Data Science 101
Are you interested in data science but not sure where to start? How about leveraging machine learning to drive your social justice campaigns? Well, Witty Webinars are here to equip you with the tools you’ll need to do just that.
This week we covered Part 1 of 2 of the fundamentals of Data Science in the Witty Tech Webinars series, introducing the applications of machine learning, data, and experimental design. The application of data science can be used to address humanity’s most pressing matters like flattening COVID-19’s curve or combatting climate change - Data Science comes with great responsibility.
Here’s the TL;DR on what we covered
Together we discovered the various applications of AI and Machine Learning in our daily lives, from facial recognition technology unlocking our iPhones to recommendation engines fueling our Netflix addictions. In going through Delta Analytics’ first module on Machine Learning, we developed an understanding of each phase of the machine learning workflow:
Introduction to Machine Learning, Delta Analytics
Although Machine Learning takes place during the modeling phase, ~80% of a Data Scientist's job is spent preparing data for analysis by refining the research question, validating and cleaning the data, and performing exploratory analysis. For an in-depth review of these concepts, check out the slideshow or rewatch the webinar recording!
Thirsty for more? Drink up.
What is machine learning? Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
How do you define a research question? A research question is a question we want our machine learning model to answer which is often refined during the data exploration phase.
What are observations? An observation is a value of something of interest you’re measuring or counting during a study or experiment: a person’s height, a bank account value at a certain point in time, or the number of animals.
What are features? With respect to a dataset, a feature represents an attribute and value combination. Color is an attribute. “Color is blue” is a feature.
What are outcome variables? An outcome variable is a measure that we want to predict. It can either be the actual values collected in the dataset or predicted outcomes from the machine learning model.
Now what?
You’re finally ready to take on Part 2 of our Introduction to Data Science: our hands-on case study. You’ll apply the principles introduced in this webinar just like a real data scientist using the Azure Notebook platform, a free and virtual development environment hosted by Microsoft Cloud. We’ll walk you through it step by step in our Part 2 webinar on 5/13! If you can’t get enough of us, join us on 5/6 for an Introduction to React.
P.S. Were you hoping we’d cover something and didn’t get to it? Take our 1-minute survey and let us know!
See you there!
The WIT Project