390 Vector courses

Description: Get introduced to the powerful vector image creation program in the Level 3 Diploma in Adobe Illustrator course. Adobe Illustrator is a vector graphics editor that allows you to create logos, icons, drawings, typography, and elaborate illustrations for any medium which is a staple for professional designers everywhere now. Through this course, you will master the advanced tricks and techniques of creating vector images with Adobe Illustrator. You will learn how to create logos, graphics, cartoons and fonts for the photo-realistic layouts of Adobe Photoshop. More importantly, get introduced to all the essential tools of this software, such as builder tool, pen tool, curvature tool, eyedropper tool, library tool, brushes, and many more. Type and font, RGB and CMYK also discussed in the course. Upon completion of the course, you can create pleasing logos and vector images that can use over a wide variety of platforms. By taking this course will teach you everything you need to know to get started with this software. Who is the course for? Anyone who wants to learn how to design with the industry-leading application. Graphic designers, motion graphics artists, Photoshop users, and any creatives who want to design their own graphics from scratch. Entry Requirement: This course is available to all learners, of all academic backgrounds. Learners should be aged 16 or over to undertake the qualification. Good understanding of English language, numeracy and ICT are required to attend this course. Assessment: At the end of the course, you will be required to sit an online multiple-choice test. Your test will be assessed automatically and immediately so that you will instantly know whether you have been successful. Before sitting for your final exam, you will have the opportunity to test your proficiency with a mock exam. Certification: After completing and passing the course successfully, you will be able to obtain an Accredited Certificate of Achievement. Certificates can be obtained either in hard copy at a cost of £39 or in PDF format at a cost of £24. Why choose us? Affordable, engaging & high-quality e-learning study materials; Tutorial videos/materials from the industry leading experts; Study in a user-friendly, advanced online learning platform; Efficient exam systems for the assessment and instant result; The UK & internationally recognised accredited qualification; Access to course content on mobile, tablet or desktop from anywhere anytime; The benefit of career advancement opportunities; 24/7 student support via email. Career Path: After completing this course you will be able to build up accurate knowledge and skills with proper confidence to enrich yourself and brighten up your career in the relevant job market. Module: 01 01 Welcome to the Course - Illustrator Essentials 2018 00:03:00 02 Download the Course Project Files 00:01:00 03 Getting Started with Adobe Illustrator 00:08:00 04 Drawing with Shapes and Lines.MP4 00:23:00 05 The Shape Builder Tool 00:08:00 06 Draw a Modern Fox with the Shape Builder Tool 00:10:00 07 Draw an Impossible Shape Logo with the Shape Builder Tool 00:05:00 08 The Curvature Tool 00:13:00 09 The Pen Tool 00:13:00 10 The Pencil Tool and Advanced Stroke Options 00:13:00 Module: 02 11 Using Brushes 00:13:00 12 The Width Tool 00:09:00 13 Type and Font 00:14:00 14 Curve Type on a Path 00:13:00 15 Break Apart and Destroy Text 00:04:00 16 What is RGB & CMYK 00:04:00 17 Using the Eye Dropper Tool 00:04:00 18 The Color Theme Tool 00:05:00 19 Gradients 00:06:00 20 Masking 00:07:00 Module: 03 21 Compound Paths 00:11:00 22 CC Libraries 00:10:00 23 The Liquify Tool 00:08:00 24 Bend and Warp 00:06:00 25 Repeating Shapes 00:05:00 26 patterns 00:09:00 27 Vectorize Images 00:12:00 28 Adobe Capture App 00:11:00 29 Free Templates from Adobe 00:04:00 30 Saving for Print 00:06:00 Module: 04 31 Exporting for Web 00:05:00 32 Redrawing the Mastercard Logo 00:05:00 33 Redrawing the Instagram Logo 00:08:00 34 Redrawing the Kadak Logo 00:11:00 35 Redrawing the eHarmony Logo 00:11:00 36 Redrawing the Tinder Logo 00:06:00 37 Redrawing the BP Logo 00:08:00 38 Adobe Illustrator Cheat Sheet and Shortcuts 00:10:00 39 Conclusion 00:02:00 Module: 05 Activity 0 - Intro to Activities 00:01:00 Activity 1 - Solution 00:11:00 Activity 2 - Swan Solution 00:14:00 Activity 3 - Coloring the Impossible Triangle 00:03:00 Activity 4 - Drawing and Coloring the Owl 00:10:00 Activity 5 - Drawing the Owl with the Pen Tool 00:09:00 Activity 6 - Drawing with the Pencil Tool 00:11:00 Activity 7 - Width Tool Love- 00:23:00 Activity 8 - Badge 00:12:00 Activity 9 - Liquify 00:09:00 Activity 10 - Repeating Shapes 00:05:00 Activity 11 - Patterns 00:06:00 Activity 12 - Vectorizing Images 00:08:00 Certificate and Transcript Order Your Certificates and Transcripts 00:00:00

BGP training course description A detailed study of BGP, from the basics of how it works through to advanced issues such as route reflectors, policy, filtering, route selection and routing registries. The course culminates with a study of an industrial strength BGP template illustrating important issues such as bogon filtering. Practical hands on with routers follow the major sessions to reinforce the theory. A multiple choice exam, leading to the LAIT III certification, is available after the course. The exam consists of 60 questions and lasts 2 hours. What will you learn Connect enterprises to the Internet, and ISPs to each other. Describe how BGP works. List, describe and configure the main BGP attributes. Implement and troubleshoot BGP. Work with route aggregation and calculate CIDR prefixes in seconds. Influence traffic paths with BGP. BGP training course details Who will benefit: Anyone who will be working with BGP. Prerequisites: TCP/IP Foundation for engineers Definitive IP routing for engineers. Duration 5 days BGP training course contents Basic BGP IGPs, EGPs, What's BGP? BGP RIB, in/out process, tables peers, adding routes. Hands on Simple configuration and troubleshooting. The Internet and peering ASs, AS numbers, Internet structure, ISP types, ISP network design, IXs, peering vs. transit, public/ private peering, bi/multi-lateral peering. Hands on AS information gathering. How BGP works Incremental updates, Path vector protocols, BGP protocol stack, the BGP header, message types, NLRI, withdrawn routes, route refresh, route dampening. Hands on More troubleshooting, packet analysis. MBGP and IPv6 Multiprotocol routing, AFI, SAFI, MBGP and multicasts, IPv6, MPLS VPNs. Hands on IPv6 BGPv4 aggregation CIDR, benefits, techniques, shortcuts, configuring BGP aggregation, leaking routes. Hands on Reducing routing table size. BGP path selection BGP attributes, attribute types, route selection order, Local preference, AS prepend, MEDs. Hands on Influencing traffic with BGP. BGP routing policies What is policy? Examples, route filtering, AS filtering, REs, applying preference selectively, peer groups. Hands on Sophisticated policies. RIPE and routing registries RIRs, Allocations, assignments, PI vs. PA. Objects, RPSL, routing registry, Hands on The RIPE database. Automating BGP configuration Automation tools, whois, IRRToolSet, Bogon lists, tracking bogon lists, HTTP, Peering, routing registries, DNS. Communities What is a community? Community names, communities for: peer types and geography. RFC 1998, default communities. Hands on Setting local preference on other routers. Route servers What are route servers? LINX route servers, route server policy control, What are route collectors, Looking glasses. Hands on Setting up and working with a route server. Peer relationships IBGP, EBGP, next hop self, advertising routes into/out of BGP, synchronisation. Hands on IBGP, troubleshooting a large BGP network. Route reflectors and confederations Full mesh IBGP, Route reflectors, RR configuration and design, confederations, migration issues. Hands on RR configuration. BGP architectures Stub vs. transit AS, when to use BGP, multihoming strategies and issues, default routes. Multihop EBGP, load balancing. Hands on Multihoming. BGP security RFC 7454, security steps, BGP TTL security, filters, RPKI, ROAs, rsync, rrdp, validators. A secure BGP template. Hands on RPKI prefix validation.

If you are looking forward to building your graphic design portfolio, then you are at the right place. This course consists of 12 plus hours of premium content that will help you build 40+ projects, which you can later add to your portfolio. You are going to design Facebook ads, Instagram posts, newsletters, YouTube thumbnails, CVs, and much more with the help of Photoshop. You need to be familiar with the basics of Photoshop to get started.

Overview This comprehensive course on R Programming for Data Science will deepen your understanding on this topic. After successful completion of this course you can acquire the required skills in this sector. This R Programming for Data Science comes with accredited certification from CPD, which will enhance your CV and make you worthy in the job market. So enrol in this course today to fast track your career ladder. How will I get my certificate? You may have to take a quiz or a written test online during or after the course. After successfully completing the course, you will be eligible for the certificate. Who is This course for? There is no experience or previous qualifications required for enrolment on this R Programming for Data Science. It is available to all students, of all academic backgrounds. Requirements Our R Programming for Data Science is fully compatible with PC's, Mac's, Laptop, Tablet and Smartphone devices. This course has been designed to be fully compatible with tablets and smartphones so you can access your course on Wi-Fi, 3G or 4G. There is no time limit for completing this course, it can be studied in your own time at your own pace. Career Path Having these various qualifications will increase the value in your CV and open you up to multiple sectors such as Business & Management, Admin, Accountancy & Finance, Secretarial & PA, Teaching & Mentoring etc. Course Curriculum 23 sections • 129 lectures • 06:25:00 total length •Introduction to Data Science: 00:01:00 •Data Science: Career of the Future: 00:04:00 •What is Data Science?: 00:02:00 •Data Science as a Process: 00:02:00 •Data Science Toolbox: 00:03:00 •Data Science Process Explained: 00:05:00 •What's Next?: 00:01:00 •Engine and coding environment: 00:03:00 •Installing R and RStudio: 00:04:00 •RStudio: A quick tour: 00:04:00 •Arithmetic with R: 00:03:00 •Variable assignment: 00:04:00 •Basic data types in R: 00:03:00 •Creating a vector: 00:05:00 •Naming a vector: 00:04:00 •Vector selection: 00:06:00 •Selection by comparison: 00:04:00 •What's a Matrix?: 00:02:00 •Analyzing Matrices: 00:03:00 •Naming a Matrix: 00:05:00 •Adding columns and rows to a matrix: 00:06:00 •Selection of matrix elements: 00:03:00 •Arithmetic with matrices: 00:07:00 •Additional Materials: 00:00:00 •What's a Factor?: 00:02:00 •Categorical Variables and Factor Levels: 00:04:00 •Summarizing a Factor: 00:01:00 •Ordered Factors: 00:05:00 •What's a Data Frame?: 00:03:00 •Creating Data Frames: 00:20:00 •Selection of Data Frame elements: 00:03:00 •Conditional selection: 00:03:00 •Sorting a Data Frame: 00:03:00 •Additional Materials: 00:00:00 •Why would you need lists?: 00:01:00 •Creating a List: 00:06:00 •Selecting elements from a list: 00:03:00 •Adding more data to the list: 00:02:00 •Additional Materials: 00:00:00 •Equality: 00:03:00 •Greater and Less Than: 00:03:00 •Compare Vectors: 00:03:00 •Compare Matrices: 00:02:00 •Additional Materials: 00:00:00 •AND, OR, NOT Operators: 00:04:00 •Logical operators with vectors and matrices: 00:04:00 •Reverse the result: (!): 00:01:00 •Relational and Logical Operators together: 00:06:00 •Additional Materials: 00:00:00 •The IF statement: 00:04:00 •IFELSE: 00:03:00 •The ELSEIF statement: 00:05:00 •Full Exercise: 00:03:00 •Additional Materials: 00:00:00 •Write a While loop: 00:04:00 •Looping with more conditions: 00:04:00 •Break: stop the While Loop: 00:04:00 •What's a For loop?: 00:02:00 •Loop over a vector: 00:02:00 •Loop over a list: 00:03:00 •Loop over a matrix: 00:04:00 •For loop with conditionals: 00:01:00 •Using Next and Break with For loop: 00:03:00 •Additional Materials: 00:00:00 •What is a Function?: 00:02:00 •Arguments matching: 00:03:00 •Required and Optional Arguments: 00:03:00 •Nested functions: 00:02:00 •Writing own functions: 00:03:00 •Functions with no arguments: 00:02:00 •Defining default arguments in functions: 00:04:00 •Function scoping: 00:02:00 •Control flow in functions: 00:03:00 •Additional Materials: 00:00:00 •Installing R Packages: 00:01:00 •Loading R Packages: 00:04:00 •Different ways to load a package: 00:02:00 •Additional Materials: 00:00:00 •What is lapply and when is used?: 00:04:00 •Use lapply with user-defined functions: 00:03:00 •lapply and anonymous functions: 00:01:00 •Use lapply with additional arguments: 00:04:00 •Additional Materials: 00:00:00 •What is sapply?: 00:02:00 •How to use sapply: 00:02:00 •sapply with your own function: 00:02:00 •sapply with a function returning a vector: 00:02:00 •When can't sapply simplify?: 00:02:00 •What is vapply and why is it used?: 00:04:00 •Additional Materials: 00:00:00 •Mathematical functions: 00:05:00 •Data Utilities: 00:08:00 •Additional Materials: 00:00:00 •grepl & grep: 00:04:00 •Metacharacters: 00:05:00 •sub & gsub: 00:02:00 •More metacharacters: 00:04:00 •Additional Materials: 00:00:00 •Today and Now: 00:02:00 •Create and format dates: 00:06:00 •Create and format times: 00:03:00 •Calculations with Dates: 00:03:00 •Calculations with Times: 00:07:00 •Additional Materials: 00:00:00 •Get and set current directory: 00:04:00 •Get data from the web: 00:04:00 •Loading flat files: 00:03:00 •Loading Excel files: 00:05:00 •Additional Materials: 00:00:00 •Base plotting system: 00:03:00 •Base plots: Histograms: 00:03:00 •Base plots: Scatterplots: 00:05:00 •Base plots: Regression Line: 00:03:00 •Base plots: Boxplot: 00:03:00 •Introduction to dplyr package: 00:04:00 •Using the pipe operator (%>%): 00:02:00 •Columns component: select(): 00:05:00 •Columns component: rename() and rename_with(): 00:02:00 •Columns component: mutate(): 00:02:00 •Columns component: relocate(): 00:02:00 •Rows component: filter(): 00:01:00 •Rows component: slice(): 00:04:00 •Rows component: arrange(): 00:01:00 •Rows component: rowwise(): 00:02:00 •Grouping of rows: summarise(): 00:03:00 •Grouping of rows: across(): 00:02:00 •COVID-19 Analysis Task: 00:08:00 •Additional Materials: 00:00:00 •Assignment - R Programming for Data Science: 00:00:00

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Start your learning journey straightaway with our Adobe Premiere Pro Introduction Training! Adobe Premiere Pro Introduction premium bundle consists of 35 precisely chosen courses on a wide range of topics essential for anyone looking to excel in this field. Each segment of Adobe Premiere Pro Introduction is meticulously designed to maximise learning and engagement, blending interactive content and audio-visual modules for a truly immersive experience. Certification You have to complete the assignment given at the end of the Adobe Premiere Pro Introduction course. After passing the Adobe Premiere Pro Introduction exam You will be entitled to claim a PDF & Hardcopy certificate accredited by CPD Quality standards completely free. CPD 350 CPD hours / points Accredited by CPD Quality Standards Who is this course for? 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The Electrical 3 Phase Power Transformers Fundamentals course is an illuminating journey into the world of electrical engineering, specifically focused on the critical aspects of power transformers. This detailed program begins with an introductory module that sets the stage for a deeper understanding of transformer technology. As you progress, the course unveils the complexities of ideal and real transformers, providing a clear distinction between theoretical models and practical applications. Each module is structured to build upon the last, ensuring a comprehensive understanding of the subject. The course is not just about learning technical details; it's about gaining a holistic view of transformers and their role in modern electrical systems. Delving further into the curriculum, learners encounter specialized topics such as instrument transformers, three-phase power transformers, and the intricacies of transformer clock system vector nomenclature. This progression of modules is designed to not only impart knowledge but also to develop a practical understanding of transformer construction, cooling, protection, and relay systems. This course is an essential resource for anyone looking to specialize in electrical engineering or enhance their understanding of power transformers, offering a blend of theory and practical insight that is rare to find. Learning Outcomes Understand the fundamental principles of ideal and real transformers. Gain in-depth knowledge of three-phase power transformers and their applications. Learn about transformer construction, cooling techniques, and protection mechanisms. Master the transformer clock system vector nomenclature. Acquire skills in the operation and maintenance of transformer relays. Why choose this Electrical 3 Phase Power Transformers Fundamentals course? Unlimited access to the course for a lifetime. Opportunity to earn a certificate accredited by the CPD Quality Standards and CIQ after completing this course. Structured lesson planning in line with industry standards. Immerse yourself in innovative and captivating course materials and activities. Assessments designed to evaluate advanced cognitive abilities and skill proficiency. Flexibility to complete the Course at your own pace, on your own schedule. Receive full tutor support throughout the week, from Monday to Friday, to enhance your learning experience. Unlock career resources for CV improvement, interview readiness, and job success. Who is this Electrical 3 Phase Power Transformers Fundamentals course for? Electrical engineering students seeking foundational knowledge in transformers. Professionals in the electrical industry aiming to deepen their expertise. Technicians involved in the maintenance and operation of power transformers. Engineers looking to specialize in power systems and transformer technology. Individuals with a technical background interested in electrical power systems. Career path Electrical Engineer - £30,000 to £50,000 Power Systems Engineer - £35,000 to £55,000 Maintenance Technician (Transformers) - £25,000 to £40,000 Transformer Design Engineer - £33,000 to £50,000 Energy Systems Analyst - £28,000 to £45,000 Power Plant Manager - £40,000 to £60,000 Prerequisites This Electrical 3 Phase Power Transformers Fundamentals does not require you to have any prior qualifications or experience. You can just enrol and start learning. This course was made by professionals and it is compatible with all PC's, Mac's, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection. Certification After studying the course materials, there will be a written assignment test which you can take at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £4.99 Original Hard Copy certificates need to be ordered at an additional cost of £8. Course Curriculum Electrical 3 Phase Power Transformers Fundamentals Module 01: Introduction 00:10:00 Module 02: The Ideal Transformer 00:55:00 Module 03: The Real Transformer 00:09:00 Module 04: Instrument Transformers 00:19:00 Module 05: Three Phase Power Transformers 01:50:00 Module 06: Transformer Clock System Vector Nomenclature 00:17:00 Module 07: Transformer Construction and Cooling 00:26:00 Module 08: Transformer Protection 00:54:00 Module 09: Transformer Relays 00:38:00 Assignment Assignment - Electrical 3 Phase Power Transformers Fundamentals 00:00:00

Duration 2 Days 12 CPD hours This course is intended for Business Analysts, Technical Managers, and Programmers Overview This intensive training course helps students learn the practical aspects of the R programming language. The course is supplemented by many hands-on labs which allow attendees to immediately apply their theoretical knowledge in practice. Over the past few years, R has been steadily gaining popularity with business analysts, statisticians and data scientists as a tool of choice for conducting statistical analysis of data as well as supervised and unsupervised machine learning. What is R ? What is R? ? Positioning of R in the Data Science Space ? The Legal Aspects ? Microsoft R Open ? R Integrated Development Environments ? Running R ? Running RStudio ? Getting Help ? General Notes on R Commands and Statements ? Assignment Operators ? R Core Data Structures ? Assignment Example ? R Objects and Workspace ? Printing Objects ? Arithmetic Operators ? Logical Operators ? System Date and Time ? Operations ? User-defined Functions ? Control Statements ? Conditional Execution ? Repetitive Execution ? Repetitive execution ? Built-in Functions ? Summary Introduction to Functional Programming with R ? What is Functional Programming (FP)? ? Terminology: Higher-Order Functions ? A Short List of Languages that Support FP ? Functional Programming in R ? Vector and Matrix Arithmetic ? Vector Arithmetic Example ? More Examples of FP in R ? Summary Managing Your Environment ? Getting and Setting the Working Directory ? Getting the List of Files in a Directory ? The R Home Directory ? Executing External R commands ? Loading External Scripts in RStudio ? Listing Objects in Workspace ? Removing Objects in Workspace ? Saving Your Workspace in R ? Saving Your Workspace in RStudio ? Saving Your Workspace in R GUI ? Loading Your Workspace ? Diverting Output to a File ? Batch (Unattended) Processing ? Controlling Global Options ? Summary R Type System and Structures ? The R Data Types ? System Date and Time ? Formatting Date and Time ? Using the mode() Function ? R Data Structures ? What is the Type of My Data Structure? ? Creating Vectors ? Logical Vectors ? Character Vectors ? Factorization ? Multi-Mode Vectors ? The Length of the Vector ? Getting Vector Elements ? Lists ? A List with Element Names ? Extracting List Elements ? Adding to a List ? Matrix Data Structure ? Creating Matrices ? Creating Matrices with cbind() and rbind() ? Working with Data Frames ? Matrices vs Data Frames ? A Data Frame Sample ? Creating a Data Frame ? Accessing Data Cells ? Getting Info About a Data Frame ? Selecting Columns in Data Frames ? Selecting Rows in Data Frames ? Getting a Subset of a Data Frame ? Sorting (ordering) Data in Data Frames by Attribute(s) ? Editing Data Frames ? The str() Function ? Type Conversion (Coercion) ? The summary() Function ? Checking an Object's Type ? Summary Extending R ? The Base R Packages ? Loading Packages ? What is the Difference between Package and Library? ? Extending R ? The CRAN Web Site ? Extending R in R GUI ? Extending R in RStudio ? Installing and Removing Packages from Command-Line ? Summary Read-Write and Import-Export Operations in R ? Reading Data from a File into a Vector ? Example of Reading Data from a File into A Vector ? Writing Data to a File ? Example of Writing Data to a File ? Reading Data into A Data Frame ? Writing CSV Files ? Importing Data into R ? Exporting Data from R ? Summary Statistical Computing Features in R ? Statistical Computing Features ? Descriptive Statistics ? Basic Statistical Functions ? Examples of Using Basic Statistical Functions ? Non-uniformity of a Probability Distribution ? Writing Your Own skew and kurtosis Functions ? Generating Normally Distributed Random Numbers ? Generating Uniformly Distributed Random Numbers ? Using the summary() Function ? Math Functions Used in Data Analysis ? Examples of Using Math Functions ? Correlations ? Correlation Example ? Testing Correlation Coefficient for Significance ? The cor.test() Function ? The cor.test() Example ? Regression Analysis ? Types of Regression ? Simple Linear Regression Model ? Least-Squares Method (LSM) ? LSM Assumptions ? Fitting Linear Regression Models in R ? Example of Using lm() ? Confidence Intervals for Model Parameters ? Example of Using lm() with a Data Frame ? Regression Models in Excel ? Multiple Regression Analysis ? Summary Data Manipulation and Transformation in R ? Applying Functions to Matrices and Data Frames ? The apply() Function ? Using apply() ? Using apply() with a User-Defined Function ? apply() Variants ? Using tapply() ? Adding a Column to a Data Frame ? Dropping A Column in a Data Frame ? The attach() and detach() Functions ? Sampling ? Using sample() for Generating Labels ? Set Operations ? Example of Using Set Operations ? The dplyr Package ? Object Masking (Shadowing) Considerations ? Getting More Information on dplyr in RStudio ? The search() or searchpaths() Functions ? Handling Large Data Sets in R with the data.table Package ? The fread() and fwrite() functions from the data.table Package ? Using the Data Table Structure ? Summary Data Visualization in R ? Data Visualization ? Data Visualization in R ? The ggplot2 Data Visualization Package ? Creating Bar Plots in R ? Creating Horizontal Bar Plots ? Using barplot() with Matrices ? Using barplot() with Matrices Example ? Customizing Plots ? Histograms in R ? Building Histograms with hist() ? Example of using hist() ? Pie Charts in R ? Examples of using pie() ? Generic X-Y Plotting ? Examples of the plot() function ? Dot Plots in R ? Saving Your Work ? Supported Export Options ? Plots in RStudio ? Saving a Plot as an Image ? Summary Using R Efficiently ? Object Memory Allocation Considerations ? Garbage Collection ? Finding Out About Loaded Packages ? Using the conflicts() Function ? Getting Information About the Object Source Package with the pryr Package ? Using the where() Function from the pryr Package ? Timing Your Code ? Timing Your Code with system.time() ? Timing Your Code with System.time() ? Sleeping a Program ? Handling Large Data Sets in R with the data.table Package ? Passing System-Level Parameters to R ? Summary Lab Exercises Lab 1 - Getting Started with R Lab 2 - Learning the R Type System and Structures Lab 3 - Read and Write Operations in R Lab 4 - Data Import and Export in R Lab 5 - k-Nearest Neighbors Algorithm Lab 6 - Creating Your Own Statistical Functions Lab 7 - Simple Linear Regression Lab 8 - Monte-Carlo Simulation (Method) Lab 9 - Data Processing with R Lab 10 - Using R Graphics Package Lab 11 - Using R Efficiently

LINX 3 training course description A detailed study of BGP, from the basics of how it works through to advanced issues such as route reflectors, policy, filtering, route selection and routing registries. The course culminates with a study of an industrial strength BGP template illustrating important issues such as bogon filtering. Practical hands on with routers follow the major sessions to reinforce the theory. A multiple choice exam, leading to the LAIT III certification, is available after the course. The exam consists of 60 questions and lasts 2 hours. What will you learn Connect enterprises to the Internet, and ISPs to each other. Describe how BGP works. List, describe and configure the main BGP attributes. Implement and troubleshoot BGP. Work with route aggregation and calculate CIDR prefixes in seconds. Influence traffic paths with BGP. LINX 3 training course details Who will benefit: Network engineers. Prerequisites: LAIT I and II OR CCNP and take LAIT I and LAIT II exams whilst on this course. Duration 5 days LINX 3 training course contents Basic BGP IGPs, EGPs, What's BGP? BGP RIB, in/out process, tables peers, adding routes. Hands on Simple configuration and troubleshooting. The Internet and peering ASs, AS numbers, Internet structure, ISP types, ISP network design, IXs, peering vs. transit, public/ private peering, bi/multi-lateral peering. Hands on AS information gathering. How BGP works Incremental updates, Path vector protocols, BGP protocol stack, the BGP header, message types, NLRI, withdrawn routes, route refresh, route dampening. Hands on More troubleshooting, packet analysis. MBGP and IPv6 Multiprotocol routing, AFI, SAFI, MBGP and multicasts, IPv6, MPLS VPNs. Hands on IPv6 BGPv4 aggregation CIDR, benefits, techniques, shortcuts, configuring BGP aggregation, leaking routes. Hands on Reducing routing table size. BGP path selection BGP attributes, attribute types, route selection order, Local preference, AS prepend, MEDs. Hands on Influencing traffic with BGP. BGP routing policies What is policy? Examples, route filtering, AS filtering, REs, applying preference selectively, peer groups. Hands on Sophisticated policies. RIPE and routing registries RIRs, Allocations, assignments, PI vs. PA. Objects, RPSL, routing registry, Hands on The RIPE database. Automating BGP configuration Automation tools, whois, IRRToolSet, Bogon lists, tracking bogon lists, HTTP, Peering, routing registries, DNS. Communities What is a community? Community names, communities for: peer types and geography. RFC 1998, default communities. Hands on Setting local preference on other routers. Route servers What are route servers? LINX route servers, route server policy control, What are route collectors, Looking glasses. Hands on Setting up and working with a route server. Peer relationships IBGP, EBGP, next hop self, advertising routes into/out of BGP, synchronisation. Hands on IBGP, troubleshooting a large BGP network. Route reflectors and confederations Full mesh IBGP, Route reflectors, RR configuration and design, confederations, migration issues. Hands on RR configuration. BGP architectures Stub vs. transit AS, when to use BGP, multihoming strategies and issues, default routes. Multihop EBGP, load balancing. Hands on Multihoming. BGP security RFC 7454, security steps, BGP TTL security, filters, RPKI, ROAs, rsync, rrdp, validators. A secure BGP template. Hands on RPKI prefix validation.

Overview This comprehensive course on Python for Data Analysis will deepen your understanding on this topic. After successful completion of this course you can acquire the required skills in this sector. This Python for Data Analysis comes with accredited certification, which will enhance your CV and make you worthy in the job market. So enrol in this course today to fast track your career ladder. How will I get my certificate? You may have to take a quiz or a written test online during or after the course. After successfully completing the course, you will be eligible for the certificate. Who is this course for? There is no experience or previous qualifications required for enrolment on this Python for Data Analysis. It is available to all students, of all academic backgrounds. Requirements Our Python for Data Analysis is fully compatible with PC's, Mac's, Laptop, Tablet and Smartphone devices. This course has been designed to be fully compatible with tablets and smartphones so you can access your course on Wi-Fi, 3G or 4G. There is no time limit for completing this course, it can be studied in your own time at your own pace. Career path Having these various qualifications will increase the value in your CV and open you up to multiple sectors such as Business & Management, Admin, Accountancy & Finance, Secretarial & PA, Teaching & Mentoring etc. Course Curriculum 19 sections • 99 lectures • 00:08:00 total length •Welcome & Course Overview: 00:07:00 •Set-up the Environment for the Course (lecture 1): 00:09:00 •Set-up the Environment for the Course (lecture 2): 00:25:00 •Two other options to setup environment: 00:04:00 •Python data types Part 1: 00:21:00 •Python Data Types Part 2: 00:15:00 •Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 1): 00:16:00 •Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 2): 00:20:00 •Python Essentials Exercises Overview: 00:02:00 •Python Essentials Exercises Solutions: 00:22:00 •What is Numpy? A brief introduction and installation instructions.: 00:03:00 •NumPy Essentials - NumPy arrays, built-in methods, array methods and attributes.: 00:28:00 •NumPy Essentials - Indexing, slicing, broadcasting & boolean masking: 00:26:00 •NumPy Essentials - Arithmetic Operations & Universal Functions: 00:07:00 •NumPy Essentials Exercises Overview: 00:02:00 •NumPy Essentials Exercises Solutions: 00:25:00 •What is pandas? A brief introduction and installation instructions.: 00:02:00 •Pandas Introduction: 00:02:00 •Pandas Essentials - Pandas Data Structures - Series: 00:20:00 •Pandas Essentials - Pandas Data Structures - DataFrame: 00:30:00 •Pandas Essentials - Handling Missing Data: 00:12:00 •Pandas Essentials - Data Wrangling - Combining, merging, joining: 00:20:00 •Pandas Essentials - Groupby: 00:10:00 •Pandas Essentials - Useful Methods and Operations: 00:26:00 •Pandas Essentials - Project 1 (Overview) Customer Purchases Data: 00:08:00 •Pandas Essentials - Project 1 (Solutions) Customer Purchases Data: 00:31:00 •Pandas Essentials - Project 2 (Overview) Chicago Payroll Data: 00:04:00 •Pandas Essentials - Project 2 (Solutions Part 1) Chicago Payroll Data: 00:18:00 •Matplotlib Essentials (Part 1) - Basic Plotting & Object Oriented Approach: 00:13:00 •Matplotlib Essentials (Part 2) - Basic Plotting & Object Oriented Approach: 00:22:00 •Matplotlib Essentials (Part 3) - Basic Plotting & Object Oriented Approach: 00:22:00 •Matplotlib Essentials - Exercises Overview: 00:06:00 •Matplotlib Essentials - Exercises Solutions: 00:21:00 •Seaborn - Introduction & Installation: 00:04:00 •Seaborn - Distribution Plots: 00:25:00 •Seaborn - Categorical Plots (Part 1): 00:21:00 •Seaborn - Categorical Plots (Part 2): 00:16:00 •Seborn-Axis Grids: 00:25:00 •Seaborn - Matrix Plots: 00:13:00 •Seaborn - Regression Plots: 00:11:00 •Seaborn - Controlling Figure Aesthetics: 00:10:00 •Seaborn - Exercises Overview: 00:04:00 •Seaborn - Exercise Solutions: 00:19:00 •Pandas Built-in Data Visualization: 00:34:00 •Pandas Data Visualization Exercises Overview: 00:03:00 •Panda Data Visualization Exercises Solutions: 00:13:00 •Plotly & Cufflinks - Interactive & Geographical Plotting (Part 1): 00:19:00 •Plotly & Cufflinks - Interactive & Geographical Plotting (Part 2): 00:14:00 •Plotly & Cufflinks - Interactive & Geographical Plotting Exercises (Overview): 00:11:00 •Plotly & Cufflinks - Interactive & Geographical Plotting Exercises (Solutions): 00:37:00 •Project 1 - Oil vs Banks Stock Price during recession (Overview): 00:15:00 •Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 1): 00:18:00 •Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 2): 00:18:00 •Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 3): 00:17:00 •Project 2 (Optional) - Emergency Calls from Montgomery County, PA (Overview): 00:03:00 •Introduction to ML - What, Why and Types..: 00:15:00 •Theory Lecture on Linear Regression Model, No Free Lunch, Bias Variance Tradeoff: 00:15:00 •scikit-learn - Linear Regression Model - Hands-on (Part 1): 00:17:00 •scikit-learn - Linear Regression Model Hands-on (Part 2): 00:19:00 •Good to know! How to save and load your trained Machine Learning Model!: 00:01:00 •scikit-learn - Linear Regression Model (Insurance Data Project Overview): 00:08:00 •scikit-learn - Linear Regression Model (Insurance Data Project Solutions): 00:30:00 •Theory: Logistic Regression, conf. mat., TP, TN, Accuracy, Specificityetc.: 00:10:00 •scikit-learn - Logistic Regression Model - Hands-on (Part 1): 00:17:00 •scikit-learn - Logistic Regression Model - Hands-on (Part 2): 00:20:00 •scikit-learn - Logistic Regression Model - Hands-on (Part 3): 00:11:00 •scikit-learn - Logistic Regression Model - Hands-on (Project Overview): 00:05:00 •scikit-learn - Logistic Regression Model - Hands-on (Project Solutions): 00:15:00 •Theory: K Nearest Neighbors, Curse of dimensionality .: 00:08:00 •scikit-learn - K Nearest Neighbors - Hands-on: 00:25:00 •scikt-learn - K Nearest Neighbors (Project Overview): 00:04:00 •scikit-learn - K Nearest Neighbors (Project Solutions): 00:14:00 •Theory: D-Tree & Random Forests, splitting, Entropy, IG, Bootstrap, Bagging.: 00:18:00 •scikit-learn - Decision Tree and Random Forests - Hands-on (Part 1): 00:19:00 •scikit-learn - Decision Tree and Random Forests (Project Overview): 00:05:00 •scikit-learn - Decision Tree and Random Forests (Project Solutions): 00:15:00 •Support Vector Machines (SVMs) - (Theory Lecture): 00:07:00 •scikit-learn - Support Vector Machines - Hands-on (SVMs): 00:30:00 •scikit-learn - Support Vector Machines (Project 1 Overview): 00:07:00 •scikit-learn - Support Vector Machines (Project 1 Solutions): 00:20:00 •scikit-learn - Support Vector Machines (Optional Project 2 - Overview): 00:02:00 •Theory: K Means Clustering, Elbow method ..: 00:11:00 •scikit-learn - K Means Clustering - Hands-on: 00:23:00 •scikit-learn - K Means Clustering (Project Overview): 00:07:00 •scikit-learn - K Means Clustering (Project Solutions): 00:22:00 •Theory: Principal Component Analysis (PCA): 00:09:00 •scikit-learn - Principal Component Analysis (PCA) - Hands-on: 00:22:00 •scikit-learn - Principal Component Analysis (PCA) - (Project Overview): 00:02:00 •scikit-learn - Principal Component Analysis (PCA) - (Project Solutions): 00:17:00 •Theory: Recommender Systems their Types and Importance: 00:06:00 •Python for Recommender Systems - Hands-on (Part 1): 00:18:00 •Python for Recommender Systems - - Hands-on (Part 2): 00:19:00 •Natural Language Processing (NLP) - (Theory Lecture): 00:13:00 •NLTK - NLP-Challenges, Data Sources, Data Processing ..: 00:13:00 •NLTK - Feature Engineering and Text Preprocessing in Natural Language Processing: 00:19:00 •NLTK - NLP - Tokenization, Text Normalization, Vectorization, BoW.: 00:19:00 •NLTK - BoW, TF-IDF, Machine Learning, Training & Evaluation, Naive Bayes : 00:13:00 •NLTK - NLP - Pipeline feature to assemble several steps for cross-validation: 00:09:00 •Resources- Python for Data Analysis: 00:00:00

This course is designed to introduce students to skills similar to what pentesters and hackers use in real-life situations. In addition, this course will provide a greater understanding of how vulnerabilities are discovered and exploited. This course will guide you on how to use pentesting tools in the real world.