This course delves into MongoDB's powerful Aggregation Framework, offering in-depth insights into data analysis and manipulation. You'll explore essential aggregation concepts, schema design, and the application of machine learning in MongoDB.
Throughout the course, you'll gain a strong foundation in the fundamentals of MongoDB aggregation, schema flexibility, and machine learning techniques. From understanding the concept of pipelines to exploring tree-like data and leveraging clustering algorithms, you'll be equipped to ask complex questions of your data and optimize your data science workflow.
By the end of the course, you'll have the knowledge and skills to utilize MongoDB and its Aggregation Framework effectively in your data analysis endeavors. The comprehensive curriculum, practical examples, and hands-on exercises ensure that you'll be well-prepared to leverage MongoDB for your data science projects.
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This course comprises three modules that cover MongoDB's Aggregation Framework, schema design, and machine learning. From fundamentals to advanced techniques, you'll gain a comprehensive understanding of MongoDB's capabilities for data analysis and manipulation.
This module focuses on building a strong foundation in the fundamentals of MongoDB Aggregation Framework. You'll explore the concept of pipelines, aggregation structure and syntax, and various stages such as $match, $project, $group, $unwind, and $lookup. Additionally, you'll gain practical experience by setting up your course environment and working with expressions and accumulators.
In this module, you'll delve into the importance of schema in MongoDB and learn about schema exploration, migration, and views. You'll also understand how to supplement schemas with accumulators and work with tree-like data in individual documents. The module concludes with insights into entity resolution and expressive lookup basics.
This module delves into the application of machine learning techniques in MongoDB. You'll explore various algorithms such as Principal Component Analysis, Linear Regressions, Decision Trees, and K-means usage on dataset. The module also covers topics like Pearson's Rho and associative rules on the Titanic dataset, providing practical insights into leveraging machine learning with MongoDB.
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