What is Python?
Opensource explains that Python is a popular general-purpose programming language that can be used for a wide variety of applications. It includes high-level data structures, dynamic typing, dynamic binding, and many more features that make it as useful for complex application development as it is for scripting or “glue code” that connects components together. It can also be extended to make system calls to almost all operating systems and to run code written in C or C++. Due to its ubiquity and ability to run on nearly every system architecture, it is a universal language found in a variety of different applications.
What is the Network Automation with Python course all about?
This intensive four-day hands-on training course is designed to help network engineers unlock the power of Python in combination with the programmability of modern networking systems. The class provides a start to finish the introduction to Python application programming for networking and network equipment. Students will learn about Python data types, program structure, functions, classes, and methods; will work with Git and understand distributed code management techniques; and will learn and use Python networking packages such as paramiko, netmiko pysnmp, and more.
Attendees will leave with a clear understanding of Python networking features and how to code with a wide range of third-party and vendor-specific libraries for controlling networking and network equipment. Students will gain hands-on experience with Python and network systems applications; will work with Cisco IOS, VMware NSX, and Arista EOS; and will learn how to use Ansible to automate network configurations.
Upon completion of the training, attendees will have the skills and information necessary to begin developing purpose-built Python applications to integrate with and control networking systems in an enterprise setting.
Available for Instructor-Led (ILT) in-person/onsite training or Virtual Instructor-Led Training (VILT) delivery; Open Enrollment options may be available.
Who should attend?
What attendees will learn?
This course is designed to help network engineers unlock the power of Python in combination with the programmability of modern networking systems.
- Overview: functions, classes, I/O, creating programs
- Python automated tests and continuous integration (CI)
- Python networking packages
- Cisco IOS, VMware NSX, and Arista EOS operations
- Ansible for Networks
Each attendee will require the ability to run a 64-bit virtual machine (provided with the course). Attendees must also have experience with networks and networking systems, as well as some programming experience.
What is the Comprehensive Data Science with Python course all about?
This programming data science training course teaches engineers, data scientists, statisticians, and other quantitative professionals the skills they need to use the Python programming language to analyze and chart data.
All students will:
- Understand the difference between Python basic data types
- Know when to use different python collections
- Ability to implement functions
- Understand control flow constructs in Python
- Handle errors via exception handling constructs
- Be able to quantitatively define an answerable, actionable question
- Import both structured and unstructured data into Python
- Parse unstructured data into structured formats
- Understand the differences between NumPy arrays and pandas data frames
- Overview of where Python fits in the Python/Hadoop/Spark ecosystem
- Simulate data through random number generation
- Understand mechanisms for missing data and analytic implications
- Explore and Clean Data
- Create compelling graphics to reveal analytic results
- Reshape and merge data to prepare for advanced analytics
- Find test for group differences using inferential statistics
- Implement linear regression from a frequentist perspective
- Understand non-linear terms, confounding, and interaction in linear regression
- Extend to logistic regression to model binary outcomes
- Understand the difference between machine learning and frequentist approaches to statistics
- Implement classification and regression models using machine learning
- Score new datasets, evaluate model fit and quantify variable importance
All attendees should have prior programming experience and an understanding of basic statistics.
- Anaconda Python 3.5 or later
- Spyder IDE (Comes with Anaconda)
Data Science with Python Programming Training Outline
- Base Introduction
- Defining Actionable, Analytic Questions
- Bringing Data In
- NumPy: Matrix Language
- Data Preparation with Pandas
- Exploratory Data Analysis with Pandas
- Exploring Data Graphically
- Advanced Graphing with Matplotlib, Pandas, and Seaborn
- Python, Hadoop, and Spark
- Missing Data
- Traditional Inferential Statistics
- Frequentist Approaches to Multivariate Statistics
- Machine Learning Approaches to Multivariate Statistics
- Supervised Learning: Regression
- Supervised Learning: Classification
Introduction to Python
This training course is an introductory level course designed for students who are new to the language and need to learn the basics as well as for students who have had some exposure and now want to take their skills to the next level by introducing new topics and reinforcing existing knowledge. After learning all the basics students progress to advanced features of the language and applying them to problem-solving. They access databases, connect to a C program, explore multi-threaded programming, and develop a simple GUI.
- Installing Python and writing basic scripts
- Using built-in data structures
- Using all flow control features
- Reading and writing from and to files
- Using Python’s extensive libraries and functions
- Accessing databases
- Connecting to C programs
- GUI development
Who Can Benefit
This class is designed for students new to Python, or for students who have some exposure and need to expand their understanding.
Good computer skills and familiarity with basic programming concepts like variables, loops, and functions.
Part 1: Basic Use
Chapter 1: Installation and Setup
Chapter 2: Getting Started
Chapter 3: Variables and Data Types
Chapter 4: Operators
Chapter 5: Control Structures
Chapter 6: Functions
Chapter 7: Exception Handling
Chapter 8: Simple File I/O
Chapter 9: Getting things done with modules and libraries
Part 2: Intermediate use
Chapter 1: Intermediate variables and operators
Chapter 2: Strings
Chapter 3: Functions
Chapter 4: Classes and Object-oriented Programming
Chapter 5: Persistence
Chapter 6: GUIs with Tkinter
Chapter 7: Numerical Processing
Chapter 8: Calling C code
Chapter 9: Threads in Python
To enroll, contact P2L today!