76 Matric courses

Mathematics isn’t just about numbers — it’s a language, a mindset, and often, a quiet rebellion against guessing your way through life. Our Advanced Mathematics Training Course is tailored for those who see logic not as a restriction, but as a thrilling challenge. Whether you’re aiming to refine your skills or looking to sharpen your edge in academia, this course will take you through the core theories and higher-level concepts that define modern mathematics. From advanced algebra to calculus, from probability to mathematical reasoning — this course isn’t here to entertain shortcuts. It’s designed to engage your analytical thinking, stretch your reasoning muscles, and polish your understanding of the deeper patterns shaping the mathematical universe. With a smartly structured learning path and clear, well-paced modules, this course speaks directly to curious minds ready to make sense of complexity — one equation at a time. Expert Support Dedicated tutor support and 24/7 customer support are available to all students with this premium quality course. Key Benefits Learning materials of the Design course contain engaging voiceover and visual elements for your comfort. Get 24/7 access to all content for a full year. Each of our students gets full tutor support on weekdays (Monday to Friday) Key Features CPD Accredited Quality License Endorsed Certificate Available Upon Course Completion Course Curriculum: Introduction Introduction Mathematical Logic Introduction to Mathematical Logic, What is Sentence,Statements and their Types Intro to Logical Connectivity,Tautology,Contradiction,Contingency,Pattern Quantitative and Quantified Statement and types and example Dual : Replacing of Connections and Symbols Negations of Compound Statement , Converse, Inverse , & Contrapositive Algebra of Statements and Law Real Life application of Logic to Switching Electric Circuit Matrices Intro to Matrices , Multiplication and Addition using Matrix Inverse of Matrix Uniqueness of Inverse,Elementary Transformation Method of REDUCTION AND INVERSION with real life example how we can implement Trigonometric Functions Introduction to Trigonometic Function General Solutions And Theorem Solution of Triangle : Polar Co-ordinates Rules and Theorems of SIn Cosine and TAN Inverse Trigonometric Function Pair Of Straight Line Introduction & Combined Equations Degrees and Types Some Theorem Lines & Planes Introduction - vector cartesian theorem Cartesian Equation & 2 Point Theorem Theorems & Problem Solving Distance of Point Line Skew Lines Distance of skew lines Distance between parallel lines Equation of Plane and Cartesian Form Linear Programming Linear Programming Introduction Introduction to LPP (Linear Programming Problem) LPP PROBLEM SOLVING Exam and Assessment MCQ based test 60% Marks to pass Instant Assessment and Feedback Certification CPD Accredited PDF and Hardcopy Certificate Level 2 QLS Endorsed Hardcopy Certificate for Certificate in Mathematics at QLS Level 2 CPD 120 CPD hours / points Accredited by CPD Quality Standards Who is this course for? This Advanced Mathematics Training course is designed to enhance your expertise and boost your CV. Learn key skills and gain a certificate of achievement to prove your newly-acquired knowledge. Requirements This Advanced Mathematics Training course is open to all, with no formal entry requirements. Career path Upon successful completion of the Advanced Mathematics Training Course, learners will be equipped with many indispensable skills and have the opportunity to grab. Certificates Certificate of completion Digital certificate - £9 Certificate of completion Hard copy certificate - £69 QLS Endorsed Certificate Hardcopy of this certificate of achievement endorsed by the Quality Licence Scheme can be ordered and received straight to your home by post, by paying - Within the UK: £69 International: £69 + £10 (postal charge) = £79 CPD Accredited Certification from One Education Hardcopy Certificate (within the UK): £15 Hardcopy Certificate (international): £15 + £10 (postal charge) = £25

Overview In the era where information is abundant and decisions are driven by data, have you ever pondered, 'what is machine learning?' or 'what is data science?' Dive into the realm of 'Data Science & Machine Learning with R from A-Z,' a comprehensive guide to unravel these complexities. This course effortlessly blends the foundational aspects of data science with the intricate depths of machine learning algorithms, all through the versatile medium of R. As the digital economy booms, the demand for machine learning jobs continues to surge. Equip yourself with the proficiency to navigate this dynamic field and transition from being an inquisitive mind to a sought-after professional in the space of data science and machine learning with R. Learning Outcomes: Understand the foundational concepts of data science and machine learning. Familiarise oneself with the R environment and its functionalities. Master data types, structures, and advanced techniques in R. Acquire proficiency in data manipulation and visual representation using R. Generate comprehensive reports using R Markdown and design web applications with R Shiny. Gain a thorough understanding of machine learning methodologies and their applications. Gain insights into initiating a successful career in the data science sector. Why buy this Data Science & Machine Learning with R from A-Z course? 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 Data Science & Machine Learning with R from A-Z there will be a written assignment test which you can take either during or at the end of the course. 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 Data Science & Machine Learning with R from A-Z course for? This course is ideal for Individuals keen on exploring the intricacies of machine learning and data science. Aspiring data analysts and scientists looking to specialise in Machine Learning with R. IT professionals aiming to diversify their skill set in the emerging data-driven market. Researchers seeking to harness the power of R for data representation and analysis. Academics and students aiming to bolster their understanding of modern data practices with R. Prerequisites This Data Science & Machine Learning with R from A-Z does not require you to have any prior qualifications or experience. You can just enrol and start learning.This Data Science & Machine Learning with R from A-Z 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 Data Scientist - Average salary range: £35,000 - £70,000 Per Annum Machine Learning Engineer - Average salary range: £50,000 - £80,000 Per Annum Data Analyst - Average salary range: £28,000 - £55,000 Per Annum R Developer - Average salary range: £30,000 - £60,000 Per Annum R Shiny Web Developer - Average salary range: £32,000 - £65,000 Per Annum Machine Learning Researcher - Average salary range: £40,000 - £75,000 Per Annum Course Curriculum Data Science and Machine Learning Course Intro Data Science and Machine Learning Introduction 00:03:00 What is Data Science 00:10:00 Machine Learning Overview 00:05:00 Who is This Course for 00:03:00 Data Science and Machine Learning Marketplace 00:05:00 Data Science and Machine Learning Job Opportunities 00:03:00 Getting Started with R Getting Started 00:11:00 Basics 00:06:00 Files 00:11:00 RStudio 00:07:00 Tidyverse 00:05:00 Resources 00:04:00 Data Types and Structures in R Unit Introduction 00:30:00 Basic Type 00:09:00 Vector Part One 00:20:00 Vectors Part Two 00:25:00 Vectors - Missing Values 00:16:00 Vectors - Coercion 00:14:00 Vectors - Naming 00:10:00 Vectors - Misc 00:06:00 Creating Matrics 00:31:00 List 00:32:00 Introduction to Data Frames 00:19:00 Creating Data Frames 00:20:00 Data Frames: Helper Functions 00:31:00 Data Frames Tibbles 00:39:00 Intermediate R Intermediate Introduction 00:47:00 Relational Operations 00:11:00 Conditional Statements 00:11:00 Loops 00:08:00 Functions 00:14:00 Packages 00:11:00 Factors 00:28:00 Dates and Times 00:30:00 Functional Programming 00:37:00 Data Import or Export 00:22:00 Database 00:27:00 Data Manipulation in R Data Manipulation in R Introduction 00:36:00 Tidy Data 00:11:00 The Pipe Operator 00:15:00 The Filter Verb 00:22:00 The Select Verb 00:46:00 The Mutate Verb 00:32:00 The Arrange Verb 00:10:00 The Summarize Verb 00:23:00 Data Pivoting 00:43:00 JSON Parsing 00:11:00 String Manipulation 00:33:00 Web Scraping 00:59:00 Data Visualization in R Data Visualization in R Section Intro 00:17:00 Getting Started 00:16:00 Aesthetics Mappings 00:25:00 Single Variable Plots 00:37:00 Two Variable Plots 00:21:00 Facets, Layering, and Coordinate Systems 00:18:00 Styling and Saving 00:12:00 Creating Reports with R Markdown Creating with R Markdown 00:29:00 Building Webapps with R Shiny Introduction to R Shiny 00:26:00 A Basic R Shiny App 00:31:00 Other Examples with R Shiny 00:34:00 Introduction to Machine Learning Machine Learning Part 1 00:22:00 Machine Learning Part 2 00:47:00 Starting A Career in Data Science Starting a Data Science Career Section Overview 00:03:00 Data Science Resume 00:04:00 Getting Started with Freelancing 00:05:00 Top Freelance Websites 00:05:00 Personal Branding 00:05:00 Importance of Website and Blo 00:04:00 Networking Do's and Don'ts 00:04:00 Assignment Assignment - Data Science & Machine Learning with R 00:00:00

Step into the world of data with the sharp edge of R — a language that speaks fluently to numbers, charts, and trends. This R Programming for Data Science course is tailored for those with a curious mind and a spreadsheet-weary soul. Whether you're peering into data for the first time or have long wrestled with rows and columns, this course guides you through the essentials of R with a touch of wit and a solid sense of direction. It’s not about learning everything; it’s about learning what matters, and learning it well. From data wrangling to visual storytelling, you'll gain the tools to make sense of messy datasets and turn them into something meaningful. Tidy code, smart analysis, and clear plots await — all from the comfort of your device. This course speaks directly to analysts, researchers, students and data enthusiasts keen to harness the power of R without the fluff. It’s structured, to the point, and just detailed enough to keep things interesting. Who said data has to be dull? Meet the Endorsement The Quality Licence Scheme has been designed specifically to recognise high-quality courses. This R Programming for Data Science course materials are recognised by Quality Licence Scheme (QLS). This ensures the deep research and quality resource allocation behind the development phase of the course. In addition, the QLS certificate enriches your CV and recognises your quality study on the relevant subject. Meet the Accreditation CPD Quality Standards (CPD QS) accreditation assure the R Programming for Data Science course training and learning activities are relevant, reliable, and upto date. Expert Support Dedicated tutor support and 24/7 customer support are available to all students with this premium quality course. Key Benefits Learning materials of the Design course contain engaging voiceover and visual elements for your comfort. Get 24/7 access to all content for a full year. Each of our students gets full tutor support on weekdays (Monday to Friday) Course Curriculum: Unit 01: Data Science Overview Unit 02: R and RStudio Unit 03: Introduction to Basics Unit 04: Vectors Unit 05: Matrices Unit 06: Factors Unit 07: Data Frames Unit 08: Lists Unit 09: Relational Operators Unit 10: Logical Operators Unit 11: Conditional Statements Unit 12: Loops Unit 13: Functions Unit 14: R Packages Unit 15: The Apply Family - lapply Unit 16: The apply Family - sapply & vapply Unit 17: Useful Functions Unit 18: Regular Expressions Unit 19: Dates and Times Unit 20: Getting and Cleaning Data Unit 21: Plotting Data in R Unit 22: Data Manipulation with dplyr How is the R Programming for Data Science Course assessed? To simplify the procedure of evaluation and acknowledgement for learners, we provide an automated assessment system. For each test, the pass mark will be set to 60%. Certificate of Achievement Endorsed Certificate of Achievement from the Quality Licence Scheme After successfully completing the R Programming for Data Science course, learners will be able to order an endorsed certificate as proof of their achievement. The hardcopy of this certificate of achievement endorsed by the Quality Licence Scheme can be ordered and received straight to your home by post, by paying - Within the UK: £109 International: £109 + £10 (postal charge) = £119 CPD Acknowledged Certificate from One Education After successfully completing this R Programming for Data Science course, you will qualify for the CPD acknowledged certificate from One Education, as proof of your continued expert development. Certificate is available in both PDF & hardcopy format, which can be received by paying - PDF Certificate: £9 Hardcopy Certificate (within the UK): £15 Hardcopy Certificate (international): £15 + £10 (postal charge) = £25 CPD 150 CPD hours / points Accredited by CPD Quality Standards Who is this course for? This R Programming for Data Science course is designed to enhance your expertise and boost your CV. Learn key skills and gain a certificate of achievement to prove your newly-acquired knowledge. Requirements This R Programming for Data Science course is open to all, with no formal entry requirements. Career path Upon successful completion of the R Programming for Data Science Course, learners will be equipped with many indispensable skills and have the opportunity to grab.

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Duration 2 Days 12 CPD hours This course is intended for Business Analysts, Technical Managers, and Programmers Overview This intensive training course helps students learn the practical aspects of the R programming language. The course is supplemented by many hands-on labs which allow attendees to immediately apply their theoretical knowledge in practice. Over the past few years, R has been steadily gaining popularity with business analysts, statisticians and data scientists as a tool of choice for conducting statistical analysis of data as well as supervised and unsupervised machine learning. What is R ? What is R? ? Positioning of R in the Data Science Space ? The Legal Aspects ? Microsoft R Open ? R Integrated Development Environments ? Running R ? Running RStudio ? Getting Help ? General Notes on R Commands and Statements ? Assignment Operators ? R Core Data Structures ? Assignment Example ? R Objects and Workspace ? Printing Objects ? Arithmetic Operators ? Logical Operators ? System Date and Time ? Operations ? User-defined Functions ? Control Statements ? Conditional Execution ? Repetitive Execution ? Repetitive execution ? Built-in Functions ? Summary Introduction to Functional Programming with R ? What is Functional Programming (FP)? ? Terminology: Higher-Order Functions ? A Short List of Languages that Support FP ? Functional Programming in R ? Vector and Matrix Arithmetic ? Vector Arithmetic Example ? More Examples of FP in R ? Summary Managing Your Environment ? Getting and Setting the Working Directory ? Getting the List of Files in a Directory ? The R Home Directory ? Executing External R commands ? Loading External Scripts in RStudio ? Listing Objects in Workspace ? Removing Objects in Workspace ? Saving Your Workspace in R ? Saving Your Workspace in RStudio ? Saving Your Workspace in R GUI ? Loading Your Workspace ? Diverting Output to a File ? Batch (Unattended) Processing ? Controlling Global Options ? Summary R Type System and Structures ? The R Data Types ? System Date and Time ? Formatting Date and Time ? Using the mode() Function ? R Data Structures ? What is the Type of My Data Structure? ? Creating Vectors ? Logical Vectors ? Character Vectors ? Factorization ? Multi-Mode Vectors ? The Length of the Vector ? Getting Vector Elements ? Lists ? A List with Element Names ? Extracting List Elements ? Adding to a List ? Matrix Data Structure ? Creating Matrices ? Creating Matrices with cbind() and rbind() ? Working with Data Frames ? Matrices vs Data Frames ? A Data Frame Sample ? Creating a Data Frame ? Accessing Data Cells ? Getting Info About a Data Frame ? Selecting Columns in Data Frames ? Selecting Rows in Data Frames ? Getting a Subset of a Data Frame ? Sorting (ordering) Data in Data Frames by Attribute(s) ? Editing Data Frames ? The str() Function ? Type Conversion (Coercion) ? The summary() Function ? Checking an Object's Type ? Summary Extending R ? The Base R Packages ? Loading Packages ? What is the Difference between Package and Library? ? Extending R ? The CRAN Web Site ? Extending R in R GUI ? Extending R in RStudio ? Installing and Removing Packages from Command-Line ? Summary Read-Write and Import-Export Operations in R ? Reading Data from a File into a Vector ? Example of Reading Data from a File into A Vector ? Writing Data to a File ? Example of Writing Data to a File ? Reading Data into A Data Frame ? Writing CSV Files ? Importing Data into R ? Exporting Data from R ? Summary Statistical Computing Features in R ? Statistical Computing Features ? Descriptive Statistics ? Basic Statistical Functions ? Examples of Using Basic Statistical Functions ? Non-uniformity of a Probability Distribution ? Writing Your Own skew and kurtosis Functions ? Generating Normally Distributed Random Numbers ? Generating Uniformly Distributed Random Numbers ? Using the summary() Function ? Math Functions Used in Data Analysis ? Examples of Using Math Functions ? Correlations ? Correlation Example ? Testing Correlation Coefficient for Significance ? The cor.test() Function ? The cor.test() Example ? Regression Analysis ? Types of Regression ? Simple Linear Regression Model ? Least-Squares Method (LSM) ? LSM Assumptions ? Fitting Linear Regression Models in R ? Example of Using lm() ? Confidence Intervals for Model Parameters ? Example of Using lm() with a Data Frame ? Regression Models in Excel ? Multiple Regression Analysis ? Summary Data Manipulation and Transformation in R ? Applying Functions to Matrices and Data Frames ? The apply() Function ? Using apply() ? Using apply() with a User-Defined Function ? apply() Variants ? Using tapply() ? Adding a Column to a Data Frame ? Dropping A Column in a Data Frame ? The attach() and detach() Functions ? Sampling ? Using sample() for Generating Labels ? Set Operations ? Example of Using Set Operations ? The dplyr Package ? Object Masking (Shadowing) Considerations ? Getting More Information on dplyr in RStudio ? The search() or searchpaths() Functions ? Handling Large Data Sets in R with the data.table Package ? The fread() and fwrite() functions from the data.table Package ? Using the Data Table Structure ? Summary Data Visualization in R ? Data Visualization ? Data Visualization in R ? The ggplot2 Data Visualization Package ? Creating Bar Plots in R ? Creating Horizontal Bar Plots ? Using barplot() with Matrices ? Using barplot() with Matrices Example ? Customizing Plots ? Histograms in R ? Building Histograms with hist() ? Example of using hist() ? Pie Charts in R ? Examples of using pie() ? Generic X-Y Plotting ? Examples of the plot() function ? Dot Plots in R ? Saving Your Work ? Supported Export Options ? Plots in RStudio ? Saving a Plot as an Image ? Summary Using R Efficiently ? Object Memory Allocation Considerations ? Garbage Collection ? Finding Out About Loaded Packages ? Using the conflicts() Function ? Getting Information About the Object Source Package with the pryr Package ? Using the where() Function from the pryr Package ? Timing Your Code ? Timing Your Code with system.time() ? Timing Your Code with System.time() ? Sleeping a Program ? Handling Large Data Sets in R with the data.table Package ? Passing System-Level Parameters to R ? Summary Lab Exercises Lab 1 - Getting Started with R Lab 2 - Learning the R Type System and Structures Lab 3 - Read and Write Operations in R Lab 4 - Data Import and Export in R Lab 5 - k-Nearest Neighbors Algorithm Lab 6 - Creating Your Own Statistical Functions Lab 7 - Simple Linear Regression Lab 8 - Monte-Carlo Simulation (Method) Lab 9 - Data Processing with R Lab 10 - Using R Graphics Package Lab 11 - Using R Efficiently

SSRS Report Builder Course Summary. Practical SSRS Report Builder course, to leavy you full conversant with queries, DML and DDL statements. Hands-on, Practical SSRS course. PCWorkshops SSRS Course Certificate. Max 4 people per course, we keep it personalised. Where is the SSRS Report Builder Course? Online: Instructor-led. Classroom: Request per email. SSRS Course Materials SSRS Coding Examples Manuals and Notes: In-depth reference materials Exercises: Practical exercises Payment Options → £600, Follow the booking link Arrange for installments Included with SSRS Report Builder Course: SSRS Course Certificate on completion. SSRS Course Manual. Practical SSRS Course exercises. SSRS Code Samples. After the course, 1 free session for questions. Max group size, online and in classroom, on this SSRS Course is 4. Course Topics SSRS Report Builder Course Topics SSRS Course topics: This course focus on producing reports using MS SQL SSRS Report Builder, to use the product features and tools related to data retrieval, visualisations and deployment. The Target Audience: end-users who will produce reports for their department or group using datasets. SSRS Course Topics Report Models: Data source views Creating a model Entities, attributes and roles Tables: Creating tables Interactive sorting Floating headers Grouping: Inserting groups Group headers/footers Displaying group totals Expanding and collapsing Expressions: The expression builder The Report Items collection Built-in fields Conditional formatting expressions Page headers and footers Page names and numbers Exporting reports: The various report formats Using RenderFormat. Parameters: Creating parameters Filtering using parameters Drop list parameters Coping with empty tables Matrices: Row and column groups Sorting and filtering matrices Creating subtotals Charts: Creating charts The parts of a chart Formatting charts Basic reports: Text boxes and images Selecting report items Formatting parts of a report Miniature charts Creating sparklines Creating data bars Graphical indicators Creating and customising indicators Linear and radial gauges Lists: Page-per-record lists Dashboard lists Other list ideas Subreports: Creating a child report Creating the main report Linking the two reports Report parts: Publishing report parts The report part gallery Updating report parts Book the SSRS Course About us Our experienced trainers are award winners. More about us FAQ's Client Comments

SSRS Report Builder Course Summary. Practical SSRS Report Builder course, to leavy you full conversant with queries, DML and DDL statements. Hands-on, Practical SSRS course. PCWorkshops SSRS Course Certificate. Max 4 people per course, we keep it personalised. Where is the SSRS Report Builder Course? Online: Instructor-led. Classroom: Request per email. SSRS Course Materials SSRS Coding Examples Manuals and Notes: In-depth reference materials Exercises: Practical exercises Payment Options → £600, Follow the booking link Arrange for installments Included with SSRS Report Builder Course: SSRS Course Certificate on completion. SSRS Course Manual. Practical SSRS Course exercises. SSRS Code Samples. After the course, 1 free session for questions. Max group size, online and in classroom, on this SSRS Course is 4. Course Topics SSRS Report Builder Course Topics SSRS Course topics: This course focus on producing reports using MS SQL SSRS Report Builder, to use the product features and tools related to data retrieval, visualisations and deployment. The Target Audience: end-users who will produce reports for their department or group using datasets. SSRS Course Topics Report Models: Data source views Creating a model Entities, attributes and roles Tables: Creating tables Interactive sorting Floating headers Grouping: Inserting groups Group headers/footers Displaying group totals Expanding and collapsing Expressions: The expression builder The Report Items collection Built-in fields Conditional formatting expressions Page headers and footers Page names and numbers Exporting reports: The various report formats Using RenderFormat. Parameters: Creating parameters Filtering using parameters Drop list parameters Coping with empty tables Matrices: Row and column groups Sorting and filtering matrices Creating subtotals Charts: Creating charts The parts of a chart Formatting charts Basic reports: Text boxes and images Selecting report items Formatting parts of a report Miniature charts Creating sparklines Creating data bars Graphical indicators Creating and customising indicators Linear and radial gauges Lists: Page-per-record lists Dashboard lists Other list ideas Subreports: Creating a child report Creating the main report Linking the two reports Report parts: Publishing report parts The report part gallery Updating report parts Book the SSRS Course About us Our experienced trainers are award winners. More about us FAQ's Client Comments