Part 7 of this video series includes specific coverage of: – The trade-offs of expanding the text analytics feature space with n-grams. – How bag-of-words representations map to the vector space model . – Usage of the dot product between document vectors as a proxy

In our next installment of introduction to text analytics, data pipelines, we take cover: – Exploration of textual data for pre-processing “gotchas” – Using the quanteda package for text analytics – Creation of a prototypical text analytics pre-processing pipeline, including : tokenization, lower casing, stop

N-grams includes specific coverage of: • Validate the effectiveness of TF-IDF in improving model accuracy. • Introduce the concept of N-grams as an extension to the bag-of-words model to allow for word ordering. • Discuss the trade-offs involved of N-grams and how Text Analytics suffers

Introduction to Azure ML – 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:

Introduction to Azure ML – 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.

In this video tutorial you will learn how to write standard web scraping commands in R, filter timely data based on time diffs, analyze or summarize key information in the text, and send an email alert of the results of your analysis. Packages used: rvest

What is A/B testing? In this quick tutorial, we go over the basics of A/B testing, as well as answer some in-depth questions such as: why should businesses conduct A/B testing? Or how do you perform an A/B test? — At Data Science Dojo, we

In this 90-minute video tutorial, we will cover an overview of solving a simple predictive analytics problem. We will use R for Feature Exploration, Visualization, and Predictive Modeling with R and Azure ML We will be using the Titanic data set for our exercise. You

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

R is currently one of the most popular data science languages in the world. However, it’s always had constraints around scaling out to big data. What happens when you expand beyond a couple gigabytes of data? You packed up your data and you used something

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 –

Introduction to Azure ML – Let’s build our first machine learning model in Azure ML. First, we have to go shopping for a machine learning model. We must identify what type of machine learning algorithm we want to choose from. We ended up using a

Introduction to Azure ML – In this tutorial we will make sure all the categorical features are treated as categories using the edit meta data module. We will also setup a holdout dataset and randomly sample our dataset in two partitions, a training set and

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

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

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

Introduction to Azure ML – Let’s understand the aggregate behavior of our features further by looking at summary statistics. Azure Machine Learning gives us easy access to mean, median, mode, min, and max. Let’s look at each measure to see what it means to the

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

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

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

Introduction to Azure ML – The machine learning model will learn from the data it has access to. Sometimes it becomes necessary to shed columns from our dataset so our machine learning model does not learn from them. In this video we’ll drop a few

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,

Introduction to Azure ML – In the last video, we prepared our dataset for a join. In this video we’ll use the join data module inside of Azure ML to cross reference each airport id with the airport table to find the airport’s city, state

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

Introduction to Azure ML – 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

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

Introduction to Azure ML – Now that we have Azure Machine Learning Studio 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

According to some estimates, bots constitute close to 50% of the overall traffic. In this introductory talk to Feature Engineering for Bot Detection, we will cover various aspects of feature engineering for bot detection of automated web traffic. We will start with understanding the impact

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

Introduction to Azure ML – To begin data mining in Azure Machine Learning Studio, we must first setup an Azure subscription. Once we have a subscription, we can create an Azure Machine Learning Studio workspace within Azure. Azure Machine Learning is in the cloud and

This presentation will discuss building a business case 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

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,

In this final tutorial of the dplyr series, we will cover ways to do feature engineering both with dplyr and base R . You’ll learn how to impute missing values as well as create new values based on existing columns. In addition, we’ll go over

Learn the basics of natural language processing: the components of NLP , enterprise applications of NLP, and finally build a simple FAQ Chatbot. About the Speaker: Chris Shei is the technical evangelist for Jet.com where he explores trending tech and helps Jet’s engineering org build

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

We introduce functions that make it easy to find overlapping and distinct values from two different data sources, intersect and setdiff. These two functions let you see the shared and unique elements from different vectors, making it easy to spot commonalities and differences. After watching

In this tutorial we will show you how to complete the Titanic Kaggle competition in Azure ML . It is helpful to have prior knowledge of Azure ML Studio, as well as have an Azure account. Check out the experiment here: Kaggle Titanic Experiment

In the final video in our Data Mining Fundamentals series, we conclude our discussion of different visualization techniques for data exploration with scatter plots and contour plots. We will define each plot, and share examples of when you can use each for your data mining.

In this Data Mining Fundamentals tutorial, we discuss different visualization techniques, starting with the most popular: histograms and box plots. We discuss the unique benefits of both, and provide examples of when you can use each for your data exploration and visualization.

In part two of using RStudio for Data Science Dojo’s titanic kaggle competition, we will show you more advanced cleaning functions for your model. If you have not seen part one,  you can view it here. Check out the data set we use here: Titanic Data

As part of the Titanic Kaggle Competition in R, you need to create a model out of the titanic data set and submit it. We will show you how you can begin by using RStudio. You can watch Part Two of this series here. Check out

In this Data Mining Fundamentals tutorial, we continue our discussion on data exploration and visualization. We discuss measuring of center such as the median and mean, and look at measures of spread such as range and variance.

In this Data Mining Fundamentals tutorial, we continue our discussion on data exploration and visualization. We discuss summary statistics and the frequency and mode of an attribute. Summary statistics are numbers that summarize properties of data, and the frequency of an attribute value is a

In this Data Mining Fundamentals tutorial, we introduce you to data exploration and visualization and what they are to data mining. Data exploration is visualization and calculation to better understand characteristics of data. We will tell you the key motivations of data exploration as well

In this Data Mining Fundamentals tutorial, we continue our discussion on similarity and dissimilarity and discuss correlation and visually evaluating it. Correlation measures the linear relationship between objects, and to visually evaluate correlation, you will need to build a scatter plot.

In this Data Mining Fundamentals tutorial, we continue our introduction to similarity and dissimilarity by discussing euclidean distance and cosine similarity. We will show you how to calculate the euclidean distance and construct a distance matrix.

Learn the basics of natural language processing: the components of NLP , enterprise applications of NLP, and finally build a simple FAQ Chatbot. About the Speaker: Chris Shei is the technical evangelist for Jet.com where he explores trending tech and helps Jet’s engineering org build

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 case 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 to Feature Engineering for Bot Detection, we will cover various aspects of feature engineering for bot detection of automated web traffic. We will start with understanding the impact

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

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

R is currently one of the most popular data science languages in the world. However, it’s always had constraints around scaling out to big data. What happens when you expand beyond a couple gigabytes of data? You packed up your data and you used something

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 video tutorial, we will cover an overview of solving a simple predictive analytics problem. We will use R for Feature Exploration, Visualization, and Predictive Modeling with R and Azure ML We will be using the Titanic data set for our exercise. You