26133 TEC courses delivered Online

Duration 3 Days 18 CPD hours This course is intended for This course is for Network, IT security, and systems administration professionals in a Security Operations position who are tasked with configuring optimum security settings for endpoints protected by Symantec Endpoint Protection 14. Overview At the completion of the course, you will be able to: Protect against Network Attacks and Enforcing Corporate Policies using the Firewall Policy. Blocking Threats with Intrusion Prevention. Introducing File-Based Threats. Preventing Attacks with SEP. Layered Security. Securing Windows Clients. Secure Mac Clients. Secure Linux Clients. Controlling Application and File Access. Restricting Device Access for Windows and Mac Clients. Hardening Clients with System Lockdown. Customizing Policies based on Location. Managing Security Exceptions. This course is designed for the network, IT security, and systems administration professionals in a Security Operations position who are tasked with configuring optimum security settings for endpoints protected by Symantec Endpoint Protection 14. Introduction Course environment Lab environment Introducing Network Threats Describing how Symantec Endpoint Protection protects each layer of the network stack Discovering the tools and methods used by attackers Describing the stages of an attack Protecting against Network Attacks and Enforcing Corporate Policies using the Firewall Policy Preventing network attacks Examining Firewall Policy elements Evaluating built-in rules Creating custom firewall rules Enforcing corporate security policy with firewall rules Blocking network attacks using protection and stealth settings Configuring advanced firewall feature Blocking Threats with Intrusion Prevention Introducing Intrusion Prevention technologies Configuring the Intrusion Prevention policy Managing custom signatures Monitoring Intrusion Prevention events Introducing File-Based Threats Describing threat types Discovering how attackers disguise their malicious applications Describing threat vectors Describing Advanced Persistent Threats and a typical attack scenario Following security best practices to reduce risks Preventing Attacks with SEP Layered Security Virus and Spyware protection needs and solutions Describing how Symantec Endpoint Protection protects each layer of the network stack Examining file reputation scoring Describing how SEP protects against zero-day threats and threats downloaded through files and email Describing how endpoints are protected with the Intelligent Threat Cloud Service Describing how the emulator executes a file in a sandbox and the machine learning engine?s role and function Securing Windows Clients Platform and Virus and Spyware Protection policy overview Tailoring scans to meet an environment?s needs Ensuring real-time protection for clients Detecting and remediating risks in downloaded files Identifying zero-day and unknown threats Preventing email from downloading malware Configuring advanced options Monitoring virus and spyware activity Securing Mac Clients Touring the SEP for Mac client Securing Mac clients Monitoring Mac clients Securing Linux Clients Navigating the Linux client Tailoring Virus and Spyware settings for Linux clients Monitoring Linux clients Providing Granular Control with Host Integrity Ensuring client compliance with Host Integrity Configuring Host Integrity Troubleshooting Host Integrity Monitoring Host Integrity Controlling Application and File Access Describing Application Control and concepts Creating application rulesets to restrict how applications run Monitoring Application Control events Restricting Device Access for Windows and Mac Clients Describing Device Control features and concepts for Windows and Mac clients Enforcing access to hardware using Device Control Discovering hardware access policy violations with reports, logs, and notifications Hardening Clients with System Lockdown What is System Lockdown? Determining to use System Lockdown in Whitelist or Blacklist mode Creating whitelists for blacklists Protecting clients by testing and Implementing System Lockdown Customizing Policies based on Location Creating locations to ensure the appropriate level of security when logging on remotely Determining the criteria and order of assessment before assigning policies Assigning policies to locations Monitoring locations on the SEPM and SEP client Managing Security Exceptions Creating file and folder exceptions for different scan types Describing the automatic exclusion created during installation Managing Windows and Mac exclusions Monitoring security exceptions

Duration 3 Days 18 CPD hours This course is intended for This course is aimed at anyone who wants to harness the power of data analytics in their organization including: Business Analysts, Data Analysts, Reporting and BI professionals Analytics professionals and Data Scientists who would like to learn Python Overview This course teaches delegates with no prior programming or data analytics experience how to perform data manipulation, data analysis and data visualization in Python. Mastery of these techniques and how to apply them to business problems will allow delegates to immediately add value in their workplace by extracting valuable insight from company data to allow better, data-driven decisions. Outcome: After attending this course, delegates will: Be able to write effective Python code Know how to access their data from a variety of sources using Python Know how to identify and fix data quality using Python Know how to manipulate data to create analysis ready data Know how to analyze and visualize data to drive data driven decisioning across your organization Becoming a world class data analytics practitioner requires mastery of the most sophisticated data analytics tools. These programming languages are some of the most powerful and flexible tools in the data analytics toolkit. From business questions to data analytics, and beyond For data analytics tasks to affect business decisions they must be driven by a business question. This section will formally outline how to move an analytics project through key phases of development from business question to business solution. Delegates will be able: to describe and understand the general analytics process. to describe and understand the different types of analytics can be used to derive data driven solutions to business to apply that knowledge to their business context Basic Python Programming Conventions This section will cover the basics of writing R programs. Topics covered will include: What is Python? Using Anaconda Writing Python programs Expressions and objects Functions and arguments Basic Python programming conventions Data Structures in Python This section will look at the basic data structures that Python uses and accessing data in Python. Topics covered will include: Vectors Arrays and matrices Factors Lists Data frames Loading .csv files into Python Connecting to External Data This section will look at loading data from other sources into Python. Topics covered will include: Loading .csv files into a pandas data frame Connecting to and loading data from a database into a panda data frame Data Manipulation in Python This section will look at how Python can be used to perform data manipulation operations to prepare datasets for analytics projects. Topics covered will include: Filtering data Deriving new fields Aggregating data Joining data sources Connecting to external data sources Descriptive Analytics and Basic Reporting in Python This section will explain how Python can be used to perform basic descriptive. Topics covered will include: Summary statistics Grouped summary statistics Using descriptive analytics to assess data quality Using descriptive analytics to created business report Using descriptive analytics to conduct exploratory analysis Statistical Analysis in Python This section will explain how Python can be used to created more interesting statistical analysis. Topics covered will include: Significance tests Correlation Linear regressions Using statistical output to create better business decisions. Data Visualisation in Python This section will explain how Python can be used to create effective charts and visualizations. Topics covered will include: Creating different chart types such as bar charts, box plots, histograms and line plots Formatting charts Best Practices Hints and Tips This section will go through some best practice considerations that should be adopted of you are applying Python in a business context.

Duration 5 Days 30 CPD hours This course is intended for Experienced system administrators System engineers System integrators Overview By the end of the course, you should be able to meet the following objectives: Introduce troubleshooting principles and procedures Practice Linux commands that aid in the troubleshooting process Use command-line interfaces, log files, and the vSphere Client to diagnose and resolve problems in the vSphere environment Explain the purpose of key vSphere log files Monitor and optimize compute, network, and storage performance on ESXi hosts Monitor and optimize vCenter Server performance Identify networking problems based on reported symptoms, validate and troubleshoot the reported problem, identify the root cause and implement the appropriate resolution Analyze storage failure scenarios using a logical troubleshooting methodology, identify the root cause, and apply the appropriate resolution to resolve the problem Troubleshoot vSphere cluster failure scenarios and analyze possible causes Diagnose common VMware vSphere High Availability problems and provide solutions Identify and validate VMware ESXi™ host and VMware vCenter Server problems, analyze failure scenarios, and select the correct resolution Troubleshoot virtual machine problems, including migration problems, snapshot problems, and connection problems Troubleshoot performance problems with vSphere components This five-day, accelerated, hands-on training course is a blend of the VMware vSphere: Optimize and Scale and VMware vSphere: Troubleshooting courses. This Fast Track course includes topics from each of these advanced courses to equip experienced VMware administrators with the knowledge and skills to effectively optimize and troubleshoot vSphere at an expert level. Course Introduction Introductions and course logistics Course objectives Introduction to Troubleshooting Define the scope of troubleshooting Use a structured approach to solve configuration and operational problems Apply a troubleshooting methodology to logically diagnose faults and improve troubleshooting efficiency Troubleshooting Tools Use command-line tools (such as Linux commands, vSphere CLI, ESXCLI) to identify and troubleshoot vSphere problems Identify important vSphere log files and interpret the log file contents Network Optimization Explain performance features of network adapters Explain the performance features of vSphere networking Use esxtop to monitor key network performance metrics Troubleshooting Virtual Networking Analyze and resolve standard switch and distributed switch problems Analyze virtual machine connectivity problems and fix them Examine common management network connectivity problems and restore configurations Storage Optimization Describe storage queue types and other factors that affect storage performance Use esxtop to monitor key storage performance metrics Troubleshooting Storage Troubleshoot and resolve storage (iSCSI, NFS, and VMware vSphere© VMFS) connectivity and configuration problems Analyze and resolve common VM snapshot problems Identify multipathing-related problems, including common causes of permanent device loss (PDL) and all paths down (APD) events and resolve these problems CPU Optimization Explain the CPU scheduler operation and other features that affect CPU performance Explain NUMA and vNUMA support Use esxtop to monitor key CPU performance metrics Memory Optimization Explain ballooning, memory compression, and host-swapping techniques for memory reclamation when memory is overcommitted Use esxtop to monitor key memory performance metrics Troubleshooting vSphere Clusters Identify and recover from problems related to vSphere HA Analyze and resolve VMware vSphere© vMotion© configuration and operational problems Analyze and resolve common VMware vSphere© Distributed Resource Scheduler? problems Troubleshooting Virtual Machines Identify possible causes and resolve virtual machine power-on problems Troubleshoot virtual machine connection state problems Resolve problems seen during VMware Tools? installations vCenter Server Performance Optimization Describe the factors that influence vCenter Server performance Use VMware vCenter© Server Appliance? tools to monitor resource use Troubleshooting vCenter Server and ESXi Analyze and fix problems with vCenter Server services Analyze and fix vCenter Server database problems Examine ESXi host and vCenter Server failure scenarios and resolve the problems

Duration 4 Days 24 CPD hours This course is intended for This course is intended for: Developers Solutions Architects Data Engineers Anyone with little to no experience with ML and wants to learn about the ML pipeline using Amazon SageMaker Overview In this course, you will learn to: Select and justify the appropriate ML approach for a given business problem Use the ML pipeline to solve a specific business problem Train, evaluate, deploy, and tune an ML model using Amazon SageMaker Describe some of the best practices for designing scalable, cost-optimized, and secure ML pipelines in AWS Apply machine learning to a real-life business problem after the course is complete This course explores how to use the machine learning (ML) pipeline to solve a real business problem in a project-based learning environment. Students will learn about each phase of the pipeline from instructor presentations and demonstrations and then apply that knowledge to complete a project solving one of three business problems: fraud detection, recommendation engines, or flight delays. By the end of the course, students will have successfully built, trained, evaluated, tuned, and deployed an ML model using Amazon SageMaker that solves their selected business problem. Module 0: Introduction Pre-assessment Module 1: Introduction to Machine Learning and the ML Pipeline Overview of machine learning, including use cases, types of machine learning, and key concepts Overview of the ML pipeline Introduction to course projects and approach Module 2: Introduction to Amazon SageMaker Introduction to Amazon SageMaker Demo: Amazon SageMaker and Jupyter notebooks Hands-on: Amazon SageMaker and Jupyter notebooks Module 3: Problem Formulation Overview of problem formulation and deciding if ML is the right solution Converting a business problem into an ML problem Demo: Amazon SageMaker Ground Truth Hands-on: Amazon SageMaker Ground Truth Practice problem formulation Formulate problems for projects Module 4: Preprocessing Overview of data collection and integration, and techniques for data preprocessing and visualization Practice preprocessing Preprocess project data Class discussion about projects Module 5: Model Training Choosing the right algorithm Formatting and splitting your data for training Loss functions and gradient descent for improving your model Demo: Create a training job in Amazon SageMaker Module 6: Model Evaluation How to evaluate classification models How to evaluate regression models Practice model training and evaluation Train and evaluate project models Initial project presentations Module 7: Feature Engineering and Model Tuning Feature extraction, selection, creation, and transformation Hyperparameter tuning Demo: SageMaker hyperparameter optimization Practice feature engineering and model tuning Apply feature engineering and model tuning to projects Final project presentations Module 8: Deployment How to deploy, inference, and monitor your model on Amazon SageMaker Deploying ML at the edge Demo: Creating an Amazon SageMaker endpoint Post-assessment Course wrap-up Additional course details: Nexus Humans The Machine Learning Pipeline on AWS 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 The Machine Learning Pipeline on AWS 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 5 Days 30 CPD hours This course is intended for The skills covered in this course converge on four areas-software development, IT operations, applied math and statistics, and business analysis. Target students for this course should be looking to build upon their knowledge of the data science process so that they can apply AI systems, particularly machine learning models, to business problems. So, the target student is likely a data science practitioner, software developer, or business analyst looking to expand their knowledge of machine learning algorithms and how they can help create intelligent decisionmaking products that bring value to the business. A typical student in this course should have several years of experience with computing technology, including some aptitude in computer programming. This course is also designed to assist students in preparing for the CertNexus Certified Artificial Intelligence (AI) Practitioner (Exam AIP-210) certification Overview In this course, you will develop AI solutions for business problems. You will: Solve a given business problem using AI and ML. Prepare data for use in machine learning. Train, evaluate, and tune a machine learning model. Build linear regression models. Build forecasting models. Build classification models using logistic regression and k -nearest neighbor. Build clustering models. Build classification and regression models using decision trees and random forests. Build classification and regression models using support-vector machines (SVMs). Build artificial neural networks for deep learning. Put machine learning models into operation using automated processes. Maintain machine learning pipelines and models while they are in production Artificial intelligence (AI) and machine learning (ML) have become essential parts of the toolset for many organizations. When used effectively, these tools provide actionable insights that drive critical decisions and enable organizations to create exciting, new, and innovative products and services. This course shows you how to apply various approaches and algorithms to solve business problems through AI and ML, all while following a methodical workflow for developing data-driven solutions. Solving Business Problems Using AI and ML Topic A: Identify AI and ML Solutions for Business Problems Topic B: Formulate a Machine Learning Problem Topic C: Select Approaches to Machine Learning Preparing Data Topic A: Collect Data Topic B: Transform Data Topic C: Engineer Features Topic D: Work with Unstructured Data Training, Evaluating, and Tuning a Machine Learning Model Topic A: Train a Machine Learning Model Topic B: Evaluate and Tune a Machine Learning Model Building Linear Regression Models Topic A: Build Regression Models Using Linear Algebra Topic B: Build Regularized Linear Regression Models Topic C: Build Iterative Linear Regression Models Building Forecasting Models Topic A: Build Univariate Time Series Models Topic B: Build Multivariate Time Series Models Building Classification Models Using Logistic Regression and k-Nearest Neighbor Topic A: Train Binary Classification Models Using Logistic Regression Topic B: Train Binary Classification Models Using k-Nearest Neighbor Topic C: Train Multi-Class Classification Models Topic D: Evaluate Classification Models Topic E: Tune Classification Models Building Clustering Models Topic A: Build k-Means Clustering Models Topic B: Build Hierarchical Clustering Models Building Decision Trees and Random Forests Topic A: Build Decision Tree Models Topic B: Build Random Forest Models Building Support-Vector Machines Topic A: Build SVM Models for Classification Topic B: Build SVM Models for Regression Building Artificial Neural Networks Topic A: Build Multi-Layer Perceptrons (MLP) Topic B: Build Convolutional Neural Networks (CNN) Topic C: Build Recurrent Neural Networks (RNN) Operationalizing Machine Learning Models Topic A: Deploy Machine Learning Models Topic B: Automate the Machine Learning Process with MLOps Topic C: Integrate Models into Machine Learning Systems Maintaining Machine Learning Operations Topic A: Secure Machine Learning Pipelines Topic B: Maintain Models in Production

Duration 3 Days 18 CPD hours This course is intended for This intermediate course is for application programmers who need to write embedded SQL COBOL or PL/I programs in either a DB2 9 or DB2 10 for z/OS environment. Overview Incorporate static SQL statements in an application program Prepare the program for execution Validate execution results are correct Produce code to support multiple rows being returned from the database manager using cursors Identify considerations regarding units of work, concurrency, and restart of programs Identify differences between static and dynamic SQL Provide test data for applications Discuss program and DB2 options relative to performance of static SQL This course enables you to acquire the skills necessary to produce application programs that manipulate DB2 databases. Emphasis is on embedding Structured Query Language (SQL) statements and preparing programs for execution. CV720G;CF82G;DB2 Concepts Identify DB2 family products Explain DB2 workstation component functions Identify DB2 objects Identify the key differences between static SQL and other application alternatives for accessing DB2 data Program Structure I Embed INSERT, UPDATE, DELETE and single-row SELECT statements in application programs Effectively communicate with DB2 when processing NULL values and determining success of statement execution Demonstrate use of DB2 coding aids Code CONNECT statements within an application program Identify connection types and impacts on a unit of work Program for the Call Attach Facility (CAF) Program Preparation Identify the additional steps necessary to prepare a program that contains embedded SQL for execution Describe the functions of the DB2 PRECOMPILE and BIND processes Describe factors relevant to the BIND process, including RUNSTATS positioning, package status, parameters, and authorization requirements Program Structure II Use DECLARE, OPEN, FETCH, and CLOSE CURSOR statements to handle select criteria that may return multiple rows in application programs Issue positioned UPDATE and DELETE statements Identify how scrollable cursors can be used Recovery and Locking Concepts Define a unit of recovery Identify the basic locking strategies used by DB2 Dynamic SQL Introduction Describe the difference between static and dynamic SQL List the types of dynamic statements Code dynamic SQL in a program Managing Test Data Identify methods to insert data into a table Use the LOAD or IMPORT utility Identify the purpose of the RUNSTATS utility Identify the purpose of the REORG utility Performance Considerations Use programming techniques that enhance DB2 application performance by following general guidelines, using indexable predicates, and avoiding unnecessary sorts Identify the access paths available to DB2 List common causes of deadlocks and avoid such causes when possible Use the EXPLAIN tools as aids to develop applications that emphasize performance Additional course details: Nexus Humans CV722 IBM DB2 11 for z/OS Application Programming Workshop 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 CV722 IBM DB2 11 for z/OS Application Programming Workshop 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 Experienced Programmers and Systems Administrators. Overview Throughout the course students will be led through a series of progressively advanced topics, where each topic consists of lecture, group discussion, comprehensive hands-on lab exercises, and lab review. This course is ?skills-centric?, designed to train attendees in core Python and web development skills beyond an intermediate level, coupling the most current, effective techniques with best practices. Working within in an engaging, hands-on learning environment, guided by our expert Python practitioner, students will learn to: ? 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 introductory and beyond-level practical, hands-on Python training course that leads the student from the basics of writing and running Python scripts to more advanced features. An Overview of Python What is python? 1 -- 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 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 o-o 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 17 ? Advanced data handling Defaultdict and Counter Prettyprinting data structures Compressed archives (zip, gzip, tar, etc.) Persistent data 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 Timeline Advantages/Disadvantages of Python Getting help with pydoc

Duration 3 Days 18 CPD hours This course is intended for Network Security Operations Workload Application Administrators Security Operations Field Engineers Network Engineers Systems Engineers Technical Solutions Architects Cisco Integrators and Partners Overview After taking this course, you should be able to: Define the Cisco telemetry and analytics approach. Explore common scenarios that Cisco Tetration Analytics can solve. Describe how the Cisco Tetration Analytics platform collects telemetry and other context information. Discuss how relative agents are installed and configured. Explore the operational aspects of the Cisco Tetration Analytics platform. Describe the Cisco Tetration Analytics support for application visibility or application insight based on the Application Dependency Mapping (ADM) feature. List the concepts of the intent-based declarative network management automation model. Describe the Cisco Tetration policy enforcement pipeline, components, functions, and implementation of application policy. Describe how to use Cisco Tetration Analytics for workload protection in order to provide a secure infrastructure for business-critical applications and data. Describe Cisco Tetration Analytics platform use cases in the modern heterogeneous, multicloud data center. List the options for the Cisco Tetration Analytics platform enhancements. Explain how to perform the Cisco Tetration Analytics administration. This course teaches how to deploy, use, and operate Cisco© Tetration Analytics? platform for comprehensive workload-protection and application and network insights across a multicloud infrastructure. You will learn how the Cisco Tetration Analytics platform uses streaming telemetry, behavioral analysis, unsupervised machine learning, analytical intelligence, and big data analytics to deliver pervasive visibility, automated intent-based policy, workload protection, and performance management. Exploring Cisco Tetration Data Center Challenges Define and Position Cisco Tetration Cisco Tetration Features Cisco Tetration Architecture Cisco Tetration Deployment Models Cisco Tetration GUI Overview Implementing and Operating Cisco Tetration Explore Data Collection Install the Software Agent Install the Hardware Agent Import Context Data Describe Cisco Tetration Operational Concepts Examining Cisco Tetration ADM and Application Insight Describe Cisco Tetration Application Insight Perform ADM Interpret ADM Results Application Visibility Examining Cisco Tetration Intent-Based Networking Describe Intent-Based Policy Examine Policy Features Implement Policies Enforcing Tetration Policy Pipeline and Compliance Examine Policy Enforcement Implement Application Policy Examine Policy Compliance Verification and Simulation Examining Tetration Security Use Cases Examine Workload Security Attack Prevention Attack Detection Attack Remediation Examining IT Operations Use Cases Key Features and IT Operations Use Cases Performing Operations in Neighborhood App-based Use Cases Examining Platform Enhancement Use Cases Integrations and Advanced Features Third-party Integration Examples Explore Data Platform Capabilities Exploring Cisco Tetration Analytics Administration Examine User Authentication and Authorization Examine Cluster Management Configure Alerts and Syslog Additional course details: Nexus Humans Cisco Implementing Cisco Tetration Analytics v1.0 (DCITET) 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 Cisco Implementing Cisco Tetration Analytics v1.0 (DCITET) 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 In order to be successful in this class, incoming attendees are required to have current, hands-on experience in developing basic web applications, and be versed in HTML5, CSS3 and JavaScript. This is an intermediate level web development class, designed for experienced web developers, new to Angular, that need to further extend their skills in modern web development. Overview At the end of this five-day course, students will have an application up and running that incorporates components, directives, custom pipes, reactive forms, routes, services, unit testing, and the REST API. They will add authentication, implement the Material library, and learn how to maintain state with NgRX. They will then take a deeper dive including making their own custom directives, lazy loading modules, and E2E testing. They will learn how to enhance their application with animations and create their own Angular library. Working within in an engaging, hands-on learning environment, guided by our expert team, attendees will learn explore: What Angular is and why should you use it How Angular reduces the amount of code that you must write to add rich functionality to both existing and new web pages What TypeScript is, why it is useful, and how to use it with Angular How to facilitate development and deployment using Angular CLI How to work with the various aspects of the Angular architecture to implement clean, responsive web interfaces How Routers can support navigation within a Single Page Application What the best practices are for using Angular so that it works unobtrusively and performs well How to use Angular with HTTP to support JSON, REST, and other services Working with the Ahead of Time compiler including its impact of developers and the development process How to defend against DOM-based XSS How to manage routing decisions based on pre-defined criteria such as a successful authentication How to meet huge data requirements by processing asynchronous data streams with RxJS Simplify server-side rendering How to facilitate unit testing Enhance an Angular user interface with animations and other advanced features Optimize Angular applications with various tools and techniques Maintain state within an Angular application What Angular 9 brings to the table and its relationship to Angular 8 Mastering Angular is a five-day, hands-on course that thoroughly explores the latest Angular features and advances, demonstrating how to solve the traditional challenges of JavaScript web application development. Throughout the course students will build custom components using application routes, form validation, and unit-testing. The course starts with an introduction of Angular CLI and TypeScript, and then delves into component-driven development with Angular components, covering data-binding, directives, services, routing, HTTP, the RxJS library, forms unit testing, and REST. Students will also learn how to add authentication, use the Material library, learn the NgRX design pattern to implement the NgRX store, make custom directives, enhance their application with animations, write an E2E test, and increase their application's efficiency by lazy loading modules and creating their own Angular library Angular Overview Overview of Angular Architecture Getting Started with Angular Getting Started with TypeScript Bootstrapping with Angular CLI Angular Project Structure Working with Angular Components and Events Third Party Libraries Dynamic Views Pipes Angular Forms Forms and the Forms API Single Page Applications and Routes Single Page Applications Services and Dependency Injection Modules Using RESTful Services Overview of REST Angular and REST Angular Best Practices Angular Style Guide What is New in Angular 9 Reactive Programming in Angular Working with RxJS Security and Authentication DomSanitizer JSON Web Tokens Route Guards Enhancing the Angular App Angular Animations Angular Material Angular Elements Deep Dive into Angular Testing and Angular Deep Dive into Components and Directives Deep Dive into Services and Dependency Injection Optimizing for the Enterprise Lazy Loading Optimizing with Universal Creating Your Own Angular Library Maintain State with NgRX NgRX Store Lesson: ES6+ Sass and SCSS for Angular and Material