IBM: Data Science and Machine Learning - Capstone Course
This course is part of Ciencia de datos con Python Professional Certificate.
In this capstone course, you'll apply your data science and machine learning knowledge to a real-world business scenario, transforming theory into practical skills that employers value. You'll explore datasets from New York City's 311 system, where residents report non-urgent problems. Focusing on complaints submitted to the Housing Preservation and Development Department, you'll analyze trends, visualize data, and build predictive models to answer crucial questions that help authorities address these complaints effectively. Throughout this project, you'll utilize Python, data ingestion techniques, exploratory data analysis, visualization, feature engineering, probabilistic modeling, and model validation. Upon completion, you'll have created a comprehensive data science project that demonstrates your job-readiness to potential employers, complete with a digital badge credential to verify your newly acquired skills.
Instructors:
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What you'll learn
Apply data science and machine learning knowledge to real-world business scenarios
Analyze and visualize data using Python programming
Perform feature engineering exercises to prepare data for modeling
Create and validate predictive machine learning models
Develop actionable insights from real-life data problems to showcase to potential employers
Earn a verified digital badge credential demonstrating your practical data science skills
Skills you'll gain
This course includes:
PreRecorded video
Assignments, projects
Access on Mobile, Tablet, Desktop
Limited Access access
Shareable certificate
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Top companies provide this course to enhance their employees' skills, ensuring they excel in handling complex projects and drive organizational success.
Module Description
This course covers essential data science skills applied to real-world scenarios using New York City's 311 system data. Students will explore datasets focusing on housing complaints, learning to perform comprehensive data analysis and build predictive models. The curriculum includes data ingestion and cleaning techniques, exploratory data analysis, data visualization methods, feature engineering approaches, probabilistic modeling, and model validation. Students will analyze the increasing volume of complaints to the Housing Preservation and Development Department, using Python and machine learning tools to develop actionable insights. The project-based approach emphasizes practical skills that employers value, culminating in a portfolio-ready project demonstrating the student's ability to solve complex data problems.
Instructors
2 Courses
Data Science Thought Leader
Sourav Mazumder is a Data Science Thought Leader and Distinguished Engineer at IBM, with over 22 years of experience in the IT industry and more than a decade specializing in artificial intelligence, data science, and big data. He has driven business innovation across sectors such as manufacturing, insurance, telecom, banking, media, healthcare, and retail in regions including the USA, Europe, Australia, Japan, and India. Sourav has deep expertise in a wide range of technologies, including Watson APIs, Spark, Hadoop, BigSQL, HBase, MongoDB, Cognos, R, Python, and Java, and has contributed to projects from MVP development to full-scale production. He is a prolific author and speaker, regularly publishing articles and presenting at industry conferences on AI, data science, and big data. Sourav holds certifications such as The Open Group Distinguished Data Scientist and IBM Master Inventor, and his work includes research and patents in areas like trustworthy AI, ML Ops, and AI governance
2 Courses
Data Science Architect & Evangelist
Linda Liu is a data science consultant in IBM's Data Science and Cloud Expert Lab, specializing in data analytics projects and technical enablement. With experience in DevOps management and consulting for leading technology companies, she focuses on implementing data-driven solutions and training programs for IBM's internal and external IT audiences. Her work spans developing analytics frameworks and collaborating on professional certification courses like "Ciencia de datos con Python" for IBM on edX.
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