2417 Programming courses in Leeds delivered Online

In this course, we'll learn how to visualize trends in temperature data with HTML, CSS, JavaScript, and jQuery. We recommend that you have some background in HTML, CSS, and JavaScript. You don't need to be an expert by any means, but you should have experience building web pages with HTML and CSS, and you should have basic programming skills with JavaScript.

This course will help you to get up and running with JMeter. You will learn how to monitor the performance of web applications and REST APIs by load testing, using the features of the JMeter tool.

Duration 5 Days 30 CPD hours This course is intended for This intermediate and beyond level course is geared for experienced technical professionals in various roles, such as developers, data analysts, data engineers, software engineers, and machine learning engineers who want to leverage Scala and Spark to tackle complex data challenges and develop scalable, high-performance applications across diverse domains. Practical programming experience is required to participate in the hands-on labs. Overview Working in a hands-on learning environment led by our expert instructor you'll: Develop a basic understanding of Scala and Apache Spark fundamentals, enabling you to confidently create scalable and high-performance applications. Learn how to process large datasets efficiently, helping you handle complex data challenges and make data-driven decisions. Gain hands-on experience with real-time data streaming, allowing you to manage and analyze data as it flows into your applications. Acquire practical knowledge of machine learning algorithms using Spark MLlib, empowering you to create intelligent applications and uncover hidden insights. Master graph processing with GraphX, enabling you to analyze and visualize complex relationships in your data. Discover generative AI technologies using GPT with Spark and Scala, opening up new possibilities for automating content generation and enhancing data analysis. Embark on a journey to master the world of big data with our immersive course on Scala and Spark! Mastering Scala with Apache Spark for the Modern Data Enterprise is a five day hands on course designed to provide you with the essential skills and tools to tackle complex data projects using Scala programming language and Apache Spark, a high-performance data processing engine. Mastering these technologies will enable you to perform a wide range of tasks, from data wrangling and analytics to machine learning and artificial intelligence, across various industries and applications.Guided by our expert instructor, you?ll explore the fundamentals of Scala programming and Apache Spark while gaining valuable hands-on experience with Spark programming, RDDs, DataFrames, Spark SQL, and data sources. You?ll also explore Spark Streaming, performance optimization techniques, and the integration of popular external libraries, tools, and cloud platforms like AWS, Azure, and GCP. Machine learning enthusiasts will delve into Spark MLlib, covering basics of machine learning algorithms, data preparation, feature extraction, and various techniques such as regression, classification, clustering, and recommendation systems. Introduction to Scala Brief history and motivation Differences between Scala and Java Basic Scala syntax and constructs Scala's functional programming features Introduction to Apache Spark Overview and history Spark components and architecture Spark ecosystem Comparing Spark with other big data frameworks Basics of Spark Programming SparkContext and SparkSession Resilient Distributed Datasets (RDDs) Transformations and Actions Working with DataFrames Spark SQL and Data Sources Spark SQL library and its advantages Structured and semi-structured data sources Reading and writing data in various formats (CSV, JSON, Parquet, Avro, etc.) Data manipulation using SQL queries Basic RDD Operations Creating and manipulating RDDs Common transformations and actions on RDDs Working with key-value data Basic DataFrame and Dataset Operations Creating and manipulating DataFrames and Datasets Column operations and functions Filtering, sorting, and aggregating data Introduction to Spark Streaming Overview of Spark Streaming Discretized Stream (DStream) operations Windowed operations and stateful processing Performance Optimization Basics Best practices for efficient Spark code Broadcast variables and accumulators Monitoring Spark applications Integrating External Libraries and Tools, Spark Streaming Using popular external libraries, such as Hadoop and HBase Integrating with cloud platforms: AWS, Azure, GCP Connecting to data storage systems: HDFS, S3, Cassandra, etc. Introduction to Machine Learning Basics Overview of machine learning Supervised and unsupervised learning Common algorithms and use cases Introduction to Spark MLlib Overview of Spark MLlib MLlib's algorithms and utilities Data preparation and feature extraction Linear Regression and Classification Linear regression algorithm Logistic regression for classification Model evaluation and performance metrics Clustering Algorithms Overview of clustering algorithms K-means clustering Model evaluation and performance metrics Collaborative Filtering and Recommendation Systems Overview of recommendation systems Collaborative filtering techniques Implementing recommendations with Spark MLlib Introduction to Graph Processing Overview of graph processing Use cases and applications of graph processing Graph representations and operations Introduction to Spark GraphX Overview of GraphX Creating and transforming graphs Graph algorithms in GraphX Big Data Innovation! Using GPT and Generative AI Technologies with Spark and Scala Overview of generative AI technologies Integrating GPT with Spark and Scala Practical applications and use cases Bonus Topics / Time Permitting Introduction to Spark NLP Overview of Spark NLP Preprocessing text data Text classification and sentiment analysis Putting It All Together Work on a capstone project that integrates multiple aspects of the course, including data processing, machine learning, graph processing, and generative AI technologies.

This course will be mainly focusing on machine learning algorithms. Throughout this course, we are preparing our machine to make it ready for a prediction test.

This course is intended for beginner-level individuals who are fascinated about quantum computing and want to learn more about it. It uses Jupyter notebook and IBM Qiskit tool to execute your learning into the actual computation.

Master MongoDB, an open-source document database and leading NoSQL database that provides high performance, high availability, and automatic scaling. This course covers the MongoDB Community version for beginners and provides over 50 live-running queries, including creating new databases and tables.

Duration 3.5 Days 21 CPD hours This course is intended for This course is aimed at students new to the language who may or may not have experience with other programming languages. Overview Learn how Python works and what it's good for. Understand Python's place in the world of programming languages Learn to work with and manipulate strings in Python. Learn to perform math operations with Python. Learn to work with Python sequences: lists, arrays, dictionaries, and sets. Learn to collect user input and output results. Learn flow control processing in Python. Learn to write to and read from files using Python. Learn to write functions in Python. Learn to handle exceptions in Python. Learn to work with dates and times in Python. In this Python training course by Webucator, Inc, students learn to program in Python. Python Basics Running Python Hello, World! Literals Python Comments Data Types Variables Writing a Python Module print() Function Named Arguments Collecting User Input Getting Help Functions and Modules Defining Functions Variable Scope Global Variables Function Parameters Returning Values Importing Modules Math Arithmetic Operators Modulus and Floor Division Assignment Operators Built-in Math Functions The math Module The random Module Seeding Python Strings Quotation Marks and Special Characters String Indexing Slicing Strings Concatenation and Repetition Common String Methods String Formatting Built-in String Functions Iterables: Sequences, Dictionaries, and Sets Definitions Sequences Unpacking Sequences Dictionaries The len() Function Sets *args and **kwargs Flow Control Conditional Statements The is and is not Operators Python's Ternary Operator Loops in Python The enumerate() Function Generators List Comprehensions File Processing Opening Files The os and os.path Modules Exception Handling Wildcard except Clauses Getting Information on Exceptions The else Clause The finally Clause Using Exceptions for Flow Control Exception Hierarchy Dates and Times Understanding Time The time Module The datetime Module Running Python Scripts from the Command Line The sys Module sys.argv

Duration 5 Days 30 CPD hours This course is intended for This course is intended for individuals who are Application designers and database developers, database administrators and web server administrators. Overview Upon successful completion of this course, students will be able to work with Oracle database programming using the PL/SQL programming language. They will learn the syntax, structure and features of the language. In this course, students will learn the foundation for the programming series and the use of database-resident stored program units for Oracle 12c. Selection & Setup of the Database Interface Considering Available Tools Selecting the Appropriate Tool Oracle Net Database Connections Oracle PAAS Database Connections Setup SQL Developer Setup SQL*Plus Setup Jdeveloper About Bind & Substitution Variables Using SQL Developer Using SQL*Plus Choosing a Database Programming Language What is Database Programming? PL?SQL Performance Advantages Integration wth Other Languages PL/SQL Language Fundamentals PL/SQL Program Structure LANGUAGE SYNTAX RULES EMBEDDING SQL WRITING READABLE CODE GENERATING DATABASE OUTPUT SQL*PLUS INPUT OF A PROGRAM BLOCK Declare Section About the Declare Section DECLARE PRIMITIVE TYPES DECLARATION OPTIONS NOT NULL CONSTANT DATA DICTIONARY INTEGRATION %TYPE DECLARE SIMPLE USER---DEFINED TYPES TYPE... TABLE TYPE... RECORD EXTENDED USER---DEFINED TYPES Begin Section About the Begin Section Manipulating Program Data Logic Control & Branching GOTO LOOP IF-THEN-ELSE CASE Exception Section ABOUT THE EXCEPTION SECTION ISOLATING THE SPECIFIC EXCEPTION PRAGMA EXCEPTION_INIT SQLCODE &SQLERRM Example SQL%ROWCOUNT &SELECT...INTO Beyond the Basics: Explicit Cursors ABOUT EXPLICIT CURSORS EXTENDED CURSOR TECHNIQUES FOR UPDATE OF Clause WHERE CURRENT OF Clause Using FOR?LOOP Cursors Beyond the Basics: Nested BlocksBeyond the Basics: Declared Subprograms USING DECLARED SUBPROGRAMS DECLARED PROCEDURE DECLARED FUNCTION Introducing Databse-Resident Program Units ABOUT DATABASE---RESIDENT PROGRAMS PHYSICAL STORAGE & EXECUTION TYPES OF STORED PROGRAM UNITS STORED PROGRAM UNIT ADVANTAGES MODULAR DESIGN PRINCIPLES Creating Stored Procedures & Functions STORED PROCEDURES & FUNCTIONS CREATE PROCEDURE / CREATE FUNCTION CREATING PROCEDURES & FUNCTIONS RAISE_SALARY() Procedure SALARY_VALID() Function THE PARAMETER SPECIFICATION DEFAULT Clause SYSTEM & OBJECT PRIVILEGES USING THE DEVELOPMENT TOOLS Executing Stored Procedures & Functions CALLING PROCEDURES & FUNCTIONS UNIT TESTING WITH EXECUTE ANONYMOUS BLOCK UNIT TESTING SPECIFYING A PARAMETER NOTATION SQL WORKSHEET UNIT TESTING CALLING FUNCTIONS FROM SQL Maintaining Stored Program Units RECOMPILING PROGRAMS Mass Recompilation Using UTL_RECOMP() DROPPING PROCEDURES & FUNCTIONS DROP PROCEDURE / FUNCTION DATA DICTIONARY METADATA Using USER_OBJECTS Using USER_SOURCE Using USER_ERRORS Using USER_OBJECT_SIZE Using USER_DEPENDENCIES Managing Dependencies DEPENDENCY INTERNALS TRACKING DEPENDENCIES THE DEPENDENCY TRACKING UTILITY SQL DEVELOPER DEPENDENCY INFO DEPENDENCY STRATEGY CHECKLISTS Creating & Maintaining Packages ABOUT PACKAGES CREATING PACKAGES MAINTAINING PACKAGES PERFORMANCE CONSIDERATIONS Advanced Package Capabilities DEFINER & INVOKER RIGHTS WHITE LITS & ACCESSIBLE BY PERSISTENT GLOBAL OBJECTS DEFINING INITIALIZATION LOGIC OBJECT ORIENTATION SUPPORT Advanced Cursor Techniques USING CUSROS VARIABLES USING SYS_REFCURSOR USING CURSOR EXPRESSIONS Using System-Supplied Package DBMS_OUTPUT() UTL_FILE() FOPEN() EXAMPLE Database Trigger Concepts ABOUT DATABASE TRIGGERS DML EVENT TRIGGER SUB---TYPES DATABASE TRIGGER SCENARIO TRIGGER EXECUTION MECHANISMS TRIGGERS WITHIN SQL WORKSHEET Creating Database Triggers STATEMENT-LEVEL TRIGGERS Using RAISE_APPLICATION_ERROR() ROW---LEVEL TRIGGERS EXAMPLES OF TRIGGERS EMPLOYEE_SALARY_CHECK Example EMPLOYEE_JOURNAL Example BUDGET_EVENT Example INSTEAD OF TRIGGERS TRIGGERS WITHIN AN APPLICATION Maintaining Database Triggers CALL SYNTAX TRIGGER MAINTENANCE TASKS SHOW ERRORS TRIGGER DROP TRIGGER ALTER TRIGGER MULTIPLE TRIGGERS FOR A TABLE HANDLING MUTATING TABLE ISSUES Implementing System Event Triggers WHAT ARE SYSTEM EVENT TRIGGERS? DEFININGTHE SCOPE AVAILABLE SYSTEM EVENTS SYSTEM EVENT ATTRIBUTES

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Assuming that you don't know anything about web scraping, Scrapy, Python, web scraping, or even the meaning of web scraping-the author starts from the complete basics. A well-balanced course with theory and practical content followed by three projects at the end ensures you have the right skills to learn scraping.