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Python, SQL, Statistics, Machine Learning.
Understand what ETL pipelines are and cccess and combine data from CSV, JSON, logs, APIs and databases.
Prepare text data for analysis with tokenization, lemmatization, and removing stop words. Use scikit-learn to transform and vectorize text data and build features with bag of words and tf-idf.
Understand the advantages of using machine learning pipelines to streamline the data preparation and modeling process. Use feature unions to perform steps in parallel and create more complex workflows and complete a case study to build a full machine learning pipeline that prepares data and creates a model for a dataset.
In this project, you’ll build a data pipeline to prepare the message data from major natural disasters around the world. You’ll build a machine learning pipeline to categorize emergency text messages based on the need communicated by the sender.
Curriculum Lead at Udacity
Juno is the curriculum lead for the School of Data Science. She has been sharing her passion for data and teaching, building several courses at Udacity. As a data scientist, she built recommendation engines, computer vision and NLP models, and tools to analyze user behavior.
Andrew has an engineering degree from Yale, and has used his data science skills to build a jewelry business from the ground up. He has additionally created courses for Udacity’s Self-Driving Car Engineer Nanodegree program.
Arpan is a computer scientist with a PhD from North Carolina State University. He teaches at Georgia Tech (within the Masters in Computer Science program), and is a coauthor of the book Practical Graph Mining with R.
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