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.

Upcoming events (4+)

Saving Lives Behind the Wheels: AI and Computer Vision for Road Safety

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More than 1.35 million people die in road crashes each year. These deaths are avoidable and unnecessary and governments all over the world are resolving to reduce them, through their “Vision-Zero” programs. We will take a technology deep-dive into this problem to first show how a lack of technological innovations has failed to lower this trend over the past 20 years. Then we will argue how three enabling trends, 1) video-analytics maturity, 2) availability of powerful edge-computing hardware and 3) proliferation of CCTV cameras, are poised to change the traffic safety landscape for the better, to reduce fatalities and crashes. We will summarize the challenges in this industry, and show how real-time edge-based deep-learning systems can tackle them effectively by retrofitting existing camera infrastructure for road safety.

Writing Unit Tests for Data Science Code

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If you type ‘unit test’ into your favorite search engine, you will receive a lot of information for Software Engineering, but very little guidance for Data Science code. In Data Science, the small piece of code that you want to test also needs to take in data, train a model, or evaluate a model, but all of these steps are complicated and consist of many smaller units. In this talk, Dr. Nile Wilson will share her Software Engineering best practices for testing Data Science Code and some of the common scenarios for data, like mocking calls or mocking data. This talk is for anyone bridging the gap between Software Engineering and Data Science, anyone in MLOps, or anyone productionalizing data.

A Crash Course on Data Wrangling using SQL

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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

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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

Past events (131)

From Data to Dashboard: make an interactive model

This event has passed

Photos (198)