37 Arithmetic courses in Nottingham delivered Live Online

Duration 2 Days 12 CPD hours This course is intended for This course is relevant to anyone who needs to work with and understand data including: Business Analysts, Data Analysts, Reporting and BI professionals Marketing and Digital Marketing professionals Digital, Web, e-Commerce, Social media and Mobile channel professionals Business managers who need to interpret analytical output to inform managerial decisions Overview This course will cover the basic theory of data visualization along with practical skills for creating compelling visualizations, reports and dashboards from data using Tableau. Outcome: After attending this course delegates will understand - How to move from business questions to great data visualizations and beyond How to apply the fundamentals of data visualization to create informative charts How to choose the right visualization type for the job at hand How to design and develop basic dashboards in Tableau that people will love to use by doing the following: Reading data sources into Tableau Setting up the roles and data types for your analysis Creating new data fields using a range of calculation types Creating the following types of charts - cross tabs, pie and bar charts, geographic maps, dual axis and combo charts, heat maps, highlight tables, tree maps and scatter plots Creating Dashboards that delight using the all of the features available in Tableau. The use of analytics, statistics and data science in business has grown massively in recent years. Harnessing the power of data is opening actionable insights in diverse industries from banking to tourism. From Business Questions to Data Visualisation and Beyond The first step in any data analysis project is to move from a business question to data analysis and then on to a complete solution. This section will examine this conversion emphasizing: The use of data visualization to address a business need The data analytics process ? from business questions to developed dashboards Introduction to Tableau ? Part 1 In this section, the main functionality of Tableau will be explained including: Selecting and loading your data Defining data item properties Create basic calculations including basic arithmetic calculations, custom aggregations and ratios, date math, and quick table calculations Creating basic visualizations Creating a basic dashboard Introduction to Tableau ? Part 2 In this section, the main functionality of Tableau will be explained including: Selecting and loading your data Defining data item properties Create basic calculations including basic arithmetic calculations, custom aggregations and ratios, date math, and quick table calculations Creating basic visualizations Creating a basic dashboard Key Components of Good Data Visualisation and The Visualisation Zoo In this section the following topics will be covered: Colour theory Graphical perception & communication Choosing the right chart for the right job Data Exploration with Tableau Exploring data to answer business questions is one of the key uses of applying good data visualization techniques within Tableau. In this section we will apply the data visualization theory from the previous section within Tableau to uncover trends within the data to answer specific business questions. The types of charts that will be covered are: Cross Tabs Pie and bar charts Geographic maps Dual axis and combo charts with different mark types Heat maps Highlight tables Tree maps Scatter plots Introduction to Building Dashboards with Tableau In this section, we will implement the full process from business question to final basic dashboard in Tableau: Introduction to good dashboard design Building dashboards in Tableau

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. 

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

Duration 5 Days 30 CPD hours This course is intended for This course is intended for new and experienced programmers that want to learn how to write and troubleshoot Python code. This is the Microsoft recommended course for preparing for the 98-381 test. Previous programming experience is not required but recommended. Overview By the end of this course, you will be able to: Create Operations using Data Types and Operators Create Control Flow Operations Create Input and Output Operations Write and Document code to solve a specified problem Troubleshoot Problems and Write Error Handling Operations Perform Operations Using Modules and Tools This five-day instructor-led course (three-day boot camp) is intended for students who want to learn how to write, debug and document Python code Module 1: Perform Operations Using Data Types and Operators Assign data types to variables Perform data and data type operations Perform Arithmetic, Comparison and Logical Operations Review Module 2: Control Flow with Decisions and Loops Construct and analyze code segments that use branching statements Construct and analyze code segments that perform iterations Review Module 3: Perform Input and Output Operations Create Python code segments that perform file input and output operations Create Python code segments that perform console input and output operations Review Module 4: Document and Structure Code Construct and analyze code segments Document code segments using comments and documentation strings Review Module 5: Perform Troubleshooting and Error Handling Analyze, Detect and Fix code segments that have errors Analyze and construct code segments that handle exceptions Review Module 6: Perform Operations Using Modules and Tools Use Built-In Modules to perform basic operations Use Built-In Modules to perform complex operations Review

Duration 2 Days 12 CPD hours This course is intended for Developers & Developer Consultants Overview Learn simple ABAP codeTrace the flow of a program and troubleshoot simple problems In this course, students will learn simple ABAP code. Basics of ABAP Programming Developing a Simple ABAP Program Introducing ABAP Syntax Implementing a Simple Dialog Customizing the ABAP Editor Coding & Debugging in ABAP Defining Simple Variables Defining Text Symbols Performing Arithmetic Operations Using Simple Variables Using System Variables Debugging a Program Creating an ABAP List Processing Character Strings Current Flow Structures in ABAP Implementing Conditional Logic Implementing Loops Runtime Errors & Error Handling Analyzing Runtime Errors Implementing Error Handling Additional ABAP Programming Techniques Retrieving Data From the Database Describing Modularization in ABAP Using Function Modules Additional course details: Nexus Humans BC100 SAP Introduction to Programming with ABAP 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 BC100 SAP Introduction to Programming with ABAP 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 2 Days 12 CPD hours This course is intended for Report authors working with dimensional data sources. Through interactive demonstrations & exercises, participants will learn how to author reports that navigate & manipulate dimensional data structures using the specific dimensional functions & features available in IBM Cognos Analytics. Introduction to Dimensional Concepts Identify different data sources and models Investigate the OLAP dimensional structure Identify dimensional data items and expressions Differentiate the IBM Cognos Analytics query language from SQL and MDX Differentiate relational and dimensional report authoring styles Introduction to Dimensional Data in Reports Work with members Identify sets and tuples in IBM Cognos Analytics Dimensional Report Context Understand the purpose of report context Understand how data is affected by default and root members Focus Your Dimensional Data Compare dimensional queries to relational queries Explain the importance of filtering dimensional queries Evaluate different filtering techniques Filter based on dimensions and members Filter based on measure values Filter using a slicer Calculations & Dimensional Functions Use IBM Cognos Analytics dimensional functions to create sets and tuples Perform arithmetic operations in OLAP queries Identify coercion errors and rules Functions for Navigating Dimesional Hierarchies Navigate dimensional data using family functions Relative Functions Navigate dimensional data using relative functions Navigate dimensional data using relative time functions Advanced Drilling Techniques & Member Sets Understand default drill-up and drill-down functionality Identify cases when you need to override default drilling behavior Configure advanced drilling behavior to support sophisticated use cases Define member sets to support advanced drilling Define member sets to support functions Set Up Drill-Through Reports Navigate from a specific report to a target report Drill down to greater detail and then navigate to target report Navigate between reports created using different data sources End-to-End Workshop Review concepts covered throughout the course

Electronics Equipment Repair 2 (Online Virtual Classroom) is a follow on course to Electronics Equipment Repair 1. This course builds on the skills gained and enables participants to build, test and fault-find more complex Analogue Electronic circuits

Duration 3.5 Days 21 CPD hours This course is intended for This course is aimed at students new to the language who may or may not have experience with other programming languages. Overview Learn how Python works and what it's good for. Understand Python's place in the world of programming languages Learn to work with and manipulate strings in Python. Learn to perform math operations with Python. Learn to work with Python sequences: lists, arrays, dictionaries, and sets. Learn to collect user input and output results. Learn flow control processing in Python. Learn to write to and read from files using Python. Learn to write functions in Python. Learn to handle exceptions in Python. Learn to work with dates and times in Python. In this Python training course by Webucator, Inc, students learn to program in Python. Python Basics Running Python Hello, World! Literals Python Comments Data Types Variables Writing a Python Module print() Function Named Arguments Collecting User Input Getting Help Functions and Modules Defining Functions Variable Scope Global Variables Function Parameters Returning Values Importing Modules Math Arithmetic Operators Modulus and Floor Division Assignment Operators Built-in Math Functions The math Module The random Module Seeding Python Strings Quotation Marks and Special Characters String Indexing Slicing Strings Concatenation and Repetition Common String Methods String Formatting Built-in String Functions Iterables: Sequences, Dictionaries, and Sets Definitions Sequences Unpacking Sequences Dictionaries The len() Function Sets *args and **kwargs Flow Control Conditional Statements The is and is not Operators Python's Ternary Operator Loops in Python The enumerate() Function Generators List Comprehensions File Processing Opening Files The os and os.path Modules Exception Handling Wildcard except Clauses Getting Information on Exceptions The else Clause The finally Clause Using Exceptions for Flow Control Exception Hierarchy Dates and Times Understanding Time The time Module The datetime Module Running Python Scripts from the Command Line The sys Module sys.argv

Course summary This Python Programming course has become essential to all professional who would like to do code using Python. Learn evenings, work while studying. Work and study Python Part Time. It is a Python beginners course and teaches Python coding from scratch to intermediate. Hands-on, practical. We code some real life examples in this course.