�ݺ�ߣshows by User: VassiliosRendoumis

�ݺ�ߣshows by User: VassiliosRendoumis / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: VassiliosRendoumis / Wed, 17 Jul 2019 15:14:21 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: VassiliosRendoumis Go /slideshow/go-156111921/156111921 certification-of-completion-training-rendoumis-vassilios-190717151421
Go Intro and Tooling,Configuration in Go, Embedding, Interfaces, Go Design Patterns (Strategy,Mediator, State, Observer, Command,Decorator, Factory, Builder, Singleton, Adapter, Bridge, Iterator, Proxy, Prototype) , Go App structure (Flat, Layered, Module, DDD), Example Router/RESTful APIs (Create & Consume), gRPC (Theory,Unary, Server Streaming,Client Streaming,Bidirectional Streaming, SSL, Errors, Deadline, Debug and Testing, gRPC vs Rest), GORM,Parallelism Vs Concurrency, Goroutine, Channels (Deadlock,Unidirectional), Worker Pool, Select, Mutex, Shared Memory (Concurency), Analyzing Performance, Testing (Unit, Integration,End-to-End, Mocking)]]>
Go Intro and Tooling,Configuration in Go, Embedding, Interfaces, Go Design Patterns (Strategy,Mediator, State, Observer, Command,Decorator, Factory, Builder, Singleton, Adapter, Bridge, Iterator, Proxy, Prototype) , Go App structure (Flat, Layered, Module, DDD), Example Router/RESTful APIs (Create & Consume), gRPC (Theory,Unary, Server Streaming,Client Streaming,Bidirectional Streaming, SSL, Errors, Deadline, Debug and Testing, gRPC vs Rest), GORM,Parallelism Vs Concurrency, Goroutine, Channels (Deadlock,Unidirectional), Worker Pool, Select, Mutex, Shared Memory (Concurency), Analyzing Performance, Testing (Unit, Integration,End-to-End, Mocking)]]> Wed, 17 Jul 2019 15:14:21 GMT /slideshow/go-156111921/156111921 VassiliosRendoumis@slideshare.net(VassiliosRendoumis) Go VassiliosRendoumis Go Intro and Tooling,Configuration in Go, Embedding, Interfaces, Go Design Patterns (Strategy,Mediator, State, Observer, Command,Decorator, Factory, Builder, Singleton, Adapter, Bridge, Iterator, Proxy, Prototype) , Go App structure (Flat, Layered, Module, DDD), Example Router/RESTful APIs (Create & Consume), gRPC (Theory,Unary, Server Streaming,Client Streaming,Bidirectional Streaming, SSL, Errors, Deadline, Debug and Testing, gRPC vs Rest), GORM,Parallelism Vs Concurrency, Goroutine, Channels (Deadlock,Unidirectional), Worker Pool, Select, Mutex, Shared Memory (Concurency), Analyzing Performance, Testing (Unit, Integration,End-to-End, Mocking) <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/certification-of-completion-training-rendoumis-vassilios-190717151421-thumbnail.jpg?width=120&height=120&fit=bounds" /> Go Intro and Tooling,Configuration in Go, Embedding, Interfaces, Go Design Patterns (Strategy,Mediator, State, Observer, Command,Decorator, Factory, Builder, Singleton, Adapter, Bridge, Iterator, Proxy, Prototype) , Go App structure (Flat, Layered, Module, DDD), Example Router/RESTful APIs (Create & Consume), gRPC (Theory,Unary, Server Streaming,Client Streaming,Bidirectional Streaming, SSL, Errors, Deadline, Debug and Testing, gRPC vs Rest), GORM,Parallelism Vs Concurrency, Goroutine, Channels (Deadlock,Unidirectional), Worker Pool, Select, Mutex, Shared Memory (Concurency), Analyzing Performance, Testing (Unit, Integration,End-to-End, Mocking)

Go from Vassilios Rendoumis

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0 Go: The Complete Developer's Guide (Golang) /slideshow/go-the-complete-developers-guide-golang/155849435 uc-ffzmopjy-190716083625
Go Packages, Imports, Function & Return Types, Slices and Slice Range Syntax, Custom Type Declaration, Receiver Functions, Multiple Return Types, Byte Slices, Joins on Slices of Strings, Error Handling, Testing, Random Number Generation, Element Assertion in Slices, Structs (Declaration, Definition), Updating and Embedding of Structs, Structures with Receiver Functions, Pass by Value, Structs with Pointers, Pointer Operations, Pointer Shortcuts, Reference vs Value Types, Maps, Manipulating Maps, Iterating over Maps, Maps vs Structs, Interfaces, Reader and Writer Interface, Go Routines, Channels, Channel Implementation, Blocking Channels, Repeating Routines, Alternative Loop Syntax, Sleeping a Routine, Function Literals]]>
Go Packages, Imports, Function & Return Types, Slices and Slice Range Syntax, Custom Type Declaration, Receiver Functions, Multiple Return Types, Byte Slices, Joins on Slices of Strings, Error Handling, Testing, Random Number Generation, Element Assertion in Slices, Structs (Declaration, Definition), Updating and Embedding of Structs, Structures with Receiver Functions, Pass by Value, Structs with Pointers, Pointer Operations, Pointer Shortcuts, Reference vs Value Types, Maps, Manipulating Maps, Iterating over Maps, Maps vs Structs, Interfaces, Reader and Writer Interface, Go Routines, Channels, Channel Implementation, Blocking Channels, Repeating Routines, Alternative Loop Syntax, Sleeping a Routine, Function Literals]]> Tue, 16 Jul 2019 08:36:24 GMT /slideshow/go-the-complete-developers-guide-golang/155849435 VassiliosRendoumis@slideshare.net(VassiliosRendoumis) Go: The Complete Developer's Guide (Golang) VassiliosRendoumis Go Packages, Imports, Function & Return Types, Slices and Slice Range Syntax, Custom Type Declaration, Receiver Functions, Multiple Return Types, Byte Slices, Joins on Slices of Strings, Error Handling, Testing, Random Number Generation, Element Assertion in Slices, Structs (Declaration, Definition), Updating and Embedding of Structs, Structures with Receiver Functions, Pass by Value, Structs with Pointers, Pointer Operations, Pointer Shortcuts, Reference vs Value Types, Maps, Manipulating Maps, Iterating over Maps, Maps vs Structs, Interfaces, Reader and Writer Interface, Go Routines, Channels, Channel Implementation, Blocking Channels, Repeating Routines, Alternative Loop Syntax, Sleeping a Routine, Function Literals <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/uc-ffzmopjy-190716083625-thumbnail.jpg?width=120&height=120&fit=bounds" /> Go Packages, Imports, Function & Return Types, Slices and Slice Range Syntax, Custom Type Declaration, Receiver Functions, Multiple Return Types, Byte Slices, Joins on Slices of Strings, Error Handling, Testing, Random Number Generation, Element Assertion in Slices, Structs (Declaration, Definition), Updating and Embedding of Structs, Structures with Receiver Functions, Pass by Value, Structs with Pointers, Pointer Operations, Pointer Shortcuts, Reference vs Value Types, Maps, Manipulating Maps, Iterating over Maps, Maps vs Structs, Interfaces, Reader and Writer Interface, Go Routines, Channels, Channel Implementation, Blocking Channels, Repeating Routines, Alternative Loop Syntax, Sleeping a Routine, Function Literals

Go: The Complete Developer's Guide (Golang) from Vassilios Rendoumis

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0 Containers & Kubernetes /slideshow/containers-kubernetes/155847881 certification-of-completion-training-vassilios-rendoumis-190716082950
Configuring applications on Kubernetes, Running jobs on Kubernetes, Managing application health on Kubernetes, Monitoring and troubleshooting applications on Kubernetes, Helm deep dive, Kubernetes under the hood, Spinning a Kubernetes cluster, Managing resources on a Kubernetes cluster, Role-based Access Control (RBAC) on a Kubernetes cluster, Kubernetes Networking, Microservice Architecture and development with Docker, Kubernetes blackbelt]]>
Configuring applications on Kubernetes, Running jobs on Kubernetes, Managing application health on Kubernetes, Monitoring and troubleshooting applications on Kubernetes, Helm deep dive, Kubernetes under the hood, Spinning a Kubernetes cluster, Managing resources on a Kubernetes cluster, Role-based Access Control (RBAC) on a Kubernetes cluster, Kubernetes Networking, Microservice Architecture and development with Docker, Kubernetes blackbelt]]> Tue, 16 Jul 2019 08:29:50 GMT /slideshow/containers-kubernetes/155847881 VassiliosRendoumis@slideshare.net(VassiliosRendoumis) Containers & Kubernetes VassiliosRendoumis Configuring applications on Kubernetes, Running jobs on Kubernetes, Managing application health on Kubernetes, Monitoring and troubleshooting applications on Kubernetes, Helm deep dive, Kubernetes under the hood, Spinning a Kubernetes cluster, Managing resources on a Kubernetes cluster, Role-based Access Control (RBAC) on a Kubernetes cluster, Kubernetes Networking, Microservice Architecture and development with Docker, Kubernetes blackbelt <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/certification-of-completion-training-vassilios-rendoumis-190716082950-thumbnail.jpg?width=120&height=120&fit=bounds" /> Configuring applications on Kubernetes, Running jobs on Kubernetes, Managing application health on Kubernetes, Monitoring and troubleshooting applications on Kubernetes, Helm deep dive, Kubernetes under the hood, Spinning a Kubernetes cluster, Managing resources on a Kubernetes cluster, Role-based Access Control (RBAC) on a Kubernetes cluster, Kubernetes Networking, Microservice Architecture and development with Docker, Kubernetes blackbelt

Containers & Kubernetes from Vassilios Rendoumis

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0 C programming - Complete Tutorial For Beginners /slideshow/c-programming-complete-tutorial-for-beginners/114429913 cprogramming-180914080048
Write C programmes independently with in-depth understanding of POINTERS, dynamic memory allocation, recursions, Arrays, Strings, functions, file handling, command line arguments, bitwise operators. Complete command over branching using if-else statement and loops (while, for and do-while) with extensive practical examples and assignments. Understand clearly Arrays and Strings, sorting arrays using bubble sort and various standard string functions. Write your own FUNCTIONS and create custom User Defined Library. Learn all about POINTERS, functions to allocate dynamic memory - malloc, calloc and realloc and free functions. Relationship between arrays and pointers. Array of pointers and simulating a dynamic 2D array using array of pointer. Command line parameter passing. File handling in details. Bitwise operators. Recursion - how it works, recursion vs iteration in depth discussion.]]>
Write C programmes independently with in-depth understanding of POINTERS, dynamic memory allocation, recursions, Arrays, Strings, functions, file handling, command line arguments, bitwise operators. Complete command over branching using if-else statement and loops (while, for and do-while) with extensive practical examples and assignments. Understand clearly Arrays and Strings, sorting arrays using bubble sort and various standard string functions. Write your own FUNCTIONS and create custom User Defined Library. Learn all about POINTERS, functions to allocate dynamic memory - malloc, calloc and realloc and free functions. Relationship between arrays and pointers. Array of pointers and simulating a dynamic 2D array using array of pointer. Command line parameter passing. File handling in details. Bitwise operators. Recursion - how it works, recursion vs iteration in depth discussion.]]> Fri, 14 Sep 2018 08:00:48 GMT /slideshow/c-programming-complete-tutorial-for-beginners/114429913 VassiliosRendoumis@slideshare.net(VassiliosRendoumis) C programming - Complete Tutorial For Beginners VassiliosRendoumis Write C programmes independently with in-depth understanding of POINTERS, dynamic memory allocation, recursions, Arrays, Strings, functions, file handling, command line arguments, bitwise operators. Complete command over branching using if-else statement and loops (while, for and do-while) with extensive practical examples and assignments. Understand clearly Arrays and Strings, sorting arrays using bubble sort and various standard string functions. Write your own FUNCTIONS and create custom User Defined Library. Learn all about POINTERS, functions to allocate dynamic memory - malloc, calloc and realloc and free functions. Relationship between arrays and pointers. Array of pointers and simulating a dynamic 2D array using array of pointer. Command line parameter passing. File handling in details. Bitwise operators. Recursion - how it works, recursion vs iteration in depth discussion. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/cprogramming-180914080048-thumbnail.jpg?width=120&height=120&fit=bounds" /> Write C programmes independently with in-depth understanding of POINTERS, dynamic memory allocation, recursions, Arrays, Strings, functions, file handling, command line arguments, bitwise operators. Complete command over branching using if-else statement and loops (while, for and do-while) with extensive practical examples and assignments. Understand clearly Arrays and Strings, sorting arrays using bubble sort and various standard string functions. Write your own FUNCTIONS and create custom User Defined Library. Learn all about POINTERS, functions to allocate dynamic memory - malloc, calloc and realloc and free functions. Relationship between arrays and pointers. Array of pointers and simulating a dynamic 2D array using array of pointer. Command line parameter passing. File handling in details. Bitwise operators. Recursion - how it works, recursion vs iteration in depth discussion.

C programming - Complete Tutorial For Beginners from Vassilios Rendoumis

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0 Introduction to Python for Data Science /slideshow/introduction-to-python-for-data-science/58096291 microsoftdat208xcertificateedx-160210113657
This course teaches basic arithmetic and variables, how to handle data structures such as Python lists, Numpy arrays and Pandas DataFrames. It introduces the Python functions and control flow as well as data visualization with Python in order to create visualizations on real data.]]>
This course teaches basic arithmetic and variables, how to handle data structures such as Python lists, Numpy arrays and Pandas DataFrames. It introduces the Python functions and control flow as well as data visualization with Python in order to create visualizations on real data.]]> Wed, 10 Feb 2016 11:36:57 GMT /slideshow/introduction-to-python-for-data-science/58096291 VassiliosRendoumis@slideshare.net(VassiliosRendoumis) Introduction to Python for Data Science VassiliosRendoumis This course teaches basic arithmetic and variables, how to handle data structures such as Python lists, Numpy arrays and Pandas DataFrames. It introduces the Python functions and control flow as well as data visualization with Python in order to create visualizations on real data. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/microsoftdat208xcertificateedx-160210113657-thumbnail.jpg?width=120&height=120&fit=bounds" /> This course teaches basic arithmetic and variables, how to handle data structures such as Python lists, Numpy arrays and Pandas DataFrames. It introduces the Python functions and control flow as well as data visualization with Python in order to create visualizations on real data.

Introduction to Python for Data Science from Vassilios Rendoumis

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0 Introduction to Functional Programming /slideshow/introduction-to-functional-programming-56757294/56757294 delftxfp101xcertificateedx-160106205740
The aim of this course is to teach the foundations of functional programming and how to apply them in the real world.]]>
The aim of this course is to teach the foundations of functional programming and how to apply them in the real world.]]> Wed, 06 Jan 2016 20:57:40 GMT /slideshow/introduction-to-functional-programming-56757294/56757294 VassiliosRendoumis@slideshare.net(VassiliosRendoumis) Introduction to Functional Programming VassiliosRendoumis The aim of this course is to teach the foundations of functional programming and how to apply them in the real world. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/delftxfp101xcertificateedx-160106205740-thumbnail.jpg?width=120&height=120&fit=bounds" /> The aim of this course is to teach the foundations of functional programming and how to apply them in the real world.

Introduction to Functional Programming from Vassilios Rendoumis

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0 Scalable Machine Learning /slideshow/certificate-51341691/51341691 certificate-150806105240-lva1-app6892
This course introduces the underlying statistical and algorithmic principles required to develop scalable real-world machine learning pipelines. We present an integrated view of data processing by highlighting the various components of these pipelines, including feature extraction, supervised learning, model evaluation, and exploratory data analysis. Students will gain hands-on experience applying these principles by using Apache Spark to implement several scalable learning pipelines.]]>
This course introduces the underlying statistical and algorithmic principles required to develop scalable real-world machine learning pipelines. We present an integrated view of data processing by highlighting the various components of these pipelines, including feature extraction, supervised learning, model evaluation, and exploratory data analysis. Students will gain hands-on experience applying these principles by using Apache Spark to implement several scalable learning pipelines.]]> Thu, 06 Aug 2015 10:52:40 GMT /slideshow/certificate-51341691/51341691 VassiliosRendoumis@slideshare.net(VassiliosRendoumis) Scalable Machine Learning VassiliosRendoumis This course introduces the underlying statistical and algorithmic principles required to develop scalable real-world machine learning pipelines. We present an integrated view of data processing by highlighting the various components of these pipelines, including feature extraction, supervised learning, model evaluation, and exploratory data analysis. Students will gain hands-on experience applying these principles by using Apache Spark to implement several scalable learning pipelines. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/certificate-150806105240-lva1-app6892-thumbnail.jpg?width=120&height=120&fit=bounds" /> This course introduces the underlying statistical and algorithmic principles required to develop scalable real-world machine learning pipelines. We present an integrated view of data processing by highlighting the various components of these pipelines, including feature extraction, supervised learning, model evaluation, and exploratory data analysis. Students will gain hands-on experience applying these principles by using Apache Spark to implement several scalable learning pipelines.

Scalable Machine Learning from Vassilios Rendoumis

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0 Introduction to big data with apache spark /slideshow/introduction-to-big-data-with-apache-spark/50377284 introductiontobigdatawithapachespark-150710064150-lva1-app6892
In this course students learned what the expected output of Data Scientist is and how they can use PySpark (part of Apache Spark) to deliver against these expectations. The course assignments included Log Mining, Textual Entity Recognition, Collaborative Filtering exercises that teach students how to manipulate data sets using parallel processing with PySpark.]]>
In this course students learned what the expected output of Data Scientist is and how they can use PySpark (part of Apache Spark) to deliver against these expectations. The course assignments included Log Mining, Textual Entity Recognition, Collaborative Filtering exercises that teach students how to manipulate data sets using parallel processing with PySpark.]]> Fri, 10 Jul 2015 06:41:50 GMT /slideshow/introduction-to-big-data-with-apache-spark/50377284 VassiliosRendoumis@slideshare.net(VassiliosRendoumis) Introduction to big data with apache spark VassiliosRendoumis In this course students learned what the expected output of Data Scientist is and how they can use PySpark (part of Apache Spark) to deliver against these expectations. The course assignments included Log Mining, Textual Entity Recognition, Collaborative Filtering exercises that teach students how to manipulate data sets using parallel processing with PySpark. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/introductiontobigdatawithapachespark-150710064150-lva1-app6892-thumbnail.jpg?width=120&height=120&fit=bounds" /> In this course students learned what the expected output of Data Scientist is and how they can use PySpark (part of Apache Spark) to deliver against these expectations. The course assignments included Log Mining, Textual Entity Recognition, Collaborative Filtering exercises that teach students how to manipulate data sets using parallel processing with PySpark.

Introduction to big data with apache spark from Vassilios Rendoumis

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0 Rationality, Decision Making and Strategic Thinking /slideshow/rationality-decision-makingstrategicthinking/48232486 rationality-decisionmakingstrategicthinking-150516205320-lva1-app6892
This advanced seminar introduced elements of rational choice and decision theory, behavioral economics and strategic thinking.]]>
This advanced seminar introduced elements of rational choice and decision theory, behavioral economics and strategic thinking.]]> Sat, 16 May 2015 20:53:20 GMT /slideshow/rationality-decision-makingstrategicthinking/48232486 VassiliosRendoumis@slideshare.net(VassiliosRendoumis) Rationality, Decision Making and Strategic Thinking VassiliosRendoumis This advanced seminar introduced elements of rational choice and decision theory, behavioral economics and strategic thinking. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/rationality-decisionmakingstrategicthinking-150516205320-lva1-app6892-thumbnail.jpg?width=120&height=120&fit=bounds" /> This advanced seminar introduced elements of rational choice and decision theory, behavioral economics and strategic thinking.

Rationality, Decision Making and Strategic Thinking from Vassilios Rendoumis

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0 Coding the Matrix: Linear Algebra through Computer Science Applications /slideshow/coursera-matrix-2015/47686394 courseramatrix2015-150502195209-conversion-gate02
In this class, you have learned key concepts and methods of linear algebra, using them to think about problems in computer science. You have implemented basic matrix and vector functionality and algorithms, and used them to process real-world data.]]>
In this class, you have learned key concepts and methods of linear algebra, using them to think about problems in computer science. You have implemented basic matrix and vector functionality and algorithms, and used them to process real-world data.]]> Sat, 02 May 2015 19:52:09 GMT /slideshow/coursera-matrix-2015/47686394 VassiliosRendoumis@slideshare.net(VassiliosRendoumis) Coding the Matrix: Linear Algebra through Computer Science Applications VassiliosRendoumis In this class, you have learned key concepts and methods of linear algebra, using them to think about problems in computer science. You have implemented basic matrix and vector functionality and algorithms, and used them to process real-world data. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/courseramatrix2015-150502195209-conversion-gate02-thumbnail.jpg?width=120&height=120&fit=bounds" /> In this class, you have learned key concepts and methods of linear algebra, using them to think about problems in computer science. You have implemented basic matrix and vector functionality and algorithms, and used them to process real-world data.

Coding the Matrix: Linear Algebra through Computer Science Applications from Vassilios Rendoumis

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0 Learnin from Data /VassiliosRendoumis/learnin-fromdata learninfromdata-141216021015-conversion-gate02
This is an introductory course in machine learning (ML) that covers the basic theory, algorithms, and applications. This course balances theory and practice, and covers the mathematical as well as the heuristic aspects.]]>
This is an introductory course in machine learning (ML) that covers the basic theory, algorithms, and applications. This course balances theory and practice, and covers the mathematical as well as the heuristic aspects.]]> Tue, 16 Dec 2014 02:10:15 GMT /VassiliosRendoumis/learnin-fromdata VassiliosRendoumis@slideshare.net(VassiliosRendoumis) Learnin from Data VassiliosRendoumis This is an introductory course in machine learning (ML) that covers the basic theory, algorithms, and applications. This course balances theory and practice, and covers the mathematical as well as the heuristic aspects. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/learninfromdata-141216021015-conversion-gate02-thumbnail.jpg?width=120&height=120&fit=bounds" /> This is an introductory course in machine learning (ML) that covers the basic theory, algorithms, and applications. This course balances theory and practice, and covers the mathematical as well as the heuristic aspects.

Learnin from Data from Vassilios Rendoumis

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0 Introduction to Data Science /slideshow/coursera-datasci-2014-40118685/40118685 courseradatasci2014-141010104108-conversion-gate02
This course covered a broad set of topics critical to practical data science: relational databases, MapReduce, NoSQL, selected topics in statistical modeling, selected topics in machine learning, and information visualization, and a variety of algorithmic topics.]]>
This course covered a broad set of topics critical to practical data science: relational databases, MapReduce, NoSQL, selected topics in statistical modeling, selected topics in machine learning, and information visualization, and a variety of algorithmic topics.]]> Fri, 10 Oct 2014 10:41:07 GMT /slideshow/coursera-datasci-2014-40118685/40118685 VassiliosRendoumis@slideshare.net(VassiliosRendoumis) Introduction to Data Science VassiliosRendoumis This course covered a broad set of topics critical to practical data science: relational databases, MapReduce, NoSQL, selected topics in statistical modeling, selected topics in machine learning, and information visualization, and a variety of algorithmic topics. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/courseradatasci2014-141010104108-conversion-gate02-thumbnail.jpg?width=120&height=120&fit=bounds" /> This course covered a broad set of topics critical to practical data science: relational databases, MapReduce, NoSQL, selected topics in statistical modeling, selected topics in machine learning, and information visualization, and a variety of algorithmic topics.

Introduction to Data Science from Vassilios Rendoumis

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0 Model Thinking /slideshow/coursera-modelthinking-2014-38771348/38771348 courseramodelthinking2014-140906061954-phpapp02
This course provided an introduction on how to think using models. Specific topics included, among others, decision-making, tipping points, economic models, crowd dynamics, Markov processes, game theory and predictive thinking.]]>
This course provided an introduction on how to think using models. Specific topics included, among others, decision-making, tipping points, economic models, crowd dynamics, Markov processes, game theory and predictive thinking.]]> Sat, 06 Sep 2014 06:19:54 GMT /slideshow/coursera-modelthinking-2014-38771348/38771348 VassiliosRendoumis@slideshare.net(VassiliosRendoumis) Model Thinking VassiliosRendoumis This course provided an introduction on how to think using models. Specific topics included, among others, decision-making, tipping points, economic models, crowd dynamics, Markov processes, game theory and predictive thinking. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/courseramodelthinking2014-140906061954-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> This course provided an introduction on how to think using models. Specific topics included, among others, decision-making, tipping points, economic models, crowd dynamics, Markov processes, game theory and predictive thinking.

Model Thinking from Vassilios Rendoumis

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0 An Introduction to Interactive Programming in Python /slideshow/coursera-interactivepython-2014-35456242/35456242 courserainteractivepython2014-140603202753-phpapp02
This introductory course taught students the basics of interactive programming in Python. Students built a collection of simple interactive games to solidify their understanding of the material.]]>
This introductory course taught students the basics of interactive programming in Python. Students built a collection of simple interactive games to solidify their understanding of the material.]]> Tue, 03 Jun 2014 20:27:53 GMT /slideshow/coursera-interactivepython-2014-35456242/35456242 VassiliosRendoumis@slideshare.net(VassiliosRendoumis) An Introduction to Interactive Programming in Python VassiliosRendoumis This introductory course taught students the basics of interactive programming in Python. Students built a collection of simple interactive games to solidify their understanding of the material. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/courserainteractivepython2014-140603202753-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> This introductory course taught students the basics of interactive programming in Python. Students built a collection of simple interactive games to solidify their understanding of the material.

An Introduction to Interactive Programming in Python from Vassilios Rendoumis

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0 Linear and Discrete Optimization /slideshow/coursera-linearopt/35401303 courseralinearopt-140602151624-phpapp02
This advanced undergraduate course treats basic principles on linear programming like the simplex algorithm, its complexity, and duality. Furthermore it gives an introduction on discrete optimization via bipartite matchings, shortest paths and the primal/dual method.]]>
This advanced undergraduate course treats basic principles on linear programming like the simplex algorithm, its complexity, and duality. Furthermore it gives an introduction on discrete optimization via bipartite matchings, shortest paths and the primal/dual method.]]> Mon, 02 Jun 2014 15:16:24 GMT /slideshow/coursera-linearopt/35401303 VassiliosRendoumis@slideshare.net(VassiliosRendoumis) Linear and Discrete Optimization VassiliosRendoumis This advanced undergraduate course treats basic principles on linear programming like the simplex algorithm, its complexity, and duality. Furthermore it gives an introduction on discrete optimization via bipartite matchings, shortest paths and the primal/dual method. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/courseralinearopt-140602151624-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> This advanced undergraduate course treats basic principles on linear programming like the simplex algorithm, its complexity, and duality. Furthermore it gives an introduction on discrete optimization via bipartite matchings, shortest paths and the primal/dual method.

Linear and Discrete Optimization from Vassilios Rendoumis

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0 Big data and social physics /slideshow/big-data-and-social-physics/35054634 bigdataandsocialphysics-140523131811-phpapp02
Social physics is a big data science that models how networks of people behave and uses these network models to create actionable intelligence. It is a quantitative science that can accurately predict patterns of human behavior and guide how to influence those patterns to (for instance) increase decision making accuracy or productivity within an organization. Included in this course is a survey of methods for increasing communication quality within an organization, approaches to providing greater protection for personal privacy, and general strategies for increasing resistance to cyber attack.]]>
Social physics is a big data science that models how networks of people behave and uses these network models to create actionable intelligence. It is a quantitative science that can accurately predict patterns of human behavior and guide how to influence those patterns to (for instance) increase decision making accuracy or productivity within an organization. Included in this course is a survey of methods for increasing communication quality within an organization, approaches to providing greater protection for personal privacy, and general strategies for increasing resistance to cyber attack.]]> Fri, 23 May 2014 13:18:11 GMT /slideshow/big-data-and-social-physics/35054634 VassiliosRendoumis@slideshare.net(VassiliosRendoumis) Big data and social physics VassiliosRendoumis Social physics is a big data science that models how networks of people behave and uses these network models to create actionable intelligence. It is a quantitative science that can accurately predict patterns of human behavior and guide how to influence those patterns to (for instance) increase decision making accuracy or productivity within an organization. Included in this course is a survey of methods for increasing communication quality within an organization, approaches to providing greater protection for personal privacy, and general strategies for increasing resistance to cyber attack. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/bigdataandsocialphysics-140523131811-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> Social physics is a big data science that models how networks of people behave and uses these network models to create actionable intelligence. It is a quantitative science that can accurately predict patterns of human behavior and guide how to influence those patterns to (for instance) increase decision making accuracy or productivity within an organization. Included in this course is a survey of methods for increasing communication quality within an organization, approaches to providing greater protection for personal privacy, and general strategies for increasing resistance to cyber attack.

Big data and social physics from Vassilios Rendoumis

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0 R Programming /slideshow/coursera-rprog-2014-34499930/34499930 courserarprog2014-140509191443-phpapp01
This course covers how to use & program in R for effective data analysis. It covers practical issues in statistical computing: programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, & organizing and commenting R code.]]>
This course covers how to use & program in R for effective data analysis. It covers practical issues in statistical computing: programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, & organizing and commenting R code.]]> Fri, 09 May 2014 19:14:43 GMT /slideshow/coursera-rprog-2014-34499930/34499930 VassiliosRendoumis@slideshare.net(VassiliosRendoumis) R Programming VassiliosRendoumis This course covers how to use & program in R for effective data analysis. It covers practical issues in statistical computing: programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, & organizing and commenting R code. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/courserarprog2014-140509191443-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> This course covers how to use & program in R for effective data analysis. It covers practical issues in statistical computing: programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, & organizing and commenting R code.

R Programming from Vassilios Rendoumis

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0 Financial Engineering and Risk Management Part II /slideshow/coursera-fe2-2014-32890785/32890785 courserafe22014-140329145307-phpapp01
This course is part II of an introduction to the theory and practice of financial engineering and risk management. The course focused on portfolio optimization and the CAPM as well as the mechanics and pricing of derivative securities in various asset classes.]]>
This course is part II of an introduction to the theory and practice of financial engineering and risk management. The course focused on portfolio optimization and the CAPM as well as the mechanics and pricing of derivative securities in various asset classes.]]> Sat, 29 Mar 2014 14:53:06 GMT /slideshow/coursera-fe2-2014-32890785/32890785 VassiliosRendoumis@slideshare.net(VassiliosRendoumis) Financial Engineering and Risk Management Part II VassiliosRendoumis This course is part II of an introduction to the theory and practice of financial engineering and risk management. The course focused on portfolio optimization and the CAPM as well as the mechanics and pricing of derivative securities in various asset classes. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/courserafe22014-140329145307-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> This course is part II of an introduction to the theory and practice of financial engineering and risk management. The course focused on portfolio optimization and the CAPM as well as the mechanics and pricing of derivative securities in various asset classes.

Financial Engineering and Risk Management Part II from Vassilios Rendoumis

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0 Social and Economic Networks: Models and Analysis /slideshow/coursera-networksonline-2014-32239215/32239215 courseranetworksonline2014-140312143802-phpapp02
This graduate-level course introduces students to a variety of models and techniques for analyzing social and economic networks, including random graph models, statistical models, and game theoretic models of network formation, diffusion, learning, and peer effects.]]>
This graduate-level course introduces students to a variety of models and techniques for analyzing social and economic networks, including random graph models, statistical models, and game theoretic models of network formation, diffusion, learning, and peer effects.]]> Wed, 12 Mar 2014 14:38:02 GMT /slideshow/coursera-networksonline-2014-32239215/32239215 VassiliosRendoumis@slideshare.net(VassiliosRendoumis) Social and Economic Networks: Models and Analysis VassiliosRendoumis This graduate-level course introduces students to a variety of models and techniques for analyzing social and economic networks, including random graph models, statistical models, and game theoretic models of network formation, diffusion, learning, and peer effects. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/courseranetworksonline2014-140312143802-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> This graduate-level course introduces students to a variety of models and techniques for analyzing social and economic networks, including random graph models, statistical models, and game theoretic models of network formation, diffusion, learning, and peer effects.

Social and Economic Networks: Models and Analysis from Vassilios Rendoumis

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0 Machine Learning /slideshow/coursera-ml-2014-30184700/30184700 courseraml2014-140119111820-phpapp02
This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI).]]>
This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI).]]> Sun, 19 Jan 2014 11:18:20 GMT /slideshow/coursera-ml-2014-30184700/30184700 VassiliosRendoumis@slideshare.net(VassiliosRendoumis) Machine Learning VassiliosRendoumis This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/courseraml2014-140119111820-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI).

Machine Learning from Vassilios Rendoumis

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0 https://cdn.slidesharecdn.com/profile-photo-VassiliosRendoumis-48x48.jpg?cb=1600674954 I am a Physicist with MSc in Mathematical Modelling and professional experience in the Fintech and Telecommunication Industry along with Data and Statistical Analysis for Financial Data. My academic research was focused in Complex systems and applications in Mathematical Epidemiology and Economics (Econophysics). Specialties: - Software Development in Python, C++, C, C#, .Net - Data Analysis with R, Python, MatLab, Excel, MySQL - Computational and Mathematical modeling with Python, MatLab, Fortran - Computational and Mathematical Finance - Research OS: - Windows, Linux http://www.unifr.ch/econophysics/ https://cdn.slidesharecdn.com/ss_thumbnails/certification-of-completion-training-rendoumis-vassilios-190717151421-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/go-156111921/156111921 Go https://cdn.slidesharecdn.com/ss_thumbnails/uc-ffzmopjy-190716083625-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/go-the-complete-developers-guide-golang/155849435 Go: The Complete Devel... https://cdn.slidesharecdn.com/ss_thumbnails/certification-of-completion-training-vassilios-rendoumis-190716082950-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/containers-kubernetes/155847881 Containers & Kubernetes