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Recognised Accreditation This course is accredited by continuing professional development (CPD). CPD UK is globally recognised by employers, professional organisations, and academic institutions, thus a certificate from CPD Certification Service creates value towards your professional goal and achievement. The Quality Licence Scheme is a brand of the Skills and Education Group, a leading national awarding organisation for providing high-quality vocational qualifications across a wide range of industries. What is CPD? Employers, professional organisations, and academic institutions all recognise CPD, therefore a credential from CPD Certification Service adds value to your professional goals and achievements. Benefits of CPD Improve your employment prospects Boost your job satisfaction Promotes career advancement Enhances your CV Provides you with a competitive edge in the job market Demonstrate your dedication Showcases your professional capabilities What is IPHM? The IPHM is an Accreditation Board that provides Training Providers with international and global accreditation. The Practitioners of Holistic Medicine (IPHM) accreditation is a guarantee of quality and skill. Benefits of IPHM It will help you establish a positive reputation in your chosen field You can join a network and community of successful therapists that are dedicated to providing excellent care to their client You can flaunt this accreditation in your CV It is a worldwide recognised accreditation What is Quality Licence Scheme? This course is endorsed by the Quality Licence Scheme for its high-quality, non-regulated provision and training programmes. The Quality Licence Scheme is a brand of the Skills and Education Group, a leading national awarding organisation for providing high-quality vocational qualifications across a wide range of industries. Benefits of Quality License Scheme Certificate is valuable Provides a competitive edge in your career It will make your CV stand out Course Curriculum Data Science Overview Introduction to Data Science 00:01:00 Data Science: Career of the Future 00:04:00 What is Data Science. 00:02:00 Data Science as a Process 00:02:00 Data Science Toolbox 00:03:00 Data Science Process Explained 00:05:00 What's Next 00:01:00 R and RStudio Engine and Coding Environment 00:03:00 Installing R and RStudio 00:04:00 RStudio: A Quick Tour 00:04:00 Introduction to Basics Arithmetic With R 00:03:00 Variable Assignment 00:04:00 Basic data types in R 00:03:00 Vectors Creating a Vector 00:05:00 Naming a Vector 00:04:00 Arithmetic Calculations On Vectors 00:07:00 Vector selection 00:06:00 Selection By Comparison 00:04:00 Matrices What's a Matrix 00:02:00 Analyzing Matrices 00:03:00 Naming a Matrix 00:05:00 Adding Columns and Rows To a Matrix 00:06:00 Selection of Matrix Elements 00:03:00 Arithmetic with Matrices 00:07:00 Factors What is Factor 00:02:00 Categorical Variables and Factor Levels 00:04:00 Summarizing a Factor 00:01:00 Ordered Factors 00:05:00 Data Frames What's a Data Frame 00:03:00 Creating a Data Frame 00:04:00 Selection of Data Frame elements 00:03:00 Conditional selection 00:03:00 Sorting a Data Frame 00:03:00 Lists Why Would You Need Lists 00:01:00 Creating Lists 00:03:00 Selecting Elements From a List 00:03:00 Adding more data to the list 00:02:00 Relational Operators Equality 00:03:00 Greater and Less Than 00:03:00 Compare Vectors 00:03:00 Compare Matrices 00:02:00 Logical Operators AND, OR, NOT Operators 00:04:00 Logical Operators with Vectors and Matrices 00:04:00 Reverse the result: (!) 00:01:00 Relational and Logical Operators Together 00:06:00 Conditional Statements The IF Statement 00:04:00 IF…ELSE 00:03:00 The ELSEIF Statement 00:05:00 Full Exercise 00:03:00 Loops Write a While Loop 00:04:00 Looping With More Conditions 00:04:00 Break: Stop the While Loop 00:04:00 What's a For Loop 00:02:00 Loop Over a Vector 00:02:00 Loop Over a List 00:03:00 Loop Over a Matrix 00:03:00 For Loop With Conditionals 00:01:00 Using Next and Break With For Loop 00:03:00 Functions What is Function 00:02:00 Arguments Matching 00:03:00 Required and Optional Arguments 00:03:00 Nested functions 00:02:00 Writing Own Functions 00:03:00 Functions With No Arguments 00:02:00 Defining Default Arguments In Functions 00:04:00 Function Scoping 00:02:00 Control Flow in Functions 00:03:00 R Packages Installing R Packages 00:01:00 Loading R Packages 00:04:00 Different Ways To Load a Package 00:02:00 The Apply Family - Lapply What Is Lapply and When Is Used. 00:04:00 Use Lapply With User-Defined Functions 00:03:00 Lapply and Anonymous Functions 00:01:00 Use lapply With Additional Arguments 00:04:00 The Apply Family - Sapply & Vapply What is Sapply 00:02:00 How to Use Sapply 00:02:00 Sapply With Your Own Function 00:02:00 Sapply With a Function Returning a Vector 00:02:00 When Can't sapply Simplify. 00:02:00 What is Vapply and Why is it Used. 00:04:00 Useful Functions Mathematical Functions 00:05:00 Data Utilities 00:08:00 Regular Expressions Grepl & Grep 00:04:00 Metacharacters 00:05:00 Sub & Gsub 00:02:00 More Metacharacters 00:04:00 Dates And Times Today and Now 00:02:00 Create and Format Dates 00:06:00 Create and Format Times 00:03:00 Calculations with Dates 00:03:00 Calculations with Times 00:07:00 Getting and Cleaning Data Get and Set Current Directory 00:04:00 Get Data From the Web 00:04:00 Loading Flat Files 00:05:00 Loading Excel files 00:03:00 Plotting Data in R Base Plotting System 00:03:00 Base plots: Histograms 00:03:00 Base plots: Scatterplots 00:05:00 Base plots: Regression Line 00:03:00 Base plots: Boxplot 00:03:00 Data Manipulation With dplyr Introduction to Dplyr Package 00:04:00 Using the Pipe Operator (%>%) 00:02:00 Columns component: select() 00:05:00 Columns component: rename() and rename_with() 00:02:00 Columns Component: Mutate() 00:02:00 Columns Ccomponent: Relocate() 00:02:00 Rows Component: Filter() 00:01:00 Rows Component: Slice() 00:04:00 Rows Component: Arrange() 00:01:00 Rows Component: Rowwise() 00:02:00 Grouping of Rows: Summarise() 00:03:00 Grouping of Rows: Across() 00:02:00 COVID-19 Analysis Task 00:08:00 Supplementary Resources Supplementary Resources - Learning R Programming for Data Science 00:00:00 Certificate of Achievement Certificate of Achievement 00:00:00 Get Your Insurance Now Get Your Insurance Now 00:00:00 Feedback Feedback 00:00:00

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Duration 3 Days 18 CPD hours This course is intended for This is an introductory- level course appropriate for those who are developing applications using relational databases, or who are using SQL to extract and analyze data from databases and need to use the full power of SQL queries. Overview This course combines expert lecture, real-world demonstrations and group discussions with machine-based practical labs and exercises. Working in a hands-on learning environment led by our expert practitioner, attendees will learn to: Maximize the potential of SQL to build powerful, complex and robust SQL queries Query multiple tables with inner joins, outer joins and self joins Construct recursive common table expressions Summarize data using aggregation and grouping Execute analytic functions to calculate ranks Build simple and correlated subqueries Thoroughly test SQL queries to avoid common errors Select the most efficient solution to complex SQL problems A company?s success hinges on responsible, accurate database management. Organizations rely on highly available data to complete all sorts of tasks, from creating marketing reports and invoicing customers to setting financial goals. Data professionals like analysts, developers and architects are tasked with creating, optimizing, managing and analyzing data from databases ? with little room for error. When databases aren?t built or maintained correctly, it?s easy to mishandle or lose valuable data. Our SQL Programming and Database Training Series provides students with the skills they require to develop, analyze and maintain data and in correctly structured, modern and secure databases. SQL is the cornerstone of all relational database operations. In this hands-on course, you learn to exploit the full potential of the SELECT statement to write robust queries using the best query method for your application, test your queries, and avoid common errors and pitfalls. It also teaches alternative solutions to given problems, enabling you to choose the most efficient solution in each situation. Introduction: Quick Tools Review Introduction to SQL and its development environments Using SQL*PLUS Using SQL Developer Using the SQL SELECT Statement Capabilities of the SELECT statement Arithmetic expressions and NULL values in the SELECT statement Column aliases Use of concatenation operator, literal character strings, alternative quote operator, and the DISTINCT keyword Use of the DESCRIBE command Restricting and Sorting Data Limiting the Rows Rules of precedence for operators in an expression Substitution Variables Using the DEFINE and VERIFY command Single-Row Functions Describe the differences between single row and multiple row functions Manipulate strings with character function in the SELECT and WHERE clauses Manipulate numbers with the ROUND, TRUNC and MOD functions Perform arithmetic with date data Manipulate dates with the date functions Conversion Functions and Expressions Describe implicit and explicit data type conversion Use the TO_CHAR, TO_NUMBER, and TO_DATE conversion functions Nest multiple functions Apply the NVL, NULLIF, and COALESCE functions to data Decode/Case Statements Using the Group Functions and Aggregated Data Group Functions Creating Groups of Data Having Clause Cube/Rollup Clause SQL Joins and Join Types Introduction to JOINS Types of Joins Natural join Self-join Non equijoins OUTER join Using Subqueries Introduction to Subqueries Single Row Subqueries Multiple Row Subqueries Using the SET Operators Set Operators UNION and UNION ALL operator INTERSECT operator MINUS operator Matching the SELECT statements Using Data Manipulation Language (DML) statements Data Manipulation Language Database Transactions Insert Update Delete Merge Using Data Definition Language (DDL) Data Definition Language Create Alter Drop Data Dictionary Views Introduction to Data Dictionary Describe the Data Dictionary Structure Using the Data Dictionary views Querying the Data Dictionary Views Dynamic Performance Views Creating Sequences, Synonyms, Indexes Creating sequences Creating synonyms Creating indexes Index Types Creating Views Creating Views Altering Views Replacing Views Managing Schema Objects Managing constraints Creating and using temporary tables Creating and using external tables Retrieving Data Using Subqueries Retrieving Data by Using a Subquery as Source Working with Multiple-Column subqueries Correlated Subqueries Non-Correlated Subqueries Using Subqueries to Manipulate Data Using the Check Option Subqueries in Updates and Deletes In-line Views Data Control Language (DCL) System privileges Creating a role Object privileges Revoking object privileges Manipulating Data Overview of the Explicit Default Feature Using multitable INSERTs Using the MERGE statement Tracking Changes in Data

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Register on the R Programming for Data Science 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 a digital certificate as a proof of your course completion. The R Programming for Data Science 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 R Programming for Data Science 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 After the successful completion of the final assessment, you will receive a CPD-accredited certificate of achievement. The PDF certificate is for £9.99, and it will be sent to you immediately after through e-mail. You can get the hard copy for £15.99, which will reach your doorsteps by post. 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 R Programming for Data Science, 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: Data Science Overview Introduction to Data Science 00:01:00 Data Science: Career of the Future 00:04:00 What is Data Science? 00:02:00 Data Science as a Process 00:02:00 Data Science Toolbox 00:03:00 Data Science Process Explained 00:05:00 What's Next? 00:01:00 Unit 02: R and RStudio Engine and coding environment 00:03:00 Installing R and RStudio 00:04:00 RStudio: A quick tour 00:04:00 Unit 03: Introduction to Basics Arithmetic with R 00:03:00 Variable assignment 00:04:00 Basic data types in R 00:03:00 Unit 04: Vectors Creating a vector 00:05:00 Naming a vector 00:04:00 Arithmetic calculations on vectors 00:07:00 Vector selection 00:06:00 Selection by comparison 00:04:00 Unit 05: Matrices What's a Matrix? 00:02:00 Analyzing Matrices 00:03:00 Naming a Matrix 00:05:00 Adding columns and rows to a matrix 00:06:00 Selection of matrix elements 00:03:00 Arithmetic with matrices 00:07:00 Additional Materials 00:00:00 Unit 06: Factors What's a Factor? 00:02:00 Categorical Variables and Factor Levels 00:04:00 Summarizing a Factor 00:01:00 Ordered Factors 00:05:00 Unit 07: Data Frames What's a Data Frame? 00:03:00 Creating Data Frames 00:20:00 Selection of Data Frame elements 00:03:00 Conditional selection 00:03:00 Sorting a Data Frame 00:03:00 Additional Materials 00:00:00 Unit 08: Lists Why would you need lists? 00:01:00 Creating a List 00:06:00 Selecting elements from a list 00:03:00 Adding more data to the list 00:02:00 Additional Materials 00:00:00 Unit 09: Relational Operators Equality 00:03:00 Greater and Less Than 00:03:00 Compare Vectors 00:03:00 Compare Matrices 00:02:00 Additional Materials 00:00:00 Unit 10: Logical Operators AND, OR, NOT Operators 00:04:00 Logical operators with vectors and matrices 00:04:00 Reverse the result: (!) 00:01:00 Relational and Logical Operators together 00:06:00 Additional Materials 00:00:00 Unit 11: Conditional Statements The IF statement 00:04:00 IFELSE 00:03:00 The ELSEIF statement 00:05:00 Full Exercise 00:03:00 Additional Materials 00:00:00 Unit 12: Loops Write a While loop 00:04:00 Looping with more conditions 00:04:00 Break: stop the While Loop 00:04:00 What's a For loop? 00:02:00 Loop over a vector 00:02:00 Loop over a list 00:03:00 Loop over a matrix 00:04:00 For loop with conditionals 00:01:00 Using Next and Break with For loop 00:03:00 Additional Materials 00:00:00 Unit 13: Functions What is a Function? 00:02:00 Arguments matching 00:03:00 Required and Optional Arguments 00:03:00 Nested functions 00:02:00 Writing own functions 00:03:00 Functions with no arguments 00:02:00 Defining default arguments in functions 00:04:00 Function scoping 00:02:00 Control flow in functions 00:03:00 Additional Materials 00:00:00 Unit 14: R Packages Installing R Packages 00:01:00 Loading R Packages 00:04:00 Different ways to load a package 00:02:00 Additional Materials 00:00:00 Unit 15: The Apply Family - lapply What is lapply and when is used? 00:04:00 Use lapply with user-defined functions 00:03:00 lapply and anonymous functions 00:01:00 Use lapply with additional arguments 00:04:00 Additional Materials 00:00:00 Unit 16: The apply Family - sapply & vapply What is sapply? 00:02:00 How to use sapply 00:02:00 sapply with your own function 00:02:00 sapply with a function returning a vector 00:02:00 When can't sapply simplify? 00:02:00 What is vapply and why is it used? 00:04:00 Additional Materials 00:00:00 Unit 17: Useful Functions Mathematical functions 00:05:00 Data Utilities 00:08:00 Additional Materials 00:00:00 Unit 18: Regular Expressions grepl & grep 00:04:00 Metacharacters 00:05:00 sub & gsub 00:02:00 More metacharacters 00:04:00 Additional Materials 00:00:00 Unit 19: Dates and Times Today and Now 00:02:00 Create and format dates 00:06:00 Create and format times 00:03:00 Calculations with Dates 00:03:00 Calculations with Times 00:07:00 Additional Materials 00:00:00 Unit 20: Getting and Cleaning Data Get and set current directory 00:04:00 Get data from the web 00:04:00 Loading flat files 00:03:00 Loading Excel files 00:05:00 Additional Materials 00:00:00 Unit 21: Plotting Data in R Base plotting system 00:03:00 Base plots: Histograms 00:03:00 Base plots: Scatterplots 00:05:00 Base plots: Regression Line 00:03:00 Base plots: Boxplot 00:03:00 Unit 22: Data Manipulation with dplyr Introduction to dplyr package 00:04:00 Using the pipe operator (%>%) 00:02:00 Columns component: select() 00:05:00 Columns component: rename() and rename_with() 00:02:00 Columns component: mutate() 00:02:00 Columns component: relocate() 00:02:00 Rows component: filter() 00:01:00 Rows component: slice() 00:04:00 Rows component: arrange() 00:01:00 Rows component: rowwise() 00:02:00 Grouping of rows: summarise() 00:03:00 Grouping of rows: across() 00:02:00 COVID-19 Analysis Task 00:08:00 Additional Materials 00:00:00