286 Data Structure courses in Bristol delivered Online

Register on the Learn DOM Manipulation with JavaScript today and build the experience, skills and knowledge you need to enhance your professional development and work towards your dream job. Study this course through online learning and take the first steps towards a long-term career. The course consists of a number of easy to digest, in-depth modules, designed to provide you with a detailed, expert level of knowledge. Learn through a mixture of instructional video lessons and online study materials. Receive online tutor support as you study the course, to ensure you are supported every step of the way. Get an e-certificate as proof of your course completion. The Learn DOM Manipulation with JavaScript is incredibly great value and allows you to study at your own pace. Access the course modules from any internet-enabled device, including computers, tablet, and smartphones. The course is designed to increase your employability and equip you with everything you need to be a success. Enrol on the now and start learning instantly! What You Get With The Learn DOM Manipulation with JavaScript Receive a e-certificate upon successful completion of the course Get taught by experienced, professional instructors Study at a time and pace that suits your learning style Get instant feedback on assessments 24/7 help and advice via email or live chat Get full tutor support on weekdays (Monday to Friday) Course Design The course is delivered through our online learning platform, accessible through any internet-connected device. There are no formal deadlines or teaching schedules, meaning you are free to study the course at your own pace. You are taught through a combination of Video lessons Online study materials Certification Upon successful completion of the course, you will be able to obtain your course completion e-certificate free of cost. Print copy by post is also available at an additional cost of £9.99 and PDF Certificate at £4.99. Who Is This Course For: The course is ideal for those who already work in this sector or are an aspiring professional. This course is designed to enhance your expertise and boost your CV. Learn key skills and gain a professional qualification to prove your newly-acquired knowledge. Requirements: The online training is open to all students and has no formal entry requirements. To study the Learn DOM Manipulation with JavaScript, all your need is a passion for learning, a good understanding of English, numeracy, and IT skills. You must also be over the age of 16.  Course Content Unit 01: Course Introduction What is the DOM? 00:02:00 Your first DOM update 00:05:00 Course Project: Code DOM Adventure 00:04:00 Unit 02: DOM Fundamentals HTML and the DOM 00:05:00 DOM standards 00:05:00 The BOM 00:04:00 The CSSOM 00:03:00 The tree (Data structure) 00:05:00 The DOM tree 00:11:00 The DOM and JavaScript 00:01:00 Unit 03: Code DOM Adventure App architecture 00:08:00 Challenge solution: The exit screen 00:04:00 App skeleton 00:05:00 The splash screen file 00:03:00 Download our asset kit now! 00:03:00 Including the assets 00:03:00 Unit 04: Creating elements Wiring the splash screen element 00:02:00 Creating elements at runtime 00:03:00 Appending HTML strings width append() 00:02:00 Appending nodes with append or append Child 00:03:00 Script order matters 00:04:00 Unit 05: Dynamic CSS Specifying classes to elements 00:04:00 Working with static styles 00:04:00 Defining styles at runtime 00:04:00 Querying the DOM to find elements 00:05:00 Unit 06: Profiling the pixel pipeline The pixel pipeline 00:02:00 Identifying bad practices 00:04:00 Profiling runtime performance 00:04:00 Batching DOM updates with document fragments 00:05:00 Too many nodes 00:04:00 Unit 07: Animation using DOM changes Removing nodes from the DOM 00:06:00 The animation frames 00:06:00 Our animate method 00:04:00 Our working animation! 00:06:00 Stopping the animation 00:08:00 Unit 08: Planning DOM changes with a state model Let's build the level! 00:02:00 2. Our state model to control the DOM from state. 00:10:00 Normalizing attributes 00:04:00 Our level class 00:04:00 Arrays, references and non-iterable empty slots 00:05:00 Building our state with an ugly oneliner 00:05:00 Module 04: The Language of Coaching 01:00:00 Write code for humans and normalize your code 00:05:00 Rendering the level element 00:05:00 Things are getting messy 00:06:00 Unit 09: Easy bundling Easy bundling 00:06:00 Bundle with the start script 00:01:00 Our dev server 00:02:00 Let's use DOMContentLoaded and ES Modules (ESM) 00:07:00 Dynamic style elements with CSS as ESM imports 00:04:00 Unit 10: DOM updates with basic state driven development Designing the shape of our state 00:02:00 Initializing our state in preparation to render DOM elements 00:05:00 DOM updates from state 00:08:00 Updates to state are reflected in the DOM 00:03:00 Modeling and render our chip walls 00:06:00 Unit 11: The player, Interacting with user input The player - Tech approach 00:02:00 Rendering the player with the DOM 00:07:00 Box model and global styles 00:06:00 Manipulating inline styles with the DOM 00:04:00 Moving the player by changing its state 00:06:00 DOM keyboard event listeners 00:07:00 Mapping and filtering DOM events data 00:04:00 Can the player move? - Tech approach 00:03:00 Preventing overlapping DOM elements 00:12:00 Prepare interactive frames 00:08:00 Resetting className and adding interactive frames on DOM events 00:06:00 Update frames without moving the element on DOM events 00:03:00 Unit 12: Interactive DOM, breaking walls Adding random DOM elements 00:06:00 DOM events when pressing the space key 00:05:00 Creating elements on DOM events 00:05:00 z-index manifest 00:04:00 Dynamic element IDs with the DOM 00:07:00 Interacting with other elements using the state model 00:06:00 Remove surrounding walls 00:04:00 Unit 13: Portal to exit the game Adding the portal to the screen 00:07:00 Random elements on the screen 00:05:00 Grouping inline CSS DOM updates 00:03:00 Exiting the game, when two elements cross paths 00:04:00 Challenge, your turn to build the exit screen 00:03:00 Challenge solution, my turn to build the exit screen 00:04:00 Hiding the portal behind a wall 00:05:00 Removing DOM event listeners 00:04:00 Unit 14: Animating all the things Rendering the splash screen 00:04:00 Swapping screens 00:02:00 Animating the portal 00:04:00 CSS kit - animations 00:03:00 Request animation frame and delaying animations 00:09:00 Animating with a parent css class 00:03:00 Old TV effect 00:02:00 Animating with delayed animation 00:11:00 Optimizing frames 00:03:00 Final frame optimizations 00:04:00 Unit 15: DOM Sound effects Dynamic audio elements 00:07:00 Interactive sound effects with DOM events 00:04:00 Delayed audio effects with callbacks and DOM events 00:04:00 Final lecture, final sound effect! exiting the game 00:03:00

Overview Mastering data science skills and expertise can open new doors of opportunities for you in a wide range of fields. Learn the fundamentals and develop a solid grasp of Python data science with the comprehensive Data Science with Python course. This course is designed to assist you in securing a valuable skill set and boosting your career. This course will provide you with quality training on the fundamentals of data analysis with Python. From the step-by-step learning process, you will learn the techniques of setting up the system. Then the course will teach you Python data structure and functions. You will receive detailed lessons on NumPy, Matplotlib, and Pandas. Furthermore, you will develop the skills for Algorithm Evaluation Techniques, visualising datasets and much more. After completing the course you will receive a certificate of achievement. This certificate will help you create an impressive resume. So join today! 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? This course Data Science with Python course is ideal for beginners in data science. It will help them develop a solid grasp of Python and help them pursue their dream career in the field of data science. Requirements The students will not require any formal qualifications or previous experience to enrol in this course. Anyone can learn from the course anytime from anywhere through smart devices like laptops, tabs, PC, and smartphones with stable internet connections. They can complete the course according to their preferable pace so, there is no need to rush. Career Path This course will equip you with valuable knowledge and effective skills in this area. After completing the course, you will be able to explore career opportunities in the fields such as Data Analyst Data Scientist Data Manager Business Analyst And much more! Course Curriculum 90 sections • 90 lectures • 10:19:00 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: 00:04:00 •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: 00:06:00 •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

12 in 1 Career Guided Programme | Python, Data Science, ML, Tableau, SQL Programming, Data Mining, Business Analysis | 130 CPD Points | Tutor Support | Lifetime Access

This second-edition JavaScript course covers fundamental concepts, including variables, data types, functions, and control flow, as well as advanced topics such as object-oriented programming, modules, and testing. With practical projects and clear explanations, learners can gain a solid understanding of the language and develop their skills.

SQL for Data Science, Data Analytics and Data Visualization Course Overview: This course offers a comprehensive introduction to SQL, designed for those looking to enhance their skills in data science, data analytics, and data visualisation. Learners will develop the ability to work with SQL databases, efficiently query and manage data, and apply these techniques for data analysis in both SQL Server and Azure Data Studio. By mastering SQL statements, aggregation, filtering, and advanced commands, learners will be equipped with the technical skills required to manage large datasets and extract meaningful insights. The course provides a solid foundation in data structures, user management, and working with multiple tables, culminating in the ability to perform complex data analysis and visualisation tasks. Course Description: This course covers a broad range of topics essential for anyone working with data in a professional capacity. From setting up SQL servers to mastering database management tools like SQL Server Management Studio (SSMS) and SQL Azure Data Studio, the course provides a thorough grounding in SQL. Learners will gain expertise in data structure statements, filtering data, and applying aggregate functions, as well as building complex SQL queries for data analysis. The course also includes instruction on SQL user management, group by statements, and JOINs for multi-table analysis. Key topics such as SQL constraints, views, stored procedures, and database backup and restore are also explored. The course incorporates SQL visualisation tools in Azure Data Studio, empowering learners to visualise data effectively. By the end of the course, learners will be proficient in SQL queries, data manipulation, and using Azure for data analysis. SQL for Data Science, Data Analytics and Data Visualization Curriculum: Module 01: Getting Started Module 02: SQL Server Setting Up Module 03: SQL Azure Data Studio Module 04: SQL Database Basic SSMS Module 05: SQL Statements for DATA Module 06: SQL Data Structure Statements Module 07: SQL User Management Module 08: SQL Statement Basic Module 09: Filtering Data Rows Module 10: Aggregate Functions Module 11: SQL Query Statements Module 12: SQL Group By Statement Module 13: JOINS for Multiple Table Data Analysis Module 14: SQL Constraints Module 15: Views Module 16: Advanced SQL Commands Module 17: SQL Stored Procedures Module 18: Azure Data Studio Visualisation Module 19: Azure Studio SQL for Data Analysis Module 20: Import & Export Data Module 21: Backup and Restore Database (See full curriculum) Who is this course for? Individuals seeking to enhance their data management and analysis skills. Professionals aiming to progress in data science, data analytics, or database administration. Beginners with an interest in data analysis and SQL databases. Anyone looking to gain expertise in SQL for Azure and SQL Server environments. Career Path: Data Analyst Data Scientist Database Administrator SQL Developer Business Intelligence Analyst Data Visualisation Specialist

Scala is doubtless one of the most in-demand skills for data scientists and data engineers. This competitive course will teach you the essential concepts and methodologies of Scala with a lot of practical implementations.

This is a comprehensive course designed to provide a solid foundation in web development principles and practices. This course is intentionally structured to provide a technical understanding of web development concepts without delving into intricate implementation details. Anyone looking to better understand how web applications are built can take this course.

Java is one of the most popular programming languages. Companies such as Facebook, Microsoft, and Apple all want Java.

Objective-C programming training course description A hands on introduction that will allow you to master Objective-C and start using it to write powerful native applications for even the newest Macs and iOS devices! Using The step-by-step approach, will let you get comfortable with Objective-C's unique capabilities and Apple's Xcode 5 development environment. Make the most of Objective-C objects and messaging. Work effectively with design patterns, collections, blocks, foundation classes, threading, Git and a whole lot more. Every session builds on what you've already learned, giving a rock-solid foundation for real-world success! What will you learn Use Xcode 5. Declare classes, instance variables, properties, methods, and actions. Use arrays, dictionaries, and sets. Expand and extend classes with protocols, delegates, categories, and extensions. Use Apple's powerful classes and frameworks. Objective-C programming training course details Who will benefit: Developers wanting to learn Objective-C. Prerequisites: Software development fundamentals. Duration 5 days Objective-C programming training course contents PART 1: GETTING STARTED WITH OBJECTIVE-C The Developer Program: Objective-C, enrolling as an Apple Developer, setting up the development environment, Xcode. Your first project. OO programming with Objective-C: OO projects, Frameworks, classes and instances, encapsulation, accessors, Inheritance. OO features in Objective-C: Messages, methods, working with id, nesting messages, method signatures and parameters. allocating and initializing objects. Using Xcode: Xcode, source code control, git and Xcode, Using a Remote Repository. Compiler Directives: Projects, Compiler Directives, Prefix headers, main.m, .h files. PART 2: OBJECTIVE-C BASICS Messaging in a Testbed App: Setting Up the Testbed Apps, Adding a Text Field and Connecting It to Your Code, Sending a Message to the Text Field, Reviewing the Message Syntax. Declaring a Class in an Interface File: Context, Creating an Instance Variable with id, What Happens When Execution Stops, dynamic binding, Creating an Instance Variable for with the Class Name and with a Superclass Name, instance variable visibility. Properties in an Interface File: Interface Variables vs Properties, Declared Properties, Using Attributes. Implementing Properties. @synthesize, @dynamic. Methods in an Interface File: Methods in a Class, class and instance methods, Method declaration, returning complex data structures from Methods. Actions in an Interface File: Actions, Actions in OS X and iOS, disconnecting actions. Routing messages with selectors: Receiver and selector objects in messages, Objective-C Runtime, SEL and @selector (), performSelector, NSInvocation, testing whether an Instance can respond to a selector. Building on the Foundation: The Foundation Framework, Foundation Classes, Foundation Paradigms and Policies; Mutability, class clusters, notifications. Defining a Class in Implementation Files: Projects, dynamic typing, creating a new App, implementing a method, expanding Classses with init Methods. Organizing Data with Collections: Collecting Objects, Property Lists, Runtime, comparing the Collection Classes, Creating a Collection, Objective-C Literal Syntax, Enumerating collections, Testing Membership in a Collection, Accessing an Object in a Collection. Managing Memory and Runtime Objects: Managing objects in memory, managing reference counts manually and with ARC, variable qualifiers, variable autorelease. PART 3: EXPANDING AND EXTENDING CLASSES Protocols and Delegates: Subclassing, Protocols, Delegates, Looking Deeper Inside Protocols. Categories and Extensions: Comparing categories and protocols, categories vs subclasses, working with categories, class extensions, informal protocols. Associative References and Fast Enumeration: Objective-C 2.0 Time-Saving Features, Extending Classes by Adding Instance Variables (Sort of), Using Fast Enumeration. Blocks: Revisiting Blocks, Callbacks, Blocks, Exploring Blocks in Cocoa, Cocoa Blocks and Memory. PART 4: BEYOND THE BASICS Handling Exceptions and Errors: Exception and Error classes: NSException, NSError, Identifying exceptions, throwing exceptions, catching exceptions. Queues and Threading: Getting Started with Concurrency, Introducing Queues, Dispatch Sources, Using Dispatch Queues. Working with the Debugger: Logging Information, Console Logs, NSLog, Smart Breakpoints, enhancing breakpoints with messages. Using Xcode Debug Gauges for Analysis: Debug Gauges, Monitoing CPU and memory utilization, monitoring energy, Using Instruments. PART 5: OPTIONAL TOPICS C Syntax Summary: Data Types, Control Structures. Apps, Packages, and Bundles: Project Bundles, lproj Files, Asset Catalogs, plist Files, Precompiled Header Files (.pch). Archiving and Packaging Apps for Development and Testing: Archiving.

OOAD training course description A workshop course providing thorough practical knowledge of object oriented analysis and design methods. What will you learn Perform Systems Analysis with Object Oriented methods. Identify key classes and objects. Expand and refine OO problem domain models. Design Class hierarchies using inheritance and polymorphism. Design programs with Object Oriented methods. OOAD training course details Who will benefit: System analysts, designers, programmers and project managers. Prerequisites: It is desirable that delegates have experience of programming in C++/Java or some other OOP language. Duration 5 days OOAD training course contents What is OO? Classes, objects, messages, encapsulation, associations, inheritance, polymorphism, reusability. What is Systems Analysis and design? Data flow diagrams, structure diagrams. The OO approach. OOA The problem domain and object modelling. Identifying classes and objects. Generalisation and inheritance. Defining attributes and methods. OOD Refining the OOA results. Designing the User Interface. Designing the algorithms and data structures using objects. Designing the methods. OOP Prototyping. Implementing OOD with OOPs and OOPLs.