The R programming language is experiencing rapid increases in popularity and wide adoption across industries. This popularity is due, in part, to R’s rich and powerful data visualization capabilities. While tools like Excel, Power BI, and Tableau are often the go-to solutions for data visualizations,

Web scraping is a very powerful tool to learn for any data professional. With web scraping, the entire internet becomes your database. In this tutorial, we show you how to parse a web page into a data file using a Python package called BeautifulSoup. There

dplyr is a a great tool to perform data manipulation. It makes your data analysis process a lot more efficient. Even better, it’s fairly simple to learn and start applying immediately to your work! Oftentimes, with just a few elegant lines of code, your data

In this video tutorial, we will take you through some common Python and R packages used for machine learning and data analysis, and go through a simple linear regression model. Also, we will help you set up Python and R on your Windows/Mac/Linux machine, run

This presentation will discuss building a business model for your machine learning idea. In this talk, our presenter, Neeti Gupta, will provide a 10-step checklist with examples for the audience to build their own business model. This 10-step business checklist is a synthesis of the

dplyr is a a great tool to perform data manipulation. It makes your data analysis process a lot more efficient. Even better, it’s fairly simple to learn and start applying immediately to your work! Oftentimes, with just a few elegant lines of code, your data

At this Data Science Dojo meetup, Phuc Duong talks about Building a Real-Time Sentiment Pipeline for Live Tweets Using Python, R, & Azure Supplementary Material found here: https://github.com/gokul180288/meetup…

At this meetup, presenter Craig Guarraci speaks about how to make sense of unstructured text with Python, MS Cognitive Services & PowerBI. – In this presentation we’ll take a broad look at industry research to see how text analytics and sentiment analysis is used –

In this meetup, I will give a quick introduction to online experimentation and A/B testing. To keep the tutorial self-contained, I will first give an overview of stats fundamentals needed to understand A/B testing. I will explain how A/B testing is done in an online

According to some estimates, bots constitute close to 50% of the overall traffic. In this introductory talk, we will cover various aspects of feature engineering & detection of automated web traffic. We will start with understanding the impact of bots on an online business and

From distorting experiments with systemic bias to imposing human ethics on machine learning models, data scientists have far more to worry about than the raw numbers in their spreadsheet. Join Raja Iqbal on an exploration of data science’s past evils and how we can pave

Continuing with data fundamentals, we introduce you to the three data set types, Record, Ordered, and Graph.

In this video we introduce Data Preprocessing, known as data cleaning, and the different strategies used to tackle it.

In this next section we introduce you to similarity and dissimilarity.

Part four of data exploration and visualization, we discuss different visualization techniques starting with the most popular histograms and box plots.

In part seven of data preprocessing, we discuss transformation of data such as attribute transformation.

This data science tutorial introduces the viewer to the exciting world of text analytics with R programming. As exemplified by the popularity of blogging and social media, textual data if far from dead – it is increasing exponentially! Not surprisingly, knowledge of text analytics is

This data science tutorial introduces the viewer to the exciting world of text analytics with R programming. As exemplified by the popularity of blogging and social media, textual data if far from dead – it is increasing exponentially! Not surprisingly, knowledge of text analytics is

All great learning opportunities are built on a solid foundation. This session is jam-packed with all the background information, technical terminology, and basic knowledge that you will need to hit the ground running on the first day of the bootcamp.

In part two of using RStudio for Data Science Dojo’s Kaggle competition, we will show you more advance cleaning functions for your model. This video assumes you have watched part one, if you have not, view it here: https://www.youtube.com/watch?v=Zx2TguRHrJE Titanic Data Set: https://www.kaggle.com/c/titanic Download RStudio:

R programming is rapidly becoming a valuable skill for data professionals of all stripes and a must-have skill for aspiring data scientists. Adding R programming to your data analyst skillset allows you to leverage powerful data visualizations, statistical analyses, and even machine learning in your

Lecture Starts at: 8:25 Business data presents a challenge for the data analyst. Business data is often aggregated, recorded over time, and tends to exhibit autocorrelation. Additionally, and most problematically, the amount of business data is usually quite limited. These characteristics lead to a situation

Nearly 100 years after Einstein predicted the existence of gravitational waves, Laser Interferometer and Gravitational Wave Observatory astounded the world by successfully detecting these waves. Detection was made possible by the advancement of laser technology and data processing techniques. Being able to distinguish the gravitational

Outline: · Setup a Spark cluster with R installed . · Wrangle data that is inside HDFS using R. · Build and deploy a machine learning model using R. R is currently one of the most popular data science languages in the world. However, it’s

Microsoft’s Power BI is a powerful technology for quickly creating rich visualizations. Power BI has many practical uses for the modern data professional including executive dashboards, operational dashboards, and visualizations for data exploration/analysis. Microsoft has also extended Power BI with support for incorporating R visualizations

Event logs are everywhere and represent a prime source of Big Data. Event log sources run the gamut from e-commerce web servers to devices participating in globally distributed Internet of Things architectures. Even Enterprise Resource Planning systems produce event logs! Given the rich and varied

This data science tutorial introduces the viewer to the exciting world of text analytics with R programming. As exemplified by the popularity of blogging and social media, textual data if far from dead – it is increasing exponentially! Not surprisingly, knowledge of text analytics is

In this tutorial we will show you how to complete the titanic Kaggle competition using Microsoft Azure Machine Learning Studio.This video assumes you have an Azure account and you understand how to use Azure. Kaggle Titanic Experiment: https://gallery.cortanaintelligence.com/Experiment/Titanic-Kaggle-Competition-1

The R programming language is experiencing rapid increases in popularity and wide adoption across industries. This popularity is due, in part, to R’s huge collection of open source machine learning algorithms. If you are a data scientist working with R, the caret package is a

As part of submitting to Data Science Dojo’s Kaggle competition you need to create a model out of the titanic data set. We will show you how to do this using RStudio. Titanic Data Set: https://www.kaggle.com/c/titanic Download RStudio: https://www.rstudio.com/products/rstudio/download/

To prepare you for Data Science Dojo’s day two homework we will explain what Kaggle is and show you how to create a Kaggle account and submit your model to the Kaggle competition. Titanic Data Set: https://www.kaggle.com/c/titanic

Storytelling is a cornerstone of the human experience. Though many elements of stories have remained the same throughout history, we have developed better tools and mediums for telling them, such as printed books, movies, and comics. This has changed storytelling styles—and perhaps most importantly, the

What’s better than machine learning? Machine learning where coding is optional! Drag and drop machine learning with a visual interface! We’re going to introduce you to a new tool to add to your data science toolkit, Azure Machine Learning Studio. Azure Machine Learning is a

To begin data mining in Azure Machine Learning Studio, we must first setup an Azure subscription. Once we have a subscription, create an Azure Machine Learning Studio workspace within Azure. Azure Machine Learning is in the cloud and allows for workspaces to be shared with

Today we’ll explore the interface of our new machine learning tool, Azure ML. How do you bring data to and from the outside world into Azure ML? The import dataset module can read in data from a variety of sources: HTTP, Azure SQL database, Hadoop

Now that Azure Machine Learning Studio is setup, let’s begin an end-to-end data science project in Azure Machine Learning. We’ll choose the flight delay data, and use it to predict whether not a flight will be late on arrival based upon the flight’s circumstances. In

Now that we have a better understanding of our dataset. Let’s go out and gather more data. There is an additional dataset inside of Azure ML we can use to look up and cross reference the airport name with airport id, then bring with it,

We introduce you to the wide world of Big Data, throwing back the curtain on the diversity and ubiquity of data science in the modern world. We also give you a bird’s eye view of the subfields of predictive analytics and the pieces of a

This data science tutorial introduces the viewer to the exciting world of text analytics with R programming. As exemplified by the popularity of blogging and social media, textual data if far from dead – it is increasing exponentially! Not surprisingly, knowledge of text analytics is

This data science tutorial introduces the viewer to the exciting world of text analytics with R programming. As exemplified by the popularity of blogging and social media, textual data if far from dead – it is increasing exponentially! Not surprisingly, knowledge of text analytics is

This data science tutorial introduces the viewer to the exciting world of text analytics with R programming. As exemplified by the popularity of blogging and social media, textual data if far from dead – it is increasing exponentially! Not surprisingly, knowledge of text analytics is

This data science tutorial introduces the viewer to the exciting world of text analytics with R programming. As exemplified by the popularity of blogging and social media, textual data if far from dead – it is increasing exponentially! Not surprisingly, knowledge of text analytics is

This data science tutorial introduces the viewer to the exciting world of text analytics with R programming. As exemplified by the popularity of blogging and social media, textual data if far from dead – it is increasing exponentially! Not surprisingly, knowledge of text analytics is

This data science tutorial introduces the viewer to the exciting world of text analytics with R programming. As exemplified by the popularity of blogging and social media, textual data if far from dead – it is increasing exponentially! Not surprisingly, knowledge of text analytics is

This data science tutorial introduces the viewer to the exciting world of text analytics with R programming. As exemplified by the popularity of blogging and social media, textual data if far from dead – it is increasing exponentially! Not surprisingly, knowledge of text analytics is

The R programming language is experiencing rapid increases in popularity and wide adoption across industries. This popularity is due, in part, to R’s rich and powerful data visualization capabilities. While tools like Excel, Power BI, and Tableau are often the go-to solutions for data visualizations,

dplyr is a a great tool to perform data manipulation. It makes your data analysis process a lot more efficient. Even better, it’s fairly simple to learn and start applying immediately to your work! Oftentimes, with just a few elegant lines of code, your data

This presentation will discuss building a business model for your machine learning idea. In this talk, our presenter, Neeti Gupta, will provide a 10-step checklist with examples for the audience to build their own business model. This 10-step business checklist is a synthesis of the

From distorting experiments with systemic bias to imposing human ethics on machine learning models, data scientists have far more to worry about than the raw numbers in their spreadsheet. Join Raja Iqbal on an exploration of data science’s past evils and how we can pave

According to some estimates, bots constitute close to 50% of the overall traffic. In this introductory talk, we will cover various aspects of feature engineering & detection of automated web traffic. We will start with understanding the impact of bots on an online business and

In this meetup, I will give a quick introduction to online experimentation and A/B testing. To keep the tutorial self-contained, I will first give an overview of stats fundamentals needed to understand A/B testing. I will explain how A/B testing is done in an online

Modern machine learning libraries make model building look deceptively easy. An unnecessary emphasis on tools like R, Python, SparkML, and techniques like deep learning is prevalent. Relying on tools and techniques while ignoring the fundamentals is the wrong approach to model building. Real-world machine learning

The R programming language is experiencing rapid increases in popularity and wide adoption across industries. This popularity is due, in part, to R’s rich and powerful data visualization capabilities. While tools like Excel, Power BI, and Tableau are often the go-to solutions for data visualizations,

Storytelling is a cornerstone of the human experience. Though many elements of stories have remained the same throughout history, we have developed better tools and mediums for telling them, such as printed books, movies, and comics. This has changed storytelling styles—and perhaps most importantly, the

The R programming language is experiencing rapid increases in popularity and wide adoption across industries. This popularity is due, in part, to R’s huge collection of open source machine learning algorithms. If you are a data scientist working with R, the caret package is a

Lecture Starts at: 8:25 Business data presents a challenge for the data analyst. Business data is often aggregated, recorded over time, and tends to exhibit autocorrelation. Additionally, and most problematically, the amount of business data is usually quite limited. These characteristics lead to a situation

R programming is rapidly becoming a valuable skill for data professionals of all stripes and a must-have skill for aspiring data scientists. Adding R programming to your data analyst skillset allows you to leverage powerful data visualizations, statistical analyses, and even machine learning in your

Event logs are everywhere and represent a prime source of Big Data. Event log sources run the gamut from e-commerce web servers to devices participating in globally distributed Internet of Things architectures. Even Enterprise Resource Planning systems produce event logs! Given the rich and varied

Microsoft’s Power BI is a powerful technology for quickly creating rich visualizations. Power BI has many practical uses for the modern data professional including executive dashboards, operational dashboards, and visualizations for data exploration/analysis. Microsoft has also extended Power BI with support for incorporating R visualizations

Outline: · Setup a Spark cluster with R installed . · Wrangle data that is inside HDFS using R. · Build and deploy a machine learning model using R. R is currently one of the most popular data science languages in the world. However, it’s

Nearly 100 years after Einstein predicted the existence of gravitational waves, Laser Interferometer and Gravitational Wave Observatory astounded the world by successfully detecting these waves. Detection was made possible by the advancement of laser technology and data processing techniques. Being able to distinguish the gravitational

At this meetup, presenter Craig Guarraci speaks about how to make sense of unstructured text with Python, MS Cognitive Services & PowerBI. – In this presentation we’ll take a broad look at industry research to see how text analytics and sentiment analysis is used –

At this Data Science Dojo meetup, Phuc Duong talks about Building a Real-Time Sentiment Pipeline for Live Tweets Using Python, R, & Azure Supplementary Material found here: https://github.com/gokul180288/meetup…

In this 90-minute talk, we will cover an overview of solving a simple predictive analytics problem. We will use R for feature exploration and visualization and build a predictive model using Azure ML. We will be using the Titanic data set for our exercise. You