256 Probability courses

Management of Risk (M_o_R®) Foundation This M_o_R® Foundation course prepares learners to demonstrate knowledge and comprehension of the four elements of the M_o_R framework: Principles, Approach, Processes, Embedding and Reviewing and how these elements support corporate governance. The M_o_R Foundation Course is also a prerequisite for the M_o_R Practitioner qualification. What you will Learn At the end of the M_o_R Foundation course, participants will gain competencies in and be able to: Describe the key characteristics of risk and the benefits of risk management List the eight M_o_R Principles List and describe the use of the key M_o_R Approach documents Create Probability and Impact scales Define and distinguish between risks and issues Create a Risk Register Create a Stakeholder map Identify the key roles in risk management Use the key techniques and describe specialisms in risk management Undertake the M_o_R Foundation examination Introduction Introduction to the M_o_R course What is a risk? What is risk management? Why is risk management so important? Basic risk definitions The development of knowledge about risk management Corporate governance and internal control Where and when should risk management be applied? M_o_R Principles The purpose of M_o_R principles Aligns with objectives Fits the context Engages stakeholders Provides clear guidance Informs decision-making Facilitates continual improvement Creates a supportive culture Achieves measurable value Risk management maturity models M_o_R Approach Relationship between the documents Risk management policy Risk management process guide Risk management strategy Risk register Issue register Risk response plan Risk improvement plan Risk communications plan M_o_R Process Common process barriers Identify contexts Identify the risks Assess estimate Assess evaluate Plan Implement Communication throughout the process M_o_R Perspectives Strategic perspective Program perspective Project perspective Operational perspective Risk Specialisms Business continuity management Incident and crisis management Health and Safety management Financial risk management Environmental risk management Reputational risk management Contract risk management

Developing the Business Case - Defining the Business Problem and Solution Scope: On-Demand This course teaches the purpose, structure, and content of a business case. It presents the basic techniques for determining financial ROI, non-tangible benefits, and the probability of meeting expectations. What You Will Learn You will learn how to: Prepare a business case Justify the business investment to solve the business need Perform feasibility studies and ROI analyses Foundation Concepts The role of the business analyst An introduction to the BABOK® Guide The business analyst and the product/project life cycle The business case deliverable Introducing the Business Case Process The business analyst and Strategy Analysis The business analyst and the business case process The business analyst during the business case process The business analyst after the business case process Importance of defining solution performance metrics Defining the Business Need Overview of defining the business need Business needs: problem/opportunity statement Product vision Objectives and constraints Exploring Business Case Solutions Overview of exploring solutions Solution identification for feasibility Solution definition for analysis Assessing project risks Justifying the Business Case Overview of justifying the business case Qualitative justification Quantitative justification Approving the Business Case Overview of business case approval Developing recommendations Preparing the decision package - Documents Preparing the decision Package - Presentations

Management of Risk (M_o_R®) Foundation: Virtual In-House Training This M_o_R® Foundation course prepares learners to demonstrate knowledge and comprehension of the four elements of the M_o_R framework: Principles, Approach, Processes, Embedding and Reviewing and how these elements support corporate governance. The M_o_R Foundation Course is also a prerequisite for the M_o_R Practitioner qualification. What you will Learn At the end of the M_o_R Foundation course, participants will gain competencies in and be able to: Describe the key characteristics of risk and the benefits of risk management List the eight M_o_R Principles List and describe the use of the key M_o_R Approach documents Create Probability and Impact scales Define and distinguish between risks and issues Create a Risk Register Create a Stakeholder map Identify the key roles in risk management Use the key techniques and describe specialisms in risk management Undertake the M_o_R Foundation examination Introduction Introduction to the M_o_R course What is a risk? What is risk management? Why is risk management so important? Basic risk definitions The development of knowledge about risk management Corporate governance and internal control Where and when should risk management be applied? M_o_R Principles The purpose of M_o_R principles Aligns with objectives Fits the context Engages stakeholders Provides clear guidance Informs decision-making Facilitates continual improvement Creates a supportive culture Achieves measurable value Risk management maturity models M_o_R Approach Relationship between the documents Risk management policy Risk management process guide Risk management strategy Risk register Issue register Risk response plan Risk improvement plan Risk communications plan M_o_R Process Common process barriers Identify contexts Identify the risks Assess estimate Assess evaluate Plan Implement Communication throughout the process M_o_R Perspectives Strategic perspective Program perspective Project perspective Operational perspective Risk Specialisms Business continuity management Incident and crisis management Health and Safety management Financial risk management Environmental risk management Reputational risk management Contract risk management

Boost Your Career with Apex Learning and Get Noticed By Recruiters in this Hiring Season! Get Hard Copy + PDF Certificates + Transcript + Student ID Card worth £160 as a Gift - Enrol Now Data analytics is a rapidly expanding discipline, and talented analysts are highly sought after in all industries. The UK and multinational organizations will undoubtedly see an increase in demand for skilled analysts in the coming years. We have created an 11-in-1 bundle course with a distinctive design to help you stand out in the related job market. It will guide you on how to read, evaluate, and display data in a way that is clear to all users and helps guide decisions. Along with this IT and Analytics course, you will get 10 premium courses, an original Hardcopy, 11 PDF Certificates (Main Course + Additional Courses) Student ID card as gifts. This Bundle Consists of the following Premium IT and Analytics courses: Course 01: Introduction to Data Analysis Course 02: Quick Data Science Approach from Scratch Course 03: Excel Pivot Tables Course 04: Google Data Studio: Data Analytics Course 05: Excel Pivot Tables, Pivot Charts, Slicers, and Timelines Course 06: Business Intelligence and Data Mining Masterclass Course 07: Statistics & Probability for Data Science & Machine Learning Course 08: RCA: Root Cause Analysis Course 09: Master JavaScript with Data Visualization Course 10: CompTIA CySA+ Cybersecurity Analyst (CS0-002) Course 11: Electronic Document Management System Step Learning Outcomes Get an overview of data analysis. Familiarise yourself with the data science approach from the ground up. Learn the link between google data studio and data analytics. Explore various Excel data tools and management. Gain a better understanding of statistics and probability in terms of data science. Learn the fundamentals of Root cause analysis. Learn javascript and data visualization. Get introduced to the CSO-002. Get insight into electronic document management systems. Curriculum Course 01: Introduction to Data Analysis Introduction Agenda and Principles of Process Management The Voice of the Process Working as One Team for Improvement Exercise: The Voice of the Customer Tools for Data Analysis The Pareto Chart The Histogram The Run Chart Exercise: Presenting Performance Data Understanding Variation The Control Chart Control Chart Example Control Chart Special Cases Interpreting the Control Chart Control Chart Exercise Strategies to Deal with Variation Using Data to Drive Improvement A Structure for Performance Measurement Data Analysis Exercise Course Project Test your Understanding ---------- Other Courses Are ---------- Course 02: Quick Data Science Approach from Scratch Course 03: Excel Pivot Tables Course 04: Google Data Studio: Data Analytics Course 05: Excel Pivot Tables, Pivot Charts, Slicers, and Timelines Course 06: Business Intelligence and Data Mining Masterclass Course 07: Statistics & Probability for Data Science & Machine Learning Course 08: RCA: Root Cause Analysis Course 09: Master JavaScript with Data Visualization Course 10: CompTIA CySA+ Cybersecurity Analyst (CS0-002) Course 11: Electronic Document Management System Step How will I get my Certificate? After successfully completing the course you will be able to order your CPD Accredited Certificates (PDF + Hard Copy) as proof of your achievement. PDF Certificate: Free (Previously it was £6*11 = £66) Hard Copy Certificate: Free (For The Title Course: Previously it was £10) CPD 120 CPD hours / points Accredited by CPD Quality Standards Who is this course for? Anyone trying to get into the IT industry. Anyone who wants to better comprehend the role of IT and business analytics. Professionals who are looking to optimize business processes. Requirements This course has been designed to be fully compatible with tablets and smartphones, so you can access your course on Wi-Fi, 3G or 4G. Career path This bundle course is a very engaging course and is proven to be beneficial to the many careers Certificates Certificate of completion Digital certificate - Included Certificate of completion is included in course price. Certificate of completion Hard copy certificate - Included You will get the Hard Copy certificate for the title course (Introduction to Data Analysis) absolutely Free! Other Hard Copy certificates are available for £10 each.

Developing the Business Case: Virtual In-House Training Business analysts must be able to create business case documents that highlight project benefits, costs, and risks. The business case is based on the real business need to be solved. These become parts of proposals, feasibility studies, and other decision support documents. This course teaches the purpose, structure, and content of a business case. It presents the basic techniques for determining financial ROI, non-tangible benefits, and the probability of meeting expectations. What you will Learn At the end of this program, you will be able to: Perform feasibility studies Justify the business investment to solve the business problem Prepare an effective business case document Plan and implement a business case approval process Foundation Concepts The role of the BA An introduction to the BABOK® Guide The business analyst and the product / project life cycle (PLC) The business case deliverable Introducing the Business Case Process The BA and strategy analysis The BA and the business case process (BCP) The BA during the business case process (BCP) The BA after the business case process (BCP) Importance of defining solution performance metrics Defining the Business Need Overview of defining the business need Business needs: problem / opportunity statement Product vision Objectives and constraints Exploring Business Case Solutions Overview of exploring solutions Solution identification for feasibility Solution definition for analysis Assessing project risks Justifying the Business Case Overview of justifying the business case Qualitative justification Quantitative justification Approving the Business Case Overview of business case approval Developing recommendations Preparing the decision package - documents Preparing the decision package - presentations

The Statistical Analysis Training Course is pivotal in the modern world, offering essential skills that are increasingly demanded across various industries. As businesses and organizations generate vast amounts of data, the ability to analyze and interpret this data becomes crucial. Learning from The Statistical Analysis Training Course equips individuals with expertise in key areas such as probability, hypothesis testing, regression analysis, and predictive analytics, enhancing their employability. In the UK, proficiency gained from this Statistical Analysis Training course can significantly boost job opportunities, with data analysts and statisticians earning an average salary of £35,000 to £50,000 annually. The demand for statistical analysis skills is on the rise, with the sector experiencing a growth rate of 33% over the past five years. Advantages of the Statistical Analysis Training course include a comprehensive understanding of both foundational and advanced statistical concepts, which are integral in roles across finance, healthcare, marketing, and technology. The Statistical Analysis Training Course ensures that learners are well-versed in modern analytical techniques, making them valuable assets in a data-driven economy. As the importance of data analytics continues to grow, so does the value of this training, making it an indispensable tool for career advancement. Key Features: CPD Certified Statistical Analysis Course Free Certificate from Reed CIQ Approved Statistical Analysis Course Developed by Specialist Lifetime Access Course Curriculum: Statistical Analysis Training Module 01: The Realm of Statistics Module 02: Basic Statistical Terms Module 03: The Center of the Data Module 04: Data Variability Module 05: Binomial and Normal Distributions Module 06: Introduction to Probability Module 07: Estimates and Intervals Module 08: Hypothesis Testing Module 09: Regression Analysis Module 10: Algorithms, Analytics and Predictions Module 11: Learning From Experience: The Bayesian Way Module 12: Doing Statistics: The Wrong Way Module 13: How We Can Do Statistics Better Learning Outcomes: Grasp fundamental statistical concepts for data analysis proficiency. Understand measures of central tendency and dispersion in datasets. Apply probability theory to make informed statistical decisions. Utilize hypothesis testing techniques to draw meaningful conclusions. Master regression analysis for predictive modelling and trend identification. Embrace Bayesian methods and enhance statistical inference capabilities. CPD 10 CPD hours / points Accredited by CPD Quality Standards Statistical Analysis Training 4:44:42 1: Module 01: The Realm of Statistics Preview 15:23 2: Module 02: Basic Statistical Terms 27:51 3: Module 03: The Center of the Data 10:00 4: Module 04: Data Variability 21:00 5: Module 05: Binomial and Normal Distributions 21:00 6: Module 06: Introduction to Probability 23:42 7: Module 07: Estimates and Intervals 21:35 8: Module 08: Hypothesis Testing 21:51 9: Module 09: Regression Analysis 21:00 10: Module 10: Algorithms, Analytics and Predictions 31:05 11: Module 11: Learning From Experience: The Bayesian Way 20:08 12: Module 12: Doing Statistics: The Wrong Way 23:39 13: Module 13: How We Can Do Statistics Better 25:28 14: CPD Certificate - Free 01:00 Who is this course for? This Statistical Analysis Training course is accessible to anyone eager to learn more about this topic. Through this course, you'll gain a solid understanding of Statistical Analysis Training. Moreover, this course is ideal for: Aspiring data analysts seeking statistical foundations for career advancement. Professionals in research roles aiming to refine statistical analysis skills. Students pursuing degrees in mathematics, economics, or related disciplines. Business professionals looking to leverage data-driven insights for strategic decisions. Anyone interested in enhancing statistical literacy and analytical reasoning abilities. Requirements There are no requirements needed to enrol into this Statistical Analysis Training course. We welcome individuals from all backgrounds and levels of experience to enrol into this Statistical Analysis Training course. Career path After finishing this Statistical Analysis Training course you will have multiple job opportunities waiting for you. Some of the following Job sectors of Statistical Analysis Training are: Data Analyst - £30K to £45K/year. Statistician - £35K to £50K/year. Market Research Analyst - £25K to £40K/year. Business Intelligence Analyst - £35K to £55K/year. Healthcare Data Analyst - £30K to £50K/year. Certificates Digital certificate Digital certificate - Included Reed Courses Certificate of Completion Digital certificate - Included Will be downloadable when all lectures have been completed.

Duration 3 Days 18 CPD hours This course is intended for This course is geared for attendees with solid Python skills who wish to learn and use basic machine learning algorithms and concepts Overview This 'skills-centric' course is about 50% hands-on lab and 50% lecture, with extensive practical exercises designed to reinforce fundamental skills, concepts and best practices taught throughout the course. Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below Getting Started & Optional Python Quick Refresher Statistics and Probability Refresher and Python Practice Probability Density Function; Probability Mass Function; Naive Bayes Predictive Models Machine Learning with Python Recommender Systems KNN and PCA Reinforcement Learning Dealing with Real-World Data Experimental Design / ML in the Real World Time Permitting: Deep Learning and Neural Networks Machine Learning Essentials with Python is a foundation-level, three-day hands-on course that teaches students core skills and concepts in modern machine learning practices. This course is geared for attendees experienced with Python, but new to machine learning, who need introductory level coverage of these topics, rather than a deep dive of the math and statistics behind Machine Learning. Students will learn basic algorithms from scratch. For each machine learning concept, students will first learn about and discuss the foundations, its applicability and limitations, and then explore the implementation and use, reviewing and working with specific use casesWorking in a hands-on learning environment, led by our Machine Learning expert instructor, students will learn about and explore:Popular machine learning algorithms, their applicability and limitationsPractical application of these methods in a machine learning environmentPractical use cases and limitations of algorithms Getting Started Installation: Getting Started and Overview LINUX jump start: Installing and Using Anaconda & Course Materials (or reference the default container) Python Refresher Introducing the Pandas, NumPy and Scikit-Learn Library Statistics and Probability Refresher and Python Practice Types of Data Mean, Median, Mode Using mean, median, and mode in Python Variation and Standard Deviation Probability Density Function; Probability Mass Function; Naive Bayes Common Data Distributions Percentiles and Moments A Crash Course in matplotlib Advanced Visualization with Seaborn Covariance and Correlation Conditional Probability Naive Bayes: Concepts Bayes? Theorem Naive Bayes Spam Classifier with Naive Bayes Predictive Models Linear Regression Polynomial Regression Multiple Regression, and Predicting Car Prices Logistic Regression Logistic Regression Machine Learning with Python Supervised vs. Unsupervised Learning, and Train/Test Using Train/Test to Prevent Overfitting Understanding a Confusion Matrix Measuring Classifiers (Precision, Recall, F1, AUC, ROC) K-Means Clustering K-Means: Clustering People Based on Age and Income Measuring Entropy LINUX: Installing GraphViz Decision Trees: Concepts Decision Trees: Predicting Hiring Decisions Ensemble Learning Support Vector Machines (SVM) Overview Using SVM to Cluster People using scikit-learn Recommender Systems User-Based Collaborative Filtering Item-Based Collaborative Filtering Finding Similar Movie Better Accuracy for Similar Movies Recommending movies to People Improving your recommendations KNN and PCA K-Nearest-Neighbors: Concepts Using KNN to Predict a Rating for a Movie Dimensionality Reduction; Principal Component Analysis (PCA) PCA with the Iris Data Set Reinforcement Learning Reinforcement Learning with Q-Learning and Gym Dealing with Real-World Data Bias / Variance Tradeoff K-Fold Cross-Validation Data Cleaning and Normalization Cleaning Web Log Data Normalizing Numerical Data Detecting Outliers Feature Engineering and the Curse of Dimensionality Imputation Techniques for Missing Data Handling Unbalanced Data: Oversampling, Undersampling, and SMOTE Binning, Transforming, Encoding, Scaling, and Shuffling Experimental Design / ML in the Real World Deploying Models to Real-Time Systems A/B Testing Concepts T-Tests and P-Values Hands-on With T-Tests Determining How Long to Run an Experiment A/B Test Gotchas Capstone Project Group Project & Presentation or Review Deep Learning and Neural Networks Deep Learning Prerequisites The History of Artificial Neural Networks Deep Learning in the TensorFlow Playground Deep Learning Details Introducing TensorFlow Using TensorFlow Introducing Keras Using Keras to Predict Political Affiliations Convolutional Neural Networks (CNN?s) Using CNN?s for Handwriting Recognition Recurrent Neural Networks (RNN?s) Using an RNN for Sentiment Analysis Transfer Learning Tuning Neural Networks: Learning Rate and Batch Size Hyperparameters Deep Learning Regularization with Dropout and Early Stopping The Ethics of Deep Learning Learning More about Deep Learning Additional course details: Nexus Humans Machine Learning Essentials with Python (TTML5506-P) training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the Machine Learning Essentials with Python (TTML5506-P) course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Mathematics is the universal language, bridging gaps, and solving mysteries of our daily lives. This Functional Skills Maths - Level 2 (Updated 2023) course has been carefully curated to elevate your mathematical understanding, offering clarity and depth on key concepts. From the world of fractions and percentages to the enigmatic arena of statistics and probability, every module ensures a robust conceptual foundation. Let's transform your approach to numbers and mathematical logic, making them allies in your quest for knowledge.

This PMI-RMP Certification Training will help you master the processes of risk management and the structured and objective approach to addressing uncertainty in projects. You will learn how to conduct risk planning, identification and analysis, and control both known and unknown risks in projects.

Telecomms training course description A comprehensive tour of the telecommunications technologies and terminology currently in use, and under development. What will you learn List and describe components of the PSTN. Explain how calls are made over the PSTN Compare analog and digital transmission methods. Describe the technologies within the transport plane. Recognise the benefits of extra features available in today's telephone networks. Telecomms training course details Who will benefit: Anyone new to the Telecommunications industry. Prerequisites: None. Duration 2 days Telecomms training course contents Telephone network architecture Handsets, local loop, distribution points, Local exchanges, main telephone switches, PBXs. Making a call - some basics Telephone call components, how a telephone call works, accessing the local exchange, loop disconnect, DTMF, standards, E.164, PSTN routing, Intelligent Networks, Special Rate Services. Analogue vs Digital Voice characteristics, PSTN bandwidth, analogue signalling, Digital encoding, PCM and the 64k, ADPCM and other voice compression methods. PBXs PABX, Call processing, networking PBXs, PBX facilities, bandwidth, blocking probability and Erlangs, Erlang models, using Erlang tables. Transmission methods Two wire transmission, 64k circuits, Nx64, E1, 2 wire to 4 wire conversion, echo, echo suppression, echo cancellers, twisted pair, coax, fibre optic, power lines, satellite systems, microwave. Signalling Analogue signalling, loop start, earth calling, E&M, AC15. Digital signalling -CAS, robbed bits and E1 slot 16 signalling. Digital signalling CCS, Q.931, SS7, Q.SIG, DPNSS, DASS2. Transport planes PDH, PDH issues, SDH, SDH architecture, SDH standards, SDH bit rates, SDH mulitplexors, DWDM. Networks Circuit Switched Networks, TDM, Packet Switched Networks, Frame Relay, Message Switching, Circuit Switching, STDM, Cell Switching, ATM, ATM cells, ATM traffic parameters, ATM QoS, MPLS. Other network access Modems, modulation, speeds, ISDN, BRI, PRI, xDSL, SDSL, ADSL. Other Services Centrex, VPNs, FeatureNet, CTI, Call Processing Systems, Voice Mail, Automated Attendant Systems, Interactive Voice Response, Call Management Systems, Call Conferencing, Star Services. Mobile communications 3 types of wireless telephone, mobile generations, base stations, cells, GSM, GPRS, 3G, UMTS, WCDMA, 4G, LTE. VoIP overview What is VoIP, VoIP benefits, What is IP? The IP header, Packetising voice, VoIP addressing, H.323, SIP, RTP. Bandwidth requirements.