807 Modeling courses

The CAIA Association is a global professional body dedicated to creating greater alignment, transparency, and knowledge for all investors, with a specific emphasis on alternative investments. Course Overview The CAIA Association is a global professional body dedicated to creating greater alignment, transparency, and knowledge for all investors, with a specific emphasis on alternative investments. A Member-driven organization representing professionals in more than 100 countries, CAIA Association advocates for the highest ethical standards. Whether you need a deep, practical understanding of the world of alternative investments, a solid introduction, or data science skills for the future in finance, the CAIA Association offers a program for you. Why CAIA? Distinguish yourself with knowledge, expertise, and a clear career advantage – become a CAIA Charterholder. CAIA® is the globally recognized credential for professionals allocating, managing, analyzing, distributing, or regulating alternative investments. The Level II curriculum takes a top-down approach and provides Candidates with the skills and tools to conduct due diligence, monitor investments, and appropriately construct an investment portfolio. In addition, the Level II curriculum contains Emerging Topic readings; articles written by academics and practitioners designed to further inform and provoke the Candidate’s investment management process. After passing the Level II exam you are eligible, with relevant professional experience, to join the CAIA Association as a Member and receive the CAIA Charter. You will be part of an elite group of more than 13,000 professionals worldwide. Only after joining the Association, you are eligible to add the CAIA designation to your professional profiles. Who will benefit from enrolling in the CAIA program? Professionals who want to develop a deep level of knowledge and demonstrated expertise in alternative investments and their contribution to the diversified portfolio should pursue the CAIA Charter including: • Asset Allocators • Risk managers • Analysts • Portfolio managers • Traders • Consultants • Business development/marketing • Operations • Advisors Curriculum Topics: Topic 1: Emerging Topics • Decentralized Finance: On Blockchain- and Smart Contract-Based Financial Markets • Technical Guide for Limited Partners: Responsible Investing in Private Equity • Channels for Exposure to Bitcoin • Assessing Long-Term Investor Performance: Principles, Policies and Metrics • Demystifying Illiquid Assets: Expected Returns for Private Equity • An Introduction to Portfolio Rebalancing Strategies • Longevity and Liabilities: Bridging the Gap • A Short Introduction to the World of Cryptocurrencies Topic 2: Ethics, Regulation and ESG • Asset Manager Code • Recommendations and Guidance • Global Regulation • ESG and Alternative Investments • ESG Analysis and Application Topic 3: Models • Modeling Overview and Interest Rate Models • Credit Risk Models • Multi-Factor Equity Pricing Models • Asset Allocation Processes and the Mean-Variance Model • Other Asset Allocation Approaches Topic 4: Institutional Asset Owners and Investment Policies • Types of Asset Owners and the Investment Policy Statement • Foundations and the Endowment Model • Pension Fund Portfolio Management • Sovereign Wealth Funds • Family Offices and the family office Model Topic 5: Risk and Risk Management • Cases in Tail Risk • Benchmarking and Performance Attribution • Liquidity and Funding Risks • Hedging, Rebalancing, and Monitoring • Risk Measurement, Risk Management, and Risk Systems Topic 6: Methods for Alternative Investing • Valuation and Hedging Using Binomial Trees • Directional Strategies and Methods • Multivariate Empirical Methods and Performance Persistence • Relative Value Methods • Valuation Methods for Private Assets: The Case of Real Estate Topic 7: Accessing Alternative Investments • Hedge Fund Replication • Diversified Access to Hedge Funds • Access to Real Estate and Commodities • Access through Private Structures • The Risk and Performance of Private and Listed Assets Topic 8: Due Diligence and Selecting Managers • Active Management and New Investments • Selection of a Fund Manager • Investment Process Due Diligence • Operational Due Diligence • Due Diligence of Terms and Business Activities Topic 9: Volatility and Complex Strategies • Volatility as a Factor Exposure • Volatility, Correlation, and Dispersion Products and Strategies • Complexity and Structured Products • Insurance-Linked and Hybrid Securities • Complexity and the Case of Cross-Border Real Estate Investing DURATION 200 Hours WHATS INCLUDED Course Material Case Study Experienced Lecturer Refreshments Certificate

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Description Register on the Essential Training on Deep Learning Heuristic using R today and build the experience, skills and knowledge you need to enhance your professional development and work towards your dream job. Study this course through online learning and take the first steps towards a long-term career. The course consists of a number of easy to digest, in-depth modules, designed to provide you with a detailed, expert level of knowledge. Learn through a mixture of instructional video lessons and online study materials. Receive online tutor support as you study the course, to ensure you are supported every step of the way. Get a certificate as proof of your course completion. The Essential Training on Deep Learning Heuristic using R course is incredibly great value and allows you to study at your own pace. Access the course modules from any internet-enabled device, including computers, tablets, and smartphones. The course is designed to increase your employability and equip you with everything you need to be a success. Enrol on the now and start learning instantly! What You Get With This Course Receive a digital certificate upon successful completion of the course Get taught by experienced, professional instructors Study at a time and pace that suits your learning style Get instant feedback on assessments 24/7 help and advice via email or live chat Get full tutor support on weekdays (Monday to Friday) Certificate of Achievement After the successful completion of the final assessment, you will receive a CPD-accredited certificate of achievement. The PDF certificate is for 9.99, and it will be sent to you immediately after through e-mail. You can get the hard copy for 15.99, which will reach your doorsteps by post.  Method of Assessment You need to attend an assessment right after the completion of this course to evaluate your progression. For passing the assessment, you need to score at least 60%. After submitting your assessment, you will get feedback from our experts immediately. Who Is This Course For The course is ideal for those who already work in this sector or are aspiring professionals. This course is designed to enhance your expertise and boost your CV. Learn key skills and gain a professional qualification to prove your newly-acquired knowledge.   Course Content Section 01: Experimental Design - Heuristics Project 1-Agriculture Course Contents 00:08:00 Creating Dataframes 00:09:00 Generating Descriptive 00:11:00 Generating Descriptive Continued 00:10:00 Section 02: Experimental Design - Heuristics Project 2-Cryptocurrencies Setting Directory and Environment 00:11:00 Assigning Variables 00:09:00 Syntax and Command Part 1 00:09:00 Syntax and Command Part 2 00:10:00 Syntax and Command Part 3 00:07:00 Setting Directory and Environment-Cryptocurrencies 00:09:00 Spearman Techniques 00:08:00 Generating Line Graphs 00:09:00 Generating Scatter Plots 00:10:00 Generating Multiple Scatter Plots 00:10:00 Section 03: Experimental Design: Heuristics Project 3-Energy Sector Understanding Regression Modeling Theory 00:10:00 Implementing Linear Regression Modeling 00:10:00 Syntax and Commands 00:10:00 Generating Scatter Plots-Energy Sector 00:09:00 Multiple Scatter Plots 00:09:00 Section 04: Experimental Design: Heuristics Project 4-Financial Markets Creating Dataframes-Financial Markets 00:08:00 Understanding Multiple 00:09:00 Implementing Multiple Regression Model in R 00:10:00 Plot and Draw Line of Fit 00:10:00 Multiple Scatter Plots in a Graphical Frame 00:07:00 Resource Resource - Essential Training on Deep Learning Heuristic using R 00:00:00 Frequently Asked Questions Are there any prerequisites for taking the course? There are no specific prerequisites for this course, nor are there any formal entry requirements. All you need is an internet connection, a good understanding of English and a passion for learning for this course. Can I access the course at any time, or is there a set schedule? You have the flexibility to access the course at any time that suits your schedule. Our courses are self-paced, allowing you to study at your own pace and convenience. How long will I have access to the course? For this course, you will have access to the course materials for 1 year only. This means you can review the content as often as you like within the year, even after you've completed the course. However, if you buy Lifetime Access for the course, you will be able to access the course for a lifetime. Is there a certificate of completion provided after completing the course? Yes, upon successfully completing the course, you will receive a certificate of completion. This certificate can be a valuable addition to your professional portfolio and can be shared on your various social networks. Can I switch courses or get a refund if I'm not satisfied with the course? We want you to have a positive learning experience. If you're not satisfied with the course, you can request a course transfer or refund within 14 days of the initial purchase. How do I track my progress in the course? Our platform provides tracking tools and progress indicators for each course. You can monitor your progress, completed lessons, and assessments through your learner dashboard for the course. What if I have technical issues or difficulties with the course? If you encounter technical issues or content-related difficulties with the course, our support team is available to assist you. You can reach out to them for prompt resolution.

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Duration 3 Days 18 CPD hours This course is intended for Data Science for Marketing Analytics is designed for developers and marketing analysts looking to use new, more sophisticated tools in their marketing analytics efforts. It'll help if you have prior experience of coding in Python and knowledge of high school level mathematics. Some experience with databases, Excel, statistics, or Tableau is useful but not necessary. Overview By the end of this course, you will be able to build your own marketing reporting and interactive dashboard solutions. The course starts by teaching you how to use Python libraries, such as pandas and Matplotlib, to read data from Python, manipulate it, and create plots, using both categorical and continuous variables. Then, you'll learn how to segment a population into groups and use different clustering techniques to evaluate customer segmentation.As you make your way through the course, you'll explore ways to evaluate and select the best segmentation approach, and go on to create a linear regression model on customer value data to predict lifetime value. In the concluding sections, you'll gain an understanding of regression techniques and tools for evaluating regression models, and explore ways to predict customer choice using classification algorithms. Finally, you'll apply these techniques to create a churn model for modeling customer product choices. Data Preparation and Cleaning Data Models and Structured Data pandas Data Manipulation Data Exploration and Visualization Identifying the Right Attributes Generating Targeted Insights Visualizing Data Unsupervised Learning: Customer Segmentation Customer Segmentation Methods Similarity and Data Standardization k-means Clustering Choosing the Best Segmentation Approach Choosing the Number of Clusters Different Methods of Clustering Evaluating Clustering Predicting Customer Revenue Using Linear Regression Understanding Regression Feature Engineering for Regression Performing and Interpreting Linear Regression Other Regression Techniques and Tools for Evaluation Evaluating the Accuracy of a Regression Model Using Regularization for Feature Selection Tree-Based Regression Models Supervised Learning: Predicting Customer Churn Classification Problems Understanding Logistic Regression Creating a Data Science Pipeline Fine-Tuning Classification Algorithms Support Vector Machine Decision Trees Random Forest Preprocessing Data for Machine Learning Models Model Evaluation Performance Metrics Modeling Customer Choice Understanding Multiclass Classification Class Imbalanced Data Additional course details: Nexus Humans Data Science for Marketing Analytics training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the Data Science for Marketing Analytics course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

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