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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

Duration 4 Days 24 CPD hours This course is intended for This course is designed for platform developers, UI developers, solution architects, and technical architects who are responsible for the setup, configuration, or maintenance of OmniStudio applications or Salesforce Industry Cloud apps. You should have a solid understanding of basic Salesforce concepts and functionality, including Lightning Web Components (LWC), as well as experience working with relational databases and familiarity with JSON. Ideally, you hold the Salesforce Administrator or Salesforce Platform Developer I credential. This class is recommended for anyone looking to earn their Salesforce Certified OmniStudio Developer credential. Overview Create FlexCards and build an OmniStudio Interaction Console to improve customer experience. Create OmniScripts to ensure productive, consistent user engagement. Use Integration Procedures to execute complex operations on the server and incorporate external data sources. Create and modify DataRaptors to get data from Salesforce, transform data, and save data back to Salesforce. Create Calculation Matrices and Calculation Procedures to execute data lookups and calculations. Discover how to develop engaging, digital-first guided experiences using OmniStudio tools. In this class, our OmniStudio experts will show you how to use FlexCards, OmniScripts, and the OmniStudio Interaction Console to configure applications that elevate the user experience. You?ll learn how to retrieve and transform internal and external data using declarative OmniStudio data tools to get a 360-degree view of customer accounts, empowering you to quickly deliver high-quality, consumer-grade experiences that your users expect. Introduction to OmniStudio Explore OmniStudio Tools and Resources OmniStudio LWC Learn the Benefits and Features of OmniStudio LWC and Component Types FlexCards and Omnistudio Interaction Consoles Design and Build Parent and Child FlexCards Assign Data Sources to FlexCards, Including External Data Sources Configure Fields to Display Data and Configure Actions to Launch OmniScripts from FlexCards Configure FlexCard Flyouts to Display Additional Data Configure Conditions to Display Different Flexcard States Build an OmniStudio Interaction Console OmniScripts Design and Build Simple and Complex OmniScripts Configure OmniScript Elements such as Type Ahead Blocks Configure Element Properties such as Branching Conditions Configure Simple Error Checking Add External Data to an OmniScript Connect an Interaction Launcher to a Console Toolbar Integration Procedures and OmniStudio Data Tools Learn How OmniStudio uses Salesforce sObjects and Fields Learn How Data Flows Between OmniScripts and Integration Procedures Build Integration Procedures and DataRaptors for OmniScripts and FlexCards Use a DataRaptor to Transform FlexCard Data JSONs Build Calculation Matrices and Procedures Test and Troubleshoot Components in the OmniStudio Interaction Console Additional course details: Nexus Humans Salesforce Build Guided Experiences with OmniStudio (OMS435) training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the Salesforce Build Guided Experiences with OmniStudio (OMS435) course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Duration 5 Days 30 CPD hours This course is intended for The primary audience for this course are Application Consultants, Business Process Architects, Business Process Owners/Team Leads/Power Users, Data Consultants/Managers, and Solution Architects. Overview Students will set up and deliver their own master data from SAP ERP to SAP SCM (APO), and make any necessary master data enhancements to ensure proper planning results in APO.Students will complete the modeling of their supply chain by creating APO master data that is necessary to activate a fully functional Supply Chain in APO. In this course, students learn how to set up and configure the standard interface between the SAP ERP system and SAP SCM with focus on SAP APO. Integration for Supply Chain Modeling Integrating SAP ERP and SAP SCM Configuring an Integration Model Using Monitoring and Error-Processing Tools Setting Up Incremental Data Transfers for Master Data Changes Organizing Integration Models Performing Routine Operations with Background Processing Supply Chain Locations Managing Locations Integrating Plant Data Integrating MRP Areas Managing Transportation Zones Integrating Customers as Locations Integrating Vendors as Locations Integrating Factory Calendars and Time Streams Supply Chain Products Integrating Products Maintaining Product Data External Procurements Relationships Integrating Purchasing Information Records Integrating Scheduling Agreements Network Modeling Creating Means of Transportation Creating Transportation Lanes Supply Chain Resources Integrating Production Resources Creating Supply Chain Management (SCM)-Specific Resources Integrating Capacity Variants Integrating Setup Groups and Matrices Manufacturing Process Modeling Preparing Integration of Master Data Mapping Bill of Materials (BOM) Fields Mapping the Routings Fields Integrating Production Master Data Transferring a Master Recipe to a Production Process Model (PPM) Transferring Characteristics and Classes Quota Arrangement Creating Quota Arrangements Supply Chain Modeling Creating an SAP liveCache Model Creating a Version in SAP SCM Using the Supply Chain Engineer (SCE) Transactional Data Integration Integrating Transactional Data Supply Chain Subcontracting Preparing Master Data for Subcontracting

Duration 3 Days 18 CPD hours This course is intended for This course is aimed at anyone who wants to harness the power of data analytics in their organization including: Business Analysts, Data Analysts, Reporting and BI professionals Analytics professionals and Data Scientists who would like to learn Python Overview This course teaches delegates with no prior programming or data analytics experience how to perform data manipulation, data analysis and data visualization in Python. Mastery of these techniques and how to apply them to business problems will allow delegates to immediately add value in their workplace by extracting valuable insight from company data to allow better, data-driven decisions. Outcome: After attending this course, delegates will: Be able to write effective Python code Know how to access their data from a variety of sources using Python Know how to identify and fix data quality using Python Know how to manipulate data to create analysis ready data Know how to analyze and visualize data to drive data driven decisioning across your organization Becoming a world class data analytics practitioner requires mastery of the most sophisticated data analytics tools. These programming languages are some of the most powerful and flexible tools in the data analytics toolkit. From business questions to data analytics, and beyond For data analytics tasks to affect business decisions they must be driven by a business question. This section will formally outline how to move an analytics project through key phases of development from business question to business solution. Delegates will be able: to describe and understand the general analytics process. to describe and understand the different types of analytics can be used to derive data driven solutions to business to apply that knowledge to their business context Basic Python Programming Conventions This section will cover the basics of writing R programs. Topics covered will include: What is Python? Using Anaconda Writing Python programs Expressions and objects Functions and arguments Basic Python programming conventions Data Structures in Python This section will look at the basic data structures that Python uses and accessing data in Python. Topics covered will include: Vectors Arrays and matrices Factors Lists Data frames Loading .csv files into Python Connecting to External Data This section will look at loading data from other sources into Python. Topics covered will include: Loading .csv files into a pandas data frame Connecting to and loading data from a database into a panda data frame Data Manipulation in Python This section will look at how Python can be used to perform data manipulation operations to prepare datasets for analytics projects. Topics covered will include: Filtering data Deriving new fields Aggregating data Joining data sources Connecting to external data sources Descriptive Analytics and Basic Reporting in Python This section will explain how Python can be used to perform basic descriptive. Topics covered will include: Summary statistics Grouped summary statistics Using descriptive analytics to assess data quality Using descriptive analytics to created business report Using descriptive analytics to conduct exploratory analysis Statistical Analysis in Python This section will explain how Python can be used to created more interesting statistical analysis. Topics covered will include: Significance tests Correlation Linear regressions Using statistical output to create better business decisions. Data Visualisation in Python This section will explain how Python can be used to create effective charts and visualizations. Topics covered will include: Creating different chart types such as bar charts, box plots, histograms and line plots Formatting charts Best Practices Hints and Tips This section will go through some best practice considerations that should be adopted of you are applying Python in a business context.