1637 Database courses delivered Online

Duration 1 Days 6 CPD hours This course is intended for The student should be familiar with Excel and the concepts of datasets and basic reporting. Overview Understand and utilize Power BI Online. Understand and examine the differences between Power BI Desktop and Power BI Online. Understand and explore the Power BI Online interface. Understand and perform importing Excel worksheets, CSV, TXT, PBIX. Understand connecting to databases. Understand other forms of data. Understand data refresh. Understand and perform creating a new Power BI report. Understand and perform adding filters to a report. Understand and perform adding KPI's to a report. Understand and perform creating a Power BI dashboard. Understand and perform pinning tiles. Understand sharing reports and dashboards. Understand Power BI Mobile. This Logical Operations courseware is no longer available on the LO Store, but can be purchased by phone orderThis one-day course familiarizes the student with Power BI Online, which allows the student to intuitively discover and automate the import, transformation, and combination of data across a variety of data sources. COURSE OVERVIEW Introduction Course Materials Facilities Prerequisites What We'll Be Discussing INTRODUCTION TO POWER BI ONLINE What is Power BI Online Examining the Differences Between Power BI Desktop and Power BI Online Exploring the Power BI Online Interface IMPORTING DATA Utilizing Excel (.xlsx or .xlsm), Comma Separated Value (.csv), and Power BI Desktop (.pbix) Files Connecting to Databases Examining Other Forms of Data Understanding Data Refresh CREATING REPORTS Creating a New Power BI Report Adding Filters to a Power BI Report Adding KPI?s to a Report CONFIGURING DASHBOARDS Creating a Power BI Dashboard Pinning Tiles SHARING REPORTS AND DASHBOARDS Sharing Reports and Dashboards Exploring Power BI Mobile

Duration 3 Days 18 CPD hours This course is intended for This course is intended for: Solutions Architects and Engineers who perform cloud migrations IT Project Managers who are involved in projects related to migrating existing workloads to the AWS Cloud Overview This course is designed to teach you how to: Explain the various cloud migration strategies Assess cloud migration readiness Discover your portfolio and plan for migration Plan and design your application migration strategy Perform and validate application migration to the cloud Optimize your applications and operations after migrating to the cloud Migrating to AWS focuses on planning and migrating existing workloads to the AWS Cloud. The course covers various cloud migration strategies with a detailed discussion on each phase of the migration process, including portfolio discovery, application migration planning and design, migration execution, and post-migration validation and application optimization. This course is designed for Solutions Architects and Engineers who perform cloud migrations, have an understanding of core AWS services and design patterns covered in Architecting on AWS. This course is also available to IT project managers involved in the planning of those migrations who have completed AWS Technical Essentials Module 1: Migrating to AWS ? Overview Migration process 'Mental Model' Cloud Migration Strategies Comparing Cloud Migration Strategies Cloud Center of Excellence (CoE) Cloud Migration Readiness Assessment AWS Cloud Migration Process Group activity: Creating a high-level migration plan Module 2: Discovery and analysis Migration Process Roadmap AWS Migration Methodology AWS Application Discovery Service Portfolio Analysis Hands-on lab 1: Performing discovery Module 3: Migration planning and design (part I) AWS Migration Hub Pricing and Availability Process Group activity: Creating a detailed migration plan Module 3: Migration planning and design (continued) Application migration ordering Application prioritization criteria Defining success criteria Migration methodology Designing for migration Module 4: Migration, integration, and validation Migration considerations Data migration AWS Snow Services AWS Data Migration Service (DMS) Server migration Demonstration: Server migration service Hands-on lab 2 : Migrating databases to AWS EC2 Hands-on lab 3 : Migrating databases to Amazon Aurora Module 5: Operations and optimization On premises vs. cloud IT operations Optimizing in the AWS Cloud Case study: Optimizing an application

Duration 3 Days 18 CPD hours This course is intended for Technology Consultant System Administrator Application Consultant Overview This course will prepare you to: Set up SLT configurations, replicate data into different targets (such as SAP HANA, SAP BW or Central Finance) and monitor the data replication. In this course, you will learn how to use the trigger based real time data replication technology of the SAP LT Replication Server (SLT). SAP Landscape Replication Server Overview Explaining Positioning and Key Concepts Identifying Use Cases Understanding SLT as Part of the DMIS Product Family SAP Landscape Transformation Server Introduction Understanding the Architectural Concept Explaining the Landscape Strategy and Sizing Aspects Outlining the SLT Installation Procedure Replication to SAP HANA Setting Up Data Replication Executing Data Replication Understanding Advanced Replication Settings Outlining Operations and Monitoring Describing SLT Handling in Special Cases Replication to SAP BW Providing a Scenario Overview Explaining Open Data Provisioning (ODP) Describing Table-Based Replication Comparing Table-Based Replication and ODP Replication to SAP Data Services Explaining SAP Data Services and Architecture Describing the Setup of Replication to SAP Data Services Replication to ABAP Systems Describing the Architecture for Replication into an ABAP System Outlining the Setup for Replication into an ABAP System Replication to Non-SAP Databases Describing the Architecture for Replication to Non-SAP Databases Understanding the Setup of ABAP to Non-ABAP Replication Replication to Central Finance Describing the Architecture for Replication to Central Finance Understanding the Setup and Configuration of Replication to Central Finance SAP Landscape Transformation Replication Server Summary Providing a Summary of the SAP Landscape Transformation Replication Server SAP Landscape Transformation Replication Server 3.0 and Recent Updates Outlining SAP Landscape Transformation Replication Server 3 Updates Additional course details: Nexus Humans SAP Real-time Replication with SAP LT Replication Server (SLT100) 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 SAP Real-time Replication with SAP LT Replication Server (SLT100) 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 1 Days 6 CPD hours This course is intended for This course is intended for the following participants:Cloud professionals interested in taking the Data Engineer certification exam.Data engineering professionals interested in taking the Data Engineer certification exam. Overview This course teaches participants the following skills: Position the Professional Data Engineer Certification Provide information, tips, and advice on taking the exam Review the sample case studies Review each section of the exam covering highest-level concepts sufficient to build confidence in what is known by the candidate and indicate skill gaps/areas of study if not known by the candidate Connect candidates to appropriate target learning This course will help prospective candidates plan their preparation for the Professional Data Engineer exam. The session will cover the structure and format of the examination, as well as its relationship to other Google Cloud certifications. Through lectures, quizzes, and discussions, candidates will familiarize themselves with the domain covered by the examination, to help them devise a preparation strategy. Rehearse useful skills including exam question reasoning and case comprehension. Tips and review of topics from the Data Engineering curriculum. Understanding the Professional Data Engineer Certification Position the Professional Data Engineer certification among the offerings Distinguish between Associate and Professional Provide guidance between Professional Data Engineer and Associate Cloud Engineer Describe how the exam is administered and the exam rules Provide general advice about taking the exam Sample Case Studies for the Professional Data Engineer Exam Flowlogistic MJTelco Designing and Building (Review and preparation tips) Designing data processing systems Designing flexible data representations Designing data pipelines Designing data processing infrastructure Build and maintain data structures and databases Building and maintaining flexible data representations Building and maintaining pipelines Building and maintaining processing infrastructure Analyzing and Modeling (Review and preparation tips) Analyze data and enable machine learning Analyzing data Machine learning Machine learning model deployment Model business processes for analysis and optimization Mapping business requirements to data representations Optimizing data representations, data infrastructure performance and cost Reliability, Policy, and Security (Review and preparation tips) Design for reliability Performing quality control Assessing, troubleshooting, and improving data representation and data processing infrastructure Recovering data Visualize data and advocate policy Building (or selecting) data visualization and reporting tools Advocating policies and publishing data and reports Design for security and compliance Designing secure data infrastructure and processes Designing for legal compliance Resources and next steps Resources for learning more about designing data processing systems, data structures, and databases Resources for learning more about data analysis, machine learning, business process analysis, and optimization Resources for learning more about data visualization and policy Resources for learning more about reliability design Resources for learning more about business process analysis and optimization Resources for learning more about reliability, policies, security, and compliance Additional course details: Nexus Humans Preparing for the Professional Data Engineer Examination 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 Preparing for the Professional Data Engineer Examination 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 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.

Duration 3 Days 18 CPD hours This course is intended for Before taking this course delegates should already be familiar with basic analytics techniques, comfortable with basic data manipulation tools such as spreadsheets and databases and already familiar with at least one programming language Overview This course teaches delegates who are already familiar with analytics techniques and at least one programming language how to effectively use the programming language for three tasks: data manipulation and preparation, statistical analysis and advanced analytics (including predictive modelling and segmentation). Mastery of these techniques will allow delegates to immediately add value in their work place by extracting valuable insight from company data to allow better, data-driven decisions. Outcomes: After completing the course, delegates will be capable of writing production-ready R code to perform advanced analytics tasks enabling their organisations make better, data-driven decisions. Becoming a world class data analytics practitioner requires mastery of the most sophisticated data analytics tools. These programming languages are some of the most powerful and flexible tools in the data analytics toolkit. Topic 1 Intro to our chosen language Topic 2 Basic programming conventions Topic 3 Data structures Topic 4 Accessing data Topic 5 Descriptive statistics Topic 6 Data visualisation Topic 7 Statistical analysis Topic 8 Advanced data manipulation Topic 9 Advanced analytics ? predictive modelling Topic 10 Advanced analytics ? segmentation

Duration 3 Days 18 CPD hours This course is intended for The course will likely be attended by SQL Server report creators who are interested in alternative methods of presenting data. Overview After completing this course, students will be able to: ? Perform Power BI desktop data transformation. ? Describe Power BI desktop modelling. ? Create a Power BI desktop visualization. ? Implement the Power BI service. ? Describe how to connect to Excel data. ? Describe how to collaborate with Power BI data. ? Connect directly to data stores. ? Describe the Power BI developer API. ? Describe the Power BI mobile app. The main purpose of the course is to give students a good understanding of data analysis with Power BI. The course includes creating visualizations, the Power BI Service, and the Power BI Mobile App. Introduction to Self-Service BI Solutions Introduction to business intelligence Introduction to data analysis Introduction to data visualization Overview of self-service BI Considerations for self-service BI Microsoft tools for self-service BI Lab : Exploring an Enterprise BI solution Introducing Power BI Power BI The Power BI service Lab : Creating a Power BI dashboard Power BI Using Excel as a data source for Power BI The Power BI data model Using databases as a data source for Power BI The Power BI service Lab : Importing data into Power BI Shaping and Combining Data Power BI desktop queries Shaping data Combining data Lab : Shaping and combining data Modelling data Relationships DAX queries Calculations and measures Lab : Modelling Data Interactive Data Visualizations Creating Power BI reports Managing a Power BI solution Lab : Creating a Power BI report Direct Connectivity Cloud data Connecting to analysis services Lab : Direct Connectivity Developer API The developer API Custom visuals Lab : Using the developer API Power BI mobile app The Power BI mobile app Using the Power BI mobile app Power BI embedded

Duration 3 Days 18 CPD hours This course is intended for This course is intended for: Solutions architects Developers Cost-optimization leads System administrators Overview In this course, you will learn to: Explain the cost of core AWS services Determine and predict costs associated with current and future cloud workloads Use strategies and best practices to reduce AWS costs Use AWS tools to manage, monitor, alert, and optimize your AWS spend Apply strategies to monitor service costs and usage Implement governance standards, including resource tagging, account structure, provisioning,permissions, and access This course is for individuals who seek an understanding of how to manage, optimize, and predict costs as you run workloads on AWS. You learn how to implement architectural best practices, explore cost optimization strategies, and design patterns to help you architect cost-efficient solutions on AWS. Module 0: Couse Overview Course introduction Module 1: Introduction to Cloud Financial Management Introduction to Cloud Financial Management Four pillars of Cloud Financial Management Module 2: Resource Tagging Tagging resources Hands-On Lab: Cost optimization: Control Resource Consumption Using Tagging and AWS Config Module 3: Pricing and Cost Fundamentals of pricing AWS Free Tier Volume discounts Savings plans and Reserved Instances Demonstration: AWS Pricing Calculator Module 4: AWS Billing, Reporting, and Monitoring Understanding AWS invoices Reporting and planning AWS Cost Explorer AWS Budgets Demonstration: AWS Billing Console Demonstration: AWS Cost Explorer Demonstration: Trusted Advisor Hands-On Lab: Cost optimization: Deploy Ephemeral Environments Using Amazon EC2 Auto Scaling Module 5: Architecting for Cost: Compute Evolution of compute efficiency Amazon EC2 right-sizing Purchasing options Architect for Amazon EC2 Spot Instance Impact of software licensing Demonstration: Compute Optimizer Demonstration: Spot Instance Advisor Hands-On Lab: Cost optimization: Right Size Amazon EC2 Instances Using Amazon CloudWatch Metrics Module 6: Architecting for Cost: Networking Data transfer costs Understand data costs for different services How to triage network costs Hands-On Lab: Cost optimization: Reduce Data Transfer Costs Using Amazon CloudFront and Endpoints Module 7: Architecting for Cost: Storage Amazon EBS cost, pricing, and best practices Amazon S3 cost, pricing, and best practices Amazon EFS cost, pricing, and best practices Hands-On Lab: Cost optimization: Reduce Storage Costs Using Amazon S3 Lifecycle Management Module 8: Architecting for Cost: Databases Amazon RDS cost, pricing, and best practices Amazon Aurora cost, pricing, and best practices Amazon DynamoDB cost, pricing, and best practices Amazon ElastiCache cost, pricing, and best practices Amazon Redshift cost, pricing, and best practices Module 9: Cost Governance Setting up AWS Organizations AWS Systems Manager Hands-On Lab: Cost optimization: Reduce Compute Costs Using AWS Instance Scheduler Module 10: Course Summary Course review

Duration 5 Days 30 CPD hours This course is intended for The primary audience for this course is as follows: Cisco Unified Communications system channel partners and resellers. System and technical support engineers. Customers who are deploying and maintaining Cisco Unified CCE solution products. Overview Upon completing this course, the learner will be able to meet these overall objectives: Understand CCE solutions, architecture, solution options, deployment models, integrated features and call flow options. Understand underlying Cisco Unified CCE processes, messaging and fault tolerance schemes. Install, upgrade and make basic configurations in Cisco Unified Communications Manager. Install, create databases, integrate, and upgrade all ICM components to include the ICM Router, Logger, Administration & Data Server, Peripheral Gateways, CTI Gateway and Cisco Finesse. Install, integrate, configure, and upgrade Cisco Unified CVP components to include the CVP Call Server, Voice XML Server, Media Server, Reporting Server and Cisco VVB. Install, upgrade, and make configurations for Cisco Unified Intelligence Center and Cisco Outbound Option Agent- and IVR-based campaigns. This course will provide the student with the underlying knowledge to understand deployment design solutions, requirements for deployment, and how to install and configure all major Cisco Unified CCE components. As a part of deployment activities, the student will understand how to install and integrate Intelligent Contact Manager (ICM) with Active Directory, how to install and integrate Cisco Unified CVP components using an IOS-based voice browser and Cisco Virtualized Voice Browser (Cisco VVB), how to install and integrate Cisco Finesse, how to install and integrate Cisco Unified Intelligence Center with Active Directory and associated Data Sources for reporting purposes, and how to install and configure Agent- and IVR-based Outbound Option dialing campaigns. And finally, the student will learn how to setup and use troubleshooting tools including RTMT, System CLI, Diagnostic Framework, and ICM command-line utilities to find status information and log files, and to track a call from the point of entry to the agent desktop. Cisco Unified Contact Center Enterprise Overview Lesson 1: Presenting Cisco Unified Contact Center Enterprise Lesson 2: Cisco Unified CCE Core Components Lesson 3: Cisco Unified CCE Options Lesson 4: Basic Call Flow Models Cisco Unified CCE Protocols, Processes and Services Lesson 1: Cisco Unified CM Lesson 2: Cisco Unified CCE/Intelligent Contact Manager Lesson 3: Cisco Unified CVP Installing Cisco Unified Communications Manager Lesson 1: Installation Prerequisites Lesson 2: Cisco Unified CM Installation Lesson 3: Post-installation Configurations Lesson 4: Creating Basic Infrastructure Lesson 5: Upgrading Cisco Unified CM Installing Intelligent Contact Manager Lesson 1: Installation Requirements Lesson 2: Pre-installation Tasks Lesson 3: Install the Main Installer Lesson 4: Install the Central Controller ?Lesson 5: Install the Administration and Data Server Lesson 6: Install the Peripheral Gateway Lesson 7: Install CTI Services Installing Cisco Unified CVP Lesson 1: Installation Prerequisites Lesson 2: Install the CVP Server Lesson 3: Configure Cisco Unified CVP Components Lesson 4: Upgrading Cisco Unified CVP Upgrade Path Installing and Configuring Cisco Unified CCE Options Lesson 1: Cisco Outbound Option Lesson 2: Cisco Unified Intelligence Center Supporting Cisco Unified CCE Lesson 1: Maintenance Activities Lesson 2: UCCE Troubleshooting Tools

Duration 3 Days 18 CPD hours This course is intended for Data Science for Marketing Analytics is designed for developers and marketing analysts looking to use new, more sophisticated tools in their marketing analytics efforts. It'll help if you have prior experience of coding in Python and knowledge of high school level mathematics. Some experience with databases, Excel, statistics, or Tableau is useful but not necessary. Overview By the end of this course, you will be able to build your own marketing reporting and interactive dashboard solutions. The course starts by teaching you how to use Python libraries, such as pandas and Matplotlib, to read data from Python, manipulate it, and create plots, using both categorical and continuous variables. Then, you'll learn how to segment a population into groups and use different clustering techniques to evaluate customer segmentation.As you make your way through the course, you'll explore ways to evaluate and select the best segmentation approach, and go on to create a linear regression model on customer value data to predict lifetime value. In the concluding sections, you'll gain an understanding of regression techniques and tools for evaluating regression models, and explore ways to predict customer choice using classification algorithms. Finally, you'll apply these techniques to create a churn model for modeling customer product choices. Data Preparation and Cleaning Data Models and Structured Data pandas Data Manipulation Data Exploration and Visualization Identifying the Right Attributes Generating Targeted Insights Visualizing Data Unsupervised Learning: Customer Segmentation Customer Segmentation Methods Similarity and Data Standardization k-means Clustering Choosing the Best Segmentation Approach Choosing the Number of Clusters Different Methods of Clustering Evaluating Clustering Predicting Customer Revenue Using Linear Regression Understanding Regression Feature Engineering for Regression Performing and Interpreting Linear Regression Other Regression Techniques and Tools for Evaluation Evaluating the Accuracy of a Regression Model Using Regularization for Feature Selection Tree-Based Regression Models Supervised Learning: Predicting Customer Churn Classification Problems Understanding Logistic Regression Creating a Data Science Pipeline Fine-Tuning Classification Algorithms Support Vector Machine Decision Trees Random Forest Preprocessing Data for Machine Learning Models Model Evaluation Performance Metrics Modeling Customer Choice Understanding Multiclass Classification Class Imbalanced Data Additional course details: Nexus Humans Data Science for Marketing Analytics 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 Data Science for Marketing Analytics 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.