573 Courses

Overview Mastering data science skills and expertise can open new doors of opportunities for you in a wide range of fields. Learn the fundamentals and develop a solid grasp of Python data science with the comprehensive Data Science with Python course. This course is designed to assist you in securing a valuable skill set and boosting your career. This course will provide you with quality training on the fundamentals of data analysis with Python. From the step-by-step learning process, you will learn the techniques of setting up the system. Then the course will teach you Python data structure and functions. You will receive detailed lessons on NumPy, Matplotlib, and Pandas. Furthermore, you will develop the skills for Algorithm Evaluation Techniques, visualising datasets and much more. After completing the course you will receive a certificate of achievement. This certificate will help you create an impressive resume. So join today! How will I get my certificate? You may have to take a quiz or a written test online during or after the course. After successfully completing the course, you will be eligible for the certificate. Who is This course for? This course Data Science with Python course is ideal for beginners in data science. It will help them develop a solid grasp of Python and help them pursue their dream career in the field of data science. Requirements The students will not require any formal qualifications or previous experience to enrol in this course. Anyone can learn from the course anytime from anywhere through smart devices like laptops, tabs, PC, and smartphones with stable internet connections. They can complete the course according to their preferable pace so, there is no need to rush. Career Path This course will equip you with valuable knowledge and effective skills in this area. After completing the course, you will be able to explore career opportunities in the fields such as Data Analyst Data Scientist Data Manager Business Analyst And much more! Course Curriculum 90 sections • 90 lectures • 10:19:00 total length •Course Overview & Table of Contents: 00:09:00 •Introduction to Machine Learning - Part 1 - Concepts , Definitions and Types: 00:05:00 •Introduction to Machine Learning - Part 2 - Classifications and Applications: 00:06:00 •System and Environment preparation - Part 1: 00:04:00 •System and Environment preparation - Part 2: 00:06:00 •Learn Basics of python - Assignment 1: 00:10:00 •Learn Basics of python - Assignment 2: 00:09:00 •Learn Basics of python - Functions: 00:04:00 •Learn Basics of python - Data Structures: 00:12:00 •Learn Basics of NumPy - NumPy Array: 00:06:00 •Learn Basics of NumPy - NumPy Data: 00:08:00 •Learn Basics of NumPy - NumPy Arithmetic: 00:04:00 •Learn Basics of Matplotlib: 00:07:00 •Learn Basics of Pandas - Part 1: 00:06:00 •Learn Basics of Pandas - Part 2: 00:07:00 •Understanding the CSV data file: 00:09:00 •Load and Read CSV data file using Python Standard Library: 00:09:00 •Load and Read CSV data file using NumPy: 00:04:00 •Load and Read CSV data file using Pandas: 00:05:00 •Dataset Summary - Peek, Dimensions and Data Types: 00:09:00 •Dataset Summary - Class Distribution and Data Summary: 00:09:00 •Dataset Summary - Explaining Correlation: 00:11:00 •Dataset Summary - Explaining Skewness - Gaussian and Normal Curve: 00:07:00 •Dataset Visualization - Using Histograms: 00:07:00 •Dataset Visualization - Using Density Plots: 00:06:00 •Dataset Visualization - Box and Whisker Plots: 00:05:00 •Multivariate Dataset Visualization - Correlation Plots: 00:08:00 •Multivariate Dataset Visualization - Scatter Plots: 00:05:00 •Data Preparation (Pre-Processing) - Introduction: 00:09:00 •Data Preparation - Re-scaling Data - Part 1: 00:09:00 •Data Preparation - Re-scaling Data - Part 2: 00:09:00 •Data Preparation - Standardizing Data - Part 1: 00:07:00 •Data Preparation - Standardizing Data - Part 2: 00:04:00 •Data Preparation - Normalizing Data: 00:08:00 •Data Preparation - Binarizing Data: 00:06:00 •Feature Selection - Introduction: 00:07:00 •Feature Selection - Uni-variate Part 1 - Chi-Squared Test: 00:09:00 •Feature Selection - Uni-variate Part 2 - Chi-Squared Test: 00:10:00 •Feature Selection - Recursive Feature Elimination: 00:11:00 •Feature Selection - Principal Component Analysis (PCA): 00:09:00 •Feature Selection - Feature Importance: 00:06:00 •Refresher Session - The Mechanism of Re-sampling, Training and Testing: 00:12:00 •Algorithm Evaluation Techniques - Introduction: 00:07:00 •Algorithm Evaluation Techniques - Train and Test Set: 00:11:00 •Algorithm Evaluation Techniques - K-Fold Cross Validation: 00:09:00 •Algorithm Evaluation Techniques - Leave One Out Cross Validation: 00:05:00 •Algorithm Evaluation Techniques - Repeated Random Test-Train Splits: 00:07:00 •Algorithm Evaluation Metrics - Introduction: 00:09:00 •Algorithm Evaluation Metrics - Classification Accuracy: 00:08:00 •Algorithm Evaluation Metrics - Log Loss: 00:03:00 •Algorithm Evaluation Metrics - Area Under ROC Curve: 00:06:00 •Algorithm Evaluation Metrics - Confusion Matrix: 00:10:00 •Algorithm Evaluation Metrics - Classification Report: 00:04:00 •Algorithm Evaluation Metrics - Mean Absolute Error - Dataset Introduction: 00:06:00 •Algorithm Evaluation Metrics - Mean Absolute Error: 00:07:00 •Algorithm Evaluation Metrics - Mean Square Error: 00:03:00 •Algorithm Evaluation Metrics - R Squared: 00:04:00 •Classification Algorithm Spot Check - Logistic Regression: 00:12:00 •Classification Algorithm Spot Check - Linear Discriminant Analysis: 00:04:00 •Classification Algorithm Spot Check - K-Nearest Neighbors: 00:05:00 •Classification Algorithm Spot Check - Naive Bayes: 00:04:00 •Classification Algorithm Spot Check - CART: 00:04:00 •Classification Algorithm Spot Check - Support Vector Machines: 00:05:00 •Regression Algorithm Spot Check - Linear Regression: 00:08:00 •Regression Algorithm Spot Check - Ridge Regression: 00:03:00 •Regression Algorithm Spot Check - Lasso Linear Regression: 00:03:00 •Regression Algorithm Spot Check - Elastic Net Regression: 00:02:00 •Regression Algorithm Spot Check - K-Nearest Neighbors: 00:06:00 •Regression Algorithm Spot Check - CART: 00:04:00 •Regression Algorithm Spot Check - Support Vector Machines (SVM): 00:04:00 •Compare Algorithms - Part 1 : Choosing the best Machine Learning Model: 00:09:00 •Compare Algorithms - Part 2 : Choosing the best Machine Learning Model: 00:05:00 •Pipelines : Data Preparation and Data Modelling: 00:11:00 •Pipelines : Feature Selection and Data Modelling: 00:10:00 •Performance Improvement: Ensembles - Voting: 00:07:00 •Performance Improvement: Ensembles - Bagging: 00:08:00 •Performance Improvement: Ensembles - Boosting: 00:05:00 •Performance Improvement: Parameter Tuning using Grid Search: 00:08:00 •Performance Improvement: Parameter Tuning using Random Search: 00:06:00 •Export, Save and Load Machine Learning Models : Pickle: 00:10:00 •Export, Save and Load Machine Learning Models : Joblib: 00:06:00 •Finalizing a Model - Introduction and Steps: 00:07:00 •Finalizing a Classification Model - The Pima Indian Diabetes Dataset: 00:07:00 •Quick Session: Imbalanced Data Set - Issue Overview and Steps: 00:09:00 •Iris Dataset : Finalizing Multi-Class Dataset: 00:09:00 •Finalizing a Regression Model - The Boston Housing Price Dataset: 00:08:00 •Real-time Predictions: Using the Pima Indian Diabetes Classification Model: 00:07:00 •Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset: 00:03:00 •Real-time Predictions: Using the Boston Housing Regression Model: 00:08:00 •Resources - Data Science & Machine Learning with Python: 00:00:00

This course on MongoDB is for absolute beginners and provides an interactive learning experience that reflects the most in-demand skills. The content will help you understand the concepts and methodology with regards to MongoDB in an effortless way. The strong basic understanding you gain initially will help you move toward learning more advanced concepts.

Kickstart your journey into deep learning and gain a strong understanding of deep neural networks through practical exercises. Develop your intuition and learn the fundamentals of artificial neural networks, activation functions, and loss functions. Gain practical experience with Python and TensorFlow 2.x, and apply your skills to build powerful deep learning models.

Duration 3 Days 18 CPD hours This course is intended for The EXIN BCS Artificial Intelligence Foundation certification is focused on individuals with an interest in, (or need to implement) AI in an organization, especially those working in areas such as science, engineering, knowledge engineering, finance, education or IT services. Overview You will be able to Describe how Artificial (AI) is Part of 'Universal Design', and 'The Fourth Industrial Revolution' Demonstrate Understanding of the Artificial Intelligence (AI) Intelligen Agent Description Explain the Benefits of Artificial Intelligence (AI) Describe how we Learn from Data - Functionality, Software and Hardware Demonstrate an Understanding that Artificial Intelligence (AI) (in Particular, Machine Learning (ML)) will Drive Humans and Machines to Work Together Describe a ''Learning from Experience'' Agile Approach to Projects Candidates should be able to demonstrate a knowledge and understanding in the application of ethical and sustainable Artificial Intelligence (AI):- Human-centric Ethical and Sustainable Human and Artificial Intelligence (AI) Ethical and Sustainable Human and Artificial Intelligence (AI) Recall the General Definition of Human and Artificial Intelligence (AI) Describe what are Ethics and Trustworthy Artificial Intelligence (AI) Describe the Three Fundamental Areas of Sustainability and the United Nationïs Seventeen Sustainability Goals Describe how Artificial Intelligence (AI) is Part of 'Universal Design', and 'The Fourth Industrial Revolution' Understand that Machine Learning (ML) is a Significant Contribution to the Growth of Artificial Intelligence (AI) Artificial Intelligence (AI) and Robotics Demonstrate Understanding of the Artificial Intelligence (AI) Intelligent Agent Description Describe what a Robot is Describe what an intelligent Robot is Applying the Benefits of Artificial Intelligence (AI) ? Challenges and Risks Describe how Sustainability Relates to Human-Centric Ethical Artificial Intelligence (AI) and how our Values will Drive our use of Artificial Intelligence (AI) and will Change Humans, Society and Organizations Explain the Benefits of Artifical Intelligence (AI) Describe the Challenges of Artificial Intelligence (AI) Projects Demonstrate Understanding of the Risks of Artificial Intelligence (AI) Projects List Opportunities for Artificial Intelligence (AI) Identify a Typical Funding Source for Artificial Intelligence (AI) Projects and Relate to the NASA Technology Readiness Levels (TRLs) Starting Artificial Intelligence (AI): how to Build a Machine Learning (ML) Toolbox ? Theory and Practice Describe how we Learn from Data - Functionality, Software and Hardware Recall which Rypical, Narrow Artificial Intelligence (AI) Capability is Useful in Machine Learning (ML9 and Artificial Intelligence (AI) AgentsïFunctionality The Management, Roles and Responsibilities of Humans and Machines Demonstrate an Understanding that Artificial Intelligence (AI) (in Particular, Machine Learning (ML)) will Drive Humans and Machines to Work Together List Future Directions of Humans and Machines Working Together Describe a ''Learning from Experience'' Agile Approach to Projects

Get ready for an exceptional online learning experience with the Data Analyst (Data Analytics)bundle! This carefully curated collection of 30 premium courses is designed to cater to a variety of interests and disciplines. Dive into a sea of knowledge and skills, tailoring your learning journey to suit your unique aspirations. The Data Analyst (Analytics) is a dynamic package, blending the expertise of industry professionals with the flexibility of digital learning. It offers the perfect balance of foundational understanding and advanced insights. Whether you're looking to break into a new field or deepen your existing knowledge, the Data Analyst (Analytics) package has something for everyone. As part of the Data Analyst (Analytics) package, you will receive complimentary PDF certificates for all courses in this bundle at no extra cost. Equip yourself with the Data Analyst (Analytics) bundle to confidently navigate your career path or personal development journey. Enrol today and start your career growth! This Bundle Comprises the Following Data Analyst (Data Analytics)CPD Accredited Courses: Course 01: Basic Data Analysis Course 02: Business Data Analysis Course 03: Introduction to Data Analytics with Tableau Course 04: Google Data Studio: Data Analytics Course 05: SQL Database Basics for Everyone Course 06: R Programming for Data Science Course 07: 2021 Data Science & Machine Learning with R from A-Z Course 08: Learn Python, JavaScript, and Microsoft SQL for Data science Course 09: Spatial Data Visualisation and Machine Learning in Python Course 10: Building Big Data Pipelines with PySpark MongoDB and Bokeh Course 11: Complete Python Machine Learning & Data Science Fundamentals Course 12: Clinical Data Management with SAS Programming Course 13: Certificate in Data Entry and Management Course 14: Quick Data Science Approach from Scratch Course 15: Web Mapping and Data Visualizations Course 16: Programming AutoCAD with SQL Server Database Using C# Course 17: Big Data Analytics with PySpark Power BI and MongoDB Course 18: Develop Big Data Pipelines with R & Sparklyr & Tableau Course 19: Develop Big Data Pipelines with R, Sparklyr & Power BI Course 20: Data Center Training Essentials: Power & Electrical Course 21: Business Intelligence and Data Mining Course 22: Set Menu Prices for your restaurant using data Course 23: Data Analysis In Excel Course 24: Data Protection Course 25: Reporting and Data Course 26: Career Development Plan Fundamentals Course 27: CV Writing and Job Searching Course 28: Networking Skills for Personal Success Course 29: Excel: Top 50 Microsoft Excel Formulas in 50 Minutes! Course 30: Decision Making and Critical Thinking What will make you stand out? Upon completion of this online Data Analyst (Data Analytics) bundle, you will gain the following: CPD QS Accredited Proficiency with this Data Analyst (Analytics) bundle After successfully completing the Data Analyst (Analytics) bundle, you will receive a FREE CPD PDF Certificates as evidence of your newly acquired abilities. Lifetime access to the whole collection of learning materials of this Data Analyst (Analytics) bundle The online test with immediate results You can study and complete the Data Analyst (Analytics) bundle at your own pace. Study for the Data Analyst (Analytics) bundle using any internet-connected device, such as a computer, tablet, or mobile device. Each course in this Data Analyst (Analytics) bundle holds a prestigious CPD accreditation, symbolising exceptional quality. The materials, brimming with knowledge, are regularly updated, ensuring their relevance. This bundle promises not just education but an evolving learning experience. Engage with this extraordinary collection, and prepare to enrich your personal and professional development. Embrace the future of learning with the Data Analyst (Analytics), a rich anthology of 30 diverse courses. Each course in the Data Analyst (Analytics) bundle is handpicked by our experts to ensure a wide spectrum of learning opportunities. ThisData Analyst (Analytics) bundle will take you on a unique and enriching educational journey. The bundle encapsulates our mission to provide quality, accessible education for all. Whether you are just starting your career, looking to switch industries, or hoping to enhance your professional skill set, the Data Analyst (Analytics) bundle offers you the flexibility and convenience to learn at your own pace. Make the Data Analyst (Data Analytics) package your trusted companion in your lifelong learning journey. CPD 300 CPD hours / points Accredited by CPD Quality Standards Who is this course for? The Data Analyst (Data Analytics) bundle is perfect for: Lifelong learners looking to expand their knowledge and skills. Professionals seeking to enhance their career with CPD certification. Individuals wanting to explore new fields and disciplines. Anyone who values flexible, self-paced learning from the comfort of home. Requirements You are cordially invited to enroll in this bundle; please note that there are no formal prerequisites or qualifications required. We've designed this curriculum to be accessible to all, irrespective of prior experience or educational background. Career path Unleash your potential with the Data Analyst (Data Analytics) bundle. Acquire versatile skills across multiple fields, foster problem-solving abilities, and stay ahead of industry trends. Ideal for those seeking career advancement, a new professional path, or personal growth. Embrace the journey with the Data Analyst (Analytics)bundle package. Certificates CPD Quality Standard Certificate Digital certificate - Included 30 CPD Quality Standard Certificates - Free

You will master the fundamentals and more advanced principles of Python programming in this beginner's tutorial. This Python course covers every Python fundamental, including installation and more complex material such as Python data science.

Aligned with the AIGP certification program, AI Governance Professional Training is for professionals tasked with implementing AI governance and risk management in their organizations. It provides baseline knowledge and strategies for responding to complex risks associated with the evolving AI landscape. This training meets the rapidly growing need for professionals who can develop, integrate and deploy trustworthy AI systems in line with emerging laws and policies. About This Course This training teaches critical artificial intelligence governance concepts that are also integral to the AIGP certification exam. While not purely a 'test prep' course, this training is appropriate for professionals who plan to certify, as well as for those who want to deepen their AI governance knowledge. Both the training and the exam are based on the same body of knowledge.   Module 1: Foundations of artificial intelligence Defines AI and machine learning, presents an overview of the different types of AI systems and their use cases, and positions AI models in the broader socio-cultural context. Module 2: AI impacts on people and responsible AI principles Outlines the core risks and harms posed by AI systems, the characteristics of trustworthy AI systems, and the principles essential to responsible and ethical AI. Module 3: AI development life cycle Describes the AI development life cycle and the broad context in which AI risks are managed. Module 4: Implementing responsible AI governance and risk management Explains how major AI stakeholders collaborate in a layered approach to manage AI risks while acknowledging AI systems' potential societal benefits. Module 5: Implementing AI projects and systems Outlines mapping, planning and scoping AI projects, testing and validating AI systems during development, and managing and monitoring AI systems after deployment. Module 6: Current laws that apply to AI systems Surveys the existing laws that govern the use of AI, outlines key GDPR intersections, and provides awareness of liability reform. Module 7: Existing and emerging AI laws and standards Describes global AI-specific laws and the major frameworks and standards that exemplify how AI systems can be responsibly governed. Module 8: Ongoing AI issues and concerns Presents current discussions and ideas about AI governance, including awareness of legal issues, user concerns, and AI auditing and accountability issues. Accreditation The associated exam is accredited by the IAPP under its ANSI Accreditation Who Should Attend? Any professionals tasked with developing AI governance and risk management in their operations, and anyone pursuing IAPP Artificial Intelligence Governance Professional certification. Prerequisites A general understanding of AI, Corporate Governance, and Business value would be of benefit to participants. Assessment As with all IAPP exams, the AIGP is a 90 question, multiple choice exam to be completed within 150 minutes. Exams are hosted by Pearsonvue and can be taken either remotely, or via any one of hundreds of exam venues globally. A passing score is achieved at 70% Our Guarantee We are an approved IAPP training provider Exam pass guarantee, or retrain until you do, for free What's Included? Participant Guide Study Guide Practice Exam Exam voucher Breakfast, lunch, coffees and snacks (Classroom courses only) Certification Logo

Learn to make interactive apps using Swift 4.2 and Xcode 10 and have your own applications in the App Store in just 10 days

***24 Hour Limited Time Flash Sale*** Penetration Test Engineer - CPD Certified Admission Gifts FREE PDF & Hard Copy Certificate| PDF Transcripts| FREE Student ID| Assessment| Lifetime Access| Enrolment Letter Feeling stuck in your career? Struggling to keep up with the ever-changing demands of the industry? You're not alone. But there's good news: Penetration Tester - CPD Certified career bundle is here to equip you with the essential skills and knowledge you need to break free and achieve your goals. With this Penetration Test Engineer course you will get 20 CPD Accredited PDF Certificates, a Hard Copy Certificate for Web Application Penetration Testing and our exclusive student ID card absolutely free. Courses Are Included In This Penetration Tester - CPD Certified Bundle: Course 01: Web Application Penetration Testing Course 02: Penetration Testing with OWASP ZAP Course 03: CompTIA PenTest+ (Ethical Hacking) Course 04: Secure Programming of Web Applications - Developers and TPMs Course 05: IT: Ethical Hacking & IT Security Course 06: IGCSE ICT: Network Essentials Course 07: Network Security Level 2 Course 08: Computer Networks Security from Scratch to Advanced Course 09: CompTIA Security+ (SY0-601) Course 10: CompTIA CySA+ Cybersecurity Analyst (CS0-002) Course 11: CompTIA Network+ Certification (N10-007) Course 12: Python Basic Programming for Absolute Beginners Course 13: Python Masterclass: Advanced Diploma Course 14: Machine Learning with Python Course Course 15: Azure Machine Learning Course 16: Introduction to Java Course 17: Essential IT Skills Level 2 Course 18: IT Support Technician Course 19: Cyber Security Awareness Training Course 20: Cyber Security Incident Handling and Incident Response Embarking on Penetration Tester - CPD Certified is more than just taking an online course; it's an investment in your future. By completing this Penetration Tester - CPD Certified bundle, you'll not only gain invaluable skills but also open doors to new career opportunities and advancements, boosting your earning potential. Don't miss this chance to elevate your career and skillset. Enrol in Penetration Tester - CPD Certified today and take the first step towards achieving your goals and dreams. Why buy this Penetration Tester - CPD Certified? Free CPD Accredited Certificate upon completion of Penetration Tester - CPD Certified Get a free student ID card with Penetration Tester - CPD Certified Lifetime access to the Penetration Tester - CPD Certified course materials Get instant access to this Penetration Tester - CPD Certified course Learn Penetration Tester - CPD Certified from anywhere in the world 24/7 tutor support with the Penetration Tester - CPD Certified course. Penetration Tester - CPD Certified is an entirely online, interactive lesson with voice-over audio. Start your learning journey straightaway with our Penetration Tester - CPD Certified Training! Penetration Tester - CPD Certified premium bundle consists of 20 precisely chosen courses on a wide range of topics essential for anyone looking to excel in this field. Each segment of Penetration Tester - CPD Certified is meticulously designed to maximise learning and engagement, blending interactive content and audio-visual modules for a truly immersive experience. Certification You have to complete the assignment given at the end of the Penetration Tester - CPD Certified course and score a minimum of 60% to pass each exam. Our expert trainers will assess your assignment and give you feedback after you submit the assignment. After passing the Penetration Tester - CPD Certified exam You will be entitled to claim a PDF & Hardcopy certificate accredited by CPD Quality standards completely free. CPD 320 CPD hours / points Accredited by CPD Quality Standards Who is this course for? This Penetration Tester - CPD Certified course is ideal for: Students seeking mastery in Penetration Tester - CPD Certified Professionals seeking to enhance Penetration Tester - CPD Certified skills Individuals looking for a Penetration Tester - CPD Certified-related career. Anyone passionate about Penetration Tester - CPD Certified Requirements This Penetration Tester - CPD Certified doesn't require prior experience and is suitable for diverse learners. Career path This Penetration Tester - CPD Certified bundle will allow you to kickstart or take your career in the related sector to the next stage. Certificates CPD Accredited Digital certificate Digital certificate - Included CPD Accredited Hard copy certificate Hard copy certificate - Included

Duration 5 Days 30 CPD hours This course is intended for The primary audience for this course is any IT, facilities or data centre professional who works in and around the data centre and has the responsibility to achieve and improve efficiency and environmental sustainability, whilst maintaining the availability and manageability of the data centre. Overview After completion of the course the participant will be able to: Understand the impact of data centres on the environment Describe the various environmental/energy management standards Understand the purpose and goals of the legally binding international treaties on climate change Implement various sustainable performance metrics and how to use them in the data centre environment Manage data centre environmental sustainability using international standards Set up the measurement, monitoring and reporting of energy usage Use power efficiency indicators in a variety of data centre designs Use best practices for energy savings in the electrical infrastructure and in the mechanical (cooling) infrastructure Use best practices for energy savings for the ICT equipment and data storage Understand the importance of water management and waste management Understand the different ways to use sustainable energy in the data centre Get practical tips and innovative ideas to make a data centre more sustainable The CDESS© course is aimed at providing knowledge of the standards and guidelines related to environmental sustainability, and how to move your data centre (existing or new) to a more environmentally sustainable design and operations. Impact of Data Centres on the Environment Predictions in 2010 Current situation Outlook and commitments What is Environmental Sustainability The importance of sustainability Senior management commitment Environmental sustainability framework Sustainability policies Performance standards and metrics Information policies Transparency Awareness Service charging models Environmental Management Environmental sustainability framework (ISO 14001) Standards and guidelines ? ISO 50001 / ISO 30134 Measurement and categories Baselining Trend analysis Reporting Power Effiðciency Indicators Various eðfficiency indicators Power Usage Effectiveness (PUE) PUE measurement levels Factors affecting PUE Measurement points and intervals PUE in mixed source environments Measuring PUE in a mixed-use building PUE reporting Impact of PUE after optimising IT load Electrical Energy Savings (Electrical) Identifying the starting point for saving energy Sizing of power DC power Generators UPS systems Power Factor (PF) Energy savings on lighting Electrical Energy Savings (Mechanical) Energy savings on the cooling infrastructure Temperature and humidity setpoints Various energy eðcient cooling technologies Energy savings on the airflow Liquid cooling Energy reusage PUE, ERE/ERF and Control Volume Electrical Energy Savings (ICT) Procurement IT equipment energy eðfficiency ITEEsv, SMPE, SMPO IT equipment utilisation Server virtualisation Open compute project Electrical Energy Savings (Data Storage) Data management Data storage management Data storage equipment effiðciency Water Management Water Usage Effectiveness (WUE) Improving WUE Water usage at the power generation source Energy Water Intensity Factor (EWIF) Waste Management Waste management policies Life-cycle assessment (Cradle to the grave) 3 R?s for waste management Reduce Reuse Second-hand market Recycle Sustainable Energy Usage Sustainable energy sources Power purchase agreements Energy attribute certificates Renewable Energy Factor (REF) Matching renewable energy supply and demand Sustainable energy storage Carbon trading Automated Environmental Management Systems Use of AI and machine learning Load migration Data Centre Infrastructure Management (DCIM) solutions