class. All concepts are reinforced by informal practice during the lecture followed by graduated lab exercises.
Python Programming is a practical introduction to a working programming language, not an academic overview of syntax and grammar. Students will immediately be able to use Python to complete tasks in the real world.
Note: This course can be taught for either Python 2.x or Python 3.x. Version-specific course materials and exercises will be provided.
Audience:
Prerequisites:
Students should already have a working, user-level knowledge of Unix/Linux, Mac, or Windows. While not required, basic skills with at least one other programming language will be helpful.
- 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
- 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
- An overview of Python
- What is python?
- Python Timeline
- Advantages/Disadvantages of Python
- Getting help with pydoc
- The Python Environment
- Starting Python
- Using the interpreter
- Running a Python script
- Python scripts on Unix/Windows
- Editors and IDEs
- Getting Started
- Using variables
- Built-in functions
- Strings
- Numbers
- Converting among types
- Writing to the screen
- Command line parameters
- Flow Control
- About flow control
- White space
- Conditional expressions
- Relational and Boolean operators
- While loops
- Alternate loop exits
- Sequences
- About sequences
- Lists and list methods
- Tuples
- Indexing and slicing
- Iterating through a sequence
- Sequence functions,keywords, and operators
- List comprehensions
- Nested sequences
- Working with files
- File overview
- Opening a text file
- Reading a text file
- Writing to a text file
- Raw (binary) data
- Dictionaries and Sets
- About dictionaries
- Creating dictionaries
- Iterating through a dictionary
- About sets
- Creating sets
- Working with sets
- Functions
- About sequences
- Function parameters
- Global variables
- Global scope
- Returning values
- Creating a library of functions
- Sorting
- The sorted() function
- Alternate keys
- Lambda functions
- Sorting collections
- Using operator.itemgetter()
- Reverse sorting
- Errors and Exception Handling
- Syntax errors
- Exceptions
- Using try/catch/else/finally
- Handling multiple exceptions
- Ignoring exceptions
- Using Modules
- The import statement
- Module search path
- Zipped libraries
- Creating Modules
- Function and Module aliases
- Regular Expressions
- RE syntax overview
- Basic patterns
- RE Objects
- Searching and matching
- Compilation flags
- Grouping
- Replacing text
- Splitting a string
- Using the Standard Library
- The sys module
- Launching external programs
- Walking directory trees
- Grabbing web pages
- Sending e-mail
- Paths, directories, and filenames
- Math functions
- Random numbers
- Dates and times
- Zipped archives
- An Introduction to Python classes
- About o-o programming
- Defining classes
- Constructors
- Instance methods
- Instance data
- Class methods and data
- Destructors
Python II Training
Course Number:
Once students have mastered the basics of Python via our introductory Python course or their own work, it’s time to move on to applying Python to daily programming needs. This course picks up where Python I leaves off, covering some topics in more detail, and adding many new ones, with a focus on enterprise development.
This is a hands-on programming class. All concepts are reinforced by informal practice during the lecture followed by lab exercises. Many labs build on earlier labs, which helps students retain the earlier material.
Python II is 40percent hands-on, 60 percent lecture, with the longest lecture segments lasting for around 45 minutes. Students “learn by doing,” with immediate opportunities to apply the material they learn to real-world problems.
Audience:
Prerequisites:
All students should be able to write simple Python scripts, using basic data types, program structures, and the standard Python library.
All students will learn to use Python to:
- Leverage OS services
- Code graphical interfaces for applications
- Create modules
- Create and run unit tests
- Define classes
- Interact with network services
- Query databases
- Process XML data
- Python Refresher
- Data types
- Sequences
- Mapping types
- Program structure
- Files and console I/O
- Conditionals
- Loops
- Built-ins
- OS Services
- The OS module
- Environment variables
- Launching external commands
- Walking directory trees
- Paths, directories, and filenames
- Working with file systems
- Dates and times
- Pythonic Programming
- The Zen of Python
- Common idioms
- Lambda functions
- List comprehensions
- Generator expressions
- String formatting
- Modules and Packages
- Initialization code
- Namespaces
- Executing modules as scripts
- Documentation
- Packages and name resolution
- Naming conventions
- Using imports
- Classes
- Defining classe
- Instance methods and data
- Initializers
- Class methods
- Static methods
- Inheritance
- Multiple inheritance
- Pseudo-private variables
- Metaprogramming
- Implicit properties
- globals() and locals()
- Attributes
- The inspect module
- Decorators
- Monkey patching
- Programmer Tools
- Analyzing programs
- Using pylint
- Testing code
- Using unittest
- Debugging
- Profiling
- Distributing Modules
- Distribution concepts
- setuptools
- Creating setup.py
- Building installers
- Running installers
- Database Access
- The DB API
- Available Interfaces
- Connecting to a server
- Creating and executing a cursor
- Fetching data
- Parameterized statements
- Metadata
- Transaction control
- GUI Programming with Tkinter
- Overview
- The main window object
- Widgets
- Colors and fonts
- GUI layout
- Event handling
- GUI Programming with PyQt4
- Overview
- A minimal QT application
- Objected-oriented PyQt4
- Using the designer
- Wiring up events
- Using predefined dialogs
- Network Programming
- Sockets
- Clients
- Servers
- Application protocols
- Forking servers
- Threaded servers
- Binary data
- The struct module
- Threads
- Why use threads?
- Threads are different
- Variables are shared
- Python threads modules
- The threading module
- The queue module
- The python thread manager
- Debugging threaded programs
- Alternatives to threading
- System Administration
- Launching programs with subprocess
- Remote access
- Permissions
- Data persistence
- Command line options
- Writing filters
- Logging
- XML and JSON
- Working with XML
- DOM and Sax
- Introducing ElementTree
- Parsing XML
- Navigating the document
- Creating a new XML document
- JSON
- Parsing JSON into Python
- Converting Python into JSON
- Extending Python
- About non-Python modules
- Overview of a C extension
- Creating functions
- Registering functions
- Initialization code
- Loading the module
Python for Data Analytics
Course Number:
The Python for Data Analytics course is intended to develop data analysts capable of driving data-driven decisions in any organization. The course begins with a refresher on the Python programming language and builds on this knowledge to introduce students to the world of data analytics in Python. Students will learn to use advanced data structures in Python, access data from external sources, use descriptive analytics, perform statistical analysis, and visualize their data in meaningful ways.
Audience:
Prerequisites:
Little to some programming knowledge in any programming language, preferably Python.
Upon completion of this course, participants should:
- Write Python scripts that that take in user input and output results
- Use Anaconda to simplify package management
- Create and use advanced data structures like vectors, matrices, and data frames
- Access data from external sources and manipulate it with Python data structures
- Use descriptive analytics and statistical analysis techniques to derive useful insight from data
- Visualize and format data in common charts using matplotlib
- Drive data-driven decisions in your organization
Part 1: Python Environment, Conventions, and Fundamentals Refresher
- What is Python?
- Brief history
- Uses
- Quirks of Python
- Introduction to Anaconda
- Writing Python Scripts
- Basic Data Types
- Functions
- Flow Control
- Decision-making Statements
- Looping Statements
- Python conventions
- Python best practices
Part 2: Introduction to Data Analytics
- The Data Analysis Process
- Descriptive Data Analytics
- Diagnostic Data Analytics
- Predictive Data Analytics
- Prescriptive Data Analytics
Part 3: Advanced Data Structures
- What are Data Structures?
- Why Do We Need Different Kinds of Data Structures?
- Lists
- Arrays / Vectors
- Matrices
- Factors (Categorical Data in pandas)
- pandas DataFrame
- CSV Files
Part 4: Accessing Data
- CSV Files in Python
- Reading CSV Files
- Parsing CSV Files
- Loading Data into Python Data Structures
- Outputting CSV Files
- Connecting to Databases in Python
- Reading Database Tables
- Accessing Data in Databases
- Loading Data into Python Data Structures
- Creating Data Tables
- Updating Data in Databases
Part 5: Data Wrangling
- Filter Out Bad or Irrelevant Data
- Derive Composite Fields
- Aggregating Data from Multiple Sources
- Standardization
- Normalization
Part 6: Introduction to Exploratory Data Analytics
- Explore the Data
- Answer the Question, “What Happened?”
- Identify Trends and Patterns
- Summary Statistics
- Clustering
- Pattern Tracking
- Regression Analysis
- Generate Primitive Reports
Part 7: Introduction to Statistical Analytics
- Statistical Analysis Basics
- Mean
- Median
- Mode
- Range
- Standard Deviation
- Linear Regression
- Hypothesis Testing
- Sample Size Determination
- How Do We Apply This Data to Business Decisions?
Part 8: Visualizing Data
- Charts
- Bar Charts
- Box Plots
- Histograms
- Line Plots
- Pie Charts
- Generate Business Reports
- Tableau, Power BI, and Other Tools
Python for Data Analytics II
Course Number:
The Python for Data Analytics II course builds on concepts from the first Python for Data Analytics course. Students will learn to leverage Python generators to conserve memory, serialize data into multiple formats, collect data from the web, integrate with Excel, use regular expressions, and perform predictive analytics. In the end, students will obtain the skills necessary to use data to make projections capable of influencing a diverse array of business decisions.
Audience:
Prerequisites:
The following are prerequisites for this course:
- Little to some programming knowledge in Python
- Python for Data Analytics course or understanding of the concepts
- Advanced Data Structures
- Accessing Data from External Sources
- Descriptive Analytics in Python
- Statistical Analysis in Python
- Data Visualization in Python
Upon completion of this course, participants should:
- Use Python generators
- Pickle data
- Serialize data to JSON, XML, CSV, bytes
- Collect data from the web
- Integrate your Python code with Microsoft Excel
- Write regular expressions for matching, searching, splitting, and replacing
- Apply predictive analysis techniques
- Develop models for predictive analysis
Part 1: Python Generators
- The yield Keyword
- Generators
- Generator Expressions
- Generator Classes
- Generator Methods
- .send()
- .throw()
- .close()
Part 2: Python Data Serialization
- The Python pickle module
- JSON
- XML
- CSV
- Bytes
Part 3: Integrating Python with Web Data
- Sources
- FTP
- RESTful Data
- Screen Scraping
Part 4: Integrating Python with Excel
- Python Modules for Excel
- xlrd
- xlwr
- xlutil
- Interacting with Excel from Python
- Creating Excel Spreadsheets
- Reading Excel Spreadsheets
- Updating Excel Spreadsheets
Part 5: Regular Expressions in Python
- Regular Expression Syntax
- Special Characters
- Repetition Characters
- The re Module
- Regular Expression Objects
- re Functions
- Searching
- Matching
- Replacing
- Splitting
Part 6: Introduction to Predictive Analytics
- Answering the Question, “What could happen in the future?”
- Case Studies of Business Applications of Predictive Analysis
- Future Cash Flow
- Investment Performance
- Staffing Needs
- Behavioral Marketing
- Manufacturing Malfunctions
- Ethical Concerns of Predictive Analysis
- Infamous and Illegal, Redlining
- Three Pillars of Data Analytics
- Needs
- Data and Technology
- Actions and Insights
Part 7: Predictive & Prescriptive Analytics Techniques
- Decision Trees
- Regression
- Single Linear Regression
- Multiple Regression
- Neural Networks
- Time Series Modeling
Python for Data Science and Machine Learning
Course Number:
The Python for Data Science and Machine Learning course builds on concepts from the Python for Data Analytics courses. Students will learn to prepare data for machine learning, implement machine learning algorithms, train machine learning models, and use evaluation metrics on their machine learning models. In the end, students will obtain the skills necessary to utilize machine learning for predictive analytics.
Audience:
Prerequisites:
The following are prerequisites for this course:
- Moderate programming knowledge in Python
- Python for Data Analytics course or understanding of the concepts
- Advanced Data Structures
- Descriptive Analytics in Python
- Statistical Analysis in Python
- Data Visualization in Python
Upon completion of this course, participants should:
- Prepare data for machine learning
- Implement machine learning algorithms
- Use SciKit-Learn, Tensorflow, and Keras for machine learning tasks
- Successfully train and fine-tune machine learning models
- Evaluate machine learning models
Part 1: Introduction to Machine Learning
- Applications of Machine Learning
- Challenges of Machine Learning
- Big Data
- Python Libraries for Machine Learning
- Artificial Neurons
- Supervised vs. Unsupervised Machine Learning
Part 1: Supervised Machine Learning
- Classification and Regression
- Generalization, Overfitting, and Underfitting
- Model Complexity and Dataset Size
- Linear Models
- Naive Bayes Classifiers
- Decision Trees
- Neural Networks and Deep Learning
- Random Forests
Part 2: Unsupervised Machine Learning
- Challenges in Unsupervised Machine Learning
- Preprocessing
- Handling Missing Data
- Handling Categorical Data
- k-means Clustering and Data Mining
- Dimensionality Reduction
Part 4: Training Models
- Linear Regression
- Gradient Decent
- Polynomial Regression
- Logistic Regression
Part 3: Model Evaluation and Improvement
- Cross Evaluation
- Grid Searching
- Evaluation Metrics
- CSV
- Bytes