43858 PE courses

Duration 2 Days 12 CPD hours This course is intended for This course is intended for software testers, architects, engineers, or other related roles, who wish to apply AI to software testing practices within their enterprise. While there are no specific pre-requisites for this course, it would be helpful is the attendee has familiarity with basic scripting (Python preferred) and be comfortable with working from the command line (for courses that add the optional hands-on labs). Attendees without basic scripting skills can follow along with the hands-on labs or demos. Overview This course introduces AI and related technologies from a practical applied software testing perspective. Through engaging lecture and demonstrations presented by our expert facilitator, students will explore: Exploring AI Introduction to Machine Learning Introduction to Deep Learning Introduction to Data Science Artificial Intelligence (AI) in Software Testing Implementing AI in Test Automation Innovative AI Test Automation Tools for the Future Implementing AI in Software Testing / AI in Test Automation is an introductory-level course for attendees new to AI, Machine Learning or Deep Learning who wish to automate software testing tasks leveraging AI. The course explores the essentials of AI, ML and DL and how the integrate into IT business operations and initiatives. Then the course moves to specifics about the skills, techniques and tools used to apply AI to common software testing requirements. Exploring AI AI-Initiatives The Priority: Excellence AI- Intelligence Types The Machine Learning Types The Quality Learning Initiative The Inception in Academics AI - Importance & Applications The Re-visit Learning Re-visited via AI Teaching in the world of AI Exploring AI for Self-Development AI In Academics Beyond Academics Introduction to Machine Learning What is Machine Learning? Why Machine Learning? Examples - Algorithms behind Machine Learning Introduction to Deep Learning What is Deep Learning? Why Deep Learning? Example - Deep Learning Vs Machine Learning Introduction to Data Science What is Data Science? Why Data Science? Examples - Use Cases of Data Science Artificial Intelligence (AI) in Software Testing What is AI in Software Testing? The Role of AI Testing Why do we Need AI in Software Testing? Pros and Cons of AI in Software Testing Applications of AI in Software Testing Is it time for Testers or QA Teams to worry about AI? Automated Testing with Artificial Intelligence Implementing AI in Test Automation Training the AI Bots Challenges with AI-powered Applications Examples - Real World use cases using Artificial Intelligence Demo - Facial Emotion Detection Using Artificial Intelligence Demo - Text Analysis API Using Artificial Intelligence Demo - EYE SPY Mobile App Using Artificial Intelligence Innovative AI Test Automation Tools for the Future Tools used for Implementing AI in Automation Testing What is NEXT? AI Test Automation Demo using Testim

Duration 3 Days 18 CPD hours This course is intended for The primary audience for this course is as follows: IT Staff and Managers Network and systems personnel and engineers Small to mid-sized organizations that require fundamental knowledge on networking terms/concepts and configuration guidance for Meraki equipment. This also includes organizations looking to implement remote sites, provide a guest wireless solution, and collect user analytics. Overview Following completion of this course, students will understand, Install, Configure, monitor, and Troubleshoot the following: Navigate and Configure the dashboard Add MX/MR/MS/MV devices to the Dashboard Understand and Configure Configuration Templates Understand and Configure Group Policies Manage/Configure/Integrate Users and Radius Policies Configure, Monitor, and Troubleshoot MX Firewalls Troubleshoot devices and Connectivity This 3-day Cisco course provide students with the skills to configure, optimize, and troubleshoot a Cisco Meraki solution. Students will learn how to install and optimize Meraki MX Firewalls. Students will also learn how to configure the Meraki Dashboard Students will troubleshoot and configure the Meraki environment and learn how to diagnose and resolve user and Network issues that may arise. Introduction to Meraki The Meraki Mission Cisco Meraki: Bringing the Cloud to Enterprise Networks Cloud-Managed Networking Architecture Benefits of a Cloud-Based Solution The Meraki Full Stack: New and Unique Value Proposition Meraki Deployment ? How it works Why Customers Choose Meraki Meraki MS Switches Overview Meraki MX Security Appliances Overview Meraki SD-WAN Overview Meraki MR Wireless Access Points Overview Cisco Meraki Systems Manager Overview Cisco Meraki MV Vision Security Cameras Overview Meraki API Overview Meraki Licensing Enterprise Support Cisco Meraki Documentation Cloud Management with the Meraki Dashboard The Meraki Dashboard Dashboard: Organizational Structure Out-of-band Cloud Management Loss of Connectivity to the Cisco Meraki Cloud Meraki Dashboard Logins Create Dashboard Accounts and Organization MSP Logins - Manage Multiple Organizations Modify an Organization View Organizations Health Meraki Dashboard Best Practices Dashboard Search Meraki Help Organizational Wide Settings Configure Monitor Create and Manage Configuration Templates Network-Wide Settings Configure Meraki MX Security Benefits of a Cloud Managed Security Solution Threat Management Solution Advanced Security Licenses Reliable, Cost Effective Connectivity with Meraki SD-WAN Site-to-Site VPN (Auto VPN) High Availability and Path Redundancy Application-Aware Intelligent Path Control Traffic Monitoring and Analytics Integrating Active Directory Cisco Meraki MX Models and Features Configuring the Local Status Page Adding Appliance to Network Device Configuration Configuring the Warm Spare Feature Device Tags & Notes Configuring Addressing & VLANs DHCP Server Configuration and Options Meraki Firewall Configuration Meraki Site-to-Site VPN One Arm VPN Concentrator Configuration Meraki Client VPN Meraki Active Directory Integration Meraki Access Control Meraki Splash Page Configuration Configuring Access Policies Teleworker VPN/L3 Roaming

Duration 1 Days 6 CPD hours This course is intended for This course is intended for those responsible for ITAD programs and other IT professionals involved in Asset Management, resource budgeting, finance, software licensing, contract management and strategic planning. Overview Students will learn the best practices in an IT Asset Management Program and align those processes with their organizations' business practices. They will be able to manage overall ITAM programs and demonstrate in-depth knowledge, operational knowledge and competence in asset disposal and process development. The IAITAM Certified IT Asset Disposition (CITAD) course prepares individuals to manage the IT asset disposal process within an organization. Best practices in IT Asset Disposition (ITAD) are broken down from policy management, data security to chain of custody transitioning. Attendees whose job responsibilities include ITAD will take away the knowledge of how to avoid risk of data loss and public exposure that surround a breakdown in ITAD process management. ITAD best practices, financial return, data security global implications and the importance of vendor management are just a few of the topics incorporated in the CITAD course. This course exposes the attendee to numerous concepts for ITAM that are relevant for both direct application and as a means of discussion for those persons who will implement, manage and direct ITAM initiatives for their organizations. This course includes the exam for CITAD certification. Course Outline Disposition Overview Disposition and ITAM Organizational Goals for Disposition ITAM Goals for Disposition Governance of Electronic Disposal Composition of E-Scrap Waste Management Laws Foundation for Disposal Management Policy Topics Relevant to Disposition Asset Standards Benefit Disposal The Role of Automation Data Security Governance Working with Vendors Selecting Vendors Due Diligence The Removal Process Software During Disposition Decision Factors for Retirement The Disposition Processes Financial Management & Measurement Additional course details: Nexus Humans Certified IT Asset Disposal (CITAD) 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 Certified IT Asset Disposal (CITAD) 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 data warehouse engineers, data platform engineers, and architects and operators who build and manage data analytics pipelines. Completed either AWS Technical Essentials or Architecting on AWS Completed Building Data Lakes on AWS Overview In this course, you will learn to: Compare the features and benefits of data warehouses, data lakes, and modern data architectures Design and implement a data warehouse analytics solution Identify and apply appropriate techniques, including compression, to optimize data storage Select and deploy appropriate options to ingest, transform, and store data Choose the appropriate instance and node types, clusters, auto scaling, and network topology for a particular business use case Understand how data storage and processing affect the analysis and visualization mechanisms needed to gain actionable business insights Secure data at rest and in transit Monitor analytics workloads to identify and remediate problems Apply cost management best practices In this course, you will build a data analytics solution using Amazon Redshift, a cloud data warehouse service. The course focuses on the data collection, ingestion, cataloging, storage, and processing components of the analytics pipeline. You will learn to integrate Amazon Redshift with a data lake to support both analytics and machine learning workloads. You will also learn to apply security, performance, and cost management best practices to the operation of Amazon Redshift. Module A: Overview of Data Analytics and the Data Pipeline Data analytics use cases Using the data pipeline for analytics Module 1: Using Amazon Redshift in the Data Analytics Pipeline Why Amazon Redshift for data warehousing? Overview of Amazon Redshift Module 2: Introduction to Amazon Redshift Amazon Redshift architecture Interactive Demo 1: Touring the Amazon Redshift console Amazon Redshift features Practice Lab 1: Load and query data in an Amazon Redshift cluster Module 3: Ingestion and Storage Ingestion Interactive Demo 2: Connecting your Amazon Redshift cluster using a Jupyter notebook with Data API Data distribution and storage Interactive Demo 3: Analyzing semi-structured data using the SUPER data type Querying data in Amazon Redshift Practice Lab 2: Data analytics using Amazon Redshift Spectrum Module 4: Processing and Optimizing Data Data transformation Advanced querying Practice Lab 3: Data transformation and querying in Amazon Redshift Resource management Interactive Demo 4: Applying mixed workload management on Amazon Redshift Automation and optimization Interactive demo 5: Amazon Redshift cluster resizing from the dc2.large to ra3.xlplus cluster Module 5: Security and Monitoring of Amazon Redshift Clusters Securing the Amazon Redshift cluster Monitoring and troubleshooting Amazon Redshift clusters Module 6: Designing Data Warehouse Analytics Solutions Data warehouse use case review Activity: Designing a data warehouse analytics workflow Module B: Developing Modern Data Architectures on AWS Modern data architectures

Duration 3 Days 18 CPD hours This course is intended for The primary audience for this course is as follows: System Engineers System Administrators Architects Channel Partners Overview Upon completing this course, the learner should be able to meet these overall objectives: Describe Cisco Webex Teams Comprehend Cisco Webex Meetings Understand Cisco Webex Messaging Recognize Cisco Webex Teams Calling Appreciate Cisco Webex Teams Care Explain Cisco Webex teams Management and Administration Describe Cisco Webex Teams Service Availability, Ordering, and Support Understand Cisco Webex Teams Board Cisco Webex Teams is an app-centric, cloud-based service that provides a complete collaboration suite for teams to create, meet, message, call, care, whiteboard, and share, regardless of whether they?re together or apart?in one continuous work stream before, during, and after meetings. Being able to understand how this works and what the features are is the key focus of this 2-day course. NterOne?s Webex Teams Collaboration Workshop should be taken by students who have a working understanding of Cisco Collaboration Products. This Cisco Webex Teams Workshop enables learners to describe Cisco Webex Teams, comprehend Cisco Webex Teams Meetings, and grasp Cisco Webex Teams Messaging, among much more. This workshop is intended to be taken by system engineers, system administrators, architects, and channel partners. Learn more below. Cisco Webex Teams Overview What is Cisco Webex Teams Webex Teams Delivery Webex Teams Security Webex Teams Collaboration Webex Teams API Meetings Messaging Calling Cisco Webex Teams Spaces Cisco Webex Teams Hybrid Services Cisco Webex Teams Benefits Cisco Webex Team Meetings Basic Meetings Instant Messages Schedule Meetings Cisco Webex Teams Advanced Meetings Overview Webex Integration Personal Room Cisco Webex Teams Desk and Room Devices and the Cisco Smart Board Cisco Webex Teams Conferencing Cisco Webex Teams Board and Room Device Setup and Use Three ways to enable pairing Cisco WebEx Teams Messaging Cisco Webex Teams Messaging Overview Key Benefits of Cisco Webex Teams Messaging Cisco Webex Teams Messaging Features Cisco Webex teams (feature in the app) Use Cases Cisco Webex Teams Board Advanced Interactive Whiteboard No Network, Bluetooth needed Microphones Voice-Tracking Technology Webex Teams Board Application Suite Camera Webex Teams APIs Any Board, anywhere Messaging Setup Cisco Webex Teams Calling Overview Traditional Calling Features in Cisco Webex Teams Features and Benefits of Cisco Webex Teams Calling Benefits of Mobility and Collaboration Features of Cisco Webex Teams PSTN Calling Phone Support Use Cases Cisco Webex Teams Care Overview Embedding Experience Cisco Webex Teams Management and Administration Overview Cisco Cloud Collaboration Management Features and Benefits Management Security Portal Cisco Webex Teams Service Availability, Ordering, and Support Country Availability Language Support Ordering Support Cisco Capital

Duration 5 Days 30 CPD hours This course is intended for This class is designed for persons who are new to the z/OS platforms but have a technical background in information technology. It is intended for those who require an in-depth understanding of z/OS. Overview Describe the system initialization process of the z/OS operating systemsState the differences between an address space, data space, and hyperspaceDescribe the process of translating a virtual address to a real addressExplain the difference between paging and swappingDefine a z/OS taskDescribe dispatching, interrupt processing, supervisor calls, cross memory services, and serializationDescribe the purpose of the Job Entry Subsystem (JES)Illustrate the flow of a job through the z/OS environmentDescribe the allocation process for data sets in the z/OS environmentsIllustrate how an I/O request is processed in a z/OS environmentDescribe how workload management is accomplished in a z/OS environmentExplain the z/OS recovery processes and list available Problem Determination ToolsDescribe z/OS storage management conceptsDescribe the UNIX System Services functions provided in the z/OS environmentsExplain the network topologies and protocol support provided in z/OSDescribe system security and network security for a z/OS environmentCreate a high-level plan for the installation and configuration of a z/OS environment This classroom course introduces the base elements, optional features, and servers that are provided in z/OS. It focuses on the system service facilities that are provided by the z/OS Base Control Program (BCP). Day 1 Unit 1: z/OS overview Including welcome and course overview Unit 2: Storage management Day 2 Unit 3: Managing work Unit 4: Input/output processing Day 3 Unit 4: Input/output processing (continued) Unit 5: Data management Unit 6: Job management Day 4 Unit 7: IPL and system initialization Unit 8: Termination and recovery analysis Unit 9: Installing and configuring Day 5 Unit 10: Communicating Unit 11: Security in z/OS Including end-of-course summary Additional course details: Nexus Humans ES15 IBM z/OS Facilities 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 ES15 IBM z/OS Facilities 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 designed for security analysts, security technical architects, offense managers, network administrators, and system administrators using QRadar SIEM. Overview After completing this course, you should be able to perform the following tasks: Describe how QRadar collects data to detect suspicious activities Describe the QRadar architecture and data flows Navigate the user interface Define log sources, protocols, and event details Discover how QRadar collects and analyzes network flow information Describe the QRadar Custom Rule Engine Utilize the Use Case Manager app Discover and manage asset information Learn about a variety of QRadar apps, content extensions, and the App Framework Analyze offenses by using the QRadar UI and the Analyst Workflow app Search, filter, group, and analyze security data Use AQL for advanced searches Use QRadar to create customized reports Explore aggregated data management Define sophisticated reporting using Pulse Dashboards Discover QRadar administrative tasks IBM Security QRadar enables deep visibility into network, endpoint, user, and application activity. It provides collection, normalization, correlation, and secure storage of events, flows, assets, and vulnerabilities. Suspected attacks and policy breaches are highlighted as offenses. In this course, you learn about the solution architecture, how to navigate the user interface, and how to investigate offenses. You search and analyze the information from which QRadar concluded a suspicious activity. Hands-on exercises reinforce the skills learned. Course Outline Unit 0: IBM Security QRadar 7.4 - Fundamentals Unit 1: QRadar Architecture Unit 2: QRadar UI - Overview Unit 3: QRadar - Log Source Unit 4: QRadar flows and QRadar Network Insights Unit 5: QRadar Custom Rule Engine (CRE) Unit 6: QRadar Use Case Manager app Unit 7: QRadar - Assets Unit 8: QRadar extensions Unit 9: Working with Offenses Unit 10: QRadar - Search, filtering, and AQL Unit 11: QRadar - Reporting and Dashboards Unit 12: QRadar - Admin Console Additional course details: Nexus Humans BQ104 IBM QRadar SIEM Foundations 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 BQ104 IBM QRadar SIEM Foundations 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 course is intended for Experienced system administrators responsible for deploying and administering JBoss Enterprise Application Platform 6 in large-scale production environments. At least 2 years' experience as a JBoss Enterprise Application Platform administrator. Be a Red Hat Certified Specialist in Enterprise Application Server Administration on Enterprise Application Platform 6 (or later) or have equivalent experience . Overview Upon successful completion of this course, students will be able to provision and manage Red Hat JBoss Enterprise Application Platform 6 in large-scale production environments. This course empowers you to provision and manage Red Hat© JBoss© Enterprise Application Platform (JBoss EAP) in large-scale production environments. Intended for experienced administrators, this course will help you gain a deeper understanding of how to work with JBoss EAP by taking a closer look at installation, clustering, deployments, scripting, management, messaging, and security with a view towards building on the skills established in the Red Hat JBoss Application Administration I (JB248) course. 1 - INSTALLATION Given the proper installation media, perform Red Hat© JBoss© Enterprise Application Platform 6 installations that are repeatable, upgradeable, and silent. 2 - CLUSTERING Demonstrate a proficient knowledge of clustering components, their configuration, and application to clustered architectures. 3 - DEPLOYMENT Deploy an application in various types of production environments. 4 - SCRIPTING Script various configuration and management scenarios using command line interface (CLI). 5 - MANAGEMENT Use various tools to monitor and manage JBoss Enterprise Application Platform. 6 - MESSAGING Learn how to manage supported messaging systems. 7 - SECURITY Configure security settings that include authentication, authorization, networking, and the management interfaces. 8 - OVERVIEW OF JBOSS OPERATIONS NETWORK Learn the functionality of JBoss Operations Network and its use cases. Also learn how to install a JBoss Operations Network server, an agent, and agent plug-ins. 9 - MONITORING RESOURCES Learn how to use JBoss Operations Network to monitor managed resources, including defining alerts, baselines, and notifications Additional course details: Nexus Humans Red Hat JBoss Application Administration II (AD348) 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 Red Hat JBoss Application Administration II (AD348) 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: Developers responsible for developing Deep Learning applications Developers who want to understand concepts behind Deep Learning and how to implement a Deep Learning solution on AWS Overview This course is designed to teach you how to: Define machine learning (ML) and deep learning Identify the concepts in a deep learning ecosystem Use Amazon SageMaker and the MXNet programming framework for deep learning workloads Fit AWS solutions for deep learning deployments In this course, you?ll learn about AWS?s deep learning solutions, including scenarios where deep learning makes sense and how deep learning works. You?ll learn how to run deep learning models on the cloud using Amazon SageMaker and the MXNet framework. You?ll also learn to deploy your deep learning models using services like AWS Lambda while designing intelligent systems on AWS. Module 1: Machine learning overview A brief history of AI, ML, and DL The business importance of ML Common challenges in ML Different types of ML problems and tasks AI on AWS Module 2: Introduction to deep learning Introduction to DL The DL concepts A summary of how to train DL models on AWS Introduction to Amazon SageMaker Hands-on lab: Spinning up an Amazon SageMaker notebook instance and running a multi-layer perceptron neural network model Module 3: Introduction to Apache MXNet The motivation for and benefits of using MXNet and Gluon Important terms and APIs used in MXNet Convolutional neural networks (CNN) architecture Hands-on lab: Training a CNN on a CIFAR-10 dataset Module 4: ML and DL architectures on AWS AWS services for deploying DL models (AWS Lambda, AWS IoT Greengrass, Amazon ECS, AWS Elastic Beanstalk) Introduction to AWS AI services that are based on DL (Amazon Polly, Amazon Lex, Amazon Rekognition) Hands-on lab: Deploying a trained model for prediction on AWS Lambda Additional course details: Nexus Humans Deep Learning on AWS 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 Deep Learning on AWS 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 course is designed for business professionals who leverage data to address business issues. The typical student in this course will have several years of experience with computing technology, including some aptitude in computer programming. However, there is not necessarily a single organizational role that this course targets. A prospective student might be a programmer looking to expand their knowledge of how to guide business decisions by collecting, wrangling, analyzing, and manipulating data through code; or a data analyst with a background in applied math and statistics who wants to take their skills to the next level; or any number of other data-driven situations. Ultimately, the target student is someone who wants to learn how to more effectively extract insights from their work and leverage that insight in addressing business issues, thereby bringing greater value to the business. Overview In this course, you will learn to: Use data science principles to address business issues. Apply the extract, transform, and load (ETL) process to prepare datasets. Use multiple techniques to analyze data and extract valuable insights. Design a machine learning approach to address business issues. Train, tune, and evaluate classification models. Train, tune, and evaluate regression and forecasting models. Train, tune, and evaluate clustering models. Finalize a data science project by presenting models to an audience, putting models into production, and monitoring model performance. For a business to thrive in our data-driven world, it must treat data as one of its most important assets. Data is crucial for understanding where the business is and where it's headed. Not only can data reveal insights, it can also inform?by guiding decisions and influencing day-to-day operations. This calls for a robust workforce of professionals who can analyze, understand, manipulate, and present data within an effective and repeatable process framework. In other words, the business world needs data science practitioners. This course will enable you to bring value to the business by putting data science concepts into practice Addressing Business Issues with Data Science Topic A: Initiate a Data Science Project Topic B: Formulate a Data Science Problem Extracting, Transforming, and Loading Data Topic A: Extract Data Topic B: Transform Data Topic C: Load Data Analyzing Data Topic A: Examine Data Topic B: Explore the Underlying Distribution of Data Topic C: Use Visualizations to Analyze Data Topic D: Preprocess Data Designing a Machine Learning Approach Topic A: Identify Machine Learning Concepts Topic B: Test a Hypothesis Developing Classification Models Topic A: Train and Tune Classification Models Topic B: Evaluate Classification Models Developing Regression Models Topic A: Train and Tune Regression Models Topic B: Evaluate Regression Models Developing Clustering Models Topic A: Train and Tune Clustering Models Topic B: Evaluate Clustering Models Finalizing a Data Science Project Topic A: Communicate Results to Stakeholders Topic B: Demonstrate Models in a Web App Topic C: Implement and Test Production Pipelines