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Are you looking to enhance your Sales and Operations Planning Manager skills? If yes, then you have come to the right place. Our comprehensive course on Sales and Operations Planning Manager Skills will assist you in producing the best possible outcome by mastering the Sales and Operations Planning Manager Skills skills. The Sales and Operations Planning Manager Skills course is for those who want to be successful. In the Sales and Operations Planning Manager Skills course, you will learn the essential knowledge needed to become well versed in Sales and Operations Planning Manager Skills. Our Sales and Operations Planning Manager Skills course starts with the basics of Sales and Operations Planning Manager Skills and gradually progresses towards advanced topics. Therefore, each lesson of this Sales and Operations Planning Manager Skills course is intuitive and easy to understand. Why would you choose the Sales and Operations Planning Manager Skills course from Compliance Central: Lifetime access to Sales and Operations Planning Manager Skills course materials Full tutor support is available from Monday to Friday with the Sales and Operations Planning Manager Skills course Learn Sales and Operations Planning Manager Skills skills at your own pace from the comfort of your home Gain a complete understanding of Sales and Operations Planning Manager Skills course Accessible, informative Sales and Operations Planning Manager Skills learning modules designed by experts Get 24/7 help or advice from our email and live chat teams with the Sales and Operations Planning Manager Skills Study Sales and Operations Planning Manager Skills in your own time through your computer, tablet or mobile device. A 100% learning satisfaction guarantee with your Sales and Operations Planning Manager Skills Course Sales and Operations Planning Manager Skills Curriculum Breakdown of the Sales and Operations Planning Manager Skills Course Course Outline: Module 01: An Introduction to Strategic Planning Module 02: Development of a Strategic Plan Module 03: Strategic Planning for Marketing Module 04: Strategic and Marketing Analysis Module 05: Internal Analysis Module 06: External Analysis Module 07: Market Segmentation, Targeting and Positioning Module 08: Approaches to Customer Analysis Module 09: Approaches to Competitor Analysis CPD 10 CPD hours / points Accredited by CPD Quality Standards Who is this course for? The Sales and Operations Planning Manager Skills course helps aspiring professionals who want to obtain the knowledge and familiarise themselves with the skillsets to pursue a career in Sales and Operations Planning Manager Skills. It is also great for professionals who are already working in Sales and Operations Planning Manager Skills and want to get promoted at work. Requirements To enrol in this Sales and Operations Planning Manager Skills course, all you need is a basic understanding of the English Language and an internet connection. Career path Sales and Operations Planning Manager: £40,000 to £70,000 per year Demand Planner: £30,000 to £50,000 per year Supply Chain Analyst: £35,000 to £55,000 per year Inventory Manager: £35,000 to £60,000 per year Procurement Specialist: £35,000 to £55,000 per year Operations Manager: £45,000 to £80,000 per year Certificates CPD Accredited PDF Certificate Digital certificate - Included CPD Accredited PDF Certificate CPD Accredited Hard Copy Certificate Hard copy certificate - £10.79 CPD Accredited Hard Copy Certificate Delivery Charge: Inside the UK: Free Outside of the UK: £9.99 each

Begin your sales journey with the ‘Diploma in Sales’ course. From sales skills development to market understanding, presentation techniques, and future trends, this course provides a foundational blueprint for success in the sales arena.

Duration 2 Days 12 CPD hours This course is intended for This course is designed for all project managers and project team leaders. Overview At course completion, students will be able to identify, analyze, respond to, and control project risks. This course focuses on improving the project manager?s risk management expertise, from initial planning through project closure. Participants will apply all course principles to a work-related case study taken directly from their work environment. Risk Management Overview Risk Management Benefit and Uses Project Risk Management Project Management Life Cycle Initiating Process Group Initiating Process Group Overview Develop Project Charter Identify Stakeholders Project Selection Criteria ROI Analysis ROI Methods Risk Complexity Index Project Selection Planning Process Group Planning Process Group Overview Scope Risks Schedule Risks Resource Risks Plan Risk Management Identify Risks Delphi Technique Subject Matter Expert Input Brainstorming Fishbone Diagrams Process Analysis Five Whys Influence Diagrams Risk Breakdown Structure (RBS) Risk Register Perform Qualitative Risk Analysis Probability and Impact Assessment Probability and Impact Chart Perform Quantitative Risk Analysis Three point estimates PERT Triangular Distributions Outlier Considerations Geometric Mean Normal Distributions Methods Tornado Diagrams Expected Monetary Value (EMV) Monte Carlo Plan Risk Responses Risk Response Strategies Overall Project Risk Checklists Executing, Monitoring and Controlling Process Groups Executing Process Group Overview Monitoring and Controlling Process Group Overview Control Risks Project Monitoring Longer Projects Closing Process Group Closing Process Group Overview Close Project or Phase Risk Activities During Closing Process Group Post-Project Risk Assessment

Duration 3 Days 18 CPD hours This course is intended for This is an introductory-level course designed to teach experienced systems administrators how to install, maintain, monitor, troubleshoot, optimize, and secure Hadoop. Previous Hadoop experience is not required. Overview Working within in an engaging, hands-on learning environment, guided by our expert team, attendees will learn to: Understand the benefits of distributed computing Understand the Hadoop architecture (including HDFS and MapReduce) Define administrator participation in Big Data projects Plan, implement, and maintain Hadoop clusters Deploy and maintain additional Big Data tools (Pig, Hive, Flume, etc.) Plan, deploy and maintain HBase on a Hadoop cluster Monitor and maintain hundreds of servers Pinpoint performance bottlenecks and fix them Apache Hadoop is an open source framework for creating reliable and distributable compute clusters. Hadoop provides an excellent platform (with other related frameworks) to process large unstructured or semi-structured data sets from multiple sources to dissect, classify, learn from and make suggestions for business analytics, decision support, and other advanced forms of machine intelligence. This is an introductory-level, hands-on lab-intensive course geared for the administrator (new to Hadoop) who is charged with maintaining a Hadoop cluster and its related components. You will learn how to install, maintain, monitor, troubleshoot, optimize, and secure Hadoop. Introduction Hadoop history and concepts Ecosystem Distributions High level architecture Hadoop myths Hadoop challenges (hardware / software) Planning and installation Selecting software and Hadoop distributions Sizing the cluster and planning for growth Selecting hardware and network Rack topology Installation Multi-tenancy Directory structure and logs Benchmarking HDFS operations Concepts (horizontal scaling, replication, data locality, rack awareness) Nodes and daemons (NameNode, Secondary NameNode, HA Standby NameNode, DataNode) Health monitoring Command-line and browser-based administration Adding storage and replacing defective drives MapReduce operations Parallel computing before MapReduce: compare HPC versus Hadoop administration MapReduce cluster loads Nodes and Daemons (JobTracker, TaskTracker) MapReduce UI walk through MapReduce configuration Job config Job schedulers Administrator view of MapReduce best practices Optimizing MapReduce Fool proofing MR: what to tell your programmers YARN: architecture and use Advanced topics Hardware monitoring System software monitoring Hadoop cluster monitoring Adding and removing servers and upgrading Hadoop Backup, recovery, and business continuity planning Cluster configuration tweaks Hardware maintenance schedule Oozie scheduling for administrators Securing your cluster with Kerberos The future of Hadoop

Duration 2 Days 12 CPD hours This course is intended for Anyone who works with IBM SPSS Statistics and wants to learn advanced statistical procedures to be able to better answer research questions. Overview Introduction to advanced statistical analysis Group variables: Factor Analysis and Principal Components Analysis Group similar cases: Cluster Analysis Predict categorical targets with Nearest Neighbor Analysis Predict categorical targets with Discriminant Analysis Predict categorical targets with Logistic Regression Predict categorical targets with Decision Trees Introduction to Survival Analysis Introduction to Generalized Linear Models Introduction to Linear Mixed Models This course provides an application-oriented introduction to advanced statistical methods available in IBM SPSS Statistics. Students will review a variety of advanced statistical techniques and discuss situations in which each technique would be used, the assumptions made by each method, how to set up the analysis, and how to interpret the results. This includes a broad range of techniques for predicting variables, as well as methods to cluster variables and cases. Introduction to advanced statistical analysis Taxonomy of models Overview of supervised models Overview of models to create natural groupings Group variables: Factor Analysis and Principal Components Analysis Factor Analysis basics Principal Components basics Assumptions of Factor Analysis Key issues in Factor Analysis Improve the interpretability Use Factor and component scores Group similar cases: Cluster Analysis Cluster Analysis basics Key issues in Cluster Analysis K-Means Cluster Analysis Assumptions of K-Means Cluster Analysis TwoStep Cluster Analysis Assumptions of TwoStep Cluster Analysis Predict categorical targets with Nearest Neighbor Analysis Nearest Neighbor Analysis basics Key issues in Nearest Neighbor Analysis Assess model fit Predict categorical targets with Discriminant Analysis Discriminant Analysis basics The Discriminant Analysis model Core concepts of Discriminant Analysis Classification of cases Assumptions of Discriminant Analysis Validate the solution Predict categorical targets with Logistic Regression Binary Logistic Regression basics The Binary Logistic Regression model Multinomial Logistic Regression basics Assumptions of Logistic Regression procedures Testing hypotheses Predict categorical targets with Decision Trees Decision Trees basics Validate the solution Explore CHAID Explore CRT Comparing Decision Trees methods Introduction to Survival Analysis Survival Analysis basics Kaplan-Meier Analysis Assumptions of Kaplan-Meier Analysis Cox Regression Assumptions of Cox Regression Introduction to Generalized Linear Models Generalized Linear Models basics Available distributions Available link functions Introduction to Linear Mixed Models Linear Mixed Models basics Hierachical Linear Models Modeling strategy Assumptions of Linear Mixed Models Additional course details: Nexus Humans 0G09A IBM Advanced Statistical Analysis Using IBM SPSS Statistics (v25) 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 0G09A IBM Advanced Statistical Analysis Using IBM SPSS Statistics (v25) 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 3 Days 18 CPD hours This course is intended for This course is geared for attendees with solid Python skills who wish to learn and use basic machine learning algorithms and concepts Overview This 'skills-centric' course is about 50% hands-on lab and 50% lecture, with extensive practical exercises designed to reinforce fundamental skills, concepts and best practices taught throughout the course. Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below Getting Started & Optional Python Quick Refresher Statistics and Probability Refresher and Python Practice Probability Density Function; Probability Mass Function; Naive Bayes Predictive Models Machine Learning with Python Recommender Systems KNN and PCA Reinforcement Learning Dealing with Real-World Data Experimental Design / ML in the Real World Time Permitting: Deep Learning and Neural Networks Machine Learning Essentials with Python is a foundation-level, three-day hands-on course that teaches students core skills and concepts in modern machine learning practices. This course is geared for attendees experienced with Python, but new to machine learning, who need introductory level coverage of these topics, rather than a deep dive of the math and statistics behind Machine Learning. Students will learn basic algorithms from scratch. For each machine learning concept, students will first learn about and discuss the foundations, its applicability and limitations, and then explore the implementation and use, reviewing and working with specific use casesWorking in a hands-on learning environment, led by our Machine Learning expert instructor, students will learn about and explore:Popular machine learning algorithms, their applicability and limitationsPractical application of these methods in a machine learning environmentPractical use cases and limitations of algorithms Getting Started Installation: Getting Started and Overview LINUX jump start: Installing and Using Anaconda & Course Materials (or reference the default container) Python Refresher Introducing the Pandas, NumPy and Scikit-Learn Library Statistics and Probability Refresher and Python Practice Types of Data Mean, Median, Mode Using mean, median, and mode in Python Variation and Standard Deviation Probability Density Function; Probability Mass Function; Naive Bayes Common Data Distributions Percentiles and Moments A Crash Course in matplotlib Advanced Visualization with Seaborn Covariance and Correlation Conditional Probability Naive Bayes: Concepts Bayes? Theorem Naive Bayes Spam Classifier with Naive Bayes Predictive Models Linear Regression Polynomial Regression Multiple Regression, and Predicting Car Prices Logistic Regression Logistic Regression Machine Learning with Python Supervised vs. Unsupervised Learning, and Train/Test Using Train/Test to Prevent Overfitting Understanding a Confusion Matrix Measuring Classifiers (Precision, Recall, F1, AUC, ROC) K-Means Clustering K-Means: Clustering People Based on Age and Income Measuring Entropy LINUX: Installing GraphViz Decision Trees: Concepts Decision Trees: Predicting Hiring Decisions Ensemble Learning Support Vector Machines (SVM) Overview Using SVM to Cluster People using scikit-learn Recommender Systems User-Based Collaborative Filtering Item-Based Collaborative Filtering Finding Similar Movie Better Accuracy for Similar Movies Recommending movies to People Improving your recommendations KNN and PCA K-Nearest-Neighbors: Concepts Using KNN to Predict a Rating for a Movie Dimensionality Reduction; Principal Component Analysis (PCA) PCA with the Iris Data Set Reinforcement Learning Reinforcement Learning with Q-Learning and Gym Dealing with Real-World Data Bias / Variance Tradeoff K-Fold Cross-Validation Data Cleaning and Normalization Cleaning Web Log Data Normalizing Numerical Data Detecting Outliers Feature Engineering and the Curse of Dimensionality Imputation Techniques for Missing Data Handling Unbalanced Data: Oversampling, Undersampling, and SMOTE Binning, Transforming, Encoding, Scaling, and Shuffling Experimental Design / ML in the Real World Deploying Models to Real-Time Systems A/B Testing Concepts T-Tests and P-Values Hands-on With T-Tests Determining How Long to Run an Experiment A/B Test Gotchas Capstone Project Group Project & Presentation or Review Deep Learning and Neural Networks Deep Learning Prerequisites The History of Artificial Neural Networks Deep Learning in the TensorFlow Playground Deep Learning Details Introducing TensorFlow Using TensorFlow Introducing Keras Using Keras to Predict Political Affiliations Convolutional Neural Networks (CNN?s) Using CNN?s for Handwriting Recognition Recurrent Neural Networks (RNN?s) Using an RNN for Sentiment Analysis Transfer Learning Tuning Neural Networks: Learning Rate and Batch Size Hyperparameters Deep Learning Regularization with Dropout and Early Stopping The Ethics of Deep Learning Learning More about Deep Learning Additional course details: Nexus Humans Machine Learning Essentials with Python (TTML5506-P) 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 Machine Learning Essentials with Python (TTML5506-P) 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 This is an introductory-level systems administration course geared for Systems Administrators and users who wish to learn how to how to install, configure and maintain an Enterprise Linux system in a networked environment. Overview This course is about 50% lab to lecture ratio, combining expert instructor-led discussions with practical hands-on skills that emphasize current techniques, best practices and standards. Working in this hands-on lab environment, guided by our expert practitioner, attendees will explore Installing the Linux operating system and configuring peripherals Performing and modifying startup and shutdown processes Configuring and maintaining basic networking services Creating and maintaining system users and groups Understanding and administering file permissions on directories and regular files Planning and creating disk partitions and file systems Performing maintenance on file systems Identifying and managing Linux processes Automating tasks with cron Performing backups and restoration of files Working with system log files Troubleshooting system problems Analyzing and taking measures to increase system performance Configuring file sharing with NFS Configuring Samba for file sharing with the Windows clients Setting up a basic Web server Understanding the components for setting up a LAMP server Implementing basic security measures Linux System Administration is a comprehensive hands-on course that teaches students how to install, configure and maintain an Enterprise Linux system in a networked environment. This lab-intensive class explores core administrative tasks such as: creating and managing users, creating and maintaining file systems, determining and implementing security measures and performing software installation and package management. Linux networking topics include installing and supporting SSH, NFS, Samba and the Apache Web server. Students will explore common security issues, as well as several tools, such as the PAM modules that help secure the operating system and network environment. Upon successful completion of this course, students will be prepared to maintain Linux systems in a networked business environment. Although the course includes installing and configuring a CentOS 7 / RHEL 7 Linux system, much of the course content also applies to Oracle, Ubuntu, Scientific and other current versions of mainstream Linux distributions. Labs include user and group maintenance, system backups and restoration, software management, administration tasks automation, file system creation and maintenance, managing remote access, working with cron, and configuring basic file sharing and Web services, as well as working with system logging utilities such as rsyslog and much more. System Administration Overview UNIX, Linux and Open Source Duties of the System Administrator Superusers and the Root Login Sharing Superuser Privileges with Others (su and sudo Commands) TCP/IP Networking Fundamentals Online Help Installation and Configuration Planning: Hardware and Software Considerations Site Planning Installation Methods and Types Installation Classes Partitions Logical Volume Manager - LVM File System Overview Swap Partition Considerations Other Partition Considerations The Linux Boot Loader: grub Software Package Selection Adding and Configuring Peripherals Printers Graphics Controllers Basic Networking Configuration Booting to Recovery Mode Booting and Shutting Down Linux Boot Sequence The systemd Daemon The systemctl Command Targets vs. Run Levels Modifying a Target Service Unit Scripts Changing System States Booting into Rescue Mode Shutdown Commands Managing Software and Devices Identifying Software Packages Using rpm to Manage Software Using yum to Manage Software Installing and Removing Software Identifying Devices Displaying Device and System Information (PCI, USB) Plug and Play Devices Device Configuration Tools Managing Users and Groups Setting Policies User File Management The /etc/passwd file The /etc/shadow file The /etc/group file The /etc/gshadow file Adding Users Modifying User Accounts Deleting User Accounts Working with Groups Setting User Environments Login Configuration Files The Linux File System Filesystem Types Conventional Directory Structure Mounting a File System The /etc/fstab File Special Files (Device Files) Inodes Hard File Links Soft File Links Creating New File Systems with mkfs The lost+found Directory Repairing File Systems with fsck The Journaling Attribute File and Disk Management Tools Linux File Security File Permissions Directory Permissions Octal Representation Changing Permissions Setting Default Permissions Access Control Lists (ACLs) The getfacl and setfacl commands SUID Bit SGID Bit The Sticky Bit Controlling Processes Characteristics of Processes Parent-Child Relationship Examining Running Processes Background Processes Controlling Processes Signaling Processes Killing Processes Automating Processes cron and crontab at and batch System Processes (Daemons) Working with the Linux Kernel Linux Kernel Components Types of Kernels Kernel Configuration Options Recompiling the Kernel Shell Scripting Overview Shell Script Fundamentals Bash Shell Syntax Overview Shell Script Examples System Backups Backup Concepts and Strategies User Backups with the tar Command System Backup Options The xfsdump and xfsrestore Commands Troubleshooting the System Common Problems and Symptoms Troubleshooting Steps Repairing General Boot Problems Repairing the GRUB 2 Boot Loader Hard Drive Problems Restoring Shared Libraries System Logs and rsyslogd Basic Networking Networking Services Overview NetworkManager Introduction Network Configuration Files Locations and Formats Enabling and Restarting Network Services with systemtcl Configuring Basic Networking Manually Configuring Basic Networking with NetworkManager LAMP Server Basics LAMP Overview Configuring the Apache Web Server Common Directives Apache Virtual Hosting Configuring an Open Source Database MySQL MariaDB PHP Basics Perl CGI Scripting Introduction to System Security Security Overview Maintaining System Security Server Access Physical Security Network Security Security Tools Port Probing with nmap Intrusion Detection and Prevention PAM Security Modules Scanning the System Maintaining File Integrity Using Firewalls Introduction to firewalld The Samba File Sharing Facility Configure Samba for Linux to Linux/UNIX File Sharing Configure Samba for Linux to Windows File Sharing Use the smbclient Utility to Transfer Files Mount/Connect Samba Shares to Linux and Windows Clients Networked File Systems (NFS) Using NFS to Access Remote File Systems Configuring the NFS Server Configuring the NFS Client Exporting File Systems from the NFS Server to the NFS Client

Learn how to use this innovative tool to analyse and validate your schedule, to add and model uncertainty and risk and to work with updated plans to compare project progress. Course overview Duration: 1 day (6.5 hours) This course looks at the powerful features of Nodes and Links. It looks at analysing and validating your schedule, adding uncertainty and risk and working with updated plans to compare project progress. Hands on practice will be gained throughout the course to ensure you can confidentially put your new skills into practice back in the workplace. This course is designed for new users of Nodes and links, no previous experience is required. You should however be familiar with risk management processes and terminology. Objectives  By the end of the course you will be able to: Import and validate plans Analyse and review plans Add and model uncertainty Add and model risk Load updated schedules Re run analysis on updated schedules Content Validating your plan Importing a baseline plan Running a health check Analysing the results Reviewing the plan Analysing critical paths Reviewing activities Reviewing resources Adding Uncertainty Setting uncertainty templates Distributions Adding uncertainty Using Inherit Using AI Reviewing activity distributions Modelling Uncertainty Running the Analysis Reviewing the results Reviewing activity results Risk Drivers Filtering for activities Setting up the Risk Register Setting Risk Templates Adding Risks to the Risk Register Independent vs Dependant Events Setting Probability and Impact Modelling Uncertainty and Risk Mapping risks to activities Running the Analysis Reviewing the results Updated Plans Importing a new plan version Comparing plans Tracking progress Trend analysis Analysing Updated Plans Using updated plans Synchronising uncertainly and risk Rerunning analysis

This course is very much a discussion, so be prepared to present and critically analyse your own and class mates work. You will also need to bring a few examples of work you have done in the past. Learning and applying best practice visualisation principles will improve effective discussions amongst decision makers throughout your organisation. As a result more end-users of your dashboards will be able to make better decisions, more quickly. This 2 Day training course is aimed at analysts with good working knowledge of BI tools (we use Tableau to present, but attendees can use their own software such as Power BI or Qlik Sense). It is a great preparation for taking advanced certifications, such as Tableau Certified Professional. Contact us to discuss the Visual Analytics Best Practice course Email us if you are interested in an on-site course, or would be interested in different dates and locations This Tableau Desktop training intermediate course is designed for the professional who has a solid foundation with Tableau and is looking to take it to the next level. Attendees should have a good understanding of the fundamental concepts of building Tableau worksheets and dashboards typically achieved from having attended our Tableau Desktop Foundation Course. At the end of this course you will be able to communicate insights more effectively, enabling your organisation to make better decisions, quickly. The Tableau Desktop Analyst training course is aimed at people who are used to working with MS Excel or other Business Intelligence tools and who have preferably been using Tableau already for basic reporting. The course includes the following topics: WHAT IS VISUAL ANALYSIS? Visual Analytics Visual Analytics Process Advantages of Visual Analysis Exercise: Interpreting Visualisations HOW DO WE PROCESS VISUAL INFORMATION? Memory and Processing Types Exercise: Identifying Types of Processing Cognitive Load Exercise: Analysing Cognitive Load Focus and Guide the Viewer Remove Visual Distractions Organise Information into Chunks Design for Proximity Exercise: Reducing Cognitive Load SENSORY MEMORY Pre-attentive Attributes Quantitatively-Perceived Attributes Categorically-Perceived Attributes Exercise: Analysing Pre-attentive Attributes Form & Attributes Exercise: Using Form Effectively Colour & Attributes Exercise: Using Colour Effectively Position & Attributes Exercise: Using Position Effectively ENSURING VISUAL INTEGRITY Informing without Misleading Gestalt Principles Visual Area Axis & Scale Colour Detail Exercise: Informing without Misleading CHOOSING THE RIGHT VISUALISATION Comparing and Ranking Categories Comparing Measures Comparing Parts to Whole Viewing Data Over Time Charts Types for Mapping Viewing Correlation Viewing Distributions Viewing Specific Values DASHBOARDS AND STORIES Exercise: Picking the Chart Type Exercise: Brainstorming Visual Best Practice Development Process for Dashboards and Stories Plan the Visualisation Create the Visualisation Test the Visualisation Exercise: Designing Dashboards and Stories This training course includes over 20 hands-on exercises to help participants “learn by doing” and to assist group discussions around real-life use cases. Each attendee receives an extensive training manual which covers the theory, practical applications and use cases, exercises and solutions together with a USB with all the materials required for the training. The course starts at 09:30 on the first day and ends at 17:00. On the second day the course starts at 09:00 and ends at 17:00. Students must bring their own laptop with an active version of Tableau Desktop 10.5 (or later) pre-installed. What People Are Saying About This Course "Steve was willing to address questions arising from his content in a full and understandable way"Lisa L. "Really enjoyed the course and feel the subject and the way it was taught was very close to my needs"James G. "The course tutor Steve was incredibly helpful and taught the information very well while making the two days very enjoyable."Bradd P. "The host and his courses will give you the tools and confidence that you need to be comfortable with Tableau."Jack S. "Steve was fantastic with his knowledge and knowhow about the product. Where possible he made sure you could put demonstrations in to working practice, to give the audience a clear understanding."Tim H. "This was a very interesting and helpful course, which will definitely help me produce smarter, cleaner visualisations that will deliver more data-driven insights within our business."Richard A. "Steve is very open to questions and will go out of his way to answer any query. Thank you"Wasif N. "Steve was willing to address questions arising from his content in a full and understandable way"Lisa L. "Really enjoyed the course and feel the subject and the way it was taught was very close to my needs"James G.

SAP Training London: Overview What is SAP? SAP is one of the largest ERP(Enterprise Resource Planning) software in the world. It provides end to end solution for Financials, Manufacturing, Logistics, Distributions etc. SAP applications, built around their latest R/3 system, provide the capability to manage financial, asset, and cost accounting, production operations and materials, personnel, plants, and archived documents. The R/3 system runs on a number of platforms including Windows and MAC and uses the client/server model. SAP Business Cases/ Live Project Our SAP training courses are designed in a way which gives the students maximum exposure of real-life scenario through no of business cases and guidance on implementation by professional SAP Consultants. Free SAP Access Free SAP Sandbox Access provided for 6 months so that you can practice even after your SAP training course. Certificate of Attendance- You receive a certificate once you finish the course from Osborne Training,  You can also do an external exam to receive certification from SAP (Optional), for details on exam booking you may visit the SAP website. SAP Training Modules Available Financial & Controlling (FICO) High-Performance Analytic Appliance (HANA) Supply Relationship Management (SRM) Customer Relationship Management (CRM)