188 Arithmetic courses delivered Online

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

Powerful 1-hour Python workshop course understand Python Basics. Practical. Instructor-led. Online. Learn to code a Password Validator in 1 hour. 

Using Ansible to automate local and cloud configuration management tasks with Playbooks

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

Learn Python programming by developing robust GUIs and games

Do you want to learn Tableau and crack the Tableau Certified Data Analyst Exam? Then this course is for you! This course is designed for absolute beginners, and it is well equipped with detailed video tutorials, exam notes PDF, tips and tricks, and full practice tests in exam format along with solutions.

This course offers a swift and straightforward way to learn Python programming. It is thoughtfully designed, packed with hands-on exercises, and tailored to assist you in embarking on your Python 3 journey. No prior programming experience is necessary to enroll in this course.

Gain the solid skills and knowledge to kickstart a successful career and learn from the experts with this

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

Standard Edition of the Deep Dive into Core Java Programming. An approach to learning Java that is both practical and effective. Become an expert in Java.