dipopotamus/custom_instruct_coursera_course_module_list huggingface dataset:
Course: Public Engagement for Scientists
Modules:
Course Overview, Instructor Introduction, Module 1 Introduction - Scientific Mistrust Through History, Knowledge Production as a Social Process, Who is an Expert Today? Who Makes Knowledge?, Science and the Public Sphere, Key Challenges When Communicating Science, Key Challenges When Communicating Science - Part 2
Module 2 Introduction - “This text is too dense!”, How to Scale Vocabulary, How to Scale Detail, How to Edit for Concision and Precision, Document Design for Comprehension, Color, Typesetting, etc.
Module 3 Introduction - Why Data Visualization is Hard, and Rhetorical, How Should I Organize this Data?, Cleaning and Organizing Data in R, Choosing the Right Data Visualization Form, Creating different types of Data Visualization in R, Captions, Legends, Labels, Making Captions, Legends, and Labels in R
Module 4 Introduction, Less is More: Helping Humans to Focus on the Right Thing in Your Data Visualization, Using Color Strategically with Data, Accessibility and Data Visualization, A Brief History Lesson: Ethically Murky Waters in Data Visualization, Ethical Framework for Data Visualization - Part 1, Ethical Framework for Data Visualization - Part 2
Module 5 Introduction - What Kinds of Writing Do Researchers Read and Why?, Goals and Expectations of the Research Community - Part 1, Goals and Expectations of the Research Community - Part 2, What is the Purpose of a Research Paper? And How Do I Read One? - Part 1, What is the Purpose of a Research Paper? And How Do I Read One? - Part 2, How Scientific Information is Communicated in a Research Paper - Part 1, How Scientific Information is Communicated in a Research Paper - Part 2, How are Technical Reports Different Than Research Papers?, How to Write Massive Amounts of Technical Detail, Accurately - Part 1, How to Write Massive Amounts of Technical Detail, Accurately - Part 2
Module 6 Introduction - Expert to Expert Communication is Still Hard, Principle 1: Bottom-line-up-front (BLUF), Principle 2: Concision and Precision - Part 1, Principle 2: Concision and Precision - Part 2, Types of Internal Documentation, Key Rhetorical Moves in Internal Documentation, How to Write a Good Memo, Types of Professional Writing
Module 7 Introduction - Genre Variation in Communicating Science to Other Professionals, Audience Theory & Reader-Based Prose, Deciding What Information Your Audience Needs, Frame Theory, Picking the Right Frame, What Will My Audience Be Able to Understand?, Editing for the “Hard Stuff” - Part 1, Editing for the “Hard Stuff” - Part 2
Module 8 Introduction - Quick review of Module 1 and How We'll Use Foundations in this Module, Why the Public Doesn't Always Believe the Data, Why the Public Doesn't Always Believe Experts - Part 2, Argument Theory - Part 1, Argument Theory - Part 2, Elements of a Strong Scientific Argument, How to Use Data as Part of Your Argument, Case Studies on (In)Effective Data Usage, Principles in Effective Data Design for Public Arguments
Summative Course Assessment
Course: Serverless Data Processing with Dataflow: Operations em Português Brasileiro
Modules:
Introdução ao curso, Como começar a usar o Google Cloud Platform e o Qwiklabs
Lista de jobs, Informações de jobs, Gráficos de jobs, Métricas de jobs, Metrics Explorer
Logging, Error Reporting
Fluxo de trabalho da solução de problemas, Tipos de problemas
Design do pipeline, Formato dos dados, Origens, coletores e sistemas externos, Shuffle e Streaming Engine
Visão geral de testes e CI/CD, Teste de unidade, Teste de integração, Criação de artefatos, Implantação
Introdução à confiabilidade, Monitoring, Geolocalização, Recuperação de desastres, Alta disponibilidade
Modelos Classic, Modelos Flex, Usar modelos Flex, Modelos fornecidos pelo Google
Resumo do curso
Course: Llama for Python Programmers
Modules:
Welcome to the Class: Llama for Python Programmers, What is Llama 2? An Open Source Large Language Model (LLM), Llamas Llamas Everywhere! Inference, Chat, and Code Completion, Open Source LLMs: Llama and Its Competitors, The Elephant in the Room: Running Large Language Models on Small Hardware, Introducing Llama 3 - Understanding the Llama 3 and Llama 3.1 Herd of Models
Llama 2 Tokenization, Building Our First Llama 2 Application with llama.cpp, Choices/Parameters, Llama 2 Chat and Conversations
Zero and Few Shot Prompting, Controlling Model Output, Using Grammars, Course Conclusion
Course: DevOps on AWS: Operate and Monitor
Modules:
Introduction to DevOps on AWS: Operate and Monitor, Why Monitoring?, First: Adding Instruments to the Cloud Environment, How to Know When Something Changes in the Cloud?, Activity and Administrative API Monitoring into the AWS Environment, Second: Instrumenting the App, Third: Network Monitoring, Exercise 1: Monitoring Pipeline Changes
Introduction to Week 2 - Operate, How to Prevent Hiccups at the End of the Infinite Loop, Detecting Unmanaged Configuration Changes to Your Cloud Resources, Operations Hub for Cloud Environments, Exploring some AWS Systems Manager Features, Remotely Run Commands in your Servers, More Node Management Features Powered by Systems Manager, Exercise 2 Video Introduction, Course Wrap-up
Course: Nutrition Hot Topics and Controversies
Modules:
Before You Start, Final Thoughts
Course: Asset Pricing Models
Modules:
Introduction, Introduction, Financial Markets with no riskless (safe) asset, Zero Beta Portfolio, CAPM without riskless asset, Risk-return tradeoff without a riskless asset /1, Risk-return tradeoff without a riskless asset /2, Risk-return tradeoff without a riskless asset /3, Risk-return tradeoff without a riskless asset /4, The importance of short selling, Asset prices under normal distribution, Log Returns, Merton's Intertemporal CAPM - step 1, Merton's Intertemporal CAPM - step 2, Merton's Intertemporal CAPM - step 3, Merton's Intertemporal CAPM - step 4, Optimal Portfolio Shares, Equilibrium stock returns - Constant Investment opportunity Set, Equilibrium stock returns - Time-Varying riskfree rate
Introduction, Realized returns and the CAPM, The Market Model /1, The Market Model /2, Empirical tests of the CAPM, Roll Critique /1, Roll Critique /2, Roll Critique /3, Time - series regressions /1, Time - series regressions /2, Time - series regressions /3, Time - series regressions /4, Cross - sectional regressions /1, Cross - sectional regressions /2, Cross - sectional regressions /3, Fama - MacBeth Method, General Findings
Introduction, Definition of Arbitrage /1, Definition of Arbitrage /2, The Arbitrage Pricing Theory, The dynamics of realized returns /1, The dynamics of realized returns /2, Assumptions of APT, The Market Model /1, The Market Model /2, The Market Model /3, The Market Model /4, The Market Model /5, Two Factors Model /1, Two Factors Model /2, Two Factors Model /3, Two Factors Model /4, Two Factors Model /5, The general case /1, The general case /2, The general case /3, Strength and Weaknesses of APT, Chen, Roll and Ross (1986), Fama and French (1993), Statistical Approach
Introduction, The CAPM assumptions, Empirical failures of the CAPM, Explore new directions, A simplified Lucas tree economy /1, A simplified Lucas tree economy /2, Solving the model, Equilibrium in the markets, Equilibrium prices, Equilibrium expected returns, Extending the model to N risky assets, Solving the model and equilibrium prices, Including also a safe asset, Dynamic Lucas tree economy, The investor dynamic problem /1, The investor dynamic problem /2, The investor dynamic problem /3, The investor dynamic problem /4, The investor dynamic problem /5, Equilibrium returns /1, Equilibrium returns /2, Equilibrium returns /3
Introduction, What is a bond? /1, What is a bond? /2, Types of bond /1, Types of bond /2, How are stocks different from bonds, Benefits from investing in bonds, Main risks from bond investing, Yield measures, Yield to Maturity, Properties of the Yield to Maturity, Yield to Maturity and portfolios of bonds, Coupon Yield, Spot Interest Rates part 1, Spot Interest Rates part 2, Term Structure of Spot Interest Rates, Forward Interest Rates /1, Forward Interest Rates /2, Forward interest rates /3, Forward interest rates /4, Duration of a bond /1, Duration of a bond /2
Course: النقود والتضخم | Money and Inflation
Modules:
مقدمة عن مفهوم التضخم (Inflation), وظائف النقود, قياس نسبة التضخم, سعر الفائدة الحقيقي (Real Interest Rate) وسعر الفائدة الاسمي (Nominal Interest Rate) وأنواع التضخم, التكاليف المرتبطة بالتضخم, الفوائد الممكنة للتضخم وأنواعه, التضخم المفرط (Hyperinflation) وآثاره, أسباب التضخم المفرط وكيف يمكن القضاء عليه
Course: Preparing for Your Cloud Architect Journey - 日本語版
Modules:
モジュールの概要, Professional Cloud Architect の役割の概要, 認定資格の価値とメリット, 認定のプロセス, 学習計画の作成
モジュールの概要, Cymbal Direct のクラウド アーキテクチャを設計する, 概要: 確認のための質問, 確認のための質問の実践デモ, 学習計画
モジュールの概要, Cymbal Direct の要件を満たすクラウド ソリューションをプロビジョニングする, 概要: 確認のための質問, 学習計画
モジュールの概要, Cymbal Direct のクラウド ソリューションのセキュリティとコンプライアンスを考慮して設計する, 概要: 確認のための質問, 学習計画
モジュールの概要, クラウドに合わせて Cymbal Direct の技術面とビジネス面のプロセスと手順を最適化する, 概要: 確認のための質問, 学習計画
モジュールの概要, Cymbal Direct での実装、運用、信頼性, 概要: 確認のための質問, 学習計画
モジュールの概要, ケーススタディを分析する:  Dress4Win, 包括的な目標を理解する, 目標を特定して箇条書きにする, 目標を分類して優先順位を付ける, 暫定的な選択肢を分析して決定する, 選択肢を絞り込んでソリューションを特定する, 週単位の学習目標
Course: Electrodynamics: Electric and Magnetic Fields
Modules:
Introduction, 1-1 Modeling Heat Flow and Membranes, 1-2 Different Spherical Solutions
2-1 Magnetic Fields and Forces, 2-2 Interactions with Magnetic Fields
3-1 Introduction to the Vector Potential, 3-2 The Vector Potentials for Specific Geometries
4-1 Mechanical and Electrical Energy, 4-2 Comparing Quantum Mechanics and Dynamics
5-1 Running Motors, 5-2 Forces From Induced Currents, Conclusion
Course: Market Risk Management: Frameworks & Strategies
Modules:
Welcome to the Course
Welcome to Module 01: Financial Instruments, Bonds, Equities, Derivatives and Managing Market Risk
Welcome to Module 02: Measuring and Analyzing Market Risk, Determining Probability of a Loss, Statistical Measures
Welcome to Module 03: Managing and Modeling Market Risk, Market Risk Measurement Systems, Downside Risk Measures, Stress Testing and Scenario Analysis
Market Risk Management​ Project - Part I, Market Risk Management​ Project - Part II, Market Risk Management​ Project - Part III
Wrap Up
Course: Inquiries Into Bipedalism
Modules:
Introductory Reading, Syllabus, Book: First Steps: How Upright Walking Made Us Human, Coursera Code of Conduct, Coursera Honor Code, Discussion Tips and Guide, Learning Journal, Human Skeleton: Intro Activity, Activity Description, Femur Matching Activity, Explainer: Femur Matching Activity, Using Footprint Data, Recap: Using Footprint Data, Hypothetical Fossil Site: Identify the Bipeds, Explainer: Identify the Bipeds, Phylogenetic Tree Based on Anatomy, Phylogenetic Tree Based on Genetics, Inquiry Activity: Last Common Ancestor
Video:  Welcome to the Hall of Human Origins, Video: A Day In the Life of Lucy and Australopithecus, Video: Walking in Their Footsteps, Video: From Bipedalism to Tool Use, Video: Learning From Fossils, Video: The Origins of Controlled Fire, Video: Reflections on Learning to Walk, Video: Paleoart, Video: Homo sapiens, Video: Creating Emotional Connections
Video: Learning From Fossils, Video: Dating Fossils, Video: Finding Fossils, Video: Formation of Fossils, Experiencing Human Origins in Southern Africa - Part 1, Experiencing Human Origins in Southern Africa - Part 2, Experiencing Human Origins in Southern Africa - Part 3
Course: Orden y manejo del tiempo
Modules:
Bienvenida, Importancia  del orden en el área de trabajo
Un lugar para cada cosa y cada cosa en su lugar
Método para lograr el orden en todas las áreas de la organización
Importancia del tiempo del directivo y su equipo
Diagnóstico personal y organizacional de administración del tiempo
Técnicas efectivas para la administración del tiempo Parte 1, Técnicas efectivas para la administración del tiempo Parte 2
Course: Gemini for Data Scientists and Analysts - Français
Modules:
Présentation de Gemini pour les professionnels des données, Analyser des données avec Gemini, Concevoir un modèle connecté à un LLM avec Gemini
Course: C for Everyone: Programming Fundamentals
Modules:
Overview, History of C, Compiling, debugging, and running a program, part 1, Compiling, debugging, and running a program part 2 (File included ➕), First Program, Example - Circle code, Example - Marathon, Simple input/output - fahrenheit, Simple input/output - miles
Character sets and tokens, Comments, Keywords, Identifiers, Operators, Expressions and precedence (File included ➕), Expression and evaluation, Declarations, Fundamental types and sizeof, The char type (File included ➕), The int type, The integer and floating point types (Files included ➕)
Logical operators, expressions, and short-circuit evaluation, The conditional statement if and if-else, The iterative statement while, while-cnt-char-explained, while-code - example, The for statement and its while analog, for statement code example, oddball operators-conditional and comma, ternary-operator code example, Break and continue and switch (File included ➕)
Function definition, Function code example, Return statement, Function prototype, Function Prototype - code example, Function variables—with call-by-value explained, Function definitions and scope rules, Storage class code example, Simple recursion, Recursion- factorial code, Recursion Fibonacci code (File included ➕)
Pointers and simple arrays, initialize arrays, array-grade-code example, What is a pointer, Pointer code example, Call-by-reference simulated, array as a parameter, array-bubble-sort code, merge sort overview, merge code -example 1, merge code example 2 (File included ➕)
Final exam
Course: Projeto Aplicado - Introdução a Gestão de Projetos
Modules:
Bem Vindo!, Avaliação entre pares
Objetivos do curso e componentes do projeto, #1 Declaração do termo de abertura, #2 Estrutura analítica de projeto, #3 Sequência de atividades do projeto, #4 Cronograma do projeto, #5 Orçamento do projeto, #6 Matriz de responsabilidade, #7 Riscos do projeto identificados e respostas a esses riscos
Conteúdos relevantes para o seu projeto aplicado
Pratique a avaliação entre pares (opcional)
Entrega final do Projeto aplicado
Avaliação do trabalho de seus pares
Course: Big Ideas in Programming: Expressing Yourself with Python
Modules:
Meet Dr. Barb Ericson, How is this course different?, What is Python?, Why work in groups?, POGIL roles, How to write code in the eBook, Reloading your code history, Welcome to Week 1, Mixed-Up Code (Parsons) Problems, Introduction to Functions, Code Visualizer (CodeLens), Integer vs Float division and the Modulo Operator, String Overview, Slicing a String, Stripping Characters, Converting Types
Welcome to Week 2, What are Conditionals?, Testing All Conditionals, Coding get_middle, Coding alarm_clock, How to Return the Result from a Boolean Expression, How to Use a Complex Conditional, Overview of Lists, Solving first_half
Welcome to Week 3, Overview of Loops, Stepping Through count_odd, Fixing sum67, More About Loops, Add Number of Guesses to Number Guessing Game, Fixing Countdown, Comparing Adjacent Values in a List, Another Way to Compare Adjacent Values in a List, Filtering Data with a Loop, What Are List Comprehensions?, Filtering Data with a List Comprehension, More About List Comprehensions
Welcome to Week 4, Overview of Tuples, Why Use Tuples?, Overview of Dictionaries, More on Dictionaries, What Does Zip Do?, A Better Way to Add a Count to a Dictionary, How to Sort Lists, Tuples, and Dictionaries, Sorting with Lambda
Course: Getting Started with Chef Cookbooks
Modules:
Overview of this Course, Chef Cookbook_Part 01, Chef Cookbook_Part 02, Setup and Configuration, Chef Installation and Configuration_Part 01, Chef Installation and Configuration_Part 02, Chef Installation and Configuration_Part 03, Chef Installation and Configuration_Part 04
Recipes and Resources_Part 01, Recipes and Resources_Part 02, Generating Chef Cookbook_Part 01, Generating Chef Cookbook_Part 02, Generating Chef Cookbook_Part 03, Attributes and Templates, Creating Cookbook using ChefDK_Part 01, Creating Cookbook using ChefDK_Part 02, Creating Cookbook using ChefDK_Part 03, Files and Directories, Chef Permissions
Testing and Validation, Chef Unit Testing Template, Chef Unit Testing_Part 01, Chef Unit Testing_Part 02, Advanced Topics_Part 01, Advanced Topics_Part 02, Security and Compliance_Part 01, Security and Compliance_Part 02, Generating RSA Key-pair, Maintenance and Troubleshooting_Part 01, Maintenance and Troubleshooting_Part 02
Course: Verification and Synthesis of Autonomous Systems
Modules:
Meet Your Instructor!, Outline of Specialization, Introduction to Course 3 - Verification and Synthesis
Introduction, Product of System and NFA, Example of Product of Simple System and NFA, An Example, Introduction, Product of System and NBA, ω-Regular Model Checking, ω-Regular Model Checking: Example, Checking Regular Safety vs ω-Regular Properties, Persistence Checking via Strongly Connected Component, Introduction, From LTL to NBA, NBA for LTL Formulas: Examples
Overview, Fixed Points, Computation of Fixed Points, The Pre Map, Synthesis for Safety Specifications, Synthesis for Safety Specifications: Example, Synthesis for Reachability Specifications, Synthesis for Reachability Specifications: Example, Synthesis for Persistence Specifications, Synthesis for Persistence Specifications: Example, Synthesis for Recurrence Specifications, More Specifications
Motivation, Definition of Feedback Refinement Relations, Example: Feedback Refinement Relation, Theorem: Feedback Refinement Relations and Behavioral Inclusion, Corollary: Feedback Refinement Relations and Behavioral Inclusion, Theorem: Computation of Abstractions, Example: Computation of Abstractions, Abstractions of Sample-and-Hold Linear Control System, SCOTS and OmegaThreads: Tools for the Synthesis of Controllers via Finite Abstractions
Course: Workforce Productivity
Modules:
Introduction to the Course & Meet Your Instructor, The Pillars of Workforce Productivity, Identifying Productivity Barriers in Operations, Introduction to Process Optimization Techniques, Lean Management Principles for Waste Reduction, Implementing Six Sigma for Process Improvement, Utilizing Technology for Efficiency Gains, Tools and Techniques for Process Mapping and Analysis New Title - Introduction to Process Mapping and Value Stream Mapping (VSM), Additional Process Mapping Tools and Techniques for Process Analysis, Setting and Measuring Performance Metrics, Fostering a Culture of Continuous Improvement, Congratulations and Continuous Learning Journey
Course: Gestão de Marca & Crise de Imagem
Modules:
Vídeo 0 - Apresentação do Curso., Aula 1 - O que é Marca?, Video 1 - A História da Evolução das Marcas, Vídeo 1 - Dúvida e a crise do poder, Vídeo 1 - Imagem e posicionamento, Vídeo 1 - Construção do Patrimônio de Marca (Brand Equity), Vídeo 1 - Endosso de marca, Vídeo 1 - Reflexão sobre a Marca Contemporânea
Vídeo 1 - O que são crises de marca?, Vídeo 1 - A difusão da Crise, Vídeo 1 - A força da sociedade e os efeitos dos memes
Vídeo 1 - O que você faria?, Vídeo 1 - A preparação, Vídeo 1 - A gestão, Vídeo 1 - A recuperação
Vídeo 1 - CIM, Vídeo 1 - Outras formas de comunicação, Vídeo 1 - Comunicação de Crise, Vídeo 1 - Avaliação da declaração dos CEOS
Vídeo 1 - Conhecendo o sistema de informação de marketing, Vídeo 1 - Caso pessoal. Crise na prática, Vídeo 1 - Caso pessoal. Crise na prática. Visão Crítica, Vídeo 1 - O perfil dos gestores de crise.
Vídeo 1 - Aprendizados, Vídeo 1 - Checklist
Course: المهارات الأساسية لمديري اليوم
Modules:
كيف تدير نفسك (Self Managing), العلاقة بين الشخصية (Personality) والأداء (Performance), المبادئ (Values) والسلوك الأخلاقي (Ethical Behavior), المساهمة (Contribution) والمسؤوليات (Responsibilities), التواصل الجيد داخل الشركة, إدارة الرئيس في العمل (Managing the Boss), مديرو المستقبل (The Managers of Tomorrow), الخلاصة
Course: Exploring and Preparing Your Data with BigQuery - Français
Modules:
Présentation de la série de cours
Problèmes d'analyse rencontrés par les analystes de données, Big data sur site et dans le cloud, Cas d'utilisation concrets, Principes de base des projets Google Cloud
Tâches des analystes de données, difficultés et outils de données Google Cloud, Neuf fonctionnalités essentielles de BigQuery, Tutoriel : Schéma d'architecture de données, Outils Google Cloud pour analystes, data scientists et ingénieurs de données, Coursera: Premiers pas avec Google Cloud Platform et Qwiklabs
Présentation de l'ensemble de données d'e-commerce Google Analytics, Techniques courantes d'exploration des données, Principes de base des requêtes, Présentation des fonctions, Filtres, agrégations et doublons, Types de données, fonctions de date et valeurs nulles, Filtres à caractères génériques avec opérateur LIKE
Tâches BigQuery, Gestion du dimensionnement et des coûts, Optimisation du coût des requêtes
Cinq principes concernant l'intégrité des ensembles de données, Forme et asymétrie des ensembles de données, Nettoyage et transformation des données à l'aide de SQL, Nettoyage et transformation des données à l'aide d'une nouvelle UI : présentation de Dataprep, Composants de Data Fusion, Construire un pipeline, Explorer des données avec Wrangler
Résumé du cours
Course: Communication Strategies for a Virtual Age
Modules:
Welcome to the Course!, What to expect?, The 3 traps of communication, First trap - Speaking in third person, Second trap - Speaking in formal language, Third trap - Speaking with too many details, Misconceptions, pros & cons, Three types of remote teams, Confessionals, Be so sensitive
Too much of a good thing?, (QCTT) + (PS) = Awesome, Interactive Awesome, What is a question?
Communication Styles, Hostile (Bullets & Categories), Indifferent (Help & Advice), Uninformed (Empathy & POV), Supportive (Everything and Everybody), Underlying  Motivations, Achievement, Affiliation, Power, Questions, Closed questions, Open Questions, Strategic Questions, Positional vs Principled Bargaining
5% Rule, Area 47, Goal Opportunity Statement, KNOW-Phrase, Belly Button Rule, The Lazy Rule, Picture Superiority Effect
Course: El liderazgo del director de proyectos
Modules:
Cualidades de un director de proyectos, Conceptos  relevantes  y útiles en el rol del líder
Importancia del proceso de comunicación, Estrategias empleadas en los canales de comunicación
Aspectos relevantes en el desarrollo de equipo de trabajo., Aspectos relevantes en los equipos de alto desempeño
Elementos relevantes en torno a la habilidad de negociación, Elementos relevantes en el proceso de negociación, Entrevista con el experto
Course: Performing Network, Path, and Text Analyses in SAS Visual Analytics
Modules:
Business Scenario: Advanced
Introduction to Network Analysis, Business Scenario, Analytics: Network, Network: Data Shape, Splitting Columns, Custom Code, Demo: Creating a Network Analysis Data Source, Practice Scenario, Uses for Network Analysis, Mapping Networks
Introduction to Path Analysis, Business Scenario, Analytics: Path, Path: Data Shape, Demo: Analyzing a Path Analysis Data Source, Practice Scenario
Introduction to Text Analysis, Business Scenario, Analytics: Text Topics, Text Topics: Data Shape, How Text Analytics Works, Derive Topics, Demo: Analyzing a Text Analysis Data Source, Practice Scenario
Course: Daten über Visualisierungen teilen
Modules:
Einführung in die Präsentation von Erkenntnissen aus Daten, Kevin: Die Macht liegt in den Datenvisualisierungen, Warum Datenvisualisierung wichtig ist, Bilder mit Daten verknüpfen, Das Rezept für effektive Visualisierungen, Dynamische Visualisierungen, Elemente der Kunst, Auswirkungen der Datenvisualisierung, Design Thinking und Visualisierungen, Überschriften, Unterüberschriften und Beschriftungen, Barrierefreie Visualisierungen, Andrew: Daten barrierefrei präsentieren
Datenvisualisierungen mit Tableau, Tableau Public und andere Online-Tools, Tableau kennenlernen, Eine Datenvisualisierung in Tableau erstellen, Das Gute, das Schlechte und das Hässliche, Kreativ werden, Daten in Tableau verknüpfen
Storytelling mit Daten, Ideen zum Leben erwecken, Zu Ihrer Zielgruppe sprechen, Carolyn: Datenjournalismus, Grundlagen zum Tableau-Dashboard, Von Filtern zu Diagrammen, Tipps für überzeugende Präsentationen, Eine Erzählung vermitteln, Sundas: So gehen Sie mit dem Hochstapler-Syndrom um
Das Gesamtbild, Mit einem Framework präsentieren, Daten in die Präsentation einbinden, Brittany: Präsentationstechniken für Nachwuchsfachkräfte für Datenanalyse, Connor: Beispiel für eine schlechte Datenpräsentation, Connor: Beispiel für eine gute Datenpräsentation, Bewährte Tipps für Präsentationen, Präsentieren wie ein Profi, Fragen im Voraus erahnen, Umgang mit Einwänden, Best Practices für Rückfragen, Connor: Daten übersetzen, Herzlichen Glückwunsch!
Course: AI For Everyone
Modules:
Week 1 Introduction, Machine Learning, What is data?, The terminology of AI, What makes an AI company?, What machine learning can and cannot do, More examples of what machine learning can and cannot do, Non-technical explanation of deep learning (Part 1, optional), Non-technical explanation of deep learning (Part 2, optional)
Week 2 Introduction, Workflow of a machine learning project, Workflow of a data science project, Every job function needs to learn how to use data, How to choose an AI project (Part 1), How to choose an AI project (Part 2), Working with an AI team, Technical tools for AI teams (optional)
Week 3 Introduction, Case study: Smart speaker, Case study: Self-driving car, Example roles of an AI team, AI Transformation Playbook (Part 1), AI Transformation Playbook (Part 2), AI pitfalls to avoid, Taking your first step in AI, Survey of major AI application areas (optional), Survey of major AI techniques (optional)
Week 4 Introduction, A realistic view of AI, Discrimination / Bias, Adversarial attacks on AI, Adverse uses of AI, AI and developing economies, AI and jobs, Conclusion
Course: Major Engineering Project Performance
Modules:
Introduction from Giorgio, Projects, sub-projects and operations, Project life cycle theories
Transaction costs
Cost and time performance of major projects, Other reasons to explain over-budget and delay
Welcome to the week, Lesson overview, What is a measure of success in major project management?, Case study: Motorola Iridium, Motorola Iridium: an analysis, Main contractor perspective, How should you measure project success?, Extended perspective, Lesson summary, Lesson overview, Turner and Zolin framework, Quiz introduction, Case study: the Tower of Pisa, Case study: the Thames Barrier, Quiz introduction, Lesson summary, Writing your reflections, References
Welcome to the week, Lesson overview, The assignment, Approaching your research, Academic referencing, Lesson summary, Creating your presentation, Preparing your presentation, Submitting your presentation, Next steps
Course: Classical papers in molecular genetics
Modules:
Introduction, Part 1 - Perspective, Part 2, Part 3, Part 4, Part 5, Part 6
Part 1, Part 2, Part 3, Part 4, Part 5, Part 6, Part 7 - Quantitative analysis of transformation
Part 1, Part 2, Part 3, Part 4
Part 1, Part 2, Part 3, Part 4, Part 5
Part 1, Part 2, Part 3, Part 4, Part 5, Part 6
Part 1, Part 2, Part 3, Part 4, Part 5, Part 6, Part 7
Part 1, Part 2, Part 3, Part 4, Part 5
Part 1, Part 2, Part 3, Part 4
Part 1, Part 2, Part 3, Part 4, Part 5, Part 6
Part 1, Part 2, Part 3, Part 4, Part 5, Part 6, Part 7
Part 1, Part 2, Part 3, Part 4
Part 1, Part 2, Part 3, Part 4, Part 5, Part 6
Course: Estrategia de Identidad de una Marca
Modules:
Bienvenida/o al curso, Presentación del Módulo 1, Introducción a la Lección 1, ¿Qué es la marca?, El valor de la marca, ¿Cuándo inició el Branding?, Introducción a la Lección 2, ¿Qué es el branding?, Diferencias entre branding y marketing, Tipos de branding, Cuándo iniciar el proceso de creación de una marca?, Principios de administración de una marca, Introducción a la Lección 3, Razones para invertir en branding y diseño, La estrategia de una marca, Los ideales de una marca
Introducción a la Lección 1, ¿Cómo realizar una investigación desde la perpectiva del branding?, Análisis y alineamiento, Introducción a la Lección 2, Conocimiento de la marca, Investigación de mercado, Introducción a la Lección 3, Auditoria de marketing, Auditoria de la competencia, Auditoria del lenguaje, Auditoría de la propiedad intelectual, Test de usabilidad, Resultado de las auditorías
Introducción a la Lección 1, Escenario estratégico, Desarrollo de la estrategia, La gran idea, Introducción a la Lección 2, Pilares de una marca, Identidad vs imagen, Posicionamiento, Mapa de posicionamiento, Propuesta única de ventas, Introducción a la Lección 3, Componentes de un informe de marca, Informe de marca Vs. Informe creativo
Introducción a la Lección 1, Diseñar la identidad, Brand Elements, Brand Engagement, Introducción a la Lección 2, ¿Cómo debe comportarse la marca?, Los valores de la marca, Los 12 arquetipos, Introducción a la Lección 3, El por qué de la marca, Las 6 preguntas de una marca, Cuando el por qué se convierte en Manifiesto
Introducción al Módulo 5, El informe creativo, Introducción a la Lección 2, Protección legal de marca, Branding in a digital world, Definir las aplicaciones ideales para la empresa, La presentación, Introducción a la Lección 3, Objetivos de contenidos, Tipos de contenidos, Branding interno y la cultura corporativa, Final del curso
Course: FinTech Security and Regulation (RegTech)
Modules:
1.1 Course & Module 1 Introduction, 1.2a Risk & Risk Management (Part 1), 1.2b Risk and Risk Management (Part 2), 1.3a Goals of Security & Regulation (Part 1), 1.3b Goals of Security & Regulation (Part 2), 1.4a What is RegTech? (Part 1), 1.4b What is RegTech? (Part 2), 1.5a What is InsurTech? (Part 1), 1.5b What is InsurTech? (Part 2), 1.6a Do regulations help society? (Part 1), 1.6b Do regulations help society? (Part 2), 1.7a How much security is optimal? (Part 1), 1.7b How much security is optimal? (Part 2), 1.8 FinTech Security and Regulation : Introduction & Overview Summary
2.1 Introduction to Risk Management & Government Control, 2.2 AML & KYC in FinTech, 2.3 Interview with the Chief Operating Officer of HSBC Asia Pacific about RegTech, 2.4a Government Regulation to Protect Consumers (Part 1), 2.4b Government Regulation to Protect Consumers (Part 2), 2.5a Regulation to Increase Tax Compliance & Control (Part 1), 2.5b Regulation to Increase Tax Compliance & Control (Part 2), 2.6a Insurance, Risk Management, and Controls to Protect Society (Part 1), 2.6b Insurance, Risk Management, and Controls to Protect Society (Part 2), 2.7a Weak Regulations lead to Financial Crisis & Meltdown: AIG case study (Part 1), 2.7b Weak Regulations lead to Financial Crisis & Meltdown: AIG case study (Part 2), 2.8 Risk Management & Government Control Summary
3.1 Introduction to Fraud, Crimes, & Security, 3.2a Cybersecurity- Data and Related Technologies (Part 1), 3.2b Cybersecurity- Data and Related Technologies (Part 2), 3.3a Cybersecurity - Human Related Issues (Part 1), 3.3b Cybersecurity - Human Related Issues (Part 2), 3.4a Digital Forensics & Electronic Evidence Discovery (Part 1), 3.4b Digital Forensics & Electronic Evidence Discovery (Part 2), 3.5a UN Model Law on E-commerce & FinTech (Part 1), 3.5b UN Model Law on E-commerce & FinTech (Part 2), 3.6 Recommendations for FinTech Firms on Cybersecurity, 3.7 Fraud,  Crimes, & Security Summary
4.1 Introduction to Global Trends & Government Initiatives in RegTech, 4.2a UK Open Banking Initiative (Part 1), 4.2b UK Open Banking Initiative (Part 2), 4.3 USA and European Regulations on FinTech, 4.4 Cryptocurrencies & ICO Regulations, 4.5 FinTech Innovation by Government - Berkeley ICO, 4.6 China Regulations and Restrictions on FinTech, 4.7 Hong Kong, Singapore, & Other Markets on RegTech, 4.8 Global Trends and Government Initiatives in RegTech Summary
5.1 Introduction to Final Project
Course: How to Implement and Evaluate Communication  Campaigns
Modules:
Introduction to Module, Client Background (Imagination Storybooks), Client Research (Imagination Storybooks), Target Audience (Imagination Storybooks), Goals, Objectives, Strategies, Tactics (Imagination Storybooks), A Tactical Look (Imagination Storybooks), Evaluation (Imagination Storybooks), Client Background (Chef Ann), 5TRIVE (Campaign Example), Slight Edge (Campaign Example)
Introduction to Module, Evaluating the Key Performance Indicators, Creating Meaningful Evaluation Measures, Evaluating Your Own Campaign, Campaign Planning vs. Reality, Making Adjustments to Reach Campaign Goal, Closing Remarks
Course: Blockchain Evolution and Technology Concepts
Modules:
Welcome to the course, Module introduction, Money and currency overview, Currency and cryptocurrency overview, Blockchain: Key terms and concepts, Crypto assets, bitcoin, and blockchain, Public versus private/consortium blockchains, Blockchain types, Blockchain mechanics and hashing, Four characteristics of bitcoin and public blockchain, Bitcoin and blockchain attributes and characteristics, Public blockchains and DLT- Litecoin (LTC), Ethereum (ETH), and Dash (DASH), Public blockchains and DLT- TrueUSD (TUSD) and IOTA (MIOTA), Permissioned and consortium blockchains, Blockchain - Core components of technology, Core components debrief, Recap and consensus algorithms, Cryptography and encryption, Wallets and the push versus pull paradigm, The custody, responsibility, and control continuum, Potential uses and benefits of blockchain, Business uses and benefits, Summary
Course: Comportamiento del Consumidor Digital
Modules:
Presentación del curso, Bienvenida al módulo 1, Fundamentos del comportamiento del consumidor digital, La transformación del consumidor en la era digital, Entrevista: Desafíos y oportunidades de la digitalización del comportamiento del consumidor, con Miguel Ángel Palacios de Ludik, Diferencias entre el consumidor digital y el consumidor físico, Entrevista: ¿Por qué es importante la investigación del comportamiento del consumidor digital? Con Miguel Ángel Palacios de Ludik, Entrevista: La importancia de la investigación del comportamiento del consumidor en la estrategia de marketing, con Oswaldo Ipanaque de Digital Taktik, Etapas de la investigación del comportamiento del consumidor digital, Herramientas digitales para la investigación del consumidor, Video de cierre del Módulo 1
Presentación del módulo 2, Introducción a las metodologías de recopilación de datos digitales, Entendiendo el comportamiento del consumidor en redes sociales, Entrevista: Experiencias en la recopilación de datos digitales, Entrevista: ¿Cómo garantizar la calidad y precisión de los datos?, La analítica en el entorno digital, Entrevista: Ventajas y desafíos de las herramientas analíticas, Entrevista: Big data para entender el rol en el comportamiento del consumidor digital, Entrevista: Skills que necesitan los profesionales en el uso de herramientas  analíticas, Cierre del Módulo 2
Video de presentación del Módulo 3, Comprendiendo la interpretación de datos digitales, Parte 1 - Entrevista: Análisis de comportamientos y tendencias del consumidor digital, Parte 2 - Entrevista: Análisis de comportamientos y tendencias del consumidor digital, Entrevista: Retos y oportunidades en el análisis de datos digitales, Interpretación de sentimientos en el mundo digital, Entrevista: ¿Cómo crear perfiles de consumidores y segmentación de mercado?, Aplicación de datos del consumidor en el diseño de productos y servicios, Parte 1 - Entrevista: Impacto de los datos del consumidor en las estrategias de marketing, Parte 2 - Entrevista: Impacto de los datos del consumidor en las estrategias de marketing, Parte 3 - Entrevista: Impacto de los datos del consumidor en las estrategias de marketing, Problemas éticos en la recopilación e interpretación de datos, Responsabilidad corporativa en el uso de los datos del consumidor, Parte 1 - Entrevista: Abordando los dilemas éticos en el manejo de los datos del consumidor, Parte 2 - Entrevista: Abordando los dilemas éticos en el manejo de los datos del consumidor, Video de Cierre del Módulo 3, Video de Cierre de Curso
Course: New paradigms in wastewater management
Modules:
The water service: from water sources to final fate of the wastewater, How the wastewater is treated today: a concise review, Challenges and opportunities in modern plants, Rethinking cities for climate resilience: the importance of water sensitive approaches, Nature based solutions (NBS) to control pollution
Energy consumption, energy neutrality/positivity in WWTPs, How to produce energy and biofuels in WWTPs
How to make wastewater fit for reuse? Challenges and opportunities, Disinfection as barrier towards pathogens spread- Pt. 1, Disinfection as barrier towards pathogens spread- Pt. 2, Contaminants of emerging concern and their removal for minimizing environmental and human health risk
How to recover materials from sludge through thermochemical processes, The role of microalgae: microalgae/bacteria interactions, Plastics and polymers from wastewater, Case study: Nosedo wastewater treatment plant
Course: How Google does Machine Learning en Español
Modules:
Vista previa de la serie, Introducción al curso
Introducción, ¿Qué es el AA?, ¿Qué tipos de problemas puede solucionar?, Introducción al lab: Definición de un problema de aprendizaje automático, Soluciones del lab: Definición de un problema de aprendizaje automático, Incorpore el AA en sus aplicaciones, Cree una estrategia de datos en torno al AA
Introducción, El factor sorpresa del AA, El ingrediente secreto, El AA y los procesos empresariales, La ruta hacia el AA, Una visión más detallada del recorrido, Profundización sobre el final de las fases
Introducción, Cómo pasar de la experimentación a la producción, Componentes de Vertex AI, Coursera: Cómo comenzar a usar Google Cloud Platform y Qwiklabs, Introducción al lab: Cómo usar un conjunto de datos de imágenes para entrenar un modelo de AutoML, Demostración del lab: Cómo usar un conjunto de datos de imágenes para entrenar un modelo de AutoML, Introducción al lab: Cómo entrenar un modelo de clasificación de videos de AutoML, Demostración del lab: Cómo entrenar un modelo de clasificación de videos de AutoML, Herramientas para interactuar con Vertex AI
Introducción, Desarrollo del aprendizaje automático con Notebooks de Vertex, Introducción al lab (opcional): SDK del creador de modelos de Vertex AI: Entrenamiento y predicciones en un modelo de AutoML, Demostración del lab (opcional): SDK del creador de modelos de Vertex AI: Entrenamiento y predicciones en un modelo de AutoML
Introducción, Prácticas recomendadas para el desarrollo del aprendizaje automático, Prácticas recomendadas para el procesamiento previo de los datos, Prácticas recomendadas para la configuración del entorno de aprendizaje automático
Introducción, Descripción general, El sesgo humano provoca sesgos en los modelos de AA, Sesgo en los datos, Evaluación de métricas con inclusión para su sistema de AA, Igualdad de oportunidades, Cómo encontrar errores en su conjunto de datos con facetas
Resumen, Recurso: Todas las preguntas del cuestionario, Recurso: Todo el material de lectura, Recurso: Todas las diapositivas
Course: Data Ethics, AI and Responsible Innovation
Modules:
The challenge of data-driven innovation, What are ethics?, Making moral decisions, Ethical considerations, Jury manipulation, Common ethical issues, Interview 1: Oliver Smith, Telefónica, Interview 2: Roland Verhaaf
Predictive policing, Statistical fairness, Group fairness measures, Individual fairness
A conversation on IoT
The algorithmic economy, Utilitarian ethics, Distributive fairness, Social welfare and equity, Maximin and Pareto efficiency, Advanced fairness criteria
Ethical benefits and risks of medical data, Neonatal genetic databanks, Robots and empathy, The datafication of work, Course summary
Course: Smart Cities – Management of Smart Urban Infrastructures
Modules:
Introduction to the Smart Cities MOOC, Introduction to week 1, Conceptualizing cities as complex socio-technical systems, What is digitalization?, General Implications of digitalization, Implications on digitalization on cities, Perspectives on Smart Cities, interview with Ms. Irene Compte Libera about Barcelona
Introduction to week 2, Conceptualization of Smart Urban Energy Systems, Interview with an Utility company (Dr. Corrodi, IWB Basel), The infrastructure layer of smart urban energy systems, Interview with Prof. Mario Paolone, The services layer of smart urban energy systems, The data/digital layer of smart urban energy systems, Managerial and Policy takeaways, Interview with an Energy company (Dr. Bolke, Schneider Electric)
Introduction to week 3, Conceptualization of smart urban transportation systems, The "improve" pathway, Interview with a transportation company (Mr Schaefer, Siemens), The "shift" pathway, Interview with a transportation operator (Ms. Leboucher, RATP), Conditions for success
Introdution to week 4, Transition in Socio-Technical systems, Interview with an urban service provider company (Dr. Staron, Veolia), Data/Digital layer, Potential of the digital layer, Interview with Prof. Montero (UNED, Madrid), Interview with Prof. Faltings (EPFL)
Challenges on the Services layer, Interview with a data company (Mr Ender & Mr Schimpel, IBM), Challenges on the Infrastructure layer, Challenges for managers, Challenges for policy-makers & Conclusion of the MOOC
Course: Managing Asthma, Allergies, Diabetes, and Seizures in School
Modules:
Welcome to School Health for Children and Adolescents, Managing Asthma, Allergies, Diabetes, and Seizures in School, What is Asthma?, Asthma Triggers, Symptoms of Asthma Attack, Asthma Management, Role of School Nurse, Medical Management Plans, Asthma Action Plan, Supporting Students with Asthma
What is Type 1 Diabetes?, Management of Type 1 Diabetes, Symptoms of Type 1 Diabetes, Diabetes Medication Management, Care of Students with T1DM
What is a Seizure Disorder?, Causes of Seizures, Common Symptoms of Seizures, Management of Seizures, Seizure Disorders: School Nurse and Staff, Seizure Emergency Action Plan
What are Food Allergies and Anaphylaxis?, Recognizing Signs of Allergies and Anaphylaxis, Common Food Allergens, Management of Allergies and Anaphylaxis, Role of Allergy and Anaphylaxis, Allergy and Anaphylaxis Plan, Ways to Minimize Exposure
Course: Customer Centric Innovation
Modules:
Course Introduction, Module 1 Introduction, Customer-Centric Innovation, Why is Innovation Important?, What is Innovation?, Successful Innovation
Module 2 Introduction, Why Do Innovations Fail Part 1, Why Do Innovations Fail Part 2, Barriers to Innovation Adaption, Overcoming Barriers to Innovation Adaption
Module 3 Introduction, Understanding Customer Needs Part 1, Understanding Customer Needs Part 2, Perceptions and Observations - Overcoming Issues with Enquiry Method, Empathy and Lead Users
Module 4 Introduction, Strategy to Innovation, Fuzzy Front End of Innovation, Ideation and Creativity, Barriers to Creativity
Course: Strategic Planning and Execution
Modules:
Introduction to Strategic Execution, Strategic Analysis, Strategic Analysis: Steps 1-3, Strategic Analysis: Step 4, Strategist'sToolkit: Hypothesis Testing, Strategy Research, Strategy Analytics, Module Lessons
Pillars of Strategy Execution, Strategy in Organizations, Strategy Formulation: The Strategic War Room, Strategy Formulation: Rapid Prototyping, Strategy Formulation: Design Thinking, Strategy Formulation: Business Model Canvas, Corporate Governance, The Human Factor, Common Strategy Execution Mistakes, Key Strategy Execution Questions, Module Lessons
The Importance of Strategy Execution, The 4A Model of Strategy Execution, Introduction to Alignment, Achieving Alignment, Introduction to Ability, Investing in Talent, Introduction to Architecture, How Architecture Drives Performance, Architecture Best Practices, Introduction to Agility, Agility through Organizational Learning, Agility through Unity and Fluidity, Module Lessons
Welcome to Our Guest Speakers, Shannon Smith, CEO Abundant Power, Byrne Murphy, DigiPlex, Carolyn Miles, Save the Children, Rick Edmunds, Strategy& PwC, Bob Hugin, Celgene, Rick Paschal, Darden '89, Sunil Ghatnekar, The Coca-Cola Company, Jerry Peng, Tranlin, Inc., Dean Scott Beardsley: Background, Dean Scott Beardsley: Perspective, Dean Scott Beardsley: Process, Dean Scott Beardsley: Advice, Conclusion
Course: Generative AI Foundations
Modules:
Course Introduction, Overview of Generative AI, Generative AI Principles, Generative AI vs. Generative AI Model, Understanding Generative AI Models, Transformer - Based , Energy - Based & Conditional Generation Models, Generative AI for Code Generation, Benefits of Generative AI for Code Generation, Introduction to ChatGPT, Log-in Process, Code Generation with ChatGPT, Leveraging ChatGPT to Learn Data Science with Python, Visualization with Gen AI, Exploratory Data Analysis, Demonstration on Exploratory Data Analysis, Exploratory Data Analysis with the help of ChatGPT
Working of Autoencoders, Variational Autoencoders, Introduction to GAN, Working of GAN, Basic GAN Architecture, Variants of GANs, BigGAN, Training GANs, About GAN, Data Compression with Autoencoders
Exploring Language Models, Types of Language Models, Transfer Models, Applications of Language Models, Summarization and Search, Introduction to GPT, Understanding GPT, BERT, Inference in BERT
Course Summary
Course: Foundations and Core Concepts of PyTorch
Modules:
Specialization Overview, PyTorch Introduction, System Setup, How to Get the Course Material, Setting Up the conda Environment, How to Work with the Course
Artificial Intelligence (101), Machine Learning (101), Machine Learning Models (101)
Deep Learning General Overview, Deep Learning Modeling 101, Performance, From Perceptron to Neural Network, Layer Types, Activation Functions, Loss Functions, Optimizers, Deep Learning Framework
Underfitting Overfitting (101), Train Test Split (101), Resampling Techniques (101)
Section Overview, Neural Network from Scratch (101), Calculating the dot-product (Coding), Neural Network from Scratch (Data Prep), Neural Network from Scratch Modeling __init__ Function, Neural Network from Scratch Modeling Helper Functions, Neural Network from Scratch Modeling Forward Function, Neural Network from Scratch Modeling Backward Function, Neural Network from Scratch Modeling Optimizer Function, Neural Network from Scratch Modeling Train Function, Neural Network from Scratch Model Training, Neural Network from Scratch Model Evaluation
Section Overview, From Tensors to Computational Graphs (101), Tensor (Coding)
Section Overview, Linear Regression from Scratch (Coding, Model Training), Linear Regression from Scratch (Coding, Model Evaluation), Model Class (Coding), Exercise: Learning Rate and Number of Epochs, Solution: Learning Rate and Number of Epochs, Batches (101), Batches (Coding), Datasets and Dataloaders (101), Datasets and Dataloaders (Coding), Saving and Loading Models (101), Saving and Loading Models (Coding), Model Training (101), Hyperparameter Tuning (101), Hyperparameter Tuning (Coding)
Course: Leading Oneself with Purpose and Meaning
Modules:
Meet your Professor (for new learners), Course Summary, Purpose, Meaning, Living With Intent
Spirituality, Mortality
Legacy, Impact
Character and Ethos, Personal Values
Perfectionism, Personal Excellence, Mastery Mindset, Outro Video
Course: Introducción al Arte Sonoro
Modules:
Introducción
Conceptos de Arte Sonoro en el marco del Arte Contemporáneo, La evolución de las tecnologías y el arte sonoro
Principios básicos de la escultura sonora, Acústica aplicada a la escultura sonora, materiales y técnicas I, Acústica aplicada a la escultura sonora, materiales y técnicas II
Paisaje sonoro. Conceptos fundamentales, Paisaje sonoro I, Paisaje sonoro II
Introducción a la psicología de la música, Introducción a la psicoacústica, Música experimental
Principio básicos de Acción Sonora, Principios básicos de la Poesía Sonora
Course: Configure and Deploy AWS PrivateLink
Modules:
Configure and Deploy AWS PrivateLink