2436 Programming courses

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In this course, we will explore some features introduced recently in the language and a few important things that should be kept in mind while programming JavaScript. Learn some important JavaScript concepts and hacks to eliminate your fear of coding and improve your JavaScript coding skills. This course expects a fair understanding of JavaScript to start with.

Network DevOps course description This course is not a soft skills course covering the concepts of DevOps but instead concentrates on the technical side of tools and languages for network DevOps. Particular technologies focussed on are ansible, git and Python enabling delegates to leave the course ready to starting automating their network. Hands on sessions follow all major sections. More detailed courses on individual aspects of this course are available. What will you learn Evaluate network automation tools. Automate tasks with ansible. Use git for version control. Use Python to manage network devices. Use Python libraries for network devices. Network DevOps course details Who will benefit: Administrators automating tasks. Prerequisites: TCP/IP Foundation Duration 5 days Network DevOps course contents What is DevOps Programming and automating networks, networks and clouds, AWS, OpenStack, SDN, DevOps for network operations. Initial configuration Configuring SSH, ZTP, POAP. Hands on Initial lab configuration. Getting started with ansible The language, the engine, the framework. Uses of ansible, orchestration. The architecture, Controlling machines, nodes, Agentless, SSH, modules. Configuration management, inventories, playbooks, modules, roles. Hands on Installing ansible, running ad hoc commands. Ansible playbooks ansible-playbook, YAML, plays, tasks, handlers, modules. Playbook variables. Register module, debug module. Hands on Running playbooks. Ansible Inventories /etc/ansible/hosts, hosts, groups, static inventories, dynamic inventories. Inventory variables, external variables. Limiting hosts. Hands on Static inventories, variables in inventory files. Ansible modules for networking Built in modules, custom modules, return values. Core modules for network operations. Cisco and/or Juniper modules. ansible_connection. Ansible 2.6 CLI. Hands on Using modules. Ansible templating and roles aConfiguration management, full configurations, partial configurations. The template module, the assemble module, connection: local, Jinja2 templates, variables, if, for, roles. Hands on Generating multiple configurations from a template. Network programming and modules Why use Python? Why use ansible? alternatives, ansible tower, Linux network devices. Programming with Python Python programming Functions. Classes, objects and instances, modules, libraries, packages. Python strings, Python file handling, pip list, pip instal. Hands on Python programming with pyping. More Python programming Functions. Classes, objects and instances, modules, libraries, packages. Python strings, Python file handling, pip list, pip install. Hands on Python programming with pyping. Git Distributed version control, repositories, Git and GitHub, Alternatives to GitHub, Installing git, git workflows, creating repositories, adding and editing files, branching and merging, merge conflicts. Hands on working with Git. Python and networking APIs, Sockets, Telnetlib, pysnmp, ncclient, ciscoconfparse. Paramiko SSH and Netmiko Integrating Python and network devices using SSH. Netmiko, Netmiko methods. Hands on Netmiko. NAPALM What is NAPALM, NAPALM operations, getters, Replace, merge, compare, commit, discard. Hands on Configuration with NAPALM. Integrating ansible and NAPALM. Python and REST REST APIs, enabling the REST API. Accessing the REST API with a browser, cURL, Python and REST, the request library. Hands on Using a REST API with network devices.

Dive into the dynamic realm of WebGL 2D/3D Programming and Graphics Rendering through this comprehensive course. Explore various sections, from foundational concepts to advanced techniques in drawing objects, applying colors and textures, transforming objects, camera movement, lighting, and shading. Participants will develop the skills to create captivating and immersive graphics using WebGL technology. Learning Outcomes: Grasp the fundamentals of WebGL programming and graphics rendering. Create and manipulate objects using WebGL for both 2D and 3D environments. Apply colors and textures to enhance the visual quality of graphics. Implement object movement and transformation techniques. Understand camera manipulation and movement for dynamic views. Master lighting and shading techniques for realistic visual effects. Apply learned concepts to real-world graphics rendering projects. Develop proficiency in WebGL programming for interactive graphics applications. Why buy this WebGL 2D/3D Programming and Graphics Rendering?  Unlimited access to the course for forever Digital Certificate, Transcript, student ID all included in the price Absolutely no hidden fees Directly receive CPD accredited qualifications after course completion Receive one to one assistance on every weekday from professionals Immediately receive the PDF certificate after passing Receive the original copies of your certificate and transcript on the next working day Easily learn the skills and knowledge from the comfort of your home Certification After studying the course materials of the WebGL 2D/3D Programming and Graphics Rendering you will be able to take the MCQ test that will assess your knowledge. After successfully passing the test you will be able to claim the pdf certificate for £5.99. Original Hard Copy certificates need to be ordered at an additional cost of £9.60. Who is this course for? This WebGL 2D/3D Programming and Graphics Rendering course is ideal for Programmers and developers interested in graphics programming using WebGL. Computer graphics enthusiasts seeking to expand their knowledge and skills. Students pursuing degrees in computer science or related fields. Designers looking to integrate interactive and visually appealing graphics into their projects. Prerequisites This WebGL 2D/3D Programming and Graphics Rendering 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. Career path Junior Web Developer: £20,000 - £30,000 WebGL Developer: £30,000 - £45,000 Graphics Programmer: £35,000 - £50,000 Senior Software Engineer (Graphics): £45,000 - £70,000 Technical Lead (Graphics Programming): £60,000 - £90,000. Course Curriculum Section 01: Introduction WebGL vs OpenGL vs OpenGL ES 00:05:00 Setup Server (Mac, Windows and Linux) 00:05:00 Setup WebGL Project 00:08:00 Section 02: Drawing Objects WebGL Rendering Pipeline 00:04:00 Drawing A Point 00:22:00 Normalised Coordinates vs Device Coordinates 00:10:00 Drawing A Simple Triangle 00:06:00 Drawing A Line Using gl.LINES 00:03:00 Drawing A Line Using gl.LINE_STRIP & gl.LINE_LOOP 00:03:00 Drawing A Triangle With Lines Using gl.TRIANGLE_STRIP & gl.TRIANGLE_FAN 00:03:00 Drawing A Quad 00:07:00 Drawing A 3D Cube 00:24:00 Setup Three.js 00:06:00 Loading & Drawing A Model Using Three.js 00:16:00 Section 03: Colours and Textures Applying Color To Shapes 00:09:00 One Color Per Triangle 00:15:00 One Color Per Vertex Using Interpolation 00:02:00 Applying A Texture To Shapes 00:23:00 Texture Coordinates 00:08:00 Section 04: Moving & Transforming Objects Moving Objects Using Translation 00:06:00 Left Handed vs Right Handed Coordinate System 00:06:00 Sizing Objects Using Scaling 00:06:00 Combining Transformations 00:07:00 Section 05: Movement & Camera Mouse Input 00:11:00 Keyboard Input 00:09:00 Fixing Rotation and Adding Individual Rotation 00:08:00 Section 06: Lighting & Shading Ambient Lighting 00:13:00 Section 07 Resource Resource 00:00:00 Assignment Assignment - WebGL 2D/3D Programming and Graphics Rendering 00:00:00

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Web API use has increased exponentially in the programming world. This course will provide significant knowledge of implementing Minimal API for your next RESTful API-related project. You will be certain to use the building concepts with ease by following a step-by-step approach. Gain the confidence to instantly create a new Minimal API project with ease.

Learn to create with SwiftUI/iOS 16. This course illustrates many features in iOS 16 development using the SwiftUI programming language, covering the basics (Stacks, Forms, Lists, ForEach, Buttons, Sliders, Pickers, Color Pickers, Scroll Views, Sheets, state, binding, and animations) and we create an Emoji Quote app using SwiftUI.

Explore the Apache Kafka ecosystem and architecture, and learn client API programming in Java

Delve into the world of data analysis with 'R Programming for Data Science,' a course designed to guide learners through the intricacies of R, a premier programming language in the data science domain. The course opens with a broad perspective on data science, illuminating the pivotal role of R in this field. Learners are then introduced to R and RStudio, equipping them with the foundational tools and interfaces essential for R programming. The curriculum progresses with an introduction to the basics of R, ensuring learners grasp the core principles that underpin more complex operations. A highlight of this course is its in-depth exploration of R's versatile data structures, including vectors, matrices, factors, and data frames. Each unit is crafted to provide learners with a comprehensive understanding of these structures, pivotal for effective data handling and manipulation. The course also emphasizes the importance of relational and logical operators in R, key elements for executing data operations. As the course advances, learners will engage with the nuances of conditional statements and loops, essential for writing efficient and dynamic R scripts. Moving into more advanced territories, the course delves into the creation and usage of functions, an integral part of R programming, and the exploration of various R packages that extend the language's capabilities. Learners will also gain expertise in the 'apply' family of functions, crucial for streamlined data processing. Further units cover regular expressions and effective strategies for managing dates and times in data sets. The course concludes with practical applications in data acquisition, cleaning, visualization, and manipulation, ensuring learners are well-prepared to tackle real-world data science challenges using R. Learning Outcomes Develop a foundational understanding of R's role in data science and proficiency in RStudio. Gain fluency in R programming basics, enabling the handling of complex data tasks. Acquire skills in managing various R data structures for efficient data analysis. Master relational and logical operations for advanced data manipulation in R. Learn to create functions and utilize R packages for expanded analytical capabilities. Why choose this R Programming for Data Science 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 R Programming for Data Science course for? Beginners in data science eager to learn R programming. Data analysts and scientists looking to enhance their skills in R. Researchers in various fields requiring advanced data analysis tools. Statisticians seeking to adopt R for more sophisticated data manipulations. Professionals in finance, healthcare, and other sectors needing data-driven insights. Career path Data Scientist (R Expertise): £30,000 - £70,000 Data Analyst (R Programming Skills): £27,000 - £55,000 Bioinformatics Scientist (R Proficiency): £35,000 - £60,000 Quantitative Analyst (R Knowledge): £40,000 - £80,000 Research Analyst (R Usage): £25,000 - £50,000 Business Intelligence Developer (R Familiarity): £32,000 - £65,000 Prerequisites This R Programming for Data Science does not require you to have any prior qualifications or experience. You can just enrol and start learning.This R Programming for Data Science 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 Unit 01: Data Science Overview 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? 00:02:00 Unit 02: R and RStudio Engine and coding environment 00:03:00 Installing R and RStudio 00:04:00 RStudio: A quick tour 00:04:00 Unit 03: Introduction to Basics Arithmetic with R 00:03:00 Variable assignment 00:04:00 Basic data types in R 00:03:00 Unit 04: Vectors Creating a vector 00:05:00 Naming a vector 00:04:00 Arithmetic calculations on vectors 00:07:00 Vector selection 00:06:00 Selection by comparison 00:04:00 Unit 05: Matrices 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 Unit 06: Factors 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 Unit 07: Data Frames 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 Unit 08: Lists 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 Unit 09: Relational Operators 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 Unit 10: Logical Operators 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 Unit 11: Conditional Statements 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 Unit 12: Loops 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 Unit 13: Functions 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 Unit 14: R Packages 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 Unit 15: The Apply Family - lapply 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 Unit 16: The apply Family - sapply & vapply 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 Unit 17: Useful Functions Mathematical functions 00:05:00 Data Utilities 00:08:00 Additional Materials 00:00:00 Unit 18: Regular Expressions grepl & grep 00:04:00 Metacharacters 00:05:00 sub & gsub 00:02:00 More metacharacters 00:04:00 Additional Materials 00:00:00 Unit 19: Dates and Times 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 Unit 20: Getting and Cleaning Data 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 Unit 21: Plotting Data in R 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 Unit 22: Data Manipulation with dplyr 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 Assignment - R Programming for Data Science 00:00:00