What we're about

Welcome to Data Science Dojo's Meetup group. Our goal is to help connect other like-minded business professionals who are interested in teaching, learning, and sharing their knowledge and understanding of data science to a larger community.

We encourage all members of this group to be pro-active in leading discussions on topics related to data science like machine learning, artificial intelligence, predictive analytics, big data, and IoT, as well as programming languages such as R, Hadoop, and Python.

Stay tuned to our Meetup calendar for future community events and be sure to follow us on Twitter at @DataScienceDojo. Also, be sure to visit our data science bootcamp (https://datasciencedojo.com/data-science-bootcamp/) for more information about our training.

We are always looking for new speakers/presenters! If you're interested, please email Nathan at npiccini@datasciencedojo.com.

Upcoming events (4+)

A Crash Course on Data Wrangling using SQL

Link visible for attendees

Data wrangling is the art of bringing data together and preparing it for analysis. It is often the most time-consuming aspect of an analytics project. A survey of analysts found that almost half of their time is spent wrangling data. Part of the complication is that analysts have several tools and languages at their disposal, making it difficult to understand which one is the right one for their data-wrangling needs.
Though this crash course focuses on data wrangling with SQL, it will build a foundation for transforming data as you develop your analytics skills. It is intended for beginners with little to no prior experience in SQL. By the end of the session, you will know:

  • How to query a dataset
  • How to join the data
  • How to append data
  • How to apply a filter to your datasets
  • How to create new data fields

How to Deploy an ML app on Azure App Service

Link visible for attendees

A deployed application can help you showcase your work in several places. Nowadays, along with coding machine learning models, it is also necessary for a person to know how to deploy those in production
In this webinar, we will focus on the deployment of a machine-learning app on Azure. We will also be discussing the step-by-step code required to develop the app. We will explore the capabilities of python packages such as Streamlit, Numpy, Pandas, and scikit-learn. This webinar is designed in such a way that people with no prior experience in web app development can also understand the concepts.
By the end of the session, you will know how to:
1. Develop a machine learning application that you can interact with
2. Deploy the ML app on Azure
3. Redeploy the app with some changes done

Telling Your Data Story With the 3Vs: Vocabulary, Voice, and Vision

Link visible for attendees

Data Leaders must create a compelling narrative to evangelize their Data Management programs and secure long-term support from enterprise stakeholders and business leadership. Data leaders who seek to improve soft skills and execute simple storytelling techniques will be more likely to gain a rightful place for their initiatives on their organization’s strategic plan.

  • Why you need a Data Management narrative vs other Data Storytelling and Data Literacy Efforts
  • Why Data Management is Macro-Trend agnostic
  • Leveraging the 3Vs: Vocabulary, Voice, and Vision

Understanding and Visualizing ResNets that Forever Revolutionized Deep Learning

Link visible for attendees

In December 2015, a published paper rocked the deep learning world. This paper is widely regarded as one of the most influential papers in modern deep learning and has been cited over 110,000 times. The name of this paper was Deep Residual Learning for Image Recognition (aka, the ResNet paper). In this session, we’ll take a brief tour through the history of computer vision, into the anatomy of a convolutional neural network, understand their limitations, and learn how the ResNet paper changed deep learning forever.
By the end of the session, you’ll know:
• What computer vision was like before convolutional neural networks (CNNs)
• The anatomy of CNNs
• The limitations of CNNs
• Residual networks and the skip connection
• How to perform image classification with ResNet with code

Past events (82)

Writing Unit Tests for Data Science Code

This event has passed

Photos (126)