3044 Software Development courses

The Oracle 11g PL/SQL Part 1 course will focus on giving you the best knowledge of coding and designing PL/SQL and SQL scripts. Initially, you'll learn how to start programming in Oracle PL/SQL. You'll understand the history of PL/SQL, installing Oracle Express Edition, connecting to Database, Relational Database, Oracle SQL in an easy-to-grasp method. This course includes mostly practical examples with explanations which makes it more valuable. Upon successful completion of this course, you can have the pleasure to code in PL/SQL which further help to boost your career. From beginners to expert learner can benefit from this course. So enrol today and see what else is in the Oracle 11g PL/SQL Part 1 course. What Will I Learn? Prepare your environment Work with Oracle database tools Understand and work with language features Work with the DECLARE clause Work with the BEGIN clause Work with the EXCEPTION clause Use explicit cursors Understand and use nested blocks Understand the difference between nested blocks and subprograms Requirements Familiarity with database and programming principles Who is the target audience? Application designers and developers Database administrators Business users and non-technical senior end users Introduction Introduction FREE 00:03:00 Preparing Your Environment Course Setup 00:37:00 Working with Oracle Database Tools Selecting the Appropriate Database Tool 00:31:00 Language Features Understanding PL/SQL 00:18:00 PL/SQL Blocks 00:15:00 Working with SQL Plus 00:11:00 The DECLARE Clause DECLARE Clause Objects 00:16:00 PL/SQL Available Data Types 00:17:00 Declaring Simple Data Types and Assignments 00:12:00 Declaring Complex Data Types and Assignments 00:11:00 Declaring Complex RowTypes and Assignments 00:07:00 Declaring Complex Tables 00:08:00 Declaring Complex Records 00:08:00 Creating and Declaring User-Defined Types 00:09:00 The BEGIN Clause What Can Be Included in the BEGIN Clause 00:07:00 Working with CHAR and VARCHAR2 Variables 00:08:00 Handling String Literals 00:08:00 Working with Numeric Variables 00:07:00 Working with Date Variables 00:06:00 Assigning and Using Boolean Variables 00:08:00 Using Comparison Operators 00:07:00 Using SQL Functions 00:06:00 SQL DML within PL/SQL 00:15:00 Using SELECT 00:07:00 Exceptions and Embedded SQL 00:11:00 Using Sequences 00:14:00 Logic Control and Branching 00:07:00 Using GOTO 00:06:00 Working with LOOPS 00:19:00 Creating and Using the Indefinite Loop 00:07:00 Creating and Using the Conditional Loop 00:08:00 Using Nested Loops 00:12:00 Using the If-Then-Else 00:19:00 Using the CASE Statement 00:18:00 The EXCEPTION Clause What Are the Types of Exceptions 00:06:00 Trapping Exceptions 00:06:00 Identify Exception Details 00:11:00 Creating User-Defined Exceptions 00:11:00 Using SQLCODE and SQLERRM Objects 00:09:00 Understand the Implicit Cursor 00:15:00 Creating User-Defined Events 00:12:00 Explicit Cursors Understanding the Concept and Purpose of Explicit Cursors 00:18:00 Learn about Cursor Attributes 00:10:00 Using the Continue Statement 00:06:00 Working with Updateable Cursors 00:19:00 Creating and Using Cursor Parameters 00:15:00 FOR...LOOP Cursors 00:07:00 Nested Blocks Understanding Nested Blocks 00:11:00 Difference between Global vs Local Objects and Exceptions 00:13:00 Declared Subprograms Subprograms: How They Differ from Nested Blocks 00:18:00 Conclusion Conclusion 00:04:00 Course Certification

Master React and Redux with our comprehensive App Development Training course. Learn to build dynamic web applications, manage state, and integrate with backend services like Firebase. Ideal for aspiring UI/UX developers and frontend developers. Enroll now to start your journey in app development!

Course Overview Visual Basic is one of the most powerful languages created by one of the largest companies in the world, Microsoft.  In this Visual Basic and VBScript Masterclass, you will learn the basic and advanced functions of Visual Basic - from how to get set up on Windows, to understanding the different data types. It will show you how to perform a wide range of tasks, from working with constructors and destructors, to object orientated programming. Throughout the training, you will be developing your basic programming skills and, which will help you to gain a head start in securing a career in this competitive field. You will gain all the knowledge needed to leverage the incredible technology behind this powerful programming language. This best selling Visual Basic and VBScript Masterclass has been developed by industry professionals and has already been completed by hundreds of satisfied students. This in-depth Visual Basic and VBScript Masterclass is suitable for anyone who wants to build their professional skill set and improve their expert knowledge. The Visual Basic and VBScript Masterclass is CPD-accredited, so you can be confident you're completing a quality training course will boost your CV and enhance your career potential. The Visual Basic and VBScript Masterclass is made up of several information-packed modules which break down each topic into bite-sized chunks to ensure you understand and retain everything you learn. After successfully completing the Visual Basic and VBScript Masterclass, you will be awarded a certificate of completion as proof of your new skills. If you are looking to pursue a new career and want to build your professional skills to excel in your chosen field, the certificate of completion from the Visual Basic and VBScript Masterclass will help you stand out from the crowd. You can also validate your certification on our website. We know that you are busy and that time is precious, so we have designed the Visual Basic and VBScript Masterclass to be completed at your own pace, whether that's part-time or full-time. Get full course access upon registration and access the course materials from anywhere in the world, at any time, from any internet-enabled device.  Our experienced tutors are here to support you through the entire learning process and answer any queries you may have via email.

This is a concise, clear, and practical course for learning modern web design, UI/UX design, and mobile app design using the Figma tool. There are various exercises to practice and learn navigating through the course; there are no hardcore prerequisites to join this course.

Are you looking to enhance your Systems Engineering skills? If yes, then you have come to the right place. Our comprehensive course on Systems Engineering will assist you in producing the best possible outcome by mastering the Systems Engineering skills. The Systems Engineering course is for those who want to be successful. In the Systems Engineering course, you will learn the essential knowledge needed to become well versed in Systems Engineering. Our Systems Engineering course starts with the basics of Systems Engineering and gradually progresses towards advanced topics. Therefore, each lesson of this Systems Engineering course is intuitive and easy to understand. Systems Engineering Curriculum Breakdown of the Systems Engineering Course Course Outline: Software Hardware Security Networking Basic IT Literacy Why would you choose the Systems Engineering course from Compliance Central: Lifetime access to Systems Engineering course materials Full tutor support is available from Monday to Friday with the Systems Engineering course Learn Systems Engineering skills at your own pace from the comfort of your home Gain a complete understanding of Systems Engineering course Accessible, informative Systems Engineering learning modules designed by experts Get 24/7 help or advice from our email and live chat teams with the Systems Engineering Study Systems Engineering in your own time through your computer, tablet or mobile device. A 100% learning satisfaction guarantee with your Systems Engineering Course CPD 35 CPD hours / points Accredited by CPD Quality Standards Who is this course for? The Systems Engineering course helps aspiring professionals who want to obtain the knowledge and familiarise themselves with the skillsets to pursue a career in Systems Engineering. It is also great for professionals who are already working in Systems Engineering and want to get promoted at work. Requirements To enrol in this Systems Engineering course, all you need is a basic understanding of the English Language and an internet connection. Career path Systems Engineer: £40,000 to £70,000 per year Project Manager (Systems Engineering): £45,000 to £80,000 per year Systems Analyst: £35,000 to £60,000 per year Systems Integration Engineer: £40,000 to £70,000 per year Technical Consultant (Systems Engineering): £50,000 to £90,000 per year Systems Architect: £55,000 to £100,000 per year 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

This course begins with a comprehensive introduction to RFID technology, focusing on both low and high-frequency cards. You'll explore the Proxmark3 RDV4 device, a powerful RFID testing tool, learning its installation and implementation to understand how RFID systems can be ethically analysed and tested.

Explore the Whistleblowing Online Certification Course, designed to empower you with the knowledge and skills to effectively report misconduct and ensure organizational integrity.

In this course, you will learn full-stack web development with React JS for the frontend and Python Django for the backend. You will learn and explore various databases such as Microsoft SQL Server, MySQL, MongoDB, PostgreSQL, and SQLite.

The course covers data science and machine learning with R.

Overview of Data Science & Machine Learning with Python Join our Data Science & Machine Learning with Python course and discover your hidden skills, setting you on a path to success in this area. Get ready to improve your skills and achieve your biggest goals. The Data Science & Machine Learning with Python course has everything you need to get a great start in this sector. Improving and moving forward is key to getting ahead personally. The Data Science & Machine Learning with Python course is designed to teach you the important stuff quickly and well, helping you to get off to a great start in the field. So, what are you looking for? Enrol now! This Data Science & Machine Learning with Python Course will help you to learn: Learn strategies to boost your workplace efficiency. Hone your skills to help you advance your career. Acquire a comprehensive understanding of various topics and tips. Learn in-demand skills that are in high demand among UK employers This course covers the topic you must know to stand against the tough competition. The future is truly yours to seize with this Data Science & Machine Learning with Python. Enrol today and complete the course to achieve a certificate that can change your career forever. Details Perks of Learning with IOMH One-To-One Support from a Dedicated Tutor Throughout Your Course. Study Online - Whenever and Wherever You Want. Instant Digital/ PDF Certificate. 100% Money Back Guarantee. 12 Months Access. Process of Evaluation After studying the course, an MCQ exam or assignment will test your skills and knowledge. You have to get a score of 60% to pass the test and get your certificate. Certificate of Achievement Certificate of Completion - Digital / PDF Certificate After completing the Data Science & Machine Learning with Python course, you can order your CPD Accredited Digital / PDF Certificate for £5.99.  Certificate of Completion - Hard copy Certificate You can get the CPD Accredited Hard Copy Certificate for £12.99. Shipping Charges: Inside the UK: £3.99 International: £10.99 Who Is This Course for? This Data Science & Machine Learning with Python is suitable for anyone aspiring to start a career in relevant field; even if you are new to this and have no prior knowledge, this course is going to be very easy for you to understand.  On the other hand, if you are already working in this sector, this course will be a great source of knowledge for you to improve your existing skills and take them to the next level.  This course has been developed with maximum flexibility and accessibility, making it ideal for people who don't have the time to devote to traditional education. Requirements You don't need any educational qualification or experience to enrol in the Data Science & Machine Learning with Python course. Do note: you must be at least 16 years old to enrol. Any internet-connected device, such as a computer, tablet, or smartphone, can access this online course. Career Path The certification and skills you get from this Data Science & Machine Learning with Python Course can help you advance your career and gain expertise in several fields, allowing you to apply for high-paying jobs in related sectors. Course Curriculum Course Overview & Table of Contents Course Overview & Table of Contents 00:09:00 Introduction to Machine Learning - Part 1 - Concepts , Definitions and Types Introduction to Machine Learning - Part 1 - Concepts , Definitions and Types 00:05:00 Introduction to Machine Learning - Part 2 - Classifications and Applications Introduction to Machine Learning - Part 2 - Classifications and Applications 00:06:00 System and Environment preparation - Part 1 System and Environment preparation - Part 1 00:04:00 System and Environment preparation - Part 2 System and Environment preparation - Part 2 00:06:00 Learn Basics of python - Assignment Learn Basics of python - Assignment 1 00:10:00 Learn Basics of python - Assignment Learn Basics of python - Assignment 2 00:09:00 Learn Basics of python - Functions Learn Basics of python - Functions 00:04:00 Learn Basics of python - Data Structures Learn Basics of python - Data Structures 00:12:00 Learn Basics of NumPy - NumPy Array Learn Basics of NumPy - NumPy Array 00:06:00 Learn Basics of NumPy - NumPy Data Learn Basics of NumPy - NumPy Data 00:08:00 Learn Basics of NumPy - NumPy Arithmetic Learn Basics of NumPy - NumPy Arithmetic 00:04:00 Learn Basics of Matplotlib Learn Basics of Matplotlib 00:07:00 Learn Basics of Pandas - Part 1 Learn Basics of Pandas - Part 1 00:06:00 Learn Basics of Pandas - Part 2 Learn Basics of Pandas - Part 2 00:07:00 Understanding the CSV data file Understanding the CSV data file 00:09:00 Load and Read CSV data file using Python Standard Library Load and Read CSV data file using Python Standard Library 00:09:00 Load and Read CSV data file using NumPy Load and Read CSV data file using NumPy 00:04:00 Load and Read CSV data file using Pandas Load and Read CSV data file using Pandas 00:05:00 Dataset Summary - Peek, Dimensions and Data Types Dataset Summary - Peek, Dimensions and Data Types 00:09:00 Dataset Summary - Class Distribution and Data Summary Dataset Summary - Class Distribution and Data Summary 00:09:00 Dataset Summary - Explaining Correlation Dataset Summary - Explaining Correlation 00:11:00 Dataset Summary - Explaining Skewness - Gaussian and Normal Curve Dataset Summary - Explaining Skewness - Gaussian and Normal Curve 00:07:00 Dataset Visualization - Using Histograms Dataset Visualization - Using Histograms 00:07:00 Dataset Visualization - Using Density Plots Dataset Visualization - Using Density Plots 00:06:00 Dataset Visualization - Box and Whisker Plots Dataset Visualization - Box and Whisker Plots 00:05:00 Multivariate Dataset Visualization - Correlation Plots Multivariate Dataset Visualization - Correlation Plots 00:08:00 Multivariate Dataset Visualization - Scatter Plots Multivariate Dataset Visualization - Scatter Plots 00:05:00 Data Preparation (Pre-Processing) - Introduction Data Preparation (Pre-Processing) - Introduction 00:09:00 Data Preparation - Re-scaling Data - Part 1 Data Preparation - Re-scaling Data - Part 1 00:09:00 Data Preparation - Re-scaling Data - Part 2 Data Preparation - Re-scaling Data - Part 2 00:09:00 Data Preparation - Standardizing Data - Part 1 Data Preparation - Standardizing Data - Part 1 00:07:00 Data Preparation - Standardizing Data - Part 2 Data Preparation - Standardizing Data - Part 2 00:04:00 Data Preparation - Normalizing Data Data Preparation - Normalizing Data 00:08:00 Data Preparation - Binarizing Data Data Preparation - Binarizing Data 00:06:00 Feature Selection - Introduction Feature Selection - Introduction 00:07:00 Feature Selection - Uni-variate Part 1 - Chi-Squared Test Feature Selection - Uni-variate Part 1 - Chi-Squared Test 00:09:00 Feature Selection - Uni-variate Part 2 - Chi-Squared Test Feature Selection - Uni-variate Part 2 - Chi-Squared Test 00:10:00 Feature Selection - Recursive Feature Elimination Feature Selection - Recursive Feature Elimination 00:11:00 Feature Selection - Principal Component Analysis (PCA) Feature Selection - Principal Component Analysis (PCA) 00:09:00 Feature Selection - Feature Importance Feature Selection - Feature Importance 00:06:00 Refresher Session - The Mechanism of Re-sampling, Training and Testing Refresher Session - The Mechanism of Re-sampling, Training and Testing 00:12:00 Algorithm Evaluation Techniques - Introduction Algorithm Evaluation Techniques - Introduction 00:07:00 Algorithm Evaluation Techniques - Train and Test Set Algorithm Evaluation Techniques - Train and Test Set 00:11:00 Algorithm Evaluation Techniques - K-Fold Cross Validation Algorithm Evaluation Techniques - K-Fold Cross Validation 00:09:00 Algorithm Evaluation Techniques - Leave One Out Cross Validation Algorithm Evaluation Techniques - Leave One Out Cross Validation 00:05:00 Algorithm Evaluation Techniques - Repeated Random Test-Train Splits Algorithm Evaluation Techniques - Repeated Random Test-Train Splits 00:07:00 Algorithm Evaluation Metrics - Introduction Algorithm Evaluation Metrics - Introduction 00:09:00 Algorithm Evaluation Metrics - Classification Accuracy Algorithm Evaluation Metrics - Classification Accuracy 00:08:00 Algorithm Evaluation Metrics - Log Loss Algorithm Evaluation Metrics - Log Loss 00:03:00 Algorithm Evaluation Metrics - Area Under ROC Curve Algorithm Evaluation Metrics - Area Under ROC Curve 00:06:00 Algorithm Evaluation Metrics - Confusion Matrix Algorithm Evaluation Metrics - Confusion Matrix 00:10:00 Algorithm Evaluation Metrics - Classification Report Algorithm Evaluation Metrics - Classification Report 00:04:00 Algorithm Evaluation Metrics - Mean Absolute Error - Dataset Introduction Algorithm Evaluation Metrics - Mean Absolute Error - Dataset Introduction 00:06:00 Algorithm Evaluation Metrics - Mean Absolute Error Algorithm Evaluation Metrics - Mean Absolute Error 00:07:00 Algorithm Evaluation Metrics - Mean Square Error Algorithm Evaluation Metrics - Mean Square Error 00:03:00 Algorithm Evaluation Metrics - R Squared Algorithm Evaluation Metrics - R Squared 00:04:00 Classification Algorithm Spot Check - Logistic Regression Classification Algorithm Spot Check - Logistic Regression 00:12:00 Classification Algorithm Spot Check - Linear Discriminant Analysis Classification Algorithm Spot Check - Linear Discriminant Analysis 00:04:00 Classification Algorithm Spot Check - K-Nearest Neighbors Classification Algorithm Spot Check - K-Nearest Neighbors 00:05:00 Classification Algorithm Spot Check - Naive Bayes Classification Algorithm Spot Check - Naive Bayes 00:04:00 Classification Algorithm Spot Check - CART Classification Algorithm Spot Check - CART 00:04:00 Classification Algorithm Spot Check - Support Vector Machines Classification Algorithm Spot Check - Support Vector Machines 00:05:00 Regression Algorithm Spot Check - Linear Regression Regression Algorithm Spot Check - Linear Regression 00:08:00 Regression Algorithm Spot Check - Ridge Regression Regression Algorithm Spot Check - Ridge Regression 00:03:00 Regression Algorithm Spot Check - Lasso Linear Regression Regression Algorithm Spot Check - Lasso Linear Regression 00:03:00 Regression Algorithm Spot Check - Elastic Net Regression Regression Algorithm Spot Check - Elastic Net Regression 00:02:00 Regression Algorithm Spot Check - K-Nearest Neighbors Regression Algorithm Spot Check - K-Nearest Neighbors 00:06:00 Regression Algorithm Spot Check - CART Regression Algorithm Spot Check - CART 00:04:00 Regression Algorithm Spot Check - Support Vector Machines (SVM) Regression Algorithm Spot Check - Support Vector Machines (SVM) 00:04:00 Compare Algorithms - Part 1 : Choosing the best Machine Learning Model Compare Algorithms - Part 1 : Choosing the best Machine Learning Model 00:09:00 Compare Algorithms - Part 2 : Choosing the best Machine Learning Model Compare Algorithms - Part 2 : Choosing the best Machine Learning Model 00:05:00 Pipelines : Data Preparation and Data Modelling Pipelines : Data Preparation and Data Modelling 00:11:00 Pipelines : Feature Selection and Data Modelling Pipelines : Feature Selection and Data Modelling 00:10:00 Performance Improvement: Ensembles - Voting Performance Improvement: Ensembles - Voting 00:07:00 Performance Improvement: Ensembles - Bagging Performance Improvement: Ensembles - Bagging 00:08:00 Performance Improvement: Ensembles - Boosting Performance Improvement: Ensembles - Boosting 00:05:00 Performance Improvement: Parameter Tuning using Grid Search Performance Improvement: Parameter Tuning using Grid Search 00:08:00 Performance Improvement: Parameter Tuning using Random Search Performance Improvement: Parameter Tuning using Random Search 00:06:00 Export, Save and Load Machine Learning Models : Pickle Export, Save and Load Machine Learning Models : Pickle 00:10:00 Export, Save and Load Machine Learning Models : Joblib Export, Save and Load Machine Learning Models : Joblib 00:06:00 Finalizing a Model - Introduction and Steps Finalizing a Model - Introduction and Steps 00:07:00 Finalizing a Classification Model - The Pima Indian Diabetes Dataset Finalizing a Classification Model - The Pima Indian Diabetes Dataset 00:07:00 Quick Session: Imbalanced Data Set - Issue Overview and Steps Quick Session: Imbalanced Data Set - Issue Overview and Steps 00:09:00 Iris Dataset : Finalizing Multi-Class Dataset Iris Dataset : Finalizing Multi-Class Dataset 00:09:00 Finalizing a Regression Model - The Boston Housing Price Dataset Finalizing a Regression Model - The Boston Housing Price Dataset 00:08:00 Real-time Predictions: Using the Pima Indian Diabetes Classification Model Real-time Predictions: Using the Pima Indian Diabetes Classification Model 00:07:00 Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset 00:03:00 Real-time Predictions: Using the Boston Housing Regression Model Real-time Predictions: Using the Boston Housing Regression Model 00:08:00 Resources Resources - Data Science & Machine Learning with Python 00:00:00