145 Data Analysis courses in Sheffield delivered Live Online

Course Overview This comprehensive course on "Data Analysis and Forecasting in Excel" provides learners with essential skills to manage, analyse, and visualise data effectively using Excel. Whether you are analysing historical data or forecasting future trends, this course covers key tools such as PivotTables, charts, and lookup functions to make your data work for you. Learners will also gain proficiency in automating tasks and creating dynamic reports, which are invaluable for decision-making processes in various professional settings. By the end of the course, you will have the capability to work with complex data sets, produce insightful reports, and apply forecasting techniques to guide future strategies. Course Description In this course, learners will delve into the full spectrum of data analysis capabilities offered by Excel. Topics include modifying worksheets, working with lists, and using advanced tools such as PivotTables, PivotCharts, and lookup functions. Learners will explore the process of visualising data through charts and sparklines, allowing them to convey complex information in an accessible manner. The course also covers automating workbook functionality and creating mapping data for better analysis. Additionally, learners will gain expertise in forecasting data trends to support strategic planning. By the end of the course, participants will have developed a comprehensive understanding of Excel’s analytical tools, enabling them to manage data with efficiency and precision in various business contexts. Course Modules Module 01: Modifying a Worksheet Module 02: Working with Lists Module 03: Analyzing Data Module 04: Visualizing Data with Charts Module 05: Using PivotTables and PivotCharts Module 06: Working with Multiple Worksheets and Workbooks Module 07: Using Lookup Functions and Formula Auditing Module 08: Automating Workbook Functionality Module 09: Creating Sparklines and Mapping Data Module 10: Forecasting Data (See full curriculum) Who is this course for? Individuals seeking to enhance their Excel data analysis skills. Professionals aiming to improve their forecasting and reporting capabilities. Beginners with an interest in data management and analysis. Those seeking to enhance their proficiency in Excel for career advancement. Career Path Data Analyst Business Analyst Financial Analyst Marketing Analyst Operations Manager Project Manager Excel Specialist in various industries such as finance, marketing, and logistics

Duration 2 Days 12 CPD hours This course is intended for New users of IBM SPSS Statistics Users who want to refresh their knowledge about IBM SPSS Statistics Anyone who is considering purchasing IBM SPSS Statistics Overview Introduction to IBM SPSS Statistics Review basic concepts in IBM SPSS Statistics Identify the steps in the research process Review basic analyses Use Help Reading data and defining metadata Overview of data sources Read from text files Read data from Microsoft Excel Read data from databases Define variable properties Selecting cases for analyses Select cases for analyses Run analyses for groups Apply report authoring styles Transforming variables Compute variables Recode values of categorical and scale variables Create a numeric variable from a string variable Using functions to transform variables Use statistical functions Use logical functions Use missing value functions Use conversion functions Use system variables Use the Date and Time Wizard Setting the unit of analysis Remove duplicate cases Create aggregate datasets Restructure datasets Merging data files Add cases from one dataset to another Add variables from one dataset to another Enrich a dataset with aggregated information Summarizing individual variables Define levels of measurement Summarizing categorical variables Summarizing scale variables Describing the relationship between variables Choose the appropriate procedure Summarize the relationship between categorical variables Summarize the relationship between a scale and a categorical variable Creating presentation ready tables with Custom Tables Identify table layouts Create tables for variables with shared categories Create tables for multiple response questions Customizing pivot tables Perform Automated Output Modification Customize pivot tables Use table templates Export pivot tables to other applications Working with syntax Use syntax to automate analyses Create, edit, and run syntax Shortcuts in the Syntax Editor Controlling the IBM SPSS Statistics environment Set options for output Set options for variables display Set options for default working folders This course guides students through the fundamentals of using IBM SPSS Statistics for typical data analysis. Students will learn the basics of reading data, data definition, data modification, data analysis, and presentation of analytical results. In addition to the fundamentals, students will learn shortcuts that will help them save time. This course uses the IBM SPSS Statistics Base; one section presents an add-on module, IBM SPSS Custom Tables. Introduction to IBM SPSS Statistics Review basic concepts in IBM SPSS Statistics Identify the steps in the research process Review basic analyses Use Help Reading data and defining metadata Overview of data sources Read from text files Read data from Microsoft Excel Read data from databases Define variable properties Selecting cases for analyses Select cases for analyses Run analyses for groups Apply report authoring styles Transforming variables Compute variables Recode values of categorical and scale variables Create a numeric variable from a string variable Using functions to transform variables Use statistical functions Use logical functions Use missing value functions Use conversion functions Use system variables Use the Date and Time Wizard Setting the unit of analysis Remove duplicate cases Create aggregate datasets Restructure datasets Merging data files Add cases from one dataset to another Add variables from one dataset to another Enrich a dataset with aggregated information Summarizing individual variables Define levels of measurement Summarizing categorical variables Summarizing scale variables Describing the relationship between variables Choose the appropriate procedure Summarize the relationship between categorical variables Summarize the relationship between a scale and a categorical variable Creating presentation ready tables with Custom Tables Identify table layouts Create tables for variables with shared categories Create tables for multiple response questions Customizing pivot tables Perform Automated Output Modification Customize pivot tables Use table templates Export pivot tables to other applications Working with syntax Use syntax to automate analyses Create, edit, and run syntax Shortcuts in the Syntax Editor Controlling the IBM SPSS Statistics environment Set options for output Set options for variables display Set options for default working folders Additional course details: Nexus Humans 0G53BG IBM SPSS Statistics Essentials (V26) 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 0G53BG IBM SPSS Statistics Essentials (V26) 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.

The Real Estate Analyst course has been taught non-stop to global real estate firms over the last 25 years, and is without doubt the core financial modelling training in your career portfolio. Whether you have an upcoming financial modelling test for a new job or an APC exam, the Real Estate Analyst course is the choice for you.

Duration 3 Days 18 CPD hours This course is intended for The primary audience for this course is people who are moving into a database role, or whose role has expanded to include database technologies. Developers that deliver content from SQL Server databases will also benefit from this material. Overview After completing this course, you will be able to: Describe key database concepts in the context of SQL Server Describe database languages used in SQL Server Describe data modelling techniques Describe normalization and denormalization techniques Describe relationship types and effects in database design Describe the effects of database design on performance Describe commonly used database objects This course is provided as an introductory class for anyone getting started with databases. It will be useful to programmers and other IT professionals whose job roles are expanding into database management. Students will learn fundamental database concepts through demonstrations and hands-on labs on a SQL Server instance. This material updates and replaces course Microsoft course 10985 which was previously published under the same title. Module 1: Introduction to databases Introduction to Relational Databases Other Databases and Storage Data Analysis SQL Server Database Languages Module 2: Data Modeling Data Modelling Designing a Database Relationship Modeling Module 3: Normalization Fundamentals of Normalization Normal Form Denormalization Module 4: Relationships Introduction to Relationships Planning Referential Integrity Module 5: Performance Indexing Query Performance Concurrency Module 6: Database Objects Tables Views Stored Procedures, Triggers and Functions

This course will show delegates how to appraise projects in Excel, incorporate and communicate updates as they happen and ultimately understand how to convert an opportunistic property investment into a core asset that can be sold on.

Project Quality Management: In-House Training In today's environment, quality is the responsibility of everyone. Project success is no longer just the fulfillment of a project on schedule, on budget, and within the scope. Today, projects aren't successful unless the customer's needs are met at the highest level of quality at the lowest cost to the organization. Project Managers must know customer needs, and manage to them throughout the project lifecycle, in order to gain acceptance. Project Quality Management provides an interactive, hands-on environment for participants to practice identification of critical quality requirements (quality planning), fulfillment of those requirements through well-designed processes (Quality Assurance), and statistical awareness of technical specifications of project deliverables (Quality Control). What You Will Learn You'll learn how to: Plan for higher quality project deliverables Measure key performance indicators on projects, processes, and products Turn data into useful project information Take action on analyzed data that will drive down non-value-added costs and drive up customer acceptance and satisfaction Reduce defects and waste in current project management processes Foundation Concepts Quality Defined Customer Focus Financial Focus Quality Management Process Management Cost of Quality Planning for Quality Project Manager Role in Planning Voice of the Customer Quality Management Plan Measurement System Accuracy Data Gathering Data Sampling Manage Quality Process Management Process Mapping Process Analysis Value Stream Mapping Standardization Visual Workplace and 5S Error Proofing (Poka-Yoke) Failure Mode and Effect Analysis Control Quality The Concept of Variation Common Cause Special Cause Standard Business Reports Tracking Key Measurements Control Charts Data Analysis Variation Root Cause Analysis Variance Management Designing for Quality

This Real Estate Financial Modelling course will enhance delegates skills in crafting sophisticated financial models tailored for debt financing within the Real Estate sector.

Duration 3 Days 18 CPD hours This course is intended for Data Analysts, Business Analysts, Business Intelligence professionals Cloud Data Engineers who will be partnering with Data Analysts to build scalable data solutions on Google Cloud Platform Overview This course teaches students the following skills: Derive insights from data using the analysis and visualization tools on Google Cloud Platform Interactively query datasets using Google BigQuery Load, clean, and transform data at scale Visualize data using Google Data Studio and other third-party platforms Distinguish between exploratory and explanatory analytics and when to use each approach Explore new datasets and uncover hidden insights quickly and effectively Optimizing data models and queries for price and performance Want to know how to query and process petabytes of data in seconds? Curious about data analysis that scales automatically as your data grows? Welcome to the Data Insights course! This four-course accelerated online specialization teaches course participants how to derive insights through data analysis and visualization using the Google Cloud Platform. The courses feature interactive scenarios and hands-on labs where participants explore, mine, load, visualize, and extract insights from diverse Google BigQuery datasets. The courses also cover data loading, querying, schema modeling, optimizing performance, query pricing, and data visualization. This specialization is intended for the following participants: Data Analysts, Business Analysts, Business Intelligence professionals Cloud Data Engineers who will be partnering with Data Analysts to build scalable data solutions on Google Cloud Platform To get the most out of this specialization, we recommend participants have some proficiency with ANSI SQL. Introduction to Data on the Google Cloud Platform Highlight Analytics Challenges Faced by Data Analysts Compare Big Data On-Premises vs on the Cloud Learn from Real-World Use Cases of Companies Transformed through Analytics on the Cloud Navigate Google Cloud Platform Project Basics Lab: Getting started with Google Cloud Platform Big Data Tools Overview Walkthrough Data Analyst Tasks, Challenges, and Introduce Google Cloud Platform Data Tools Demo: Analyze 10 Billion Records with Google BigQuery Explore 9 Fundamental Google BigQuery Features Compare GCP Tools for Analysts, Data Scientists, and Data Engineers Lab: Exploring Datasets with Google BigQuery Exploring your Data with SQL Compare Common Data Exploration Techniques Learn How to Code High Quality Standard SQL Explore Google BigQuery Public Datasets Visualization Preview: Google Data Studio Lab: Troubleshoot Common SQL Errors Google BigQuery Pricing Walkthrough of a BigQuery Job Calculate BigQuery Pricing: Storage, Querying, and Streaming Costs Optimize Queries for Cost Lab: Calculate Google BigQuery Pricing Cleaning and Transforming your Data Examine the 5 Principles of Dataset Integrity Characterize Dataset Shape and Skew Clean and Transform Data using SQL Clean and Transform Data using a new UI: Introducing Cloud Dataprep Lab: Explore and Shape Data with Cloud Dataprep Storing and Exporting Data Compare Permanent vs Temporary Tables Save and Export Query Results Performance Preview: Query Cache Lab: Creating new Permanent Tables Ingesting New Datasets into Google BigQuery Query from External Data Sources Avoid Data Ingesting Pitfalls Ingest New Data into Permanent Tables Discuss Streaming Inserts Lab: Ingesting and Querying New Datasets Data Visualization Overview of Data Visualization Principles Exploratory vs Explanatory Analysis Approaches Demo: Google Data Studio UI Connect Google Data Studio to Google BigQuery Lab: Exploring a Dataset in Google Data Studio Joining and Merging Datasets Merge Historical Data Tables with UNION Introduce Table Wildcards for Easy Merges Review Data Schemas: Linking Data Across Multiple Tables Walkthrough JOIN Examples and Pitfalls Lab: Join and Union Data from Multiple Tables Advanced Functions and Clauses Review SQL Case Statements Introduce Analytical Window Functions Safeguard Data with One-Way Field Encryption Discuss Effective Sub-query and CTE design Compare SQL and Javascript UDFs Lab: Deriving Insights with Advanced SQL Functions Schema Design and Nested Data Structures Compare Google BigQuery vs Traditional RDBMS Data Architecture Normalization vs Denormalization: Performance Tradeoffs Schema Review: The Good, The Bad, and The Ugly Arrays and Nested Data in Google BigQuery Lab: Querying Nested and Repeated Data More Visualization with Google Data Studio Create Case Statements and Calculated Fields Avoid Performance Pitfalls with Cache considerations Share Dashboards and Discuss Data Access considerations Optimizing for Performance Avoid Google BigQuery Performance Pitfalls Prevent Hotspots in your Data Diagnose Performance Issues with the Query Explanation map Lab: Optimizing and Troubleshooting Query Performance Advanced Insights Introducing Cloud Datalab Cloud Datalab Notebooks and Cells Benefits of Cloud Datalab Data Access Compare IAM and BigQuery Dataset Roles Avoid Access Pitfalls Review Members, Roles, Organizations, Account Administration, and Service Accounts

Project Quality Management: Virtual In-House Training In today's environment, quality is the responsibility of everyone. Project success is no longer just the fulfillment of a project on schedule, on budget, and within the scope. Today, projects aren't successful unless the customer's needs are met at the highest level of quality at the lowest cost to the organization. Project Managers must know customer needs, and manage to them throughout the project lifecycle, in order to gain acceptance. Project Quality Management provides an interactive, hands-on environment for participants to practice identification of critical quality requirements (quality planning), fulfillment of those requirements through well-designed processes (Quality Assurance), and statistical awareness of technical specifications of project deliverables (Quality Control). What You Will Learn You'll learn how to: Plan for higher quality project deliverables Measure key performance indicators on projects, processes, and products Turn data into useful project information Take action on analyzed data that will drive down non-value-added costs and drive up customer acceptance and satisfaction Reduce defects and waste in current project management processes Foundation Concepts Quality Defined Customer Focus Financial Focus Quality Management Process Management Cost of Quality Planning for Quality Project Manager Role in Planning Voice of the Customer Quality Management Plan Measurement System Accuracy Data Gathering Data Sampling Manage Quality Process Management Process Mapping Process Analysis Value Stream Mapping Standardization Visual Workplace and 5S Error Proofing (Poka-Yoke) Failure Mode and Effect Analysis Control Quality The Concept of Variation Common Cause Special Cause Standard Business Reports Tracking Key Measurements Control Charts Data Analysis Variation Root Cause Analysis Variance Management Designing for Quality

Business Intelligence: In-House Training Business Intelligence (BI) refers to a set of technology-based techniques, applications, and practices used to aggregate, analyze, and present business data. BI practices provide historical and current views of vast amounts of data and generate predictions for business operations. The purpose of Business Intelligence is the support of better business decision making. This course provides an overview of the technology and application of BI and how it can be used to improve corporate performance. What you will Learn You will learn how to: Specify a data warehouse schema Identify the data and visualization to be used for data mining and Business Intelligence Design a Business Intelligence user interface Getting Started Introductions Agenda Expectations Foundation Concepts The challenge of decision making What is Business Intelligence? The Business Intelligence value proposition Business Intelligence taxonomy Business Intelligence management issues Sources of Business Intelligence Data warehousing Data and information Information architecture Defining the data warehouse and its relationships Facts and dimensions Modeling, meta-modeling, and schemas Alternate architectures Building the data warehouse Extracting Transforming Loading Setting up the data and relationships Dimensions and the Fact Table Implementing many-to-many relationships in data warehouse Data marts Online Analytical Processing (OLAP) What is OLAP? OLAP and OLTP OLAP functionality Multi-dimensions Thinking in more than two dimensions What are the possibilities? OLAP architecture Cubism Tools OLAP variations - MOLAP, ROLAP, HOLAP BI using SOA Applications of Business Intelligence Applying BI through OLAP Enterprise Resource Planning and CRM Business Intelligence and financial information Business Intelligence User Interfaces and Presentations Data access Push-pull data access Types of decision support systems Designing the front end Presentation formats Dashboards Types of dashboards Common dashboard features Briefing books and scorecards Querying and Reporting Reporting emphasis Retrofitting Talking back Key Performance Indicators Report Definition and Visualization Typical reporting environment Forms of visualization Unconstrained views Data mining What is in the mine? Applications for data mining Data mining architecture Cross Industry Standard Process for Data Mining (CISP-DM) Data mining techniques Validation The Business Intelligence User Experience The business analyst role Business analysis and data analysis Five-step approach Cultural impact Identifying questions Gathering information Understand the goals The strategic Business Intelligence cycle Focus of Business Intelligence Design for the user Iterate the access Iterative solution development process Review and validation questions Basic approaches Building ad-hoc queries Building on-demand self-service reports Closed loop Business Intelligence Coming attractions - future of Business Intelligence Best practices in Business Intelligence