12632 Courses

Duration 1 Days 6 CPD hours This course is intended for Individuals who have knowledge, skills, and experience developing front-end and/or back-end JavaScript applications for the web stack. Developers who have experience designing, developing, testing, and deploying applications using an object-oriented programming language and would like to transfer those skills to building applications with JavaScript. Overview When you complete this course, you will be able to: Understand the different exam objectives and their weighting on the exam. Know which JavaScript concepts to focus on to best prepare for your exam. Study the provided repository of JavaScript sample code. Are you ready to take the next step in your career by becoming a Salesforce Certified JavaScript Developer I? By covering the details around the exam structure and objectives, this course will help hone your problem-solving skills and reinforce your knowledge of key features and concepts of the JavaScript programming language. This course includes a voucher to sit for the Salesforce JavaScript Developer I certification exam. JavaScript Basics Data Types and Variables Type Conversion (explicit and implicit) Collections Working with Strings, Numbers, and Dates Working with JSON Objects, Functions, and Classes Creating Objects Defining Functions Object Prototypes Declaring Classes Using JavaScript Modules Browser and Events Document Object Model DOM Events Browser Dev Tools Debugging and Error Handling Throwing and Catching Errors Working with the Console Asynchronous Programming Callback Functions Promises Async/Await Server Side JavaScript Node.js CLI Node.js Libraries Debugging in Node.js npm Testing Assertions Types of Testing Additional course details: Nexus Humans Salesforce Certification Preparation for Salesforce JavaScript Developer I (CRT600) training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the Salesforce Certification Preparation for Salesforce JavaScript Developer I (CRT600) course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Duration 4 Days 24 CPD hours This course is intended for The workshop is designed for data scientists who currently use Python or R to work with smaller datasets on a single machine and who need to scale up their analyses and machine learning models to large datasets on distributed clusters. Data engineers and developers with some knowledge of data science and machine learning may also find this workshop useful. Overview Overview of data science and machine learning at scale Overview of the Hadoop ecosystem Working with HDFS data and Hive tables using Hue Introduction to Cloudera Data Science Workbench Overview of Apache Spark 2 Reading and writing data Inspecting data quality Cleansing and transforming data Summarizing and grouping data Combining, splitting, and reshaping data Exploring data Configuring, monitoring, and troubleshooting Spark applications Overview of machine learning in Spark MLlib Extracting, transforming, and selecting features Building and evaluating regression models Building and evaluating classification models Building and evaluating clustering models Cross-validating models and tuning hyperparameters Building machine learning pipelines Deploying machine learning models Spark, Spark SQL, and Spark MLlib PySpark and sparklyr Cloudera Data Science Workbench (CDSW) Hue This workshop covers data science and machine learning workflows at scale using Apache Spark 2 and other key components of the Hadoop ecosystem. The workshop emphasizes the use of data science and machine learning methods to address real-world business challenges. Using scenarios and datasets from a fictional technology company, students discover insights to support critical business decisions and develop data products to transform the business. The material is presented through a sequence of brief lectures, interactive demonstrations, extensive hands-on exercises, and discussions. The Apache Spark demonstrations and exercises are conducted in Python (with PySpark) and R (with sparklyr) using the Cloudera Data Science Workbench (CDSW) environment. The workshop is designed for data scientists who currently use Python or R to work with smaller datasets on a single machine and who need to scale up their analyses and machine learning models to large datasets on distributed clusters. Data engineers and developers with some knowledge of data science and machine learning may also find this workshop useful. Overview of data science and machine learning at scaleOverview of the Hadoop ecosystemWorking with HDFS data and Hive tables using HueIntroduction to Cloudera Data Science WorkbenchOverview of Apache Spark 2Reading and writing dataInspecting data qualityCleansing and transforming dataSummarizing and grouping dataCombining, splitting, and reshaping dataExploring dataConfiguring, monitoring, and troubleshooting Spark applicationsOverview of machine learning in Spark MLlibExtracting, transforming, and selecting featuresBuilding and evauating regression modelsBuilding and evaluating classification modelsBuilding and evaluating clustering modelsCross-validating models and tuning hyperparametersBuilding machine learning pipelinesDeploying machine learning models Additional course details: Nexus Humans Cloudera Data Scientist Training training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the Cloudera Data Scientist Training course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Duration 2 Days 12 CPD hours This course is intended for This introductory-level course is intended for Business Analysts and Data Analysts (or anyone else in the data science realm) who are already comfortable working with numerical data in Excel or other spreadsheet environments. No prior programming experience is required, and a browser is the only tool necessary for the course. Overview This course is approximately 50% hands-on, combining expert lecture, real-world demonstrations and group discussions with machine-based practical labs and exercises. Our engaging instructors and mentors are highly experienced practitioners who bring years of current 'on-the-job' experience into every classroom. Throughout the hands-on course students, will learn to leverage Python scripting for data science (to a basic level) using the most current and efficient skills and techniques. Working in a hands-on learning environment, guided by our expert team, attendees will learn about and explore (to a basic level): How to work with Python interactively in web notebooks The essentials of Python scripting Key concepts necessary to enter the world of Data Science via Python This course introduces data analysts and business analysts (as well as anyone interested in Data Science) to the Python programming language, as it?s often used in Data Science in web notebooks. This goal of this course is to provide students with a baseline understanding of core concepts that can serve as a platform of knowledge to follow up with more in-depth training and real-world practice. An Overview of Python Why Python? Python in the Shell Python in Web Notebooks (iPython, Jupyter, Zeppelin) Demo: Python, Notebooks, and Data Science Getting Started Using variables Builtin 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, Arrays, Dictionaries and Sets 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 Dictionaries Working with Sets Working with files File overview Opening a text file Reading a text file Writing to a text file Reading and writing raw (binary) data Functions Defining functions Parameters Global and local scope Nested functions Returning values Essential Demos Sorting Exceptions Importing Modules Classes Regular Expressions The standard library Math functions The string module Dates and times Working with dates and times Translating timestamps Parsing dates from text Formatting dates Calendar data Python and Data Science Data Science Essentials Pandas Overview NumPy Overview SciKit Overview MatPlotLib Overview Working with Python in Data Science Additional course details: Nexus Humans Python for Data Science: Hands-on Technical Overview (TTPS4873) training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the Python for Data Science: Hands-on Technical Overview (TTPS4873) course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Duration 3 Days 18 CPD hours This course is intended for This course is geared for attendees with solid Python skills who wish to learn and use basic machine learning algorithms and concepts Overview This 'skills-centric' course is about 50% hands-on lab and 50% lecture, with extensive practical exercises designed to reinforce fundamental skills, concepts and best practices taught throughout the course. Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below Getting Started & Optional Python Quick Refresher Statistics and Probability Refresher and Python Practice Probability Density Function; Probability Mass Function; Naive Bayes Predictive Models Machine Learning with Python Recommender Systems KNN and PCA Reinforcement Learning Dealing with Real-World Data Experimental Design / ML in the Real World Time Permitting: Deep Learning and Neural Networks Machine Learning Essentials with Python is a foundation-level, three-day hands-on course that teaches students core skills and concepts in modern machine learning practices. This course is geared for attendees experienced with Python, but new to machine learning, who need introductory level coverage of these topics, rather than a deep dive of the math and statistics behind Machine Learning. Students will learn basic algorithms from scratch. For each machine learning concept, students will first learn about and discuss the foundations, its applicability and limitations, and then explore the implementation and use, reviewing and working with specific use casesWorking in a hands-on learning environment, led by our Machine Learning expert instructor, students will learn about and explore:Popular machine learning algorithms, their applicability and limitationsPractical application of these methods in a machine learning environmentPractical use cases and limitations of algorithms Getting Started Installation: Getting Started and Overview LINUX jump start: Installing and Using Anaconda & Course Materials (or reference the default container) Python Refresher Introducing the Pandas, NumPy and Scikit-Learn Library Statistics and Probability Refresher and Python Practice Types of Data Mean, Median, Mode Using mean, median, and mode in Python Variation and Standard Deviation Probability Density Function; Probability Mass Function; Naive Bayes Common Data Distributions Percentiles and Moments A Crash Course in matplotlib Advanced Visualization with Seaborn Covariance and Correlation Conditional Probability Naive Bayes: Concepts Bayes? Theorem Naive Bayes Spam Classifier with Naive Bayes Predictive Models Linear Regression Polynomial Regression Multiple Regression, and Predicting Car Prices Logistic Regression Logistic Regression Machine Learning with Python Supervised vs. Unsupervised Learning, and Train/Test Using Train/Test to Prevent Overfitting Understanding a Confusion Matrix Measuring Classifiers (Precision, Recall, F1, AUC, ROC) K-Means Clustering K-Means: Clustering People Based on Age and Income Measuring Entropy LINUX: Installing GraphViz Decision Trees: Concepts Decision Trees: Predicting Hiring Decisions Ensemble Learning Support Vector Machines (SVM) Overview Using SVM to Cluster People using scikit-learn Recommender Systems User-Based Collaborative Filtering Item-Based Collaborative Filtering Finding Similar Movie Better Accuracy for Similar Movies Recommending movies to People Improving your recommendations KNN and PCA K-Nearest-Neighbors: Concepts Using KNN to Predict a Rating for a Movie Dimensionality Reduction; Principal Component Analysis (PCA) PCA with the Iris Data Set Reinforcement Learning Reinforcement Learning with Q-Learning and Gym Dealing with Real-World Data Bias / Variance Tradeoff K-Fold Cross-Validation Data Cleaning and Normalization Cleaning Web Log Data Normalizing Numerical Data Detecting Outliers Feature Engineering and the Curse of Dimensionality Imputation Techniques for Missing Data Handling Unbalanced Data: Oversampling, Undersampling, and SMOTE Binning, Transforming, Encoding, Scaling, and Shuffling Experimental Design / ML in the Real World Deploying Models to Real-Time Systems A/B Testing Concepts T-Tests and P-Values Hands-on With T-Tests Determining How Long to Run an Experiment A/B Test Gotchas Capstone Project Group Project & Presentation or Review Deep Learning and Neural Networks Deep Learning Prerequisites The History of Artificial Neural Networks Deep Learning in the TensorFlow Playground Deep Learning Details Introducing TensorFlow Using TensorFlow Introducing Keras Using Keras to Predict Political Affiliations Convolutional Neural Networks (CNN?s) Using CNN?s for Handwriting Recognition Recurrent Neural Networks (RNN?s) Using an RNN for Sentiment Analysis Transfer Learning Tuning Neural Networks: Learning Rate and Batch Size Hyperparameters Deep Learning Regularization with Dropout and Early Stopping The Ethics of Deep Learning Learning More about Deep Learning Additional course details: Nexus Humans Machine Learning Essentials with Python (TTML5506-P) training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the Machine Learning Essentials with Python (TTML5506-P) course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Duration 5 Days 30 CPD hours This course is intended for System administrator Network administrator Technician DevOps Overview The Linux Professional Institute(LPI) is the go to certification body for vendor independent Linux certifications. This course covers fundamental Linuxskills such as file management and manipulation, text processing, command line use, package management,filesystems, hardware, and many more. Students will feel confident taking the LPI LPIC-1 101 exam with in classroomassessments and practice exams. This course prepares students to take the 101 exam of the LPI level 1 certification. Work on the Command Line LPI Objectives Covered Role of Command Shell Shells Gathering System Info Identifying the Shell Changing the Shell Shell Prompts Bash: Bourne-Again Shell Navigating the Filesystem Help from Commands and Documentation Getting Help Within the Graphical Desktop Getting Help with man & info Bash: Command Line History Bash: Command Editing Bash: Command Completion Shell and Environment Variables Key Environment Variables LAB TASKS Use Streams, Pipes, and Redirects LPI Objectives Covered File Redirection Piping Commands Together Filename Matching File Globbing and Wildcard Patterns Brace Expansion General Quoting Rules Nesting Commands Gotchas: Maximum Command Length LAB TASKS Manage File Permissions and Ownership LPI Objectives Covered Filesystem Hierarchy Standard Displaying Directory Contents Filesystem Structures Determining Disk Usage With df and du File Ownership Default Group Ownership File and Directory Permissions File Creation Permissions with umask Changing File Permissions SUID and SGID on files SGID and Sticky Bit on Directories User Private Group Scheme LAB TASKS Create, Delete, Find, and Display Files LPI Objectives Covered Directory Manipulation File Manipulation Deleting and Creating Files Physical Unix File Structure Filesystem Links File Extensions and Content Which and Type Where is Searching the Filesystem Alternate Search Method Manually Installed Shared Libraries LAB TASKS Work with Archives and Compression LPI Objectives Covered Archives with tar Archives with cpio The gzip Compression Utility The bzip2 Compression Utility The XZ Compression Utility The PKZIP Archiving/Compression format LAB TASKS Process Text Streams Using Filters LPI Objectives Covered Producing File Statistics The Streaming Editor Replacing Text Characters Text Sorting Duplicate Removal Utility Extracting Columns of Text Displaying Files Prepare Text for Display Previewing Files Displaying Binary Files Combining Files and Merging Text LAB TASKS Search Text Files Using Regular Expressions LPI Objectives Covered Searching Inside Files Regular Expression Overview Regular Expressions RE Character Classes Regex Quantifiers RE Parenthesis LAB TASKS Perform Basic File Editing Operations Using VI LPI Objectives Covered Text Editing vi and Vim Learning Vim Basic vi Intermediate vi LAB TASKS Create, Monitor, and Kill Processes LPI Objectives Covered What is a Process? Process Lifecycle Process States Viewing Processes Signals Tools to Send Signals Managing Processes Tuning Process Scheduling Job Control Overview Job Control Commands Nohup and Disown Uptime & w Persistent Shell Sessions with Screen Using screen Advanced Screen LAB TASKS Use RPM, YUM, and Debian Package Management LPI Objectives Covered Managing Software RPM Architecture Working With RPMs Querying and Verifying with RPM Installing Debian Packages Querying and Verifying with dpkg The alien Package Conversion Tool Managing Software Dependencies Using the Yum command yum downloader Configuring Yum The deselect & APT Frontends to dpkg Aptitude Configuring APT LAB TASKS Work with Partitions, Filesystem, and Disk Quotas LPI Objectives Covered Partition Considerations Logical Volume Management Filesystem Planning Partitioning Disks with fdisk & gdisk Resizing a GPT Partition with gdisk Partitioning Disks with parted Non-Interactive Disk Partitioning with sfdisk Filesystem Creation Filesystem Support Unix/Linux Filesystem Features Swap Selecting a Filesystem Filesystem Maintenance Mounting Filesystems Mounting Filesystems Managing an XFS Filesystem NFS SMB Filesystem Table (/etc/fstab) Configuring Disk Quotas Setting Quotas Viewing and Monitoring Quotas LAB TASKS Linux Boot Process LPI Objectives Covered Booting Linux on PCs GRUB 2 GRUB 2 Configuration GRUB Legacy Configuration Boot Parameters Uinit Linux Runlevels Aliases Systemd local-fs.target and sysinit.target Runlevel Implementation System Boot Method Overview Systemd System and Service Manager Modifying systemd services Systemd Targets Using systemd Shutdown and Reboot System Messaging Commands Controlling System Messaging LAB TASKS Determine and Configure Hardware Settings LPI Objectives Covered Managing Linux Device Files Hardware Discovery Tools Configuring New Hardware with hwinfo PC Architecture and Bus DMA & IRQ USB Devices USB Architecture Configuring Kernel Components and Modules Kernel Modules Handling Module Dependencies Configuring the Kernel via /proc/ LAB TASKS Linux Fundamentals Unix and its Design Principles FSF and GNU GPL ƒ?? General Public License The Linux Kernel Components of a Distribution Red Hat Linux Products SUSE Linux Products Debian Ubuntu Logging In got root? Switching User Contexts Gathering Login Session Info LAB TASKS Additional course details: Nexus Humans Linux Professional Institute Certification (LPIC) 101 training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the Linux Professional Institute Certification (LPIC) 101 course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Duration 1 Days 6 CPD hours This course is intended for Software Engineers Overview The objective of this course is to learn the key language concepts to machine learning, Spark MLlib, and Spark ML. This course will teach you the key language concepts to machine learning, Spark MLlib, and Spark ML. The course includes coverage of collaborative filtering, clustering, classification, algorithms, and data volume. This course will teach you the key language concepts to machine learning, Spark MLlib, and Spark ML. The course includes coverage of collaborative filtering, clustering, classification, algorithms, and data volume.

Duration 5 Days 30 CPD hours This course is intended for New users of BSM 9.0 -- 9.2,including:? IT Tools engineers? Database administrators? System administrators? Network administrators? Operations managers? Availability engineers Overview After completing this course, you should be able to:? Identify HP Business Service Management (BSM)deployment scenarios? Monitor application performance and availability fromthe end user perspective? Integrate HP SiteScope with the BSM environment? Monitor the real-time status of Key PerformanceIndicators (KPIs), view performance metrics, and workwith reports containing historical performance data? Configure and work with an IT model to build atopology of your system, populate the model withconfiguration items (CIs) and relationships, and use themodel to measure and manage critical businessprocesses? Create and analyze reports that present and organizespecific data your organization might need? Create and manage service level agreements (SLAs)representing your department contracts with serviceproviders, customers, and internal business units? Perform administrative tasks to enable user access,configure licenses, and enhance system performance? Work with the following applications: End UserManagement (EUM), Business Process Monitor (BPM),SiteScope/System Availability Manager (SAM), Run-Time Service Model (RTSM), BSM applications ? ServiceHealth (SH), MyBSM, Service Level Management (SLM). This entry-level, instructor-led classroom training offers technical personnel, who are new to HP Business Service Management (BSM) 9.0 ? 9.21, the opportunity to develop hands-on experience in applying the fundamental concepts, principles This entry-level, instructor-led classroom training offers technical personnel, who are new to HP Business Service Management (BSM) 9.0 ? 9.21, the opportunity to develop hands-on experience in applying the fundamental concepts, principles

Duration 4 Days 24 CPD hours This course is intended for This course is intended for: Network administrators Network engineers with little or no programming or Python experience Network managers Systems engineers Overview After taking this course, you should be able to: Create a Python script Describe data types commonly used in Python coding Describe Python strings and their use cases Describe Python loops, conditionals, operators, and their purposes and use cases Describe Python classes, methods, functions, namespaces, and scopes Describe the options for Python data manipulation and storage Describe Python modules and packages, their uses, and their benefits Explain how to manipulate user input in Python Describe error and exception management in Python Describe Python code debugging methods The Programming for Network Engineers (PRNE) v2.0 course is designed to equip you with fundamental skills in Python programming. Through a combination of lectures and lab experience in simulated network environments, you will learn to use Python basics to create useful and practical scripts with Netmiko to retrieve data and configure network devices. Upon completion of this course, you should have a basic understanding of Python, including the knowledge to create, apply, and troubleshoot simple network automation scripts. Course Outline Introducing Programmability and Python for Network Engineers Scripting with Python Examining Python Data Types Manipulating Strings Describing Conditionals, Loops, and Operators Exploring Classes, Methods, Functions, Namespaces, and Scopes Exploring Data Storage Options Exploring Python Modules and Packages Gathering and Validating User Input Analyzing Exceptions and Error Management Examining Debugging Methods

Duration 1 Days 6 CPD hours This course is intended for This course is designed for sales representatives who are managing their sales territories, with the goal of converting sales leads into sales opportunities and sales opportunities into closed sales. You may be selling products or services, or you may or may not be familiar with other CRM systems and may be new to Salesforce CRM. Overview In this course, students will create and manage records in the trial version of Salesforce© Sales Cloud© Lightning Enterprise Edition.Students will:- Create a basic Salesforce Sales Cloud environment and navigate the interface.- Optimize lead qualification.- Manage accounts and contacts.- Manage the opportunity pipeline.- Use Salesforce email.- Work with reports and dashboards. This course will give students the knowledge and skills they need to use Salesforce© in the real world?ultimately enabling them to meet and exceed their sales targets. Getting Started with Salesforce Set Up a Trial Version of Salesforce Sales Cloud Enterprise Edition Navigate the Salesforce LE Interface Navigate in the Salesforce Classic Interface Optimizing Lead Qualification View Leads Manage Leads Work Leads Convert Leads Managing Accounts and Contacts Manage Accounts Manage Contacts Managing the Opportunity Pipeline Manage Opportunities Work Opportunities Close Opportunities Using Salesforce Email Use Salesforce Email in Salesforce Classic Use Salesforce Email in Salesforce LE Working with Reports and Dashboards View Reports and Dashboards Create and Edit Reports and Dashboards

Duration 4 Days 24 CPD hours This course is intended for This course is intended for: Network administrators Network engineers with little or no programming or Python experience Network managers Systems engineers Overview After taking this course, you should be able to: Create a Python script Describe data types commonly used in Python coding Describe Python strings and their use cases Describe Python loops, conditionals, operators, and their purposes and use cases Describe Python classes, methods, functions, namespaces, and scopes Describe the options for Python data manipulation and storage Describe Python modules and packages, their uses, and their benefits Explain how to manipulate user input in Python Describe error and exception management in Python Describe Python code debugging methods The Programming for Network Engineers (PRNE) v2.0 course is designed to equip you with fundamental skills in Python programming. Through a combination of lectures and lab experience in simulated network environments, you will learn to use Python basics to create useful and practical scripts with Netmiko to retrieve data and configure network devices. Upon completion of this course, you should have a basic understanding of Python, including the knowledge to create, apply, and troubleshoot simple network automation scripts. Course Outline Introducing Programmability and Python for Network Engineers Scripting with Python Examining Python Data Types Manipulating Strings Describing Conditionals, Loops, and Operators Exploring Classes, Methods, Functions, Namespaces, and Scopes Exploring Data Storage Options Exploring Python Modules and Packages Gathering and Validating User Input Analyzing Exceptions and Error Management Examining Debugging Methods Course Summary