dipopotamus/custom_instruct_coursera_course_module_list huggingface dataset:
Course: Fundamentals of Social Media Advertising
Modules:
Introduction to the Meta Social Media Marketing Professional Certificate, Fundamentals of Social Media Advertising Overview, Introduction to Daniel Kob, Careers in Digital Marketing, Introduction to Social Media Advertising Overview, Why and When to Pay for Ads, Organic Posts vs. Paid Ads, Organic Posts vs. Paid Ads on Facebook and Instagram, Ad Formats on Popular Social Media Platforms, The Five Components of a Social Media Ad, Anatomy of a Facebook and Instagram Ad, Evaluating the Effectiveness of an Ad, Introduction to Social Media Advertising Conclusion
Creating Effective Ads on Social Media Overview, How Your Business Goals Determine Your KPIs, Tips for Sourcing and Creating Ad Visuals, Writing Effective Copy for Your Ad, Collaborating with Internal and External Teams, Working with Creative Briefs, Creating Effective Ads on Social Media Conclusion
Data, Privacy and Policies on Social Media Introduction, Data-Based Advertising, How Data is Collected Online, Different Data Formats, Who Collects and Stores Data?, Intro to Data Protection Policies, Government Regulations: GDPR, CCPA & COPPA, Industry Self-Regulation, Company Data Policies, Data, Privacy and Policies on Social Media Conclusion
Getting Started with Advertising on Facebook and Instagram Overview, Case Study: Charleston Gourmet Burger Company, Build an Ad from Your Facebook Business Page, Choose the Ad Objective for Your Business Goal, Case Study: Blooms France, Build an Ad from Your Instagram Business Account, Instagram Stories Ads, Creative Tips for Ads in Stories, Getting Started with Advertising on Facebook and Instagram Conclusion
Project Overview: Create a Creative Brief for Your Social Media Ad, Congratulations!, What's Next?
Course: Old Norse Mythology in the Sources
Modules:
Course Introduction, Geography and People, Language, Runes, Written Sources, Skaldic Poetry, Eddic Poetry, Snorri's Edda, The Conversion to Christianity, The Prologue to Snorri's Edda, The Delusion of Gylfi, The Language of Poetry, Pre-Christian Scandinavia, The Old Norse gods 1, The Old Norse gods 2, The Old Norse gods 3
The Collection of Eddic Poems, The Prophecy of the Seeress, The Sayings of the High One, Grimnir's Sayings, Vafthrudnir's Sayings, Harbard's Song, Hymir's Poem, Thrym's Poem, Skirnir's Journey, Loki's Quarrel, The Poem of Volund, All-Wise's Sayings
Baldr's Dreams, The Song of Hyndla, Rig's List, The Song of Grotti, Baldr in Saxo's Gesta Danorum, Odin in Saxo's Gesta Danorum, Hading in Saxo's Gesta Danorum, The Use of Eddic Poetry in Snorri's Edda, The Use of Skaldic Poetry in Snorri's Edda, The Saga of the Ynglings Between History and Myth
Final Exam
Course: English for Business B1
Modules:
Looking for a job, What information to include in your CV, What to ask in an interview
The elevator pitch, How to give an effective presentation of your company, How to approach a product presentation
Formal and informal emails, Team-meetings and how to prepare the agenda, Mastering reports writing  in business English, A couple of questions from your tutors
The pros and cons of in-person conferences, What's important when you..., Making notes, Expert tips on how to negotiate effectively
Well-being in the workplace, How to combat stress, Solutions to workplace stress: 2) Breathing and meditation, How to decrease workplace stress
Course: Architecting Google Kubernetes Engine Production in italiano
Modules:
Introduzione al corso
Introduzione, Autenticazione e autorizzazione, Cloud IAM, Tipi di ruoli IAM, Kubernetes RBAC, Associazione di ruoli, Sicurezza del piano di controllo Kubernetes, Sicurezza dei pod, Applicazione dei criteri di sicurezza dei pod, Introduzione lab, Introduzione a Google Cloud e Qwiklabs, Introduzione lab, Riepilogo
Introduzione, Suite operativa di Google Cloud, Nozioni di base sul logging, Esecuzione del logging con Kubernetes, Monitoraggio, Introduzione lab, Probe, Introduzione lab, Riepilogo
Introduzione, Utilizzo dei servizi Google Cloud, Utilizzo delle API, Utilizzo di Cloud Storage, Utilizzo dei database di Google Cloud, Bigtable, Firestore, Cloud SQL, Cloud Spanner, Bigquery, Introduzione lab, Albero decisionale, Riepilogo
Introduzione, Panoramica di pipeline CI/CD, Spettro CI/CD, Esempi di strumenti CI/CD, Introduzione lab, Soluzione del lab, Esempi CI/CD, Esempi di architetture CI/CD, Riepilogo
Course: Feature Engineering en Español
Modules:
Introducción al curso
Introducción, Beneficios de Feature Store, Terminología y conceptos de Feature Store, El modelo de datos de Feature Store, Cómo crear un Feature Store, Entrega de atributos: Por lotes y en línea
Introducción, Descripción general de la ingeniería de atributos, De los datos sin procesar a los atributos, Atributos buenos frente a atributos malos, Los atributos se deben conocer en el momento de la predicción, Los atributos deben ser numéricos, Los atributos deben tener suficientes ejemplos, El aporte de las estadísticas manuales, Representación de atributos
Introducción, Aprendizaje automático frente a las estadísticas, Ingeniería de atributos básica, Coursera: Empezar a usar Google Cloud y Qwiklabs, Introducción al lab: Realiza ingeniería de atributos básica en BigQuery ML, Ingeniería de atributos avanzada: Combinaciones de atributos, Funciones Bucketize y Transform, Predicción de precios de viviendas, Estimación de tarifas de taxis, Atributos temporales y de ubicación geográfica, Introducción al lab: Ingeniería de atributos básica en Keras, Introducción al lab: Ingeniería de atributos avanzada en Keras
Introducción, Apache Beam y Dataflow, Términos y conceptos de Dataflow
Introducción, ¿Qué es una combinación de atributos?, Discretización, Introducción al lab: TensorFlow Playground: Use combinaciones de atributos para crear un buen clasificador, Introducción al lab: TensorFlow Playground: Demasiado de algo bueno
Introducción, TensorFlow Transform, Fase de análisis, Fase de transformación, Asistencia para entrega
Resumen, Recurso: Todas las preguntas del cuestionario, Recurso: Todo el material de lectura, Recurso: Todas las diapositivas (English only)
Course: Think Again IV: How to Avoid Fallacies
Modules:
Introduction to the Specialization
Introduction to Fallacies, Argument from the Heap, Vagueness, Conceptual Slippery Slopes, Fairness Slippery Slopes, Causal Slippery Slopes, Ambiguity, Semantic and Syntactic Ambiguity, Fallacies of Equivocation
Fallacies of Relevance and Vacuity, Fallacies of Relevance: Ad Hominem, Silencers, Dismissers, Deniers, Appeals to Authority, Amplifiers, Supporters, Affirmers, Appeals to Popular Opinion
Fallacies of Vacuity, Circularity and Begging the Question, Self-Sealers
Refutation, Refutation by Parallel Reasoning, False Dichotomy, Reductio Ad Absurdum, Counterexamples, Attacking a Straw Man, Why Walter Should Shave His Head
The Great Shave
Course: Working with Data in Android
Modules:
Introduction to the course, What you know about HTTP, RESTfulness, Naming conventions, Essential tools for API development, REST best practices, Security and authentication in REST API, Access control, Module summary: Introduction to REST APIs
Contacting Servers, HTTP protocol and asynchronous calls, HTTP protocol and asynchronous calls in action, What you've learned about JSON, Working with JSON, Module summary: Interacting with REST APIs in Android
What is a database?, How is data related?, What is Structured Query Language?, SQL usage, SQL syntax introduction, CREATE and DROP database, What are tables in databases?, Numeric data types, String data types, Default values, CREATE TABLE statement, ALTER TABLE statement, INSERT statement, SELECT statement, Updating data, Deleting data, Module summary: Introduction to Databases & SQL
What is SharedPreferences?, Working with SharedPreferences, Connecting SharedPreferences to State, What is SQLite?, Setting up Room, Connecting Room to State, Module summary: SQL Data in Android
Course Recap: Working with data in Android, Course wrap up
Course: VR and 360 Video Production
Modules:
Instructor Introductions, What is 360 Video?, 3-DoF vs 6-DoF, Position and Orientation, Agency, Presence, and Immersion, Point of View (POV), Mono vs. Stereo, Parallax, Equirectangular Projection & Cubemaps, Field of View (FOV), Zenith and Nadir, Stitch Lines
Questions to Ask Yourself, Achieving Presence, Space and Setting, Plot, Character, and Dialogue, Guiding Cues, Storyboarding, Overhead Diagrams and Blocking Shots, Shot Lists & Scheduling, Crewing up for a 360 Production
Resolution, Lenses, Frame Rate, Dynamic Range, Synchronization, Stabilization, Movement, & Balance, Settings, Distance of Subject to Camera, Hiding Equipment and Crew, Plate Shots & Post-Production Masking, Lighting & Practicals, Monophonic vs. Stereophonic Audio, Spatial Audio
Editing in 360, Equirectangular Editing, Match on Attention
Course: Análisis de datos empresariales con R
Modules:
Bienvenida, Políticas y lineamientos de calificación, Políticas de Coursera
Bienvenida, Lectura de Datos Multivariados, Funciones de Análisis del Data Frame, Análisis de Covarianza y correlación, Diseño de la Gráfica de Correlación, Subconjunto de un Data Frame, Representación con Boxplot, Análisis de normalidad por gráfica de cuantiles, Función de usuario para graficar, Prueba de Normalidad de Anderson – Darling
Introducción, Lectura de Datos Multivariados - Wine, Preparando el Data Frame, Análisis de Pares, Diseño de la Gráfica de Correlación, Diseño de Gráficas tipo Boxplot, Ajustando los parámetros de graficación, Graficando con qplot, Creación de líneas de tendencia, Agregando Facetas a la grafica
Cálculo de Dendogramas, Ejemplo de aplicación clúster jerárquico, Calculando la matriz de distancias, Generando los grupos de clúster, Graficando los clústeres por variables, Construyendo la función clusplot(), Graficando con la función clusplot(), Calculando varios números de clústeres, Generando clústeres con datos normalizados
Bienvenida, Generando el análisis PCA, Análisis de contribución de los individuos al PCA, Análisis de los individuos en PCA, El círculo de Correlación, Calidad de la representación y gráfica Biplot
Revisión de conocimientos 1, Revisión de conocimientos 2, Revisión de conocimientos 3
Despedida
Course: Root Cause Analysis: Principles and Benefits
Modules:
Introduction to the Course & Meet Your Instructor, Understanding Root Cause Analysis (RCA), Common RCA Tools & Techniques, Real-World Applications of RCA, The 3 "Rs" of Performing Root Cause Analysis, Deep Dive into the 5 Whys Method, Mastering the Fishbone Diagram, Utilizing Pareto Analysis in RCA, From Identification to Action - Implementing Corrective Measures, Case Study Analysis: Success Stories of RCA in Action, Fostering a Culture of Proactive Problem-Solving, Congratulations! What's Next?
Course: Bacteria and Chronic Infections
Modules:
1.1 Introduction to Bacteria, by Bioinformatician, PhD, Peder Worning, 1.2 Planktonic and Biofilm growing bacteria, by Professor Thomas Bjarnsholt, 1.3 Infection Pathogenesis, by Associate professor Oana Ciofu, 1.4  Bacterias and biofilms, interview with Professor Niels Høiby
2.1 Biofilms Properties, by Project Leader Morten Alhede, 2.2 Chronic Infections - Host response - Part 1, by M.sc.,Ph.D, Peter Østrup Jensen, 2.3  Chronic Infections - Host response - Part 2, by M.sc.,Ph.D, Peter Østrup Jensen, 2.4 Chronic Infections - Treatment failure by Associate professor Oana Ciofu, 2.5 Chronic Infections Persistency - Interview with O. Ciofu and P. Østrup
3.1 Introduction to week 3, by Associate professor Mette Burmølle, 3.2 Oral Biofilms, by Assistant professor Mette Keller, 3.3 Skin Microbiology, by Professor Magnus Ågren, 3.4 Commensal Biofilm Gut Flora, by Søren Sørensen, 3.5 Bacteria and Biofilms are everywhere, Interview with Leif Parcival
4.1 Cystic Fibrosis, by Professor Thomas Bjarnsholt, 4.2 Chronic Wounds, by MD , Chief surgeon Klaus Kirketerp-Møller, 4.3 Implants, by Anders Odgaard, 4.4 Tissue Fillers, by Senior pathologist Lise Christensen, 4.5 Otitis Media, by Preben Homøe, 4.6  Intravascular Catheter, by Doctor Claus Moser
5.1 Diagnosis of Chronic infections, by Professor Thomas Bjarnsholt, 5.2 Treatment of Chronic Infections  by Associate professor Oana Ciofu, 5.3 Treatment of Chronic infections by MD , Chief surgeon Klaus Kirketerp-Møller, 5.4 Diagnosis in Clinical practice, Interview with Doctor Claus Moser
6.1 Adaptation of bacteria to chronic infections, by Lars Jelsbak, 6.2 Evolution in biofilms - Part 1, by Associate professor Mette Burmølle, 6.3 Evolution in biofilms - Part 2, by Associate professor Steve Diggle, 6.4 Adaptation and evolution in bacteria – Interview with Mette Burmølle and Lars Jelsbak
Course: Liderazgo de equipos
Modules:
01.01 - Introducción: Cómo diseñar su equipo para la excelencia, 01.02 - Diseñando su equipo pensando en el cliente, 01.03 - ¿Cuál es el tamaño óptimo del equipo?, 01.04 - Características demográficas: Cómo afecta la diversidad demográfica a los equipos, 01.05 - Fallas demográficas: ¡Tenga mucho cuidado!, 01.06 - Personalidad: Cómo la diversidad en la personalidad afecta a los equipos, 01.07 - Diversidad basada en valores en equipos (Parte 1), 01.08 - Diversidad basada en valores en equipos (Parte 2), 01.09 - Valores de equipo y ajuste persona-equipo, 01.10 - Construyendo un clima colaborativo, 01.11 - Establecimiento de metas en equipos, 01.12 - Gestión de las expectativas individuales, 01.13 - Enfatizando los objetivos compartidos, 01.14 - Evitar la autosemejanza en la composición de equipos, 01.15 - Resumen: Diseñando su equipo para la excelencia
02.01 - Estructurando su equipo para la excelencia: Estudio de caso de Apolo 13, 02.02 - División en departamentos: Cómo agrupar y organizar trabajos en el equipo, 02.03 - Centralización: Cómo estructurar la toma de decisiones en el equipo, 02.04 - Recompensas: Cómo estructurar las recompensas en el equipo, 02.05 - Recompensas y castigos de equipo, 02.06 - Virtualidad: Cómo administrar las estructuras del equipo virtual, 02.07 - Funciones: Cómo definir y organizar roles en el equipo, 02.08 - Todos los roles importan en los equipos, 02.09 - Mantener a las personas motivadas cuando no obtienen su papel preferido, 02.10 - Normas: Cómo crear normas productivas en el equipo, 02.11 - Creando una cultura de trabajo en equipo a través de normas y expectativas, 02.12 - Estatutos de equipo: Una herramienta para diseñar su equipo, 02.13 - Resumen: Estructurando su equipo para la excelencia
03.01 - Coordinación, 03.02 - Efecto de la información común, 03.03 - Holgazanería social, 03.04 - Idioma, 03.05: Trabajar entre culturas, 03.06 - Gestión de conflictos, 03.07 - Resolución de conflictos de tareas, 03.08 - Resumen: Gestionar la dinámica del equipo
04.01: Evaluación del desempeño del equipo, 04.02: Evaluación del desempeño, 04.03 - Gestión de la satisfacción de los compañeros de equipo, 04.04 - Gestión del desempeño del equipo, 04.05 - Creatividad, 04.06 - Seguridad psicológica, 04.07 - Comprometer a su equipo, 04.08 - Golpear y abrazar, 04.09 - Construir un entorno seguro, 04.10 - Memoria transactiva, 04.11: Facilitar el aprendizaje en equipo, 04.12 - Aprender de la adversidad, 04.13: Inculcar una mentalidad de aprendizaje, 04.14 - Éxito del equipo de fabricación, 04.15 - Creación de un desempeño de equipo sostenible
Course: Manage Security Operations
Modules:
Course introduction, What is Azure Monitor?, Explore Azure Monitor, Introduction to full-stack monitoring in Azure, Configure and monitor metrics and logs, Enable Log Analytics, Responding to critical situations, Enable Azure Monitor Alerts, Configure properties for diagnostic logging, Module summary
Recap: What is Microsoft Defender for Cloud?, Review the Cyber Kill Chain, Implement Microsoft Defender for Cloud, Centralized policy management with Microsoft Defender for Cloud, Overview of Microsoft Defender for servers, Configure Microsoft Defender for Cloud policies, Manage and implement Microsoft Defender for Cloud recommendations, Explore secure score, Deploy Microsoft Defender for Cloud, What are brute-force attacks?, Implement Just-in-time VM access, Understand the malware threat, Module summary
What is Microsoft Sentinel?, How does Microsoft Sentinel work?, Data connections in Microsoft Sentinel, Visualize Microsoft Sentinel data using workbooks, Create workbooks to explore Microsoft Sentinel data, Enable rules to create incidents in Microsoft Sentinel, Configure playbooks in Microsoft Sentinel, Turn on auditing and health monitoring for Microsoft Sentinel, Hunt and investigate potential breaches, Search across long time spans in large datasets, Overview of threat modeling, Module summary
Course summary, About the graded assessment, Congratulations, Next steps
Course: Hacking COVID-19 — Course 5: Tracing SARS-CoV-2's Evolution
Modules:
Accessing Course Content
Stepik
Course: 데이터 스토리텔링
Modules:
태블로에서 산점도 만들기, 태블로에서 상관관계 만들기
시각화를 만들 때 필요한 인간 중심의 데이터 설계 전략, 태블로에서 히스토그램 만들기, 태블로에서 히트맵 만들기, 태블로에서 박스플롯 만들기
추론하기
윤리, 사기 및 오류, 잘못된 차트, 근본적인 데이터 문제, 보충 자료
Course: Corporate Strategy
Modules:
About Deepak Somaya, Learn on Your Terms
Introduction: Corporate Scope, Vertical Scope Part 1, Vertical Scope Part 2, Horizontal Scope Part 1, Horizontal Scope Part 2, Horizontal Scope Part 3, Managing Integrated Firms, Like this course? Learn more with the iMBA! (optional)
Introduction: Corporate Transactions, Mergers and Acquisitions Part 1, Mergers and Acquisitions Part 2, Mergers and Acquisitions Part 3, Divestitures, Strategic Alliances, A Portfolio View, Jamie Cleghorn Interview, Brett Conradt Interview, Disney Case, Disney Case Wrap Up
Globalization, Foreign Market Entry Part 1, Foreign Market Entry Part 2, National Origins of Competitive Advantage, MNE Strategies
Public Firms and Their Role in Society, Corporate Social Responsibility, Mechanisms of Corporate Governance, Recent Developments
Course: Pharmacokinetics
Modules:
Course Welcome, Interview with Sujal Deshmukh, PKS, Routes of Administration, IV Bolus Cp Time Curves, Oral Dose Cp Time Curves
PK of Biologics, Phase I & Phase II Metabolism, Additional Aspects of Metabolism, Metabolic Stability & Clearance, Membranes, Permeability, & Transporters
Plasma Protein Binding, Drug-Drug Interactions, Pharmacokinetic/Pharmacodynamic Relationships, Anticipated Human Dose, Farewell
Course: Proyecto final sobre negociación para un mejor clima laboral
Modules:
Bienvenida
Meditando el caso
El proceso de la planeación parte 1, El proceso de la planeación parte 2, El proceso de la planeación parte 3
h) Estándares de equidad para nuestras opciones de solución, i) Alternativa estrella, k) Definir propuestas clave, intercambios y concesiones, l) Definir áreas de efectividad, m) Definir estrategias y visión futura, n) Asignación de responsabilidades, o) Agenda de la negociación, p) Resultados de la negociación, Mensaje final del módulo
Conclusión del caso, Negociación para un mejor clima laboral en el contexto de la pandemia por COVID19
Course: Security Governance & Compliance
Modules:
Course Overview, Goals of Cybersecurity, Threats, Vulnerabilities, Risks, & Countermeasures
Security Governance
Legal and Regulatory Compliance
Policies, Standards, & Procedures
Course: Providing Technical Support for AWS Workloads
Modules:
Course Introduction, Module 1 Introduction, Review of AWS Infrastructure, AWS Support Methods, Troubleshooting mindset, Customer scenario for support, Contacting technical support, Module 1 closing
Module 2 introduction, Avoiding common issues in EC2, EC2 instance connectivity issues, Customer Scenario for EC2, Avoiding common issues in AWS Lambda, Customer scenario for AWS Lambda, Avoiding common connectivity issues in Amazon VPC, Amazon VPC Flow Logs, Customer scenario for Amazon VPC, Module 2 closing
Week 3 introduction, Amazon S3 preventative measures, Customer scenarios for Amazon S3, Amazon Relational Database Service preventative measures, AWS Identity and Access Management preventative measures, Customer scenario for IAM, AWS Organizations preventative measures, Customer scenario for AWS Organizations, Course closing
Course: Gestion de projet Agile
Modules:
Introduction au Cours 5, Un bref aperçu d'Agile, Faire la distinction entre Agile et Waterfall, Les quatre valeurs du Manifeste Agile, Les 12 principes du Manifeste Agile, Adopter un état d'esprit Agile, Appliquer Agile dans un environnement VUCA, Introduction à Scrum, Introduction à Kanban, XP et Lean, Mélanger les approches de gestion de projet, Dana: Exécution de projets Agile chez Google, Synthèse
Introduction: Scrum 101, Les trois piliers Scrum, Les cinq valeurs Scrum, Rôles essentiels Scrum, Caractéristiques d'un(e) Scrum Master efficace, Pete: Ce qui rend un Scrum Master efficace, Caractéristiques d'un(e) responsable de produit efficace, Caractéristiques d'une équipe de développement efficace, Synthèse
Introduction: Mise en œuvre de Scrum, Création d'un carnet de produit, Rédaction de récits utilisateurs, Créer un carnet de produit dans Asana, Affinement du carnet et estimation de l'effort, Ajout d’une estimation dans Asana, Introduction au sprint, Planification du sprint, Créer et gérer des sprints dans Asana, Le Scrum quotidien et la revue de sprint, Sarah: Les avantages d’une réunion quotidienne, La rétrospective de sprint, Graphiques de vitesse et d’avancement, Utilisation de tableaux Kanban, Outils de transparence et de collaboration, Synthèse
Introduction: Mettre en œuvre Agile dans l'organisation, Maximiser la livraison axée sur la valeur, Camron: Comment le cadre Agile peut générer de la valeur, Composants d'une feuille de route de valeur, Créer une feuille de route efficace, Faciliter le changement organisationnel, Accompagnement d'une équipe Agile, Défis de l'équipe Agile, Défis courants de l’accompagnement Agile, L'évolution d’Agile, Jez: Mon avis sur Agile, Occasion de gestion de projet Agile, Résumé du cours, Création d'un plan de recherche d'emploi
Course: Advanced Ruby Programming and Object-Oriented Design
Modules:
Review of Blocks, The yield Keyword, Block Return Values, The block_given? Method, Block Parameters, A Custom each Method, Procs, More Proc Examples, Methods with Proc Parameters, Intro to Lambdas, Differences between Lambdas and Procs, Section Review [SLIDES]
Intro to Classes, Create a Class, The new Method on Ruby's Native Objects, Instance Variables and the initialize Method, Instance Methods, Override the to_s Method, The self Keyword, Omitting the self Keyword, Getter Methods (Read Methods), Setter Methods (Write Methods), The attr_reader and attr_writer Methods, The attr_accessor Method, Add Parameters to initialize Method, Section Review [SLIDES]
Private Methods, Protected Methods, Add Validation to Setter Methods, Derived Values, Class Methods, Alternative Class Method Syntax, Class Variables, Building a Class Over Time, Monkey Patching, Section Review [SLIDES]
Hash as initialize Argument, Required Keyword Arguments, Optional Keyword Arguments, Positional Arguments and Keyword Arguments, New Version of Candidate Class with Keyword Arguments, Section Review [SLIDES]
Intro to Inheritance [SLIDES], Create Subclasses, The superclass and ancestors Class Methods, Check Inheritance Hierarchy of our Subclasses, The is_a? and instance_of? Methods, The methods Method, Exclusive Instance Methods in Subclasses, Override Methods in a Subclass, The super Keyword I, The super Keyword II, Defining Equality, Duck Typing, Section Review [SLIDES]
Read from a Text File, Write to a Text File, Rename and Delete a File, Command Line Arguments (ARGV), Load A Ruby File with load Method, The require and require_relative Methods, Section Review [SLIDES]
Intro to Modules, Modules with Identical Methods, Import Modules into the Current File, The Math Module, The uri and net/http Modules, The Enumerable Mixin, The Comparable Mixin, Mixing in Our Own Module, The ancestors Method in Depth, The prepend Keyword, The extend Keyword, Mixing in Multiple Modules, Multiple Declarations for the Same Module, Modules within Modules, Section Review [SLIDES]
Intro to Sets, Add and Delete Items from Set, Explore set.rb on GitHub, Section Review [SLIDES]
The Date Object, The Time Object, Add or Subtract Time, The Comparable Methods on a Time Object, Convert Time Object to Formatted String, The parse and strptime Methods, Section Review [SLIDES]
Intro to Regular Expressions, The scan Method, The Digit Symbol, The Wildcard Symbol, Anchors, Exclude Characters, The sub and gsub Methods with Regular Expressions, Rubular.com, Section Review [SLIDES]
The begin and rescue Keywords, Catching Error Object, The retry Keyword, The ensure Keyword, Using begin and rescue at Top Level, The raise Keyword, Custom Exceptions, Section Review [SLIDES]
Intro to Testing, Testing a Class, The setup and teardown Methods, Assert Inclusion, Assert Error was Raised, Final Argument to Assertion Methods, Section Review [SLIDES]
Reading Documentation, Use RDoc to Generate Documentation
Intro to Ruby Gems, Updating Gem Program, Installing Faker Gem, Use Faker Gem
Conclusion to the Specialization
Course: Etkili Konuşma (Effective Speaking)
Modules:
1.1 Nefes-Diyaframın Tanıtılması ve Uygulamaları, 1.2 Ses Kullanımı, 1.3 Konuşma Organlarının Çalıştırılması Uygulamaları-1, 1.4 Konuşma Organlarının Çalıştırılması Uygulamaları-2, 1.5 Uygulama: Tekerleme Okuması
2.1 Yazı Söyleyiş Farkı, 2.2 Uygulama: Kişisel Mükemmellik Andı, 2.3 Fonetik: Uzun ve Kısa a’lar, 2.4 Fonetik: Açık ve Kapalı e’ler, 2.5 Türkçe'nin Ünsüzleri
3.1 Konuşmada Tempo, Ritim ve Durak, 3.2 Uygulama: Masalların Masalı (Nazım Hikmet), 3.3 Uygulama: Ayak İzleri İsimli Masal, 3.4 Uygulama: Memleketimden İnsan Manzaraları (Nazım Hikmet), 3.5 Uygulama: Marianne’in Kalbi (Alfred De Musset)
4.1 Türkçe'de Vurgulama Nedir? Nasıl Kullanılmalıdır?, 4.2 Shakespeare Metinleriyle Vurgulama Örnekleri, 4.3 Uygulama: Yaşadıklarımdan Öğrendiğim Bir Şey Var (Ataol Behramoğlu), 4.4 Açıklamalarla Sık Yapılan Yazım ve Söyleyiş Yanlışları-1, 4.5 Açıklamalarla Sık Yapılan Yazım ve Söyleyiş Yanlışları-2
5.1 Tonlama Nedir?, 5.2 Uygulama: Evet’li Sözcüklerle, 5.3 Uygulama: Paragraf Paragraf Duyguların Değiştirilmesi, 5.4 Uygulama: Elsa’ya Şiirler (Luis Aragon)
6.1 Uygulama: Ömer Hayyam Şiirleri, 6.2 Uygulama: Kuvayi Milliye - Kambur Kerim Hikayesi (Nazım Hikmet), 6.3 Vurgu Tonlama, Ritim, Tempo ve Durak Konularının Tekrarı, 6.4 Genel Tekrar, 6.5 Hazırlıksız Konuşma Uygulamaları
Course: Intro to TensorFlow em Português Brasileiro
Modules:
Introdução ao curso, Primeiros passos com o Google Cloud e o Qwiklabs
Introdução ao curso, Hierarquia da API TensorFlow, Componentes do TensorFlow: tensores e variáveis, Introdução ao laboratório sobre tensores e variáveis, Introdução ao laboratório sobre como escrever programas de nível inferior do TensorFlow
Visão geral, Como trabalhar na memória e com arquivos, Como preparar os dados para o treinamento do modelo, Introdução ao laboratório sobre carregamento de dados CSV e numpy, Introdução ao laboratório sobre carregamento de dados de imagem, Introdução ao laboratório sobre colunas de atributos, Introdução ao laboratório opcional sobre TFRecord e tf.Example, Treinamento em grandes conjuntos de dados com a API tf.data, Introdução ao laboratório sobre manipulação de dados com a API TensorFlow Dataset, Introdução ao laboratório opcional sobre análise de atributos usando a validação de dados e os atributos do TensorFlow
Visão geral, Funções de ativação, Funções de ativação: problemas a serem evitados na retropropagação, Redes neurais com a API Keras Sequential, Introdução ao laboratório sobre a API Keras Sequential
Redes neurais com a API Keras Functional, Regularização: os fundamentos, Regularização: L1, L2 e parada antecipada, Regularização: dropout, Exibição de modelos no Cloud, Introdução ao laboratório sobre a API Keras Functional
Resumo do curso
Course: Childbirth: A Global Perspective
Modules:
Introduction, A Closer Look at the Numbers, Causes of Death, Signs of Progress, Accelerating the Global Response
Introduction, Trends and Disparities in U.S. Maternal Mortality, Severe Maternal Morbidity, Primary Prevention of Maternal Deaths, Interview with Diana Childers and Dr. Wendy Book, Secondary Prevention of Maternal Death, When Prevention Fails: Maternal Mortality Review
Health Care Workforce Shortages, Maldistribution & Migration, Shortage, Maldistribution, & Migration Solutions, Task Shifting vs. Task Sharing, The Effectiveness of Task Shifting & Task Sharing, Policy Issues
Introduction, EmOC Signal Functions, United Nations Process Indicators for Monitoring EmOC, EmOC Monitoring and Assessment, Quality Improvement in EmOC, Challenges to EmOC Implementation in Developing Country Contexts
Introduction, Participatory Learning and Action in Practice, Effectiveness Research, Center for Black Women's Wellness, Dominican Republic: Community-Based Participatory Research Project, Essentials for Sustained Empowerment
MaNHEP Introduction, MaNHEP Theory of Action, MaNHEP Intervention Prongs, MaNHEP Collaborative Quality Improvement, MaNHEP Behavior Change Communications, MaNHEP Monitoring, Learning, and Evaluation, MaNHEP Key Project-Level Results
Course: 更年期综合管理
Modules:
更年期综合管理与团队合作, 女性全生命周期的保健问题, 性激素化验单解读, 围绝经期生理和病理的变化, 更年期症状的判断
更年期的饮食指导（上）, 更年期的饮食指导（下）, 围绝经期的常用药物, 更年期的药物指导（上）, 更年期的药物指导（下）
更年期盆腔器官脱垂的防治, 更年期月经管理, 更年期健康大步走, 更年期盆底功能训练
阴道微生态解析, 宫颈癌的筛查, 子宫内膜癌的防治, 卵巢癌的防治
围绝经期血脂的管理, 围绝经期血压的管理, 围绝经期心律失常的防治
围绝经期体重的管理, 围绝经期糖代谢异常及对策, 更年期与甲状腺功能异常（上）, 更年期与甲状腺功能异常（下）
骨质疏松的预防和治疗, 骨关节病, 腰间盘突出
乳腺癌患者的妇科问题, 乳腺癌的危险因素, 乳腺癌的早期筛查
Course: Farmacovigilancia ocular
Modules:
Características morfofisiológicas del ojo, Segmento anterior, Segmento posterior, Proceso inmunológico ocular
Bases de farmacología, Absorción ocular, Distribución y biotransformación ocular, Eliminación y barreras oculares
Conociendo la farmacovigilancia, Buenas prácticas clínicas en Farmacovigilancia, ¿Qué es una RAM?, ¿Qué es una ESAVI?, ¿Qué es tecnovigilancia?
El ojo y los fármacos, Anamnesis del paciente con reacción adversa, Evaluación visual de reacciones adversas. Parte 1, Evaluación visual de reacciones adversas. Parte 2, Diagnósticos diferenciales de reacciones adversas, Seguimiento a reacciones adversas, Farmacovigilancia ocular en el contexto de la pandemia por COVID19
Course: Alteryx Advanced
Modules:
Introduction to the Course, Using Multiple Files and Calgary, Sampling, Tiles and Binning, Cleaning, Wild Matches and Grouping, Regex and Parsing XML, Gathering Numbers
In-Database, Blending, Select and Summarize
Level-Up Your Apps and Macros Part 1, Level-Up Your Apps and Macros Part 2, Batch Macros, App Errors and Conditions, App and Macro Customization
Machine Learning Part 1, Machine Learning Part 2, Machine Learning Part 3, Machine Learning Part 4, Machine Learning Part 5
Exercise 1, Exercise 2, Exercise 3, Exercise 4, Exercise 5, Ending Video
Course: Machine Teaching for Autonomous AI
Modules:
0.1 – Specialization Preview: A glimpse at what you'll learn, 0.2 – Who is this specialization for?, 0.3 – What will you encounter in this course?, 1.1 – Real Life Examples of Autonomous AI, 1.4 – The Teacher's Mindset
2.1 – The "Skills Gap", 2.1a – Autonomous AI in Action: The invisible line on the balance sheet, 2.2 – The Value of the Problem, 2.2a – Autonomous AI in Action: The devastating effect of downtime, 2.3 – An Introduction to Math, Menus, & Manuals, 2.3a – Math – Making Predictable Decisions with Control Theory, 2.3b – Menus - Searching for the Right Decision with Optimization Algorithms, 2.3c – Manuals - The Human Factor in Expert Rules and High Stakes Decisions, 2.5 – Interviewing Skills: The teacher's toolset
3.1 – Machine Learning - Algorithms that can learn, 3.1a – Autonomous AI in Action: Curve fitting with WOOD, 3.2 – Deep Reinforcement Learning – Learning by trial & error, 3.3 The Role of Strategy, 3.4 – Machine Teaching – The superpowers of autonomous AI, 3.4a – Autonomous AI in Action: Listening to machines with NOV
4.1 – The Value of Storytelling in Autonomous AI, 4.1a – Autonomous AI in Action: Building a drone laboratory at Bell Flight, 4.3a – Autonomous AI in Action: A story of process improvement at PepsiCo, 4.3b – Autonomous AI in Action: 'The internet of REALLY OLD things', 4.4 – Components of  Storytelling
Course: Fundamentals of Business Analysis
Modules:
Introduction to the course and the instructor, The definition of business analysis, The role of business analyst, A career development path for a business analyst, Core concepts of business analysis, Identifying needs, Define the future state, Recommend solutions, Identifying stakeholders, Key business analyst responsibilities, Scope, Requirements from the business, Requirements from the stakeholders, Functional requirements, Non-functional requirements, Transition requirements, Quality Assurance and Quality Control, Process improvement, Solution evaluation, Lessons learned, Managing requirements through the lifecycle, Stakeholder collaboration, Supporting org. change, What makes a great business analyst
Course: Teaching LGBTQ+ Health
Modules:
Course Team, Disclaimers and CME Credit Instructions, Introduction, LGBTQ+ Health Vocabulary, LGBTQ+ Health: Social and Behavioral Determinants
Meet Carla, Meet Jesse, Meet Teddy
References, Teaching Resources, Claiming CME Credit Instructions
Course: Getting Started with Bottlerocket
Modules:
Getting Started with Bottlerocket
Course: Algorithms, Part II
Modules:
Course Introduction
Introduction to Graphs, Graph API, Depth-First Search, Breadth-First Search, Connected Components, Graph Challenges
Introduction to Digraphs, Digraph API, Digraph Search, Topological Sort, Strong Components
Introduction to MSTs, Greedy Algorithm, Edge-Weighted Graph API, Kruskal's Algorithm, Prim's Algorithm, MST Context
Shortest Paths APIs, Shortest Path Properties, Dijkstra's Algorithm, Edge-Weighted DAGs, Negative Weights
Introduction to Maxflow, Ford–Fulkerson Algorithm, Maxflow–Mincut Theorem, Running Time Analysis, Java Implementation, Maxflow Applications
Strings in Java, Key-Indexed Counting, LSD Radix Sort, MSD Radix Sort, 3-way Radix Quicksort, Suffix Arrays
R-way Tries, Ternary Search Tries, Character-Based Operations
Introduction to Substring Search, Brute-Force Substring Search, Knuth–Morris–Pratt, Boyer–Moore, Rabin–Karp
Regular Expressions, REs and NFAs, NFA Simulation, NFA Construction, Regular Expression Applications
Introduction to Data Compression, Run-Length Coding, Huffman Compression, LZW Compression
Introduction to Reductions, Designing Algorithms, Establishing Lower Bounds, Classifying Problems
Brewer's Problem, Simplex Algorithm, Simplex Implementations, Linear Programming Reductions
Introduction to Intractability, Search Problems, P vs. NP, Classifying Problems, NP-Completeness, Coping with Intractability
Course: A Bridge to the World: Korean Language for Intermediate1
Modules:
강의 소개1(Introduction to Lecture1), 강의 소개2(Introduction to Lecture2), 학습 목표(Learning Objective ), 핵심 표현과 대화(Key Expression and Conversation), 문법 1: (이)라고 하다(Grammar 1), 문법 2: -고 있다(Grammar 2), 내용 확인 및 자기 평가(Content check-up and Self-Assessment), 학습 목표(Learning Objective ), 핵심 표현과 대화(Key Expression and Conversation), 문법 1: -다가(Grammar 1), 문법 2: -게 되다(Grammar 2), 내용 확인 및 자기 평가(Content check-up and Self-Assessment)
학습 목표(Learning Objective), 핵심 표현과 대화(Key Expression and Conversation), 문법 1: -(으)ㄴ 적이 있다(Grammar 1), 문법 2: -(으)ㄹ까 하다(Grammar 2), 내용 확인 및 자기 평가(Content check-up and Self-Assessment), 학습 목표(Learning Objective), 핵심 표현과 대화(Key Expression and Conversation), 문법 1: -고 나서(Grammar 1), 문법 2: -거나, (이)나(Grammar 2), 내용 확인 및 자기 평가(Content check-up and Self-Assessment)
학습 목표(Learning Objective), 핵심 표현과 대화(Key Expression and Conversation), 문법 1: -아도/어도/여도 되다(Grammar 1), 문법 2: -(으)ㄹ 줄 알다/모르다(Grammar 2), 내용 확인 및 자기 평가(Content check-up and Self-Assessment), 학습 목표(Learning Objective), 핵심 표현과 대화(Key Expression and Conversation), 문법 1: 밖에(Grammar 1), 문법 2: -(으)면 좋겠다(Grammar 2), 내용 확인 및 자기 평가(Content check-up and Self-Assessment)
학습 목표(Learning Objective), 핵심 표현과 대화(Key Expression and Conversation), 문법 1: -아/어/여 가지고(Grammar 1), 문법 2: 처럼(Grammar 2), 내용 확인 및 자기 평가(Content check-up and Self-Assessment), 학습 목표(Learning Objective), 핵심 표현과 대화(Key Expression and Conversation), 문법 1: -게(Grammar 1), 문법 2: -아/어/여지다(Grammar 2), 내용 확인 및 자기 평가(Content check-up and Self-Assessment)
학습 목표(Learning Objective), 핵심 표현과 대화(Key Expression and Conversation), 문법 1: -(으)ㄹ까 봐(서)(Grammar 1), 문법 2: -(으)ㄹ 테니까(Grammar 2), 내용 확인 및 자기 평가(Content check-up and Self-Assessment), 학습 목표(Learning Objective), 핵심 표현과 대화(Key Expression and Conversation), 문법 1: 마다(Grammar 1), 문법 2: ㄹ탈락(Grammar 2), 내용 확인 및 자기 평가(Content check-up and Self-Assessment)
학습 목표(Learning Objective), 핵심 표현과 대화(Key Expression and Conversation), 문법 1: -아/어/여(평서형, 의문형 1)(Grammar 1), 문법 2: -아/어/여(평서형, 의문형 2)(Grammar 2), 내용 확인 및 자기 평가(Content check-up and Self-Assessment), 학습 목표(Learning Objective), 핵심 표현과 대화(Key Expression and Conversation), 문법 1: -아/어/여(명령형, 청유형, 감탄형)(Grammar 1), 문법 2: -자(Grammar 2), 내용 확인 및 자기 평가(Content check-up and Self-Assessment)
Course: The Challenges of Modern Caregiving
Modules:
Introduction to the Challenges of Modern Caregiving, Care Implications Part I: The Four Forces of Modernity, Care Implications Part II: Implications of Changes for Care
Is there a care deficit? Part I: Evidence of More Care, Is there a care deficit? Part II: Evidence of Less Care, Is there a care deficit? Part III: Different Approaches to Providing Care in Modern Society
Care Outside the Family Part I: What Volunteering Can Teach Us About Paid Care, Care Outside the Family Part II: How Professional Caregivers Engage in Emotional Detachment, Care Outside the Family Part III: The Emotional Costs of Professionalization, Care Outside the Family Part IV: Caregivers and Professionalism
Care and Emotions Part I: Managing Emotions, Care and Emotions Part II: Emotional Labor, Care and Emotions Part III: How the Negative Consequences of Emotional Management Can be Minimized
Re-humanized Care Work Part I: The Classical Theorists on Emotions in the Workplace, Re-humanized Care Work Part II: Attempts to Re-humanize Care Work, Re-humanized Care Work Part III: Scenarios, Bureaucratic Systems Part I: The Tendency to Trust and Obey Bureaucracies, Bureaucratic Systems Part II: The Example of Parenting, Bureaucratic Systems Part III: Vignettes
Course: Managerial Accounting: Cost Behaviors, Systems, and Analysis
Modules:
Welcome and Course Overview, Learn on Your Terms, Learning Objectives and What is Managerial Accounting, The Difference Between Financial and Managerial Accounting, Contemporary Issues, What We've Learned in Lesson 1.1, Learning Objectives and Terminology, Cost Framework 1 - Objects, Cost Framework 2 - Behavior, Cost Framework 2 - Behavior Example, What We've Learned in Lesson 1.2, Module 1 Review
Learning Objectives and Flow of Costs Through Financial Statements, Two Traditional Systems, Extension - Overhead Costs, What We've Learned in Lesson 2.1, Learning Objectives and Example Scenario, The Manager Did What?, What We've Learned in Lesson 2.2, Module 2 Review
Learning Objectives and General Approach and Overview, Fundamental Components, ABC Organization and Steps to Implementation, What We've Learned in Lesson 3.1, Learning Objectives and Steps Through an Example, Other Considerations, What We've Learned in Lesson 3.2, Module 3 Review
Learning Objectives and Cost-Volume-Profit Analysis Intro, Cost-Volume-Profit Analysis Example 1, Cost-Volume-Profit Analysis Example 2 - Taxes, What We've Learned in Lesson 4.1, Learning Objectives and Cost-Volume-Profit Example 3 - Multiple Products, What We've Learned in Lesson 4.2, Module 4 Review
Course: Probability & Statistics for Machine Learning & Data Science
Modules:
Course Introduction, A note on programming experience, What is Probability?, What is Probability? - Dice Example, Complement of Probability, Sum of Probabilities (Disjoint Events), Sum of Probabilities (Joint Events), Independence, Birthday problem, Conditional Probability - Part 1, Conditional Probability  - Part 2, Bayes Theorem - Intuition, Bayes Theorem - Mathematical Formula, Bayes Theorem - Spam example, Bayes Theorem - Prior and Posterior, Bayes Theorem - The Naive Bayes Model, Probability in Machine Learning, Random Variables, Probability Distributions (Discrete), Binomial Distribution, (Optional) Binomial Coefficient, Bernoulli Distribution, Probability Distributions (Continuous), Probability Density Function, Cumulative Distribution Function, Uniform Distribution, Normal Distribution, (Optional) Chi-Squared Distribution, Sampling from a Distribution, Week 1 - Conclusion
Expected Value, Other measures of central tendency: median and mode, Expected value of a Function, Sum of expectations, Variance, Standard Deviation, Sum of Gaussians, Standardizing a Distribution, Skewness and Kurtosis: Moments of a Distribution, Skewness and Kurtosis - Skewness, Skewness and Kurtosis - Kurtosis, Quantiles and Box-Plots, Visualizing data: Box-Plots, Visualizing data: Kernel density estimation, Visualizing data: Violin Plots, Visualizing data: QQ plots, Joint Distribution (Discrete) - Part 1, Joint Distribution (Discrete) - Part 2, Joint Distribution (Continuous), Marginal and Conditional Distribution, Conditional Distribution, Covariance of a Dataset, Covariance of a Probability Distribution, Covariance Matrix, Correlation Coefficient, Multivariate Gaussian Distribution, Week 2 - Conclusion
Population and Sample, Sample Mean, Sample Proportion, Sample Variance, Law of Large Numbers, Central Limit Theorem - Discrete Random Variable, Central Limit Theorem - Continuous Random Variable, Point Estimation, Maximum Likelihood Estimation: Motivation, MLE: Bernoulli Example, MLE: Gaussian Example, MLE: Linear Regression, Regularization, Back to "Bayesics", Bayesian Statistics - Frequentist vs. Bayesian, Bayesian Statistics - MAP, Bayesian Statistics - Updating Priors, Bayesian Statistics - Full Worked Example, Relationship between MAP, MLE and Regularization, Week 3 - Conclusion
Confidence Intervals - Overview, Confidence Intervals - Changing the Interval, Confidence Intervals - Margin of Error, Confidence Intervals - Calculation Steps, Confidence Intervals - Example, Calculating Sample Size, Difference Between Confidence and Probability, Unknown Standard Deviation, Confidence Intervals for Proportion, Defining Hypotheses, Type I and Type II errors, Right-Tailed, Left-Tailed, and Two-Tailed Tests, p-Value, Critical Values, Power of a Test, Interpreting Results, t-Distribution, t-Tests, Two Sample t-Test, Paired t-Test, ML Application: A/B Testing, Week 4 - Conclusion
Course: 파이썬의 응용 소셜 네트워크 분석
Modules:
네트워크: 네트워크의 정의와 네트워크를 연구하는 이유, 네트워크 정의 및 용어, 노드 및 간선 속성, 이분 그래프, TA 데모: NetworkX에서 그래프 불러오기
클러스터링 계수, 거리 척도, 연결된 구성 요소, 네트워크 견고성, TA 데모: NetworkX의 간단한 네트워크 시각화
연결 및 근접 중심성, 매개 중심성, 기본 페이지 랭크, 확장된 페이지 랭크, 허브 및 권한, 중심성 예
선호적 연결 모델, 작은 세상 네트워크, 링크 예측
Course: Business Value Creation
Modules:
Meet your Instructor
Four Cornerstones of Value Creation, Case Study Solution
Key Value Drivers, Case Study Solution
Course: Becoming a changemaker: Introduction to Social Innovation
Modules:
Becoming a changemaker: Introduction to Social Innovation, About this course, RLabs: Journey of Hope Part 1, What is Social Innovation?, Simple, complicated and complex, Wicked problems, The 5 Whys, Case study: Mothers2mothers, Reflecting with RLabs: problems, Week 1 Peer Assignment guidance
RLabs: Journey of Hope  Part 2, Discovering resources, Appreciative inquiry, Case study: The Street Store, Finding hidden resources, Reflecting with RLabs: resources
RLabs: Journey of Hope Part 3, Bricolage: recombining ideas and people, Thinking about difference, Engaging difference, Negotiating difference, Reflecting with RLabs: comfort zones, Week  3 peer assessment
Rlabs: Journey of Hope  Part 4, Generating ideas, Design thinking principles, Design thinking steps, Design thinking case studies, Discussing design thinking, Reflecting with RLabs: design in social innovation, Week 4 Peer Assignment guidance, Week 4 peer assessment artefact feedback
RLabs: Journey of Hope Part 5, What's innovative about your ideas?, Social innovation as system innovation, Seeing system impact, Deepening system impact, Reflecting with RLabs: changing the system
Rlabs: Journey of Hope  Part 6, The evolving world of social innovation, Organizing social innovation, Financing social innovation, Scaling social innovation, Reflecting with RLabs: moving forward, The journey ahead
Course: Programming for Data Science
Modules:
Welcome to Programming for Data Science, Learning to program, Introduction to Python, Introduction to Jupyter Notebook, Python as a Calculator
Overview of Basic data Types in Python, Introduction to Structured data Types in Python
Introduction to Control Structures, Introduction to Functions
Course: Introduction to Privacy - Part 1
Modules:
Introduction to CIPM, Stakeholders Vision and Alignment, Why Invest in Privacy, Data Life Cycle Approach and Management System Model
Privacy Team and Data Governance Models, Pitching Privacy
Principles and Standards, Laws and Regulations, Privacy Management Solutions
Where is Safe and Why, International Transfers; Justifying Transfers, International Transfers between the US and EU, Around the World
Course: Ser más creativos
Modules:
¿Qué es eso de la creatividad?, Cómo definimos la creatividad, Procesos creativos, Fijación cognitiva, Personas creativas, Plazas que promueven la creatividad, Productos creativos, ¿Nacemos o nos volvemos creativos?, ¿Se me escapó algo?
¿Qué factores importan?, El lado oscuro: Frases suicidas y asesinas, El lado oscuro: Ineficacia personal y boicot profesional, Cambio de perspectiva y pensamiento lateral, Más sobre pensamiento lateral, Hábitos recomendados, Liderazgo y creatividad, Creatividad constructiva y prototipos, ¿Lo recuerdo todo?
¿Qué podemos hacer para potenciar la creatividad?, Pensar sin pensar directamente: historias, Pensar sin pensar directamente: metáforas, Ingeniería reversa, Listas combinadas, Transferencia de dominios, Proceso compartido: lluvia de ideas, Un ejemplo de lluvia de ideas, ¡Ábrase en caso de emergencia!, ¿Recuerdo todas las estrategias?
¿Qué son las heurísticas y qué otros caminos hay para ser más creativos?, ¿Qué son las heurísticas?, Heurísticas positivas, El modelo de Osborn y Parnes: primera parte, El modelo de Osborn y Parnes: segunda parte, Design thinking: conceptos básicos, Propuestas de Brown, La experiencia del usuario, ¿Tengo todo lo aprendido en mente?
¿Qué otras cuestiones debemos saber de la creatividad?, Frasear todo en términos solucionables, Humor, estado de ánimo y creatividad, Un poco más sobre estados de ánimo, Las restricciones, Ejemplos del uso de restricciones, Gracias!!!
Course: 공급망금융의 주요 성공 요소
Modules:
전문 과정 소개, 강사 소개, 강좌 개요 및 목표, 공급망금융 프로그램 수립 및 관리 과정에서의 주요 과제, 성공 요소 개요, 주제 01: 공급망금융을 시작한 기업의 이상적인 예, 주제 02: 기업 구매자 규모, 주제 03: 신용 등급, 주제 04: 직접 지출 vs. 간접 지출, 주제 05: 이해관계자
주제 01: 개요, 주제 02: 현황 평가, 주제 03: 목표 - 공급망 위험, 주제 04: 목표 - 운용 자금 및 이윤, 주제 05: 목표 - 이니셔티브 I, 주제 06: 목표 - 이니셔티브 II
주제 01: 기업이 추구하는 바는 무엇일까요?, 주제 02: 플랫폼 예시, 주제 01: 기업이 추구하는 바는 무엇일까요? (1부), 주제 02: 기업이 추구하는 바는 무엇일까요? (2부), 금리부터 실행, 수취채권을 거래하는 공급자에 이르기까지, 공급망금융을 수립하는 과정에서 기업의 주요 단계는 무엇인가요?, 주제 01: 예시, 주제 02: 예시: 사전 영업 분석(Brown-Forman), 주제 03: 사전 영업 분석의 중요성
주제 01: 수익은 어떻게 창출되며 가격은 어떻게 책정하나요?, 주제 01: 지금까지 무엇을 배웠나요?, 주제 01: 절차, 주제 02: 공급자 선정 기준, 주제 03: 구매자 및 공급자 분석의 예, 주제 04: 스프레드시트 예시, 주제 01: 절차, 주제 02: 예시
주제 01: 핵심 역할, 주제 01: 온보딩 절차, 주제 02: 거래 파트너에 대한 소개, 주제 03: 공급자 온보딩 - 예시, 주제 04: 잘못된 의사소통의 예, 주제 05: 거래 파트너 교육, 주제 06: 거래 파트너의 등록, 공급자 측의 공급망금융 사용이 저조한 이유는 무엇인가요?, 주제 01: 구매자 고려 사항 1부, 주제 02: 구매자 고려 사항 2부, 주제 03: 회계 예시 - 일반 & 특별목적회사(SPV), 주제 04: 공급자/판매자 고려 사항, 주제 05: Abengoa, 화이트보드 토론: 공급자와 구매자 간의 관계에 대한 심화 토론