dipopotamus/custom_instruct_coursera_course_module_list huggingface dataset:
Course: Intro to Snowflake for Devs, Data Scientists, Data Engineers
Modules:
Intro and Course Overview, Worksheets and a Simple Example - Part I, Worksheets and a Simple Example - Part II, Virtual Warehouses Overview, Virtual Warehouses Scaling - Part I, Virtual Warehouses Scaling - Part II, Stages and Basic Ingestion - Part I, Stages and Basic Ingestion - Part II, Databases and Schemas - Part I, Databases and Schemas - Part II, Tables - Part I, Tables - Part II, Views - Part I, Views - Part II, Semi-Structured Data Types, Semi-Structured Data Manipulation, Snowflake Architecture Overview, Wrap-up of Snowflake’s Core Objects and Architecture
Intro to Snowflake Feature Overview, Time Travel - Part I, Time Travel - Part II, Permanent, Transient, and Temporary Tables, Cloning, Resource Monitors, User-Defined Functions (UDFs) - Part I, User-Defined Functions (UDFs) - Part II, User-Defined Table Functions (UDTFs), Stored Procedures - Part I, Stored Procedures - Part II, Role-based Access Control (RBAC) - Part I, Role-based Access Control (RBAC) - Part II [Tether], VS Code Extension, Snowpark DataFrames - Part I, Snowpark DataFrames - Part II, Snowflake CLI, Wrap-up of Snowflake Feature Overview
Intro to Overview of Builder Workloads, Snowflake Data Engineering Overview - Part I, Snowflake Data Engineering Overview - Part II, Snowpipe - Part I, Snowpipe - Part II, Snowflake GenAI Overview - Part I, Snowflake GenAI Overview - Part II, Snowflake Cortex LLM Functions - Part I, Snowflake Cortex LLM Functions - Part II, Snowflake ML Overview - Part I, Snowflake ML Overview - Part II, Snowpark ML Modeling - Part I, Snowpark ML Modeling - Part II, Snowpark ML Modeling - Part III, Snowflake Applications Overview - Part I, Snowflake Applications Overview - Part II, Streamlit in Snowflake - Part I, Streamlit in Snowflake - Part II, The Data Cloud, Wrap-up of Overview of Builder Workloads, Course Conclusion
Course: Simple Regression Analysis in Public Health
Modules:
Introduction, Simple Regression: An Overview, Simple Linear Regression with a Binary (or Nominal Categorical) Predictor, Simple Linear Regression with a Categorical Predictor, Simple Linear Regression with a Continuous Predictor, Simple Linear Regression Model: Estimating the Regression Equation—Accounting for Uncertainty in the Estimates, Measuring the Strength of a Linear Association, Additional Examples
Introduction, Simple Logistic Regression: An Overview, Simple Logistic Regression with a Binary (or Categorical) Predictor, Simple Logistic Regression with a Continuous Predictor, Simple Logistic Regression: Accounting for Uncertainty in the Estimates, Estimating Risk and Functions of Risk from Logistic Regression Results, Additional Examples
Introduction, Simple Cox Regression: An Overview, Simple Cox Regression with a Binary (or Categorical) Predictor, Simple Cox Regression with a Continuous Predictor, Simple Cox Regression: Accounting for Uncertainty in the Estimates, Estimating Survival Curves from Cox Regression Results, Additional Examples
Introduction, Confounding: A Formal Definition and Some Examples, Adjusted Estimates: Presentation, Interpretation, and Utility for Assessing Confounding, Adjusted Estimates: The General Idea Behind the Computations, Additional Examples, Effect modification: Introduction with an analytic example, Effect modification: Tree damage and elevation example, Effect modification: Examples from the literature, Confounding versus effect modification: A review, Additional examples
Biostatistical Consulting Project
Course: HI-FIVE: Health Informatics For Innovation, Value & Enrichment (Social/Peer Perspective)
Modules:
Introduction to Social/Peer Perspective Course
Introduction and Definitions: Population Health and Health IT, Population Health Management and Health IT, What is Care Coordination?, Care Coordination Models, Long-Term Care Coordination
Introduction to Value-Based Care, New Models of Care Delivery, Accountable Care Organizations, ACO Performance, The Patient-Centered Medical Home
Patient-Initiated Information Exchange, Patient Preferences for Information Sharing, Information-Driven Patient Education, Health IT-Supported Communication Between Primary Care Provider and Specialist, Methods for Patient Engagement (Part 1), Methods for Patient Engagement (Part 2)
Communicating Health Risk: Overview, Communicating Health Risk: Evidence-Based Methods, Shared Decision-Making: Introduction, Shared Decision-Making: Patient Decision Aids
Evaluation Survey and Next Steps
Course: Introduction to Data Analytics in Google Cloud
Modules:
Welcome to the Google Cloud Data Analytics Certificate, Introduction to Course 1, Welcome to module 1, Ben: The cloud's impact on education, Cloud computing infrastructure, Cloud computing service models, Benefits of cloud computing, Traditional versus cloud computing, Cloud data warehouses, Introduction to BigQuery, Wrap-up
Welcome to module 2, Steps for effective cloud migration, Explore cloud deployment models, Cloud's role in advancing data analytics, Cost considerations of cloud services, Cloud data analytics in different industries, Overview of cloud architecture, Cost optimization for the cloud, Wrap-up
Welcome to module 3, Introduction to data management, Safety and privacy in the cloud, Stages of the data lifecycle, The data lifecycle in action, Common roles on a data team, Safa: Data analysts are essential in the cloud, Importance of automation in the data lifecycle, Introduction to data retention policies, Overview of version control and holds, Wrap-up
Welcome to module 4, Data analysis in the cloud, Overview of Google's cloud data analytics tools, Data access management for secure data, Introduction to business data requests, Tips for ensuring accurate data, Cloud data storage and management tools, Introduction to Dataproc, Introduction to process management, Strategies for handling data requests, Clear documentation benefits the whole team, Wrap-up, Vince: Characteristics of strong data analyst candidates, Vince and Monica: Interview role play, Course wrap-up
Course: Simple Past Tense
Modules:
Meets Your Teachers, Simple Past BE, Simple Past Regular Verbs, Simple Past Irregular Verbs
Simple Past Negative BE, Simple Past Negative Verbs, Object Pronouns
Simple Past Yes/No Questions BE, Simple Past Yes/No Questions Verbs, Simple Past Information Questions
This, That, These & Those, Comparatives, Infinitives with Want, Need & Like
Course: Applying Machine Learning to Your Data with GC - 日本語版
Modules:
コースの概要
機械学習の概要, デモ: Google フォト, ディープ ラーニングとは, ビジネス向け機械学習アプリケーション, ML の用語: インスタンス、特徴、ラベル, さまざまな機械学習ツール
事前トレーニング済み機械学習モデル, デモ: Cloud Translation API と Cloud Vision API, デモ: Natural Language Processing API, Coursera:  Google Cloud Platform と Qwiklabs を使用する
機械学習に適したデータセットについて, 適切な特徴を選択する, データの探索、前処理を行う, データ パイプライン作成用のその他のツール, 未知の情報を把握する, 再現性のあるデータセット スプリットを作成する
BigQuery の機械学習の概要, モデル構築のフェーズ, ラボの概要: BigQuery で訪問者の再購入を予測する
コースのまとめ
Course: Data Science Capstone
Modules:
Welcome to the Capstone Project, Welcome from SwiftKey, You Are a Data Scientist Now, Introduction to Task 0: Understanding the Problem, Introduction to Task 1: Getting and Cleaning the Data, Regular Expressions: Part 1 (Optional), Regular Expressions: Part 2 (Optional)
Introduction to Task 2: Exploratory Data Analysis, Introduction to Task 3: Modeling
Introduction to Task 4: Prediction Model
Introduction to Task 5: Creative Exploration
Introduction to Task 6: Data Product
Introduction to Task 7: Slide Deck
Congratulations!
Course: Introduction to Generative AI - 繁體中文
Modules:
生成式 AI 簡介
Course: Fase final de Marketing en redes sociales de Meta
Modules:
Introducción al Certificado de profesional del marketing en redes sociales de Facebook, Presentación de Anke Audenaert, Introducción de la semana, Carreras en marketing digital
Introducción de la semana, Conclusión de la semana
Presentación del examen de Facebook, ¡Felicitaciones!
Course: Smart Analytics, Machine Learning, and AI on Google Cloud
Modules:
Course Introduction
Module introduction, What is AI?, From ad-hoc data analysis to data-driven decisions, Options for ML models on Google Cloud
Module introduction, Unstructured data is hard, ML APIs for enriching data, Lab Intro: Using the Natural Language API to Classify Unstructured Text, Getting Started with Google Cloud and Qwiklabs
Module introduction, What’s a Notebook?, BigQuery magic and ties to Pandas, Lab Intro: BigQuery in JupyterLab on Vertex AI
Module introduction, Ways to do ML on Google Cloud, Vertex AI Pipelines, TensorFlow Hub, Lab Intro: Running Pipelines on Vertex AI, Summary
Module introduction, BigQuery ML for Quick Model Building, Supported models, Lab Intro: Predict Bike Trip Duration with a Regression Model in BigQuery ML, Lab Intro: Movie Recommendations in BigQuery ML, Summary
Module introduction, Why AutoML?, AutoML Vision, AutoML Natural Language Processing, AutoML Tables, Summary
Course Summary
Course: Advanced Cybersecurity
Modules:
Introduction to Cloud Computing, Types of Cloud Computing Services, Responsibilities in On-Premises, Responsibilities in Cloud, Cloud Deployment Models, Understanding Virtualization, Cloud Security Control Layers, Benefits of Virtualization in the Cloud, Cloud Computing Threats, Cloud Computing Attacks, Cloud Computing Security Tools, Hacking Mobile Platforms, Vulnerable Areas for Mobiles, Mobile Platform Vulnerabilities and Risks, DEMO: Hacking Mobile Phone, Android OS, Architecture of Android OS, Android Rooting Tools, Android Pen Testing, Counter Measures for Mobile Security
What Is Cryptography?, Types of Cryptography, DEMO: Cryptographic Techniques, RSA Algorithm, Steganography and Its Types, How Steganography Works, Use of Algorithms and Transformation, DEMO: Perform Stegnography Using Open Stego, DEMO: Perform Stegnography Using Snow Tool, Denial-of-Service (DoS) Attack, Distributed Denial-of-Service (DDoS) Attack, Symptoms of DoS and DDoS Attacks, How DDoS Attacks Work, DEMO: Perform a DoS Attack on a Virtual Machine, Categories of DoS and DDoS Attack Vectors, Techniques of DoS and DDoS Attacks, What Is Botnet?, Techniques to Protect against Botnets, DoS and DDoS Protection Tools, SQL Injection, Scope of SQL Injections, How SQL Query Works, Types of SQL Injection, DEMO: SQL Injections, Extract Information through Error Messages, SQL Injection Tools, How to Defend from an SQL Injection Attack, DEMO: SQL Injection Using Havij Tool
Course: Exploring Data Transformation with Google Cloud - Français
Modules:
Présentation du cours
Introduction, Comment les données créent-elles de la valeur ?, Libérer de la valeur commerciale avec les données, Concepts de la gestion des données, Le rôle des données dans la transformation numérique, La chaîne de valeur des données, Gouvernance des données
Introduction, Stockage de données non structurées, Stockage de données structurées, Stockage de données semi-structurées, Choisir le bon produit de stockage, Migration et modernisation de base de données
Introduction, Informatique décisionnelle et insights avec Looker, Analyse de flux, Pub/Sub et Dataflow
Résumé du cours
Course: Planning with Climate Change in Mind
Modules:
Introduction to to the NCA, Water, Transportation, Energy, Agriculture, Forests, Human Health, Coasts and Oceans
East (NE and SE), West (SW and NW), Middle (Midwest and GP), Alaska, Islands (HI and Caribbean), Interview with David Reidmiller
Module 3 Introduction, Household, Community - Part 1, Community - Part 2, Business - Part 1, Business - Part 2, Where Do You Fit?
Course: Gender Foundations in Health Data: A Data for Health Course
Modules:
Pre-Course Survey (optional), Pre-Course Info, Post-Course Survey (optional)
Course: Personalised Medicine from a Nordic Perspective
Modules:
Introduction to Personalised Medicine from a Nordic Perspective – Saedis Saevarsdottir, Sisse Ostrowski, Applied examples – Hans Tomas Björnsson, Richard Rosenquist Brandell, Henning Bundgaard, What is personalised medicine? – Sisse Ostrowski, Why the Nordic perspective? – Saedis Saevarsdottir, (Suited for listening) Initiatives to address common diseases – Engilbert Sigurðsson, Aarno Palotie, Ole A Andreassen, Runolfur Palsson
What is health data? Data generation pre-birth and throughout life – Alma Möller, Patient information used to generate personalised medicine – Johan Askling, Optimizing clinical decision making with data-driven approaches – Carsten Utoft Niemann, Data cleaning and validation – Rudi Agius, Mapping disease trajectories from longitudinal health data – Søren Brunak, Digital tools for patient engagement – Sofia Ernestam, Saemundur Oddsson
What is a biomarker? – Saedis Saevarsdottir, Studying and implementing useful biomarkers in clinical practice – Henrik Ullum, Henning Bundgaard, (Suited for listening) Clinical sequencing and genetic  testing – 'science fiction' a few years ago – Hans Tomas Björnsson, Patrick Sulem, Omics and beyond – smart ways to identify biomarkers – Simon Rasmussen, The use of genetic information in clinical medicine – David O. Arnar
What is evidence? Shift in paradigm driven by personalised medicine – Jens Lundgren, Novel trials to generate evidence – Merete Lund Hetland, Anders Perner, How to generate evidence from a legal and ethical perspective – Heidi Bentzen, Innovation: From research to clinical practice – Henning Langberg
Screening for rare and common diseases – Sigurdur Kristinsson, How to communicate risk – Thor Aspelund, Patient-centered care as a complement to personalised medicine – Jeanette Knox, Diagnosed with a risk variant? – Sigurdis Haraldsdottir, Media hype and horror – Hakon Heimer, Lone Frank, Heidi Bentzen
Ethical concerns – Mette Nordahl Svendsen, Legal regulation – Heidi Bentzen, Ethical and social aspects – Bjørn Hofmann, (Suited for listening) Morten Søgaard, Pfizer – Why invest in genetics and omics, (Suited for listening) Kari Stefansson, deCODE genetics – Why invest in genetics and omics, Reflections on the future – Saedis Saevarsdottir, Sisse Ostrowski
Course: Strategy and Game Theory for Management
Modules:
Course Introduction, Some Questions in Game theory, What is a Game?, Examples of Simultaneous Games, What is a Dominant Strategy?, Prisoners' Dilemma I, Prisoners' Dilemma II, Applications of Prisoners' Dilemma, Revisiting Dominant Strategy, Iterated Elimination I, Iterated Elimination II
What is the Idea of Nash Equilibrium?, A Simple Illustration of of Nash Equilibrium?, Stag Hunt – Does Equilibrium mean Efficient Outcome?, Battle of Sexes – Does Equilibrium mean Equality?, Hawk-Dove – Does Equilibrium mean Playing Symmetric Strategies?, Stag Hunt and Development of Economies, Digital Markets - Stag Hunt and Single Homing, Digital Markets - Stag Hunt and Multi Homing, Digital Markets – Some Recent Issues, Competing with Rivals in Coordination Games, Inequality Inherent in Equilibrium, Introduction to Mixed Strategies, Mixed Strategies: Zero Sum Games, Expected Values and Mixed Strategies, Computing Mixed Strategies, Behavioral Aspects to Mixed Strategies, Action Bias and Mixed Strategies
Basics of Backward Induction, Analyzing Backward Induction: Centipede Game, Backward Induction: Ultimatum Game, Behavior and Ultimatum Games, Emotions and Ultimatum Games, Trust Games, What Happens When there is No Intellectual Property?, Backward Induction and Innovation, Arguments against Intellectual Property, Rare Diseases and Backward Induction, What Happens when Prisoners’ Dilemma is Repeated Finitely?, Introduction to Tit-For-Tat in Large Repetitions, Outlook of the Future and Cooperation, What does Cooperation Entail?, The Economics of Cartels
Second Price Sealed Bid Auction vs First Price Sealed Bid Auction, Revenue Equivalence, Experimental Evidence in Auctions, Some Common Auction Experiments, The Idea of Winners' Curse, Winners' Curse in Mergers and Acquisitions World, Why do Winners' Curse Happen?, Avoiding Winners' Curse, Problem of Lemons and Peacock's Feathers, Problem of Adverse Selection, Why do People Study and Why do Peacocks have Bright Feathers, Screening and Multiple Contracts, Conclusion
Peer Review Assignment
Course: Machine Learning in Healthcare: Fundamentals & Applications
Modules:
Meet Your Faculty: Paul Cerrato, Meet Your Faculty: Sonya Makhni, Module Overview, Defining Data Mining, Differences Between Machine Learning and Deep Learning, Linear Regression, Dataset Construction, Dataset Preparation
Module Overview, Logistic Regression, Decision Trees and Random Forest Modeling, Gradient Boosting, Clustering, Neural Networks
Module Overview, Applying Data Mining and Machine Learning to Real-World Problems Part 1, Applying Data Mining and Machine Learning to Real-World Problems Part 2, Comparing AI Performance to Clinician Performance Part 1, Analyzing the EAGLE Study, Comparing AI Performance to Clinician Performance Part 2
Module Overview, Why Clinicians Resist AI-Enabled Algorithms, Addressing Validation Issues, Internal/External Validation, Clinical Validation Studies, Mayo Clinic on Health AI Part 1, Mayo Clinic on Health AI Part 2
Course: Introduction to Financial Engineering and Risk Management
Modules:
Course Overview
Discrete Random Variable and Distribution, Bayes' Theorem, Continuous Random Variable and Distribution, Conditional Expectation and Variance, Multivariate Distribution and Independence, The Multivariate Normal Distribution, Introduction to Martingale, Martingales Example 1, Martingales Example 2, Introduction to Brownian Motion, Geometric Brownian Motion, Vector: Independence and Basis, Vector: norm and inner Product, Matrix: Matrix Operations, Matrix: Linear Functions and Rank, Linear Optimization: Hedging Problem, Linear Optimization: Duality, Nonlinear Optimization: Unconstrained Nonlinear Problem, Nonlinear Optimization: Largrangian Method
Introduction to No-Arbitrage, Present Value of Cash Flow, Fixed Income Instruments, Floating Rate Bonds, Term Structure of Interest Rates, Forward Contracts: Introduction, Forward Contracts: An Example
Swaps, Futures, Hedging Using Futures, Futures Excel, Options, Properties of Options, Introduction to Options Pricing, A Paradox Example, The 1-Period Binomial Model, Option Pricing in the 1-Period Binomial Model, Pricing Derivative Security int he 1-Period Binomial Model
The Multi-Period Binomial Model, An Example: 3-Period Binomial Model, What’s Going On?, Pricing American Options, Replicating Strategies and Self-Financing, Dynamic Replication and Risk-Neutral Price, Pricing with Dividends with Binomial Model, Pricing Forwards and Futures with Binomial model, The Black-Scholes Model, An Example: Pricing a European Put on a Futures Contract
Course: Generative AI: Elevate your Software Development Career
Modules:
Course Introduction, Introduction to Generative AI in Software Development, Leveraging Generative AI in Software Development Lifecycle, Large Language Models (LLM) and Transformers, Natural Language Processing (NLP) and its Significance, Leveraging AI for Technical Help and Best Practices on Design Patterns and Architecture, AI for Coding and Development, Demo: GitHub CoPilot for Code Generation and Troubleshooting, Generating Static Website using Generative AI, Demo: Generating Prototypes with Generative AI, Demo: Generating Diagram using Generative AI
Introduction to CI/CD using AI, Generative AI for Security in Software, Popular Generative AI Tools for Security in Software, Generative AI for Software Testing, Integration of Generative AI into Software Development Workflows, Innovation with Generative AI in Software Development
Final Project Overview, Congratulations and Next Steps, Thanks from the Course Team
Course: TensorFlow を使った畳み込みニューラルネットワーク
Modules:
はじめに：アンドリューとの対話, アンドリューとの対話, 「猫と犬」のデータセットを使用したトレーニング, ノートブックに取り組む, クロッピングで修正を行う, 畳み込みの効果を視覚化する, 精度と損失を調べる, 1 週目のまとめ
アンドリューとの対話, 拡張の紹介, ImageDataGeneratorで拡張をコーディングする, 「猫と犬」での過学習を実演する, 「猫と犬」に拡張を追加する, 「馬と人間」での拡張を探求する, 2 週目のまとめ
アンドリューとの対話, 転移学習を理解する: コンセプト, Inceptionモードでの転移学習をコーディングする, 転移した特徴を使った自分のモデルをコーディングする, Dropout を探求する, Inceptionでの転移学習を探求する, 3 週目のまとめ
アンドリューとの対話, ２値分類から多クラス分類へ移行する, 「グー、チョキ、パー」のデータセットを使った多クラスを探求する, 「グー、チョキ、パー」で分類器を訓練する, 「グー、チョキ、パー」分類器をテストする, アンドリューとの対話
Course: Network Function Virtualization
Modules:
Introduction to NFV, Overview, What are Network Functions?, Middleboxes, Examples of Middleboxes, Network Management and proliferation of Middleboxes, Network Services as Software Entities, Virtualization Technology, Eliminating Overhead of Virtualization for NF, Putting it all together, Lesson 1 Wrap-Up, Workshop 1: Docker and Open vSwitch
Lesson 2 Introduction, Developing Virtual Network Functions, Virtual Network Functions, Performance Issues Implementing VNF, Performance-conscious Implementation of VNF, Data Plane Development Kit, NIC Ring Buffer, Implementation of VNF, Multi-core Implementation Challenges, Putting it all together, Lesson 2 Wrap-Up, Workshop 2: Firewall and NAT
Lesson 3 Introduction, Outline, Limitations of Monolithic Software Middleboxes, Need for an NF Control Plane, Elements of NF Control Plane, NF Control Plane Architecture, Virtualization Platform Tasks, Network Programming Tasks, NFVs that modify Packets, Putting them all together, Lesson 3 Wrap-Up, Workshop 3: SDN for basic traffic forwarding to NF
Lesson 1 Introduction, Benefits of using Managed Cloud Services, Techniques for offloading NF to Managed Cloud, Observed Performance of NF Offloading, Mobile edge Computing, Cloud-RAN, Lesson 4 Wrap-Up, Workshop 4: Connection-affinity with multiple NF instances
Introduction to Service Chaining over WAN, Switchboard Architecture, Switchboard Implementation and Performance Evaluation
Project - NFV Orchestration
Course: Public Health in Humanitarian Crises 1
Modules:
Introduction to Public Health in Humanitarian Crisis, Introduction to Humanitarian Disasters and Public Health, Introduction, Disaster Trends, Introduction to the Role of Public Health in Humanitarian Emergencies, The Role of Public Health in Humanitarian Emergencies
Introduction to Humanitarian Actors and Coordination, Humanitarian Actors and Coordination, Introduction to Humanitarian Coordination, Humanitarian Coordination, Humanitarian Coordination Models, A Practical Example of Humanitarian Coordination
Introduction to Water and Sanitation, Water and Sanitation, Sanitation Options, Water Supply Options, The Priorities
Introduction to Livelihoods and Food Security, Livelihoods and Food Security in Humanitarian Crises, Food Security, Livelihoods Support Programming
Introduction to Nutrition in Humanitarian Crises, Nutrition in Humanitarian Crises, A Case Study in Malnutrition, Types of Malnutrition, Treatment and Prevention of Acute Malnutrition
Introduction to Management of Diseases in Humanitarian Crises, Management of Diseases in Humanitarian Crises, Managing an Outbreak of a Communicable Disease, Non-Communicable Disease In Disasters
Introduction Health Care in Humanitarian Emergencies, Health Care in Humanitarian Emergencies, Parts of a Health Service, Introduction to Types of Health Services, Types of Health Services
Introduction to Shelter in a Humanitarian Setting, Shelter in a Humanitarian Setting, Shelter Solutions
Introduction to Protection of Displaced Populations, Protection of Displaced Populations: Human Rights and Humanitarian Law, The Displaced Populations, International Law, International Humanitarian Law (IHL), Refugee Law and Refugee Protection, Solutions for Refugee Situations
Introduction to Emerging Risks, Challenges, and Opportunities for Future Humanitarian Emergencies, Changing Risk Patterns for Humanitarian Emergencies, Introduction to Challenges and Opportunities for Future Humanitarian Emergencies, Challenges and Opportunities for Future Humanitarian Emergencies, Future Opportunities, Conclusion
Course: Wind resources for renewable energies
Modules:
Energy units, Primary, final and useful energy, Variability and capacity factors of power plants
Atmospheric layers, Equations of motion:GEOSTROPHIC AND HYDROSTATIC BALANCE, General circulation of the atmosphere, Wind veering & Local effects
Turbulence/ Intensity, Reynolds decomposition, RANS, closure relation, Eddy viscosity, Prandtl closure, logarithmic law, Convective boundary layer, diurnal cycle, Generic wind profiles: logarithmic, power law and others, In-situ measurements, Remote sensing measurements, Windcube: a compact wind lidar
Standard Betz law, Betz law for confined turbines, Power curves / ideal case, Power curves / impact of density, Power curves / impact of vertical shear, Power curves / impact of turbulence, Isolated turbine wake, Multiple turbine interaction
Organising data, Weibull  Rayleigh distribution, Energy Production Assessment
Variability of the vertical wind shear, Probability distribution of the wind velocity, An example of wind turbine power production assessment
Course: Sonidos digitales: síntesis y procesamiento en el computador
Modules:
Video Introductorio a Software Pure Data, Video Introductorio a Software Max, Video Introductorio a Faust
Bienvenida al curso, 1. ¿Qué es el sonido?, 2. El sonido como fenómeno físico, 3. El sonido como fenómeno perceptual, 1. Tipos de sonidos, 2. Sinusoides, 3. Superposición de sonidos, 1. Amplitud e intensidad, 2. Periodo, frecuencia y fase, 3. Timbre y estructura armónica, 4. Representación gráfica: tiempo vs frecuencia
1. Audio digital versus analógico, 2. Discretización, 3. Teorema del muestreo y aliasión, 4. Cuantización, 1. Osciladores y tablas de ondas, 2. Envolventes, 3. Tasa de audio y de control y la cadena de audio
1. Filtros selectivos en frecuencia, 2. Filtros recursivos y no recursivos, 3. Ecualizadores, 1. Líneas de retardo (delay lines), 2. Filtros Comb, 3. Phaser, flanger y chorus, 4. Wah-wah, distorción y pitch shifter, 5. Vocoder, 6. Control de amplitud, 7. Reverberación
1. Síntesis y procesamientos de sonidos, 2. Síntesis aditiva, 3. Modulación, 4. Modulación de amplitud (AM), 5. Modulación de frecuencia (FM), 1. Waveshaping y síntesis no lineal, 2. Síntesis granular, 3. Modelamiento físico, 4. Síntesis espectral: DFT y FFT, 5. Síntesis espectral: ventanas y proceso
1. Construcción de un sintetizador, 2. Multi-efecto paso a paso, 1. Explorando un sintetizador, 2. Explorando un procesador multi-efecto
Course: Arm Cortex-M Processors Overview
Modules:
Specialization welcome, Specialization objectives
Introduction to the Cortex-M Processors Overview, Arm introduction - part 1, Arm introduction - part 2, Arm introduction - part 3, Arm resources and support - part 1, Arm resources and support - part 2, Cortex-M overview, Armv6-M Cortex processors, Armv7-M Cortex processors - part 1, Armv7-M Cortex processors - part 2, Armv8-M Cortex processors - part 1, Armv8-M Cortex processors - part 2, Processor selection, End of course message
Course: Preparing  Data for Analysis with Microsoft Excel
Modules:
Introduction to the program, Course introduction: Preparing data for analysis with Microsoft Excel, Day in the life, Welcome to Generative AI for everyone, Understanding the Capabilities of Generative AI for Business Functions, Generative AI - Under the hood, The potential pitfalls and shortcomings of GEN AI, Introduction to Microsoft Excel, Entering and formatting data, Reading large data blocks, Sorting and filtering data in Excel, Module summary: Excel fundamentals
What is a formula?, Controlling calculations, Controlling calculations in action, What is a function formula and how to write it, Using the INSERT function, Module summary: Formulas and functions
How inconsistent data can affect data analysis, Why understanding date calculations is important, Dynamic date and time entries, Using the IF function, Using nested IF and IFS function, Module summary: preparing data for analysis using functions
Course recap: Preparing data for analysis with Microsoft Excel, Congratulations
Course: Curso con proyecto final: Comienza tu futuro en fintech
Modules:
Revisión de la especialización en el sector tecnofinanciero en mercados emergentes, Introducción al proyecto del curso, Encuadra tu desafío comercial, Entender a tus clientes
Diseñar el lienzo de modelo de negocio
Crear tu presentación
Dar tu presentación, Presentación de “Proof of Steak”, Comentarios de la presentación de “Proof of Steak”, Presentación de “Registree”, Comentarios de la presentación de “Registree”
Course: Leveraging Unstructured Data with Cloud Dataproc on Google Cloud em Português Brasileiro
Modules:
Bem-vindo, Introdução ao Cloud Dataproc, Como definir dados não estruturados?, Como extrair valor de dados não estruturados, Abordagens ao trabalhar com Big Data, Origens do MapReduce e do Hadoop, Excesso de sobrecarga com o uso do Hadoop no local, Comparação entre o Cloud Dataproc e as opções do Hadoop, Como criar um cluster do Dataproc, Personalização do Dataproc, O Dataproc e a CLI, Laboratório 1: visão geral, Laboratório 1: demonstração e avaliações, Tipos de máquina personalizados, VMs preemptivas, Resumo do módulo
Visão geral da execução dos jobs do Dataproc, Métodos para envio de jobs, Laboratório 2: visão geral, Laboratório 2: demonstração e avaliações, Separação do armazenamento e da computação, A evolução do processamento de dados, A importância da rede no processamento de dados, Como separar o armazenamento do processamento com o Spark, Como enviar jobs do Spark, Visão geral dos conceitos do Spark, Visão geral do laboratório, Laboratório 3: demonstração e avaliações, Resumo do módulo
Introdução ao uso do GCP, Suporte do BigQuery, Laboratório 4: visão geral, Laboratório 4: demonstração e avaliações, Personalização do cluster, Como instalar um software em um cluster do Dataproc, Laboratório 5: visão geral, Laboratório 5: demonstração e avaliações, Resumo do módulo, Avaliações
Saiba mais sobre o aprendizado de máquina, Aprendizado de máquina em detalhes, Exemplos da aplicação do aprendizado de máquina, Processamento de linguagem natural em detalhes, Laboratório 6: visão geral, Laboratório 6: demonstração e avaliações, Resumo do módulo
Course: Web Design: Strategy and Information Architecture
Modules:
Course Introduction, Why Peer Review?, Peer Review Tips, How to apply feedback
Introduction to Week 1, Defining the term "user experience", User-centric design, What could possibly go wrong?, The UX Phases (part 1), The UX Phases (part 2), Waterfall vs. Agile, Web vs. App, Project Ideas: Alyson and Bradley
Introduction to Week 2, User Research, Inspiration, Analytics, User Needs and Client Needs, Target Audience, Strategy: Alyson and Bradley, Interview with Craig Cooke: Determining success
Introduction to Week 3, Introduction to Outline of Scope, Content and Functionality, Outline of Scope Example, Scope: Alyson and Bradley, Interview with Melissa Kuo
Introduction to Week 4, Introduction to Sitemaps, Information Architecture, Sitemap Concerns, Sitemapping: an annotated process, Sitemap Elements, Sitemap Example, Treejack Introduction, Treejack Analysis, Sitemap: Alyson and Bradley
Course: Deploying Microservices to Kubernetes using Azure DevOps
Modules:
Introduction and Course Overview, Pre-requisites and Setup, Azure DevOps Overview, Kubernetes Overview Part 1, Kubernetes Overview Part 2, Kubernetes in Azure DevOps - Part 1, Kubernetes in Azure DevOps - Part 2, Kubernetes in Azure DevOps Demo, Work Items Introduction, Demo: Work Items, Boards and Process Templates
Azure Repos Introduction, Branch Policies and Pull Requests, Gitflow patterns and Project Policies, App Repo Creation and First Commits, App Repo PR Process and Agile Process Overview, App Work Item Backlog Creation Demo, Azure Container Registry Overview, Azure Container Registry Demo, Azure Pipelines Overview, Azure Pipelines Demo, Environments and Agent Pools, Environments and Agent Pools Demo, Pipeline Secrets and Service Connections, Pipeline Secrets and Service Connections Demo, Introduction to Azure Artifacts Part 1, Introduction to Azure Artifacts Part 2, Azure Artifacts Usage Demo, Work Item Query and Dashboards Overview, WI Query Dashboard and Docs Demo - Part 1, WI Query Dashboard and Docs Demo - Part 2
Mermaid Graphs and Documentation Updates, App Setup Part 3: Containerization, AKS, ACR and full pipeline, App Setup Part 3: Setup AKS, ACR, Service Principals and Pipeline, App Setup Part 3: Debugging Containerized Services in AKS, App Setup Part 4: Iteration Dates, Dashboard, Sprint planning, App Setup Part 4: Work Item Query, Wiki and Dashboards, App Demo 5 Intro: Work Items and Registering DNS, App Demo 5 Intro and Demo: Ingress, Cert Manager and SSL with LE, App Demo 5 Demo: Ingress and PVCs, App Demo 5 Demo DR and Autoscaling AKS - Part 1, App Demo 5 Demo DR and Autoscaling AKS - Part 2, Introduction To Test Plans, Test Plan Usage Demo, App Setup Part 6: Forms and Wrap up, App Setup Part 6 Demo: Reports Page Creation, App Setup Part 6 Demo: Debugging Microservices and Final App Wrap Up, Conclusion - Knowledge Check and What's Next - Part 1, Conclusion - Knowledge Check and What's Next - Part 2, Conclusion - Knowledge Check and What's Next - Part 3
Course: Logging and Monitoring in Google Cloud - Español
Modules:
Introducción al curso
Descripción general del módulo, Necesidad de observabilidad de Google Cloud, Cloud Monitoring, Cloud Logging, Error reporting, Herramientas de administración del rendimiento de aplicaciones, Resumen del módulo
Descripción general del módulo, Descripción general de Monitoring, Patrones de la arquitectura de Cloud Monitoring, Supervisión de múltiples proyectos, Modelo de datos y paneles, Métricas de consultas, Verificaciones de tiempo de actividad, Coursera: Empezar a usar Google Cloud y Qwiklabs, Resumen del módulo
Descripción general del módulo, SLI, SLO y ANS, Desarrollar una estrategia de alertas, Crear alertas, Supervisión de servicios
Descripción general del módulo, Descripción general y arquitectura de Cloud Logging, Tipos y colecciones de registros, Almacenamiento, enrutamiento y exportación de los registros, Consulta y visualización de registros, Uso de métricas basadas en registros, Análisis de registros, Resumen del módulo
Descripción general del módulo, Registros de auditoría de Cloud, Registros de auditoría de acceso a los datos, Formato de entrada de los registros de auditoría, Prácticas recomendadas, Resumen del módulo
Resumen del curso 1
Course: Esports Management Capstone Project
Modules:
Honor Code & Plagiarism, Peer Review, Grading Logistic, FAQs, Project Description
Peer Review Draft
Final Peer Review
Course: Google Workspace Security
Modules:
Welcome and getting started guide, Introduction to common security settings, Use of an exception group, Introduction to SSO, Secure LDAP, Introduction to application security, Security tools overview, Security center, Recap
Course: Від уподобань до потенційних клієнтів: взаємодія з клієнтами онлайн
Modules:
Вступ до 3-го курсу, Вітаємо з початком 1-го тижня, Переваги маркетингу в соціальних мережах, П’ять основних принципів маркетингу в соціальних мережах, Здобуті, власні та платні медіаканали в соцмережах, Маркетингова лійка в соціальних мережах, Підсумки
Вітаємо з початком 2-го тижня, Визначення цілей стратегії в соціальних мережах, Визначення цільової аудиторії в соціальних мережах, Вибір соціальних мереж для кампанії, Типи контенту в соціальних мережах, Анна – як використовувати здобуті, власні та платні медіаканали в маркетингових кампаніях у соціальних мережах, Визначення періодичності та часу розміщення дописів, Використання календаря соціальних мереж, Підсумки
Вітаємо з початком 3-го тижня, Важливість моніторингу соціальних мереж, Стратегії моніторингу соціальних мереж, Залученість у соціальних мережах, Камілла – відповіді на коментарі в соціальних мережах, Збільшення кількості підписників у Twitter, Написання контенту для соціальних мереж, Розробка голосу бренду в соціальних мережах, Перепрофілювання контенту в соціальних мережах, Кетрін – залучення аудиторії в соціальних мережах через сторітелінг, Підсумки
Вітаємо з початком 4-го тижня, Розуміння аналітики соціальних мереж, Показники, які слід відстежувати за допомогою аналітики соціальних мереж, Використання даних соціальних мереж для формування маркетингової стратегії, Джон – Як аналітика даних і соціальних мереж впливає на прийняття рішень та стратегію, Розуміння звітів про соціальні мережі, Представлення звіту про соціальні мережі, Підсумки
Вітаємо з початком 5-го тижня, Переваги платної реклами в соцмережах, Інтеграція реклами в соцмережах у маркетингову стратегію, Розробка стратегії для платної реклами в соцмережах, Ремаркетинг у соціальних мережах, Складові бюджету платної реклами в соціальних мережах, Вартість реклами в соціальних мережах, Сабріна – вирішення конфліктів і реакція на критику, Підсумки, Підсумки курсу
Course: Phases of Matter: Solid, Liquid, Gas and Beyond
Modules:
Emergence, The Problem with Brute Force Solution, Seeking Simplicity in Complexity, Phases of Water, Phases of Matter, Supplemental: The Thermodynamic Limit, Supplemental: Phase Transitions, Module 1 Summary
Symmetries: What They Are, Symmetries: What They Are Good For, Supplemental: Lightning Introduction to Group Theory, Fun Aside: Wallpaper Groups, Supplemental: Representation Theory, Spontaneous Symmetry Breaking, Supplemental: Group Theory Applied to Symmetry Breaking, Analyzing Phases in Terms of Symmetries and Conclusion
Introducing Energy, Energy and Ordering, Entropy, Ordering and Entropy, Free Energy as Function of Order Parameter, Order Parameter Near Phase Transitions, Module 3 Summary
Mean Field Theory, Supplemental: Quantitative Analysis of Free Energy, Continuous Symmetries, Statistical Symmetries, Honors Continuous Statistical Symmetries, Fluctuations, Review 1, Review 2, Review 3
Course: Learning Experience Design: Orientation to the Profession
Modules:
Welcome to the Course, Learning with Technology in this Course, Why LXD? Why Now?, Three Dimensions of LXD, Voices from Academia, An Introduction to Design, Key Interactions in Support of Learning, Essential Components of LXD Processes, Creating Your Professional Development Plan
Inclusive Design, Design Lenses for Diversity, Equity, Inclusion, and Justice, Learner Variability is a Given, Getting Started with Universal Design for Learning Guidelines, Multiple Means of Engagement, Multiple Means of Representation, Multiple Means of Action and Expression, Designing with Accessibility in Mind, Conversation with Reginé Gilbert
The  Role of Technology for LXD, Using Different Technologies Throughout the Design Process, Using Different Technologies to Support Learning
A Look Back at User-Centered Design, Moving from User-Centered Design to Learner-Centered Design, LXD Core Competencies, Challenges for New Designers: Panel Discussion, The Fuzzy Front End of Design (Video Option)
Course: Introduction to Machine Learning in Sports Analytics
Modules:
Introduction, What is Machine Learning?, The Machine Learning Workflow, Our First Model: NHL Game Outcomes, Building the Logistic Regression Model, Considerations in Deploying The Model, Wrap Up
Introduction to Support Vector Machines (SVMs), Polynomial Support Vector Machines, Cross Validation, A Real World SVM Model: Boxing Punch Classification
Decision Trees, A Multiclass Tree Approach, Model Trees, Tuning and Inspecting Model Trees
Ensembles, Additional Machine Learning Concepts, Baseball Hall of Fame Prediction, Baseball Hall of Fame Demonstration Part 1, Baseball Hall of Fame Demonstration Part 2
Course: Project Lifecycle, Information Sharing, and Risk Management
Modules:
Course Introduction, Lifecycle Overview, Expert Viewpoints: Lifecycle Selection Management, Adaptive Lifecycles, Project Initiation, Project Planning, Project Executing, Project Monitoring and Controlling, Expert Viewpoints: Project Failure
Communications Basics, Communications Planning, Expert Viewpoints: Communications Planning, Manage Communications - Part 1, Manage Communications - Part 2, Manage Communications, Tools, and Techniques, Monitor Communications, Expert Viewpoints: Monitoring and Controlling Communications
Identify Stakeholders, Plan Stakeholder Engagement, Expert Viewpoints: Stakeholder Management Tools, Monitor Stakeholder Engagement, Expert Viewpoints: Ground Rules and Norms, Demo: Team Charter
Project Risk Management Overview, Risk Defined, Risk Metalanguage, SWOT Analysis, The Risk Management Plan, Expert Viewpoints: Risk Planning, Demo: Risk Register, Expert Viewpoints: Risk Management
Respond to Risk, Expert Viewpoints: Risk Responses, Risk Report, Risk Reassessment (Review), Expert Viewpoints: Risk Monitoring, Risk Matrix
Final Project Overview, Key Takeaways, Congratulations and Next Steps, Thanks from the Course Team
Course: Hybrid Electric Vehicles Technical System
Modules:
Introduction to the course and Instructor, Introduction to hybrid electric vehicles, Types of hybrid systems, HEV components and layout, HEV powertrain components, Energy flow in HEVs, Battery types and management, Overview of HEV control systems, HEV sensors and actuators, Software and firmware in HEVs
Introduction to Module 2, Driving dynamics of EVs, Energy conservation strategies for HEVs, Regenerative braking systems in HEVs, Routine HEV maintenance, Safety procedures in HEV servicing, HEV battery care and maintenance, Tools for HEV diagnostics, Service information for HEVs, Up-to-date HEV service techniques
Introduction to module 3, Diagnostic tools and applications for HEVs, Understanding diagnostic codes in HEVs, Identifying common HEV issues, Diagnosing HEV battery and charging system, Electric motor, generator and transmission diagnostics in HEVs, Diagnosing HEV power electronics, Systematic HEV troubleshooting, Real-world HEV diagnostic scenarios, HEV data logging and analysis
Introduction to module 4, Revolutionizing diagnostics: The new age tools for HEVs, Deep dive into HEV battery diagnostics, Powertrain diagnostics: Advanced techniques for HEVs, Navigating through complex diagnostic challenges in HEVs, Advanced battery health assessment, Diagnosing electronic control systems in HEVs, Anticipating tomorrow: Challenges in the HEV landscape, Sustainable practices in HEV service, Mastering HEV service: Success stories and insights, Wrap-up & key takeaways
Course: Relationship Management
Modules:
Meet the Professor
Observation and Objectives, Coaching vs Counseling, Providing Feedback, Consequences of Unskilled Communication, Strategies for Supportive Communication, Active Listening, Receiving Feedback, Communication Style, Tool box: Feedback Checklist, Dave's Journaling Topic - The Hammer, Summary
Observation and Objectives, What Is It and Why Do We Want It?, Sources of Power, Influence Strategies, Neutralizing Influence Strategies, Self Assessment Discussion, Tool box - The Power of Saying Thank You, Dave's Journaling Topic - Upward Selling Story, Summary
Observation and Objectives, Sources and Effects of Conflict, Conflict Management Styles, Collaborative Problem Solving, Collaborative Problem Solving, Summarized, Tool box - Ladder of Inference, Self-Assessments, Dave's Journaling Topic - Ineffective Problem Solving Example, Summary
Engineer Interview Writing Assignment, Movie Review Writing Assignment
Observation and Objectives, Types of Meetings, Purposes of Meetings, Pre-Meeting Planning, Conducting the Meeting, Post Meeting Actions, Tool box: Getting out of the Ditch, Dave's Journaling Topic - Ineffective Meeting Example, Summary
Course: Algorithms for DNA Sequencing
Modules:
Module 1 Introduction, Lecture: Why study this?, Lecture: DNA sequencing past and present, Lecture: Genomes as strings, reads as substrings, Lecture: String definitions and Python examples, Practical: String basics, Practical: Manipulating DNA strings, Practical: Downloading and parsing a genome, Lecture: How DNA gets copied, Optional lecture: How second-generation sequencers work, Optional lecture: Sequencing errors and base qualities, Lecture: Sequencing reads in FASTQ format, Practical: Working with sequencing reads, Practical: Analyzing reads by position, Lecture: Sequencers give pieces to genomic puzzles, Lecture: Read alignment and why it's hard, Lecture: Naive exact matching, Practical: Matching artificial reads, Practical: Matching real reads
Week 2 Introduction, Lecture: Boyer-Moore basics, Lecture: Boyer-Moore: putting it all together, Lecture: Diversion: Repetitive elements, Practical: Implementing Boyer-Moore, Lecture: Preprocessing, Lecture: Indexing and the k-mer index, Lecture: Ordered structures for indexing, Lecture: Hash tables for indexing, Practical: Implementing a k-mer index, Lecture: Variations on k-mer indexes, Lecture: Genome indexes used in research, Lecture: Approximate matching, Hamming and edit distance, Lecture: Pigeonhole principle, Practical: Implementing the pigeonhole principle
Module 3 Introduction, Lecture: Solving the edit distance problem, Lecture: Using dynamic programming for edit distance, Practical: Implementing dynamic programming for edit distance, Lecture: A new solution to approximate matching, Lecture: Meet the family: global and local alignment, Practical: Implementing global alignment, Lecture: Read alignment in the field, Lecture: Assembly: working from scratch, Lecture: First and second laws of assembly, Lecture: Overlap graphs, Practical: Overlaps between pairs of reads, Practical: Finding and representing all overlaps
Module 4 introduction, Lecture: The shortest common superstring problem, Practical: Implementing shortest common superstring, Lecture: Greedy shortest common superstring, Practical: Implementing greedy shortest common superstring, Lecture: Third law of assembly: repeats are bad, Lecture: De Bruijn graphs and Eulerian walks, Practical: Building a De Bruijn graph, Lecture: When Eulerian walks go wrong, Lecture: Assemblers in practice, Lecture: The future is long?, Lecture: Computer science and life science, Lecture: Thank yous
Course: Reimagining Consumer Experience
Modules:
Marketing Automation, Conflict in Online Communities, Sales Funnel, Sales Funnel Efficiency
Search Engine Marketing: An Introduction, Search Engine Optimisation, Paid Search Marketing, Online Public Relations
Assessing the Influence of Influencers, Managing Affiliate Marketing: Affiliate Networks, Online Sponsorships, Programmatic Advertising
Email Marketing, Social Media and Viral Marketing, E-commerce Support: Offline Promotion, Traffic Building
Course: Act on Climate: Steps to Individual, Community, and Political Action
Modules:
Welcome to the course, Meet Dahlia, Meet the expert - Dr. Rosina Bierbaum
Meet Chris, Meet the Expert: Dr. Sara Soderstrom
Meet Benjamin, Meet the expert - Dr. Kaitlin Raimi
Meet Stephanie, Meet the expert - Brandon Schoettle
Meet Haley, Meet the expert - Doug Kelbaugh
Meet Priscila, Meet the expert - Dr. Jennifer Hirsch
Meet Sachi, Meet the expert - Dr. Raymond De Young, Meet the expert - State Representative Yousef Rabhi, Meet the expert - Mayor Amanda Edmonds, Dr. Michaela Zint concludes the course
Course: Preparing for Your Cloud Architect Journey - Español
Modules:
Descripción general del módulo, Introducción al papel del Professional Cloud Architect, Valor y beneficios de la certificación, Proceso de certificación, Crea tu plan de estudio
Descripción general del módulo, Diseña la arquitectura en la nube de Cymbal Direct, Introducción: Preguntas de diagnóstico, Demostración de práctica de las preguntas de diagnóstico, Tu plan de estudio
Descripción general del módulo, Aprovisiona una solución en la nube para que cumpla con los requisitos de Cymbal Direct, Introducción: Preguntas de diagnóstico, Tu plan de estudio
Descripción general del módulo, Diseña para lograr seguridad y cumplimiento con la solución en la nube de Cymbal Direct, Introducción: Preguntas de diagnóstico, Tu plan de estudio
Descripción general del módulo, Optimiza los procedimientos y procesos técnicos y comerciales para la nube de Cymbal Direct., Introducción: Preguntas de diagnóstico, Tu plan de estudio
Descripción general del módulo, Implementación, operaciones y confiabilidad en Cymbal Direct, Introducción: Preguntas de diagnóstico, Tu plan de estudio
Descripción general del módulo, Analizar un caso de éxito: Dress4Win, Comprender los objetivos generales, Identificar y detallar los objetivos, Categorizar y priorizar los objetivos, Analizar y determinar las opciones preliminares, Definir mejor las opciones e identificar las soluciones, Objetivos de estudio semanales