A small presentation with some quick explanation of what D3.js is and a few examples of what it can do for you. It can be used for a quick presentation (20-30 mins) after which you should have an idea of whether you can use D3.js for your project.
Visual Exploration of Large Data sets with D3, crossfilter and dc.jsFlorian Georg
My talk at this year's Jazoon about data visualization and exploration with D3, crossfilter and dc.js
It should give you a good introduction on how/when to use these frameworks and how they relate to each other.
More info on http://datavisual.mybluemix.net
This presentation has been prepared by Oleksii Prohonnyi for LvivJS 2015 conference (http://lvivjs.org.ua/)
See the speech in Russian by the following link: https://youtu.be/oi7JhB8eWnA
Feature Engineering - Getting most out of data for predictive models - TDC 2017Gabriel Moreira
How should data be preprocessed for use in machine learning algorithms? How to identify the most predictive attributes of a dataset? What features can generate to improve the accuracy of a model?
Feature Engineering is the process of extracting and selecting, from raw data, features that can be used effectively in predictive models. As the quality of the features greatly influences the quality of the results, knowing the main techniques and pitfalls will help you to succeed in the use of machine learning in your projects.
In this talk, we will present methods and techniques that allow us to extract the maximum potential of the features of a dataset, increasing flexibility, simplicity and accuracy of the models. The analysis of the distribution of features and their correlations, the transformation of numeric attributes (such as scaling, normalization, log-based transformation, binning), categorical attributes (such as one-hot encoding, feature hashing, Temporal (date / time), and free-text attributes (text vectorization, topic modeling).
Python, Python, Scikit-learn, and Spark SQL examples will be presented and how to use domain knowledge and intuition to select and generate features relevant to predictive models.
A small presentation with some quick explanation of what D3.js is and a few examples of what it can do for you. It can be used for a quick presentation (20-30 mins) after which you should have an idea of whether you can use D3.js for your project.
Visual Exploration of Large Data sets with D3, crossfilter and dc.jsFlorian Georg
My talk at this year's Jazoon about data visualization and exploration with D3, crossfilter and dc.js
It should give you a good introduction on how/when to use these frameworks and how they relate to each other.
More info on http://datavisual.mybluemix.net
This presentation has been prepared by Oleksii Prohonnyi for LvivJS 2015 conference (http://lvivjs.org.ua/)
See the speech in Russian by the following link: https://youtu.be/oi7JhB8eWnA
Feature Engineering - Getting most out of data for predictive models - TDC 2017Gabriel Moreira
How should data be preprocessed for use in machine learning algorithms? How to identify the most predictive attributes of a dataset? What features can generate to improve the accuracy of a model?
Feature Engineering is the process of extracting and selecting, from raw data, features that can be used effectively in predictive models. As the quality of the features greatly influences the quality of the results, knowing the main techniques and pitfalls will help you to succeed in the use of machine learning in your projects.
In this talk, we will present methods and techniques that allow us to extract the maximum potential of the features of a dataset, increasing flexibility, simplicity and accuracy of the models. The analysis of the distribution of features and their correlations, the transformation of numeric attributes (such as scaling, normalization, log-based transformation, binning), categorical attributes (such as one-hot encoding, feature hashing, Temporal (date / time), and free-text attributes (text vectorization, topic modeling).
Python, Python, Scikit-learn, and Spark SQL examples will be presented and how to use domain knowledge and intuition to select and generate features relevant to predictive models.
Andrii Gordiichuk, Software Developer
“Visualization of Big Data in Web Applications”
- Data in our life
- Patterns for data visualization
- Technologies for data visualization
- SVG and Canvas
- Frameworks for data visualization. Selection criteria
- D3.js and Highcharts.js
With information available in more systems than ever, how do we make sense of it all? Here are a few examples of how people have blended large amounts of data across the web and enterprise, and turned it into something useful and visually pleasing.
Overview of WPF in light of Ribbon UI ControlAbhishek Sur
The slides introduces Ribbon UI control as released on Aug 2010 with the basic overview of WPF and XAML. I (Abhishek Sur) have demonstrated this on Community Tech Days session at Kolkata on 28th November 2010
Eclipse Con Europe 2014 How to use DAWN Science ProjectMatthew Gerring
This is a talk given at Eclipse Con Europe 2014 on how to use the open source project DAWN, Data Analysis Workbench. This project has two papers with more than three hundred citations of using the software.
Presentation / Workshop which will teach you the core patterns, concepts and visualisation options of D3.js (v4). Accompanying exercises can be found here: https://github.com/josdirksen/d3exercises
International Journal of Engineering Research and Development is an international premier peer reviewed open access engineering and technology journal promoting the discovery, innovation, advancement and dissemination of basic and transitional knowledge in engineering, technology and related disciplines.
Andrii Gordiichuk, Software Developer
“Visualization of Big Data in Web Applications”
- Data in our life
- Patterns for data visualization
- Technologies for data visualization
- SVG and Canvas
- Frameworks for data visualization. Selection criteria
- D3.js and Highcharts.js
With information available in more systems than ever, how do we make sense of it all? Here are a few examples of how people have blended large amounts of data across the web and enterprise, and turned it into something useful and visually pleasing.
Overview of WPF in light of Ribbon UI ControlAbhishek Sur
The slides introduces Ribbon UI control as released on Aug 2010 with the basic overview of WPF and XAML. I (Abhishek Sur) have demonstrated this on Community Tech Days session at Kolkata on 28th November 2010
Eclipse Con Europe 2014 How to use DAWN Science ProjectMatthew Gerring
This is a talk given at Eclipse Con Europe 2014 on how to use the open source project DAWN, Data Analysis Workbench. This project has two papers with more than three hundred citations of using the software.
Presentation / Workshop which will teach you the core patterns, concepts and visualisation options of D3.js (v4). Accompanying exercises can be found here: https://github.com/josdirksen/d3exercises
International Journal of Engineering Research and Development is an international premier peer reviewed open access engineering and technology journal promoting the discovery, innovation, advancement and dissemination of basic and transitional knowledge in engineering, technology and related disciplines.