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About the course “Quantum Computing for Finance” is an emerging multidisciplinary field of quantum physics, finance, mathematics, and computer science, in which quantum computations are applied to solve complex problems. “Introduction to Quantitative and Computational Finance” provides a basis to step into the world of Quantum Computing for Finance. This introductory course will develop fundamental concepts required for an understanding of quantum algorithms and more advanced topics in computational finance. Through this course, you will learn the basics of derivative products, the Black-Scholes-Merton model for pricing vanilla derivatives, and how to compute the price of exotic options with a computer. This course is designed for all those who wish to develop their skills and start a career in quantitative finance. This course is the first part of the specialised training program: “Quantum Computing for Finance”. What Skills you will learn The fundamentals of derivative products, their types – forwards and options, and their pricing. An understanding of the Black-Scholes-Merton model, hedging and volatility modelling. The computational and modelling techniques for pricing options such as Monte-Carlo simulations and the Finite Difference method. A strong foundation in quantitative and computational skills for modelling and solving complex financial problems using Python programming language. The skills for a career in the finance industry, including quantitative asset management and trading, financial engineering, risk management, and applied research. Course Prerequisites All potential learners should have prior knowledge of the following content areas, either through completion of academic studies or relative professional preparation: Basic calculus (partial derivatives) Probability theory (with an exposure to measure theory if possible) Basic linear algebra (matrix operations) Numerical Python (NumPy essentially) The course contains several Python based programming exercises. We recommend that you install Python on your local system to practice and implement the programs explained throughout the course. For instructions and tutorials for beginners, please click on the following link: Python installation instructions and tutorials for beginners Duration The estimated duration to complete this course is approximately 4 weeks (~3hrs/week). Course assessment To complete the course and earn certification, you must pass all the quizzes at the end of each lesson by scoring 80% or more. Instructors QuantFiQuantFi is a French start-up research firm formed in 2019 with the objective of using the science of quantum computing to provide solutions to the financial services industry. With its staff of PhD's and PhD students, QuantFi engages in fundamental and applied research in in the field of quantum finance, collaborating with industrial partners and universities in seeking breakthroughs in such areas as portfolio optimisation, asset pricing, and trend detection.

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Learn C++ from Scratch: The Complete Coding Guide premium bundle consists of 30 precisely chosen courses on a wide range of topics essential for anyone looking to excel in this field. Each segment of Learn C++ from Scratch: The Complete Coding Guide 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 Learn C++ from Scratch: The Complete Coding Guide course. After passing the Learn C++ from Scratch: The Complete Coding Guide exam You will be entitled to claim a PDF & Hardcopy certificate accredited by CPD Quality standards completely free. CPD 300 CPD hours / points Accredited by CPD Quality Standards Who is this course for? 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This Data Analysis and Data Science Bundle Consists of the following Premium courses: Course 01: Introduction to Data Analysis Course 02: Python for Data Analysis Course 03: Statistical Analysis Course 04: SQL NoSQL Big Data and Hadoop Course 05: Complete Microsoft Power BI 2021 Course 06: Data Analysis in Excel Level 3 Course Course 07: Data Analytics with Tableau Course 08: Basic Google Data Studio Course 09: Business Analytics Course 10: Complete Introduction to Business Data Analysis Level 3 Course 11: Business Intelligence and Data Mining Masterclass Course 12: Research Methods in Business Course 13: Computer Science: Graph Theory Algorithms Course 14: Data Protection and Data Security Level 2 Enrol now in Data Analysis and Data Science to advance your career, and use the premium study materials from Apex Learning. How will I get my Certificate? 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Curriculum of Bundle Course 01: Introduction to Data Analysis Module 01: Introduction Module 02: Agenda and Principles of Process Management Module 03: The Voice of the Process Module 04: Working as One Team for Improvement Module 05: Exercise: The Voice of the Customer Module 06: Tools for Data Analysis Module 07: The Pareto Chart Module 08: The Histogram Module 09: The Run Chart Module 10: Exercise: Presenting Performance Data Module 11: Understanding Variation Module 12: The Control Chart Module 13: Control Chart Example Module 14: Control Chart Special Cases Module 15: Interpreting the Control Chart Module 16: Control Chart Exercise Module 17: Strategies to Deal with Variation Module 18: Using Data to Drive Improvement Module 19: A Structure for Performance Measurement Module 20: Data Analysis Exercise Module 21: Course Project Module 22: Test your Understanding Course 02: Python for Data Analysis Welcome, Course Introduction & overview, and Environment set-up Python Essentials Python for Data Analysis using NumPy Python for Data Analysis using Pandas Python for Data Visualization using matplotlib Python for Data Visualization using Seaborn Python for Data Visualization using pandas Python for interactive & geographical plotting using Plotly and Cufflinks Capstone Project - Python for Data Analysis & Visualization Python for Machine Learning (ML) - scikit-learn - Linear Regression Model Python for Machine Learning - scikit-learn - Logistic Regression Model Python for Machine Learning - scikit-learn - K Nearest Neighbors Python for Machine Learning - scikit-learn - Decision Tree and Random Forests Python for Machine Learning - scikit-learn -Support Vector Machines (SVMs) Python for Machine Learning - scikit-learn - K Means Clustering Python for Machine Learning - scikit-learn - Principal Component Analysis (PCA) Recommender Systems with Python - (Additional Topic) Python for Natural Language Processing (NLP) - NLTK - (Additional Topic) Course 03: Statistical Analysis 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 Course 04: SQL NoSQL Big Data and Hadoop Module 01: Introduction Module 02: Relational Database Systems Module 03: Database Classification Module 04: Key-Value Store Module 05: Document-Oriented Databases Module 06: Search Engines Module 07: Wide Column Store Module 08: Time Series Databases Module 09: Graph Databases Module 10: Hadoop Platform Module 11: Big Data SQL Engines Module 12: Distributed Commit Log Module 13: Summary Course 05: Complete Microsoft Power BI 2021 Module 01: Introduction Module 02: Preparing our Project Module 03: Data Transformation - The Query Editor Module 04: Data Transformation - Advanced Module 05: Creating a Data Model Module 06: Data Visualization Module 07: Power BI & Python Module 08: Storytelling with Data Module 09: DAX - The Essentials Module 10: DAX - The CALCULATE function Module 11: Power BI Service - Power BI Cloud Module 12: Row-Level Security Module 13: More data sources Module 14: Next steps to improve & stay up to date Course 06: Data Analysis in Excel Level 3 Course Modifying a Worksheet Working with Lists Analyzing Data Visualizing Data with Charts Using PivotTables and PivotCharts Working with Multiple Worksheets and Workbooks Using Lookup Functions and Formula Auditing Automating Workbook Functionality Creating Sparklines and Mapping Data Forecasting Data Course 07: Data Analytics with Tableau Module 01: Introduction to the Course Module 02: Project 1: Discount Mart (Sales and Profit Analytics) Module 03: Project 2: Green Destinations (HR Analytics) Module 04: Project 3: Superstore (Sales Agent Tracker) Module 05: Northwind Trade (Shipping Analytics) Module 06: Project 5: Tesla (Stock Price Analytics) Module 07: Bonus: Introduction to Database Concepts Module 08: Tableau Stories Course 08: Basic Google Data Studio Module 01: Introduction to GDS Module 02: Data Visualization Module 03: Geo-visualization Module 04: A Socio-Economic Case Study Course 09: Business Analytics Module 01: What is business analysis? Module 02: Strategy analysis Module 03: Collaboration Module 04: Requirements analysis and Design definition Module 05: Requirements lifecycle management Module 06: Solution quality Module 07: Stakeholder management Module 08: BA Governance Module 09: Legal notes and Copyright information Course 10: Complete Introduction to Business Data Analysis Level 3 Module 1: Statistics Fundamentals Module 2: Data Analysis Module 3: Probability Module 4: Random Variables and Discrete Distributions Module 5: Continuous Distributions Module 6: Sampling Distributions Module 7: Confidence Interval Module 8: Hypothesis Testing with One Sample Module 9: Hypothesis Testing with Two Samples Module 10: The Chi-Square Distribution Module 11: F Distribution and One-Way ANOVA Module 12: Correlation analysis Module 13: Simple Linear Regression Analysis Course 11: Business Intelligence and Data Mining Masterclass Module 01: What is Business Intelligence? Module 02: Starting Case in understanding BI needs in diff phase of business Module 03: Decision Making Process and Need of IT systems Module 04: Problem Structure and Decision Support System Module 05: Introduction to BI Applications Module 06: Dashboard presentation systems Module 07: Different Types of Charts used in 131 Dashboards Module 08: Good Dashboard and BSC Module 09: Examples of Bad Dashboards 1 Module 10: Examples of Bad Dashboards 2 And much more... Course 12: Research Methods in Business Section 01: Applied Project & Research Methods in Business Section 02: Writing a Purpose / Quantitative and Qualitative Research Approaches Section 03: Mixed Method Research Approaches, Ethical Considerations & Writing Effectively Written Methodology Part 3 !@@ Section 04: Writing Data Collection Tools, Qualitative & Quantitative Data Analysis Section 05: Comparing Findings to Literature and Writing the Final Paper Course 13: Computer Science: Graph Theory Algorithms Module 00: Promo Module 01: Introduction Module 02: Common Problem Module 03: Depth First Search Module 04: Breadth First Search Module 05: Breadth First Search Shortest Path on a Grid And much more... Course 14: Data Protection and Data Security Level 2 GDPR Basics GDPR Explained Lawful Basis for Preparation Rights and Breaches Responsibilities and Obligations CPD 165 CPD hours / points Accredited by CPD Quality Standards Who is this course for? Anyone from any background can enrol in this Data Analysis and Data Science bundle. Requirements Our Data Analysis and Data Science course is fully compatible with PCs, Macs, laptops, tablets and Smartphone devices. Career path Having this Data Analysis and Data Science expertise will increase the value of your CV and open you up to multiple job sectors. Certificates Certificate of completion Digital certificate - Included You will get the PDF Certificate for the title course (Introduction to Data Analysis) absolutely Free! 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. Please Note: The delivery charge inside the UK is £3.99, and the international students must pay a £9.99 shipping cost.

Duration 2 Days 12 CPD hours This course is intended for If you are a data analyst, data scientist, or a business analyst who wants to get started with using Python and machine learning techniques to analyze data and predict outcomes, this book is for you. Basic knowledge of computer programming and data analytics is a must. Familiarity with mathematical concepts such as algebra and basic statistics will be useful. Overview By the end of this course, you will have the skills you need to confidently use various machine learning algorithms to perform detailed data analysis and extract meaningful insights from data. This course is designed to give you practical guidance on industry-standard data analysis and machine learning tools in Python, with the help of realistic data. The course will help you understand how you can use pandas and Matplotlib to critically examine a dataset with summary statistics and graphs, and extract the insights you seek to derive. You will continue to build on your knowledge as you learn how to prepare data and feed it to machine learning algorithms, such as regularized logistic regression and random forest, using the scikit-learn package. You?ll discover how to tune the algorithms to provide the best predictions on new and unseen data. As you delve into later sections, you?ll be able to understand the working and output of these algorithms and gain insight into not only the predictive capabilities of the models but also their reasons for making these predictions. Data Exploration and Cleaning Python and the Anaconda Package Management System Different Types of Data Science Problems Loading the Case Study Data with Jupyter and pandas Data Quality Assurance and Exploration Exploring the Financial History Features in the Dataset Activity 1: Exploring Remaining Financial Features in the Dataset Introduction to Scikit-Learn and Model Evaluation Introduction Model Performance Metrics for Binary Classification Activity 2: Performing Logistic Regression with a New Feature and Creating a Precision-Recall Curve Details of Logistic Regression and Feature Exploration Introduction Examining the Relationships between Features and the Response Univariate Feature Selection: What It Does and Doesn't Do Building Cloud-Native Applications Activity 3: Fitting a Logistic Regression Model and Directly Using the Coefficients The Bias-Variance Trade-off Introduction Estimating the Coefficients and Intercepts of Logistic Regression Cross Validation: Choosing the Regularization Parameter and Other Hyperparameters Activity 4: Cross-Validation and Feature Engineering with the Case Study Data Decision Trees and Random Forests Introduction Decision trees Random Forests: Ensembles of Decision Trees Activity 5: Cross-Validation Grid Search with Random Forest Imputation of Missing Data, Financial Analysis, and Delivery to Client Introduction Review of Modeling Results Dealing with Missing Data: Imputation Strategies Activity 6: Deriving Financial Insights Final Thoughts on Delivering the Predictive Model to the Client

***24 Hour Limited Time Flash Sale*** Data Visualisation Professional - CPD Certified Admission Gifts FREE PDF & Hard Copy Certificate| PDF Transcripts| FREE Student ID| Assessment| Lifetime Access| Enrolment Letter Are you a professional feeling stuck in your career, struggling to keep up with the ever-changing demands of the industry? Or perhaps you're a beginner, unsure of where to start or how to break into your desired field. Whichever stage you're in, our exclusive Data Visualisation Professional - CPD Certified Bundle provides unique insights and tools that can help you achieve your goals. Designed to cater to the needs of both seasoned professionals and aspiring newcomers, our Data Visualisation Professional - CPD Certified bundle is a comprehensive program that will equip you with the essential skills and knowledge you need to succeed. Whether you're looking to advance in your current role or embark on a new career journey, this bundle has everything you need to take your professional life to the next level. But that's not all. When you enrol in Data Visualisation Professional - CPD Certified Online Training, you'll receive 30 CPD-Accredited PDF Certificates, Hard Copy Certificates, and our exclusive student ID card, all absolutely free. Courses Are Included In this Data Visualisation Professional - CPD Certified Career Bundle: Course 01: Master JavaScript with Data Visualization Course 02: SQL for Data Science, Data Analytics and Data Visualization Course 03: Spatial Data Visualization and Machine Learning in Python Course 04: Data Analysis Course 05: Excel Data Analysis Course 06: Excel Pivot Tables, Pivot Charts, Slicers, and Timelines Course 07: Python Programming Bible Course 08: Python Data Science with Numpy, Pandas and Matplotlib Course 09: Data Science & Machine Learning with Python Course 10: Statistics & Probability for Data Science & Machine Learning Course 11: R Programming for Data Science Course 12: Quick Data Science Approach from Scratch Course 13: Mastering SQL Programming Course 14: Learn Python, JavaScript, and Microsoft SQL for Data science Course 15: Web Scraping and Mapping Dam Levels in Python and Leaflet Course 16: Business Data Analysis Course 17: Business Intelligence and Data Mining Diploma Course 18: Google Data Studio: Data Analytics Course 19: Google Analytics for Everyone Course 20: Big Data Analytics with PySpark Tableau Desktop and MongoDB Course 21: Big Data Analytics with PySpark Power BI and MongoDB Course 22: Introduction to Data Analytics with Tableau Course 23: Microsoft Power BI - Master Power BI in 90 Minutes! Course 24: PowerBI Formulas Course 25: Develop Big Data Pipelines with R & Sparklyr & Tableau Course 26: Develop Big Data Pipelines with R, Sparklyr & Power BI Course 27: Microsoft Access Tables and Queries Course 28: Statistical Analysis Course 29: Research Methods in Business Course 30: Customer Analytics Training With Data Visualisation Professional - CPD Certified, you'll embark on an immersive learning experience that combines interactive lessons with voice-over audio, ensuring that you can learn from anywhere in the world, at your own pace. And with 24/7 tutor support, you'll never feel alone in your journey, whether you're a seasoned professional or a beginner. Don't let this opportunity pass you by. Enrol in Data Visualisation Professional - CPD Certified today and take the first step towards achieving your goals and dreams. Why buy this Data Visualisation Professional - CPD Certified? Free CPD Accredited Certificate upon completion of Data Visualisation Professional - CPD Certified Get a free student ID card with Data Visualisation Professional - CPD Certified Lifetime access to the Data Visualisation Professional - CPD Certified course materials Get instant access to this Data Visualisation Professional - CPD Certified course Learn Data Visualisation Professional - CPD Certified from anywhere in the world 24/7 tutor support with the Data Visualisation Professional - CPD Certified course. Start your learning journey straightaway with our Data Visualisation Professional - CPD Certified Training! Data Visualisation Professional - CPD Certified premium bundle consists of 30 precisely chosen courses on a wide range of topics essential for anyone looking to excel in this field. Each segment of the Data Visualisation Professional - 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 Data Visualisation Professional - CPD Certified course. After passing the Data Visualisation Professional - CPD Certified exam You will be entitled to claim a PDF & Hardcopy certificate accredited by CPD Quality standards completely free. CPD 300 CPD hours / points Accredited by CPD Quality Standards Who is this course for? This Data Visualisation Professional - CPD Certified course is ideal for: Students seeking mastery in Data Visualisation Professional - CPD Certified Professionals seeking to enhance Data Visualisation Professional - CPD Certified skills Individuals looking for a Data Visualisation Professional - CPD Certified-related career. Anyone passionate about Data Visualisation Professional - CPD Certified Requirements This Data Visualisation Professional - CPD Certified doesn't require prior experience and is suitable for diverse learners. Career path After the assessment, you will be ready for countless job opportunities. Some of them are- Business Analyst Business Intelligence Analyst Analytics Manager Data Analyst Data Scientist However, in the growing market of the UK, these professionals are expected to earn between £25k to £80k yearly on average. Certificates CPD Accredited Digital Certificate Digital certificate - Included CPD Accredited Hard Copy Certificate Hard copy certificate - Included If you are an international student, then you have to pay an additional 10 GBP for each certificate as an international delivery charge.

About this training course Business Impact: The main aim is to provide insight and understanding of data analytics and machine learning principles through applications. Field data is used to explain data-analysis workflows. Using easy to follow solution scripts, the participants will assess and extract value from the data sets. Hands-on solution approach will give them confidence to try out applicable techniques on data from their field assets. Data analysis means cleaning, inspecting, transforming, and modeling data with the goal of discovering new, useful information and supporting decision-making. In this hands-on 2-day training course, the participants learn some data analysis and data science techniques and workflows applied to petroleum production (specifically artificial lift) while reviewing code and practicing. The focus is on developing data-driven models while keeping our feet closer to the underlying oil and gas production principles. Unique Features: Eight business use cases covering their business impact, code walkthroughs for most all and solution approach. Industry data sets for participants to practice on and take home. No software or complicated Python frameworks required. Training Objectives After the completion of this training course, participants will be able to: Understand digital oil field transformation and its impact on business Examine machine learning methods Review workflows and code implementations After completing the course, participants will have a set of tools and some pathways to model and analyze their data in the cloud, find trends, and develop data-driven models Target Audience This training course is suitable and will greatly benefit the following specific groups: Artificial lift, production and facilities engineers and students to enhance their knowledge base, increase technology awareness, and improve the facility with different data analysis techniques applied on large data sets Course Level Intermediate Advanced Training Methods The course discusses several business use-cases that are amenable to data-driven workflows. For each use case, the instructor will show the solution using a data analysis technique with Python code deployed in the Google cloud. Trainees will solve a problem and tweak their solution. Course Duration: 2 days in total (14 hours). Training Schedule 0830 - Registration 0900 - Start of training 1030 - Morning Break 1045 - Training recommences 1230 - Lunch Break 1330 - Training recommences 1515 - Evening break 1530 - Training recommences 1700 - End of Training The maximum number of participants allowed for this training course is 20. This course is also available through our Virtual Instructor Led Training (VILT) format. Prerequisites: Understanding of petroleum production concepts Knowledge of Python is not a must but preferred to get the full benefit. The training will use the Google Collaboratory environment available in Google-Cloud for hands-on exercises Trainees will need to bring a computer with a Google Chrome browser and a Google email account (available for free) Trainer Your expert course leader has over 35 years' work-experience in multiphase flow, artificial lift, real-time production optimization and software development/management. His current work is focused on a variety of use cases like failure prediction, virtual flow rate determination, wellhead integrity surveillance, corrosion, equipment maintenance, DTS/DAS interpretation. He has worked for national oil companies, majors, independents, and service providers globally. He has multiple patents and has delivered a multitude of industry presentations. Twice selected as an SPE distinguished lecturer, he also volunteers on SPE committees. He holds a Bachelor's and Master's in chemical engineering from the Gujarat University and IIT-Kanpur, India; and a Ph.D. in Petroleum Engineering from the University of Tulsa, USA. Highlighted Work Experience: At Weatherford, consulted with clients as well as directed teams on digital oilfield solutions including LOWIS - a solution that was underneath the production operations of Chevron and Occidental Petroleum across the globe. Worked with and consulted on equipment's like field controllers, VSDs, downhole permanent gauges, multiphase flow meters, fibre optics-based measurements. Shepherded an enterprise-class solution that is being deployed at a major oil and gas producer for production management including artificial lift optimization using real time data and deep-learning data analytics. Developed a workshop on digital oilfield approaches for production engineers. Patents: Principal inventor: 'Smarter Slug Flow Conditioning and Control' Co-inventor: 'Technique for Production Enhancement with Downhole Monitoring of Artificially Lifted Wells' Co-inventor: 'Wellbore real-time monitoring and analysis of fracture contribution' Worldwide Experience in Training / Seminar / Workshop Deliveries: Besides delivering several SPE webinars, ALRDC and SPE trainings globally, he has taught artificial lift at Texas Tech, Missouri S&T, Louisiana State, U of Southern California, and U of Houston. He has conducted seminars, bespoke trainings / workshops globally for practicing professionals: Companies: Basra Oil Company, ConocoPhillips, Chevron, EcoPetrol, Equinor, KOC, ONGC, LukOil, PDO, PDVSA, PEMEX, Petronas, Repsol, , Saudi Aramco, Shell, Sonatrech, QP, Tatneft, YPF, and others. Countries: USA, Algeria, Argentina, Bahrain, Brazil, Canada, China, Croatia, Congo, Ghana, India, Indonesia, Iraq, Kazakhstan, Kenya, Kuwait, Libya, Malaysia, Oman, Mexico, Norway, Qatar, Romania, Russia, Serbia, Saudi Arabia, S Korea, Tanzania, Thailand, Tunisia, Turkmenistan, UAE, Ukraine, Uzbekistan, Venezuela. Virtual training provided for PetroEdge, ALRDC, School of Mines, Repsol, UEP-Pakistan, and others since pandemic. POST TRAINING COACHING SUPPORT (OPTIONAL) To further optimise your learning experience from our courses, we also offer individualized 'One to One' coaching support for 2 hours post training. We can help improve your competence in your chosen area of interest, based on your learning needs and available hours. This is a great opportunity to improve your capability and confidence in a particular area of expertise. It will be delivered over a secure video conference call by one of our senior trainers. They will work with you to create a tailor-made coaching program that will help you achieve your goals faster. Request for further information post training support and fees applicable Accreditions And Affliations

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In this self-paced course, you will learn how to use TensorFlow 2 to build recurrent neural networks (RNNs). You will learn about sequence data, forecasting, Elman Unit, GRU, and LSTM. You will also learn how to work with image classification and how to get stock return predictions using LSTMs. We will also cover Natural Language Processing (NLP) and learn about text preprocessing and classification.