5523 Software courses delivered Online

With this GraphQL Training with NodeJs you will learn how to start GraphQL and NodeJs, do CURD Operations with NodeJs, MongoDB, and GraphQL, and fragment, union, and interfaces with GraphQL. The course will teach you to learn how to set page number, make caching and batching, filtering, sorting and to know about the subscription and data loaders in GraphQL. You will master fundamental concepts behind structuring graphql servers. Learn to build apis with graphql. You will explore to integrating graphql with nodejs and mongodb and, graphql with nodejs and sequelize. You will also learn to test the data authentication and authorization with Jest in MngoDB, NodeJs, and GraphQL. Who is this course for? GraphQL Training with NodeJs is suitable for anyone who wants to gain extensive knowledge, potential experience and professional skills in the related field. This course is CPD accredited so you don't have to worry about the quality. Requirements Our GraphQL Training with NodeJs is open to all from all academic backgrounds and there are no specific requirements to attend this course. It is compatible and accessible from any device including Windows, Mac, Android, iOS, Tablets etc. CPD Certificate from Course Gate  At the successful completion of the course, you can obtain your CPD certificate from us. You can order the PDF certificate for £9 and the hard copy for £15. Also, you can order both PDF and hardcopy certificates for £22. Career path This course opens a new door for you to enter the relevant job market and also gives you the opportunity to acquire extensive knowledge along with required skills to become successful. You will be able to add our qualification to your CV/resume which will help you to stand out in the competitive job industry. Course Curriculum Getting Started Create Express Server in NodeJs 00:04:00 Create GraphQL Server using express 00:08:00 Understand GraphQL Resolver Arguments 00:00:00 Connect GraphQL App with MongoDB using Mongoose 00:04:00 CURD(Create, Read,Update,Delete) Operations with Nodejs, GraphQL, and MongoDB Create Record in MongoDB Using GraphQL Mutation 00:06:00 Get Records From MongoDB Using GraphQL Query 00:02:00 Update Records From MongoDB Using GraphQL Mutation 00:02:00 Delete Record From MongoDB Using GraphQL Mutation 00:02:00 Authentication and Authorization in GraphQL, Nodejs and MongoDB Create GraphQL Server with GraphQL Yoga 00:03:00 Split up your schema definition, resolvers, and Query type into multiple files 00:07:00 Create a Mongoose User model for Authentication in GraphQL 00:03:00 Register and Encrypt User Data and Return JWT Payload with Graph 00:07:00 Operations and Variables in GraphQL 00:02:00 Validate Input in GraphQL with Joi 00:04:00 Authenticate a User using a JSON Web Token in GraphQL 00:03:00 Merge GraphQL Resolvers into a Single Object using lodash.merge 00:02:00 Create a Middleware for Authentication in GraphQL 00:07:00 Apply Authentication Middleware on GraphQL Resolvers 00:03:00 Create Nested Resolvers to remove duplicate code from individual 00:07:00 Pagination, Filtering, And Sorting in GraphQL Paginate List of Data in GraphQL 00:08:00 Filter Nodes with Matching Rule GraphQL Queries 00:03:00 Sort GraphQL Query Results by Field 00:02:00 Cursor Based Pagination 00:09:00 Fragments, Interfaces, and Unions in GraphQL Send Multiple Queries in a Single Request using Aliases in GraphQL 00:00:00 Enhancing Fields Reusability with Fragments in GraphQL 00:02:00 Create Enum to represents a collection of related values 00:03:00 Create an Interface to represent the reusable fields in GraphQL 00:06:00 Interface Demo with - ResolveType 00:05:00 Create Union to return more than object type from GraphQL field 00:11:00 Subscriptions and DataLoaders in GraphQL Introduction to Subscriptions 00:01:00 Adding real-time functionality with GraphQL Subscriptions 00:06:00 Create DataLoader in GraphQL 00:05:00 Batching in GraphQL 00:05:00 Caching in GraphQL 00:03:00 Testing with Jest in GraphQL and NodeJs Write Unit test for Resolvers in GraphQL 00:04:00 Write Integration Test for Queries And Mutations in GraphQL 00:04:00 Certificate and Transcript Order Your Certificates or Transcripts 00:00:00

Gain the skills and credentials to kickstart a successful career and learn from the experts with this step-by-step

Gain the skills and credentials to kickstart a successful career and learn from the experts with this step-by-step

Javascript for Data Structures: Dive into the world of data structures using JavaScript. Learn to implement arrays, linked lists, stacks, queues, trees, and more. Perfect for aspiring developers.

Discover the art of modern web design with our comprehensive course on HTML5, CSS3, and Bootstrap. Learn the latest techniques to create visually stunning and responsive websites. Master the essential tools and frameworks used by industry professionals to bring your web design skills to the next level. Enroll now for a hands-on learning experience that blends theory with practical application in the dynamic world of web development.

Welcome to a brand-new course, where you can learn how to create modern and beautiful web projects and templates; if you want to develop and customize your portfolio, become an experienced developer, then this is the right course for you. Level-up your HTML, CSS, and JavaScript coding skills with this course.

Are you looking to enhance your Game Development skills? If yes, then you have come to the right place. Our comprehensive course on Game Development will assist you in producing the best possible outcome by mastering the Game Development skills. The Game Development course is for those who want to be successful. In the Game Development course, you will learn the essential knowledge needed to become well versed in Game Development. Our Game Development course starts with the basics of Game Development and gradually progresses towards advanced topics. Therefore, each lesson of this Game Development course is intuitive and easy to understand. Why would you choose the Game Development course from Compliance Central: Lifetime access to Game Development course materials Full tutor support is available from Monday to Friday with the Game Development course Learn Game Development skills at your own pace from the comfort of your home Gain a complete understanding of Game Development course Accessible, informative Game Development learning modules designed by experts Get 24/7 help or advice from our email and live chat teams with the Game Development Study Game Development in your own time through your computer, tablet or mobile device. A 100% learning satisfaction guarantee with your Game Development Course Game Development Curriculum Breakdown of the Game Development Course Section 01: Introduction Section 02: Basic Building Blocks Section 03: Putting Blocks Together Section 04: Winning Managers Section 05: Creating Basic Game Section 06: Advanced Scratching And Winning Options Section 07: Finishing The Game Section 08: Creating Custom Animation System Section 09: Creating Game Animations Section 10: Building Your Game Section 11: Initial Scratching CPD 10 CPD hours / points Accredited by CPD Quality Standards Who is this course for? The Game Development course helps aspiring professionals who want to obtain the knowledge and familiarise themselves with the skillsets to pursue a career in Game Development. It is also great for professionals who are already working in Game Development and want to get promoted at work. Requirements To enrol in this Game Development course, all you need is a basic understanding of the English Language and an internet connection. Career path The Game Development course will enhance your knowledge and improve your confidence in exploring opportunities in various sectors related to Game Development. Certificates CPD Accredited PDF Certificate Digital certificate - Included CPD Accredited PDF Certificate CPD Accredited Hard Copy Certificate Hard copy certificate - £10.79 CPD Accredited Hard Copy Certificate Delivery Charge: Inside the UK: Free Outside of the UK: £9.99 each

The Statistical Analysis Training Course is pivotal in the modern world, offering essential skills that are increasingly demanded across various industries. As businesses and organizations generate vast amounts of data, the ability to analyze and interpret this data becomes crucial. Learning from The Statistical Analysis Training Course equips individuals with expertise in key areas such as probability, hypothesis testing, regression analysis, and predictive analytics, enhancing their employability. In the UK, proficiency gained from this Statistical Analysis Training course can significantly boost job opportunities, with data analysts and statisticians earning an average salary of £35,000 to £50,000 annually. The demand for statistical analysis skills is on the rise, with the sector experiencing a growth rate of 33% over the past five years. Advantages of the Statistical Analysis Training course include a comprehensive understanding of both foundational and advanced statistical concepts, which are integral in roles across finance, healthcare, marketing, and technology. The Statistical Analysis Training Course ensures that learners are well-versed in modern analytical techniques, making them valuable assets in a data-driven economy. As the importance of data analytics continues to grow, so does the value of this training, making it an indispensable tool for career advancement. Key Features: CPD Certified Statistical Analysis Course Free Certificate from Reed CIQ Approved Statistical Analysis Course Developed by Specialist Lifetime Access Course Curriculum: Statistical Analysis Training Module 01: The Realm of Statistics Module 02: Basic Statistical Terms Module 03: The Center of the Data Module 04: Data Variability Module 05: Binomial and Normal Distributions Module 06: Introduction to Probability Module 07: Estimates and Intervals Module 08: Hypothesis Testing Module 09: Regression Analysis Module 10: Algorithms, Analytics and Predictions Module 11: Learning From Experience: The Bayesian Way Module 12: Doing Statistics: The Wrong Way Module 13: How We Can Do Statistics Better Learning Outcomes: Grasp fundamental statistical concepts for data analysis proficiency. Understand measures of central tendency and dispersion in datasets. Apply probability theory to make informed statistical decisions. Utilize hypothesis testing techniques to draw meaningful conclusions. Master regression analysis for predictive modelling and trend identification. Embrace Bayesian methods and enhance statistical inference capabilities. CPD 10 CPD hours / points Accredited by CPD Quality Standards Statistical Analysis Training 4:44:42 1: Module 01: The Realm of Statistics Preview 15:23 2: Module 02: Basic Statistical Terms 27:51 3: Module 03: The Center of the Data 10:00 4: Module 04: Data Variability 21:00 5: Module 05: Binomial and Normal Distributions 21:00 6: Module 06: Introduction to Probability 23:42 7: Module 07: Estimates and Intervals 21:35 8: Module 08: Hypothesis Testing 21:51 9: Module 09: Regression Analysis 21:00 10: Module 10: Algorithms, Analytics and Predictions 31:05 11: Module 11: Learning From Experience: The Bayesian Way 20:08 12: Module 12: Doing Statistics: The Wrong Way 23:39 13: Module 13: How We Can Do Statistics Better 25:28 14: CPD Certificate - Free 01:00 Who is this course for? This Statistical Analysis Training course is accessible to anyone eager to learn more about this topic. Through this course, you'll gain a solid understanding of Statistical Analysis Training. Moreover, this course is ideal for: Aspiring data analysts seeking statistical foundations for career advancement. Professionals in research roles aiming to refine statistical analysis skills. Students pursuing degrees in mathematics, economics, or related disciplines. Business professionals looking to leverage data-driven insights for strategic decisions. Anyone interested in enhancing statistical literacy and analytical reasoning abilities. Requirements There are no requirements needed to enrol into this Statistical Analysis Training course. We welcome individuals from all backgrounds and levels of experience to enrol into this Statistical Analysis Training course. Career path After finishing this Statistical Analysis Training course you will have multiple job opportunities waiting for you. Some of the following Job sectors of Statistical Analysis Training are: Data Analyst - £30K to £45K/year. Statistician - £35K to £50K/year. Market Research Analyst - £25K to £40K/year. Business Intelligence Analyst - £35K to £55K/year. Healthcare Data Analyst - £30K to £50K/year. Certificates Digital certificate Digital certificate - Included Reed Courses Certificate of Completion Digital certificate - Included Will be downloadable when all lectures have been completed.

Overview This comprehensive course on Kotlin Programming : Android Coding will deepen your understanding on this topic. After successful completion of this course you can acquire the required skills in this sector. This Kotlin Programming : Android Coding 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 Kotlin Programming : Android Coding. It is available to all students, of all academic backgrounds. Requirements Our Kotlin Programming : Android Coding 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 Learning this new skill will help you to advance in your career. It will diversify your job options and help you develop new techniques to keep up with the fast-changing world. This skillset will help you to- Open doors of opportunities Increase your adaptability Keep you relevant Boost confidence And much more! Course Curriculum 11 sections • 73 lectures • 05:38:00 total length •Introduction To This Course: 00:02:00 •Windows Setup: 00:13:00 •Mac Setup: 00:10:00 •Linux Setup: 00:12:00 •Online Code Editor: 00:02:00 •Variables: 00:06:00 •Data Types: 00:06:00 •String: 00:05:00 •Array: 00:07:00 •Data Type Conversion: 00:05:00 •Comments: 00:04:00 •Arithmetic Operators: 00:07:00 •Relational Operators: 00:06:00 •Assignment Operators: 00:06:00 •Unary Operators: 00:05:00 •Bitwise Operators: 00:09:00 •Logical Operators: 00:04:00 •Input & Output: 00:03:00 •Conditional if Statement: 00:05:00 •when Statement: 00:07:00 •For Loop: 00:04:00 •while Loop: 00:04:00 •do while Loop: 00:04:00 •Break Statement: 00:04:00 •Continue Statement: 00:04:00 •Basic Functions: 00:03:00 •Function Parameters: 00:04:00 •Return Values: 00:04:00 •Recursion: 00:04:00 •Default & Named Arguments: 00:06:00 •Lambda Function: 00:04:00 •Higher Order Function: 00:05:00 •Inline Function: 00:02:00 •Try Catch Block: 00:04:00 •Try Catch Expression: 00:05:00 •Multiple Catch Block: 00:05:00 •Nested Try Catch Block: 00:05:00 •Finally Block: 00:02:00 •Throw Keyword: 00:02:00 •Nullable & Non Nullable Types: 00:03:00 •Smart Cast: 00:02:00 •Unsafe and Safe Cast: 00:03:00 •Elvis Operator: 00:04:00 •List: listOf Function: 00:06:00 •List: mutableListOf Function: 00:05:00 •List: arrayListOf Function: 00:06:00 •Map: mapOf Function: 00:07:00 •Map: HashMap: 00:08:00 •Map: hashMapOf Function: 00:05:00 •Map: mutableMapOf Function: 00:04:00 •Set: setOf Function: 00:04:00 •Set: mutableSetOf Function: 00:04:00 •Set: HashSet: 00:04:00 •Basic Example: 00:07:00 •Nested and Inner Class: 00:06:00 •Constructors: 00:05:00 •Visibility Modifiers: 00:06:00 •Inheritance: 00:05:00 •Method Overriding: 00:04:00 •Property Overriding: 00:02:00 •Abstract Class: 00:03:00 •Superclass: 00:03:00 •Data Class: 00:05:00 •Multiple Class Inheritance & Interfaces: 00:03:00 •Sealed Class: 00:03:00 •Extension Function: 00:03:00 •Generics: 00:05:00 •Integer Type Range: 00:05:00 •Regex: 00:04:00 •Call Java from Kotlin: 00:03:00 •Call Kotlin from Java: 00:02:00 •Resource: 00:00:00 •Assignment - Kotlin Masterclass Programming Course: Android Coding Bible: 00:00:00

Overview This comprehensive course on Data Science & Machine Learning with Python will deepen your understanding on this topic. After successful completion of this course you can acquire the required skills in this sector. This Data Science & Machine Learning with Python 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 Data Science & Machine Learning with Python. It is available to all students, of all academic backgrounds. Requirements Our Data Science & Machine Learning with Python 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 Learning this new skill will help you to advance in your career. It will diversify your job options and help you develop new techniques to keep up with the fast-changing world. This skillset will help you to- Open doors of opportunities Increase your adaptability Keep you relevant Boost confidence And much more! Course Curriculum 2 sections • 90 lectures • 10:24:00 total length •Course Overview & Table of Contents: 00:09:00 •Introduction to Machine Learning - Part 1 - Concepts , Definitions and Types: 00:05:00 •Introduction to Machine Learning - Part 2 - Classifications and Applications: 00:06:00 •System and Environment preparation - Part 1: 00:08:00 •System and Environment preparation - Part 2: 00:06:00 •Learn Basics of python - Assignment 1: 00:10:00 •Learn Basics of python - Assignment 2: 00:09:00 •Learn Basics of python - Functions: 00:04:00 •Learn Basics of python - Data Structures: 00:12:00 •Learn Basics of NumPy - NumPy Array: 00:06:00 •Learn Basics of NumPy - NumPy Data: 00:08:00 •Learn Basics of NumPy - NumPy Arithmetic: 00:04:00 •Learn Basics of Matplotlib: 00:07:00 •Learn Basics of Pandas - Part 1: 00:06:00 •Learn Basics of Pandas - Part 2: 00:07:00 •Understanding the CSV data file: 00:09:00 •Load and Read CSV data file using Python Standard Library: 00:09:00 •Load and Read CSV data file using NumPy: 00:04:00 •Load and Read CSV data file using Pandas: 00:05:00 •Dataset Summary - Peek, Dimensions and Data Types: 00:09:00 •Dataset Summary - Class Distribution and Data Summary: 00:09:00 •Dataset Summary - Explaining Correlation: 00:11:00 •Dataset Summary - Explaining Skewness - Gaussian and Normal Curve: 00:07:00 •Dataset Visualization - Using Histograms: 00:07:00 •Dataset Visualization - Using Density Plots: 00:06:00 •Dataset Visualization - Box and Whisker Plots: 00:05:00 •Multivariate Dataset Visualization - Correlation Plots: 00:08:00 •Multivariate Dataset Visualization - Scatter Plots: 00:05:00 •Data Preparation (Pre-Processing) - Introduction: 00:09:00 •Data Preparation - Re-scaling Data - Part 1: 00:09:00 •Data Preparation - Re-scaling Data - Part 2: 00:09:00 •Data Preparation - Standardizing Data - Part 1: 00:07:00 •Data Preparation - Standardizing Data - Part 2: 00:04:00 •Data Preparation - Normalizing Data: 00:08:00 •Data Preparation - Binarizing Data: 00:06:00 •Feature Selection - Introduction: 00:07:00 •Feature Selection - Uni-variate Part 1 - Chi-Squared Test: 00:09:00 •Feature Selection - Uni-variate Part 2 - Chi-Squared Test: 00:10:00 •Feature Selection - Recursive Feature Elimination: 00:11:00 •Feature Selection - Principal Component Analysis (PCA): 00:09:00 •Feature Selection - Feature Importance: 00:07:00 •Refresher Session - The Mechanism of Re-sampling, Training and Testing: 00:12:00 •Algorithm Evaluation Techniques - Introduction: 00:07:00 •Algorithm Evaluation Techniques - Train and Test Set: 00:11:00 •Algorithm Evaluation Techniques - K-Fold Cross Validation: 00:09:00 •Algorithm Evaluation Techniques - Leave One Out Cross Validation: 00:05:00 •Algorithm Evaluation Techniques - Repeated Random Test-Train Splits: 00:07:00 •Algorithm Evaluation Metrics - Introduction: 00:09:00 •Algorithm Evaluation Metrics - Classification Accuracy: 00:08:00 •Algorithm Evaluation Metrics - Log Loss: 00:03:00 •Algorithm Evaluation Metrics - Area Under ROC Curve: 00:06:00 •Algorithm Evaluation Metrics - Confusion Matrix: 00:10:00 •Algorithm Evaluation Metrics - Classification Report: 00:04:00 •Algorithm Evaluation Metrics - Mean Absolute Error - Dataset Introduction: 00:06:00 •Algorithm Evaluation Metrics - Mean Absolute Error: 00:07:00 •Algorithm Evaluation Metrics - Mean Square Error: 00:03:00 •Algorithm Evaluation Metrics - R Squared: 00:04:00 •Classification Algorithm Spot Check - Logistic Regression: 00:12:00 •Classification Algorithm Spot Check - Linear Discriminant Analysis: 00:04:00 •Classification Algorithm Spot Check - K-Nearest Neighbors: 00:05:00 •Classification Algorithm Spot Check - Naive Bayes: 00:04:00 •Classification Algorithm Spot Check - CART: 00:04:00 •Classification Algorithm Spot Check - Support Vector Machines: 00:05:00 •Regression Algorithm Spot Check - Linear Regression: 00:08:00 •Regression Algorithm Spot Check - Ridge Regression: 00:03:00 •Regression Algorithm Spot Check - Lasso Linear Regression: 00:03:00 •Regression Algorithm Spot Check - Elastic Net Regression: 00:02:00 •Regression Algorithm Spot Check - K-Nearest Neighbors: 00:06:00 •Regression Algorithm Spot Check - CART: 00:04:00 •Regression Algorithm Spot Check - Support Vector Machines (SVM): 00:04:00 •Compare Algorithms - Part 1 : Choosing the best Machine Learning Model: 00:09:00 •Compare Algorithms - Part 2 : Choosing the best Machine Learning Model: 00:05:00 •Pipelines : Data Preparation and Data Modelling: 00:11:00 •Pipelines : Feature Selection and Data Modelling: 00:10:00 •Performance Improvement: Ensembles - Voting: 00:07:00 •Performance Improvement: Ensembles - Bagging: 00:08:00 •Performance Improvement: Ensembles - Boosting: 00:05:00 •Performance Improvement: Parameter Tuning using Grid Search: 00:08:00 •Performance Improvement: Parameter Tuning using Random Search: 00:06:00 •Export, Save and Load Machine Learning Models : Pickle: 00:10:00 •Export, Save and Load Machine Learning Models : Joblib: 00:06:00 •Finalizing a Model - Introduction and Steps: 00:07:00 •Finalizing a Classification Model - The Pima Indian Diabetes Dataset: 00:07:00 •Quick Session: Imbalanced Data Set - Issue Overview and Steps: 00:09:00 •Iris Dataset : Finalizing Multi-Class Dataset: 00:09:00 •Finalizing a Regression Model - The Boston Housing Price Dataset: 00:08:00 •Real-time Predictions: Using the Pima Indian Diabetes Classification Model: 00:07:00 •Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset: 00:03:00 •Real-time Predictions: Using the Boston Housing Regression Model: 00:08:00 •Resources - Data Science & Machine Learning with Python: 00:00:00