edXData ScienceFree courseIntermediate levelCertificate

Data Science and Machine Learning Capstone Project

Create a project that you can use to showcase your Data Science skills to prospective employers. Apply various data science and machine learning techniques to analyze and visualize a data set involving a real life business scenario and build a predictive model.

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Course Description

Employers really care about how well can you apply your knowledge and skills to solve real world problems. Now that you've taken several courses on Data Science and Machine Learning, its time to put your learning to practice and work on a data problem involving a real life scenario.
New Yorkers use 311 system to report complaints for the non-emergency problems they face. Various agencies in New York get assigned to these problems. The data related to these Complaints are available in New York City Open Dataset. On investigation one can see that in last few years the 311 complaints coming to The Department of Housing Preservation and Development in New York City has increased significantly.
In this Capstone project your task would be to find out answers to some questions that would help The Department of Housing Preservation and Development in New York City to effectively tackle 311 complaints coming to them. You need to use Python and Data Science and Machine Learning techniques such as Data Ingestion, Data Exploration, Data Visualization, Feature Engineering, Probabilistic Modeling, Model Validation, etc.
By the end of this course you will have used real world Data Science tools to create a showcase project and demostrate to employers that you are job ready and a worthy candidate in the field of Data Science.

Intermediate Level

Time: 6 Weeks - 3-4 hours per week

Instructor Details

Linda Liu

Linda is a data science consultant in IBM Data Science and Cloud Expert Lab focusing in Data and Analytics projects and enablement. She has worked with training enablement for IBM internal and external IT technical audiences. Prior working in IBM, Linda worked as a professional consultant and DevOps manager for leading technology companies, implementing Data and Analytics solutions.