An average data scientist (ML Practitioner, AI expert) spends a significant amount of time designing and running machine learning experiments (and waiting for them to complete)
In this webinar, we will explore what is a data engineer. This includes discussing what are the goals, skills, and tools that they use on a daily basis. We wanted to take a moment to provide insights to the other …
In part two of our introduction to web scraping in r, we will use taskscheduleR to set up our automated web scraping script to run as a background task on our computer. This means we do not have to manually …
In this R tutorial, we show you how to automatically web scrape using rvest periodically so you can analyze timely/frequently updated data. This talk was given by one of our instructors who teach our data science bootcamp!
In part 1 of introduction to web scraping in r, 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 …
The focus of the webinar will be using ggplot2 to analyze your data visually with a specific focus on discovering the underlying signals/patterns of your business. As an example, R’s ggplot2 package provides the R programmer with dozens of print-quality …
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 …
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 …
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 …
We go over some basic functions of dplyr including the mighty group_by and summarize combo that makes dividing up datasets a breeze, as well as arrange, select, and filter that help get the data in a cleaner and more organized …
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