À propos de ce cours : This course will teach you how to perform data analysis using MongoDB's powerful Aggregation Framework. You'll begin this course by building a foundation of essential aggregation knowledge. By understanding these features of the Aggregation Framework you will learn how to ask complex questions of your data. This will lay the groundwork for the remainder of the course where you'll dive deep and learn about schema design, relational data migrations, and machine learning with MongoDB. By the end of this course you'll understand how to best use MongoDB and its Aggregation Framework in your own data science workflow.
Les destinataires de ce cours : This course has been primarily designed for learners who have prior experience as a data analyst or data scientist. You should already have experience with Python and Jupyter Notebooks.
Créé par : MongoDB Inc.
Enseigné par : Nathan Leniz, Curriculum Engineer
Education
Enseigné par : Kirby Kohlmorgen, Senior Curriculum Engineer
Education
| Niveau | Intermédiaire |
| Langue | English |
| Comment réussir | Réussissez tous les devoirs notés pour terminer le cours. |
| Notes des utilisateurs |
Programme de cours
SEMAINE 1
The Fundamentals of MongoDB Aggregation
In this module you'll learn the fundamentals of MongoDB's Aggregation Framework. This will cover basics like filtering and sorting, as well as how to transform array data, how to group documents together, how to join data, and how to traverse graph data.
15 vidéos, 1 lecture, 8 quiz pour s'exercer
- Vidéo: Course Logistics and Prerequisites
- Practice Quiz: Course Logistics
- Reading: Setting Up Your Course Environment
- Vidéo: Aggregation Introduction
- Practice Quiz: Aggregation Introduction
- Vidéo: The Concept of Pipelines
- Practice Quiz: The Concept of Pipelines
- Vidéo: Aggregation Structure and Syntax
- Practice Quiz: Aggregation Structure and Syntax
- Vidéo: $match: Filtering Documents
- Practice Quiz: $match: Filtering Documents
- Vidéo: Using $project
- Practice Quiz: Using $project
- Vidéo: Using Expressions
- Practice Quiz: Using Expressions
- Vidéo: Cursor Like Methods Stages: Part 1
- Vidéo: Cursor Like Methods Stages: Part 2
- Vidéo: The $group Stage
- Vidéo: Using Accumulator Expressions
- Vidéo: $unwind
- Vidéo: The $lookup Stage
- Practice Quiz: The $lookup Stage
- Vidéo: $graphLookup Stage
Noté: $project and Expressions
Noté: Changing Document Shape with $project
Noté: Cursor Like Methods
Noté: $group Accumulators
Noté: $unwind and $group
Noté: $lookup
Noté: $graphLookup
SEMAINE 2
Leveraging MongoDB's Flexible Schema
This module is going to be focused on the different ways you can leverage MongoDB's flexible schema. You'll learn how to migrate a relational schema, how to enhance existing schemas, and how to merge datasets via an entity resolution technique.
11 vidéos, 7 quiz pour s'exercer
- Practice Quiz: Intro to Week 2
- Vidéo: mongoimport
- Practice Quiz: mongoimport
- Vidéo: Importance of Schema
- Practice Quiz: Importance of Schema
- Vidéo: Exploring Schemas
- Practice Quiz: Exploring Schemas
- Vidéo: Migrating Your Schema
- Practice Quiz: Migrating Your Schema
- Vidéo: Views
- Practice Quiz: Views
- Vidéo: Supplementing Schemas with Accumulators
- Vidéo: Tree-like Data in Individual Documents (Part 1)
- Vidéo: Tree-like Data in Individual Documents (Part 2)
- Practice Quiz: Tree-like Data in Individual Documents
- Vidéo: Expressive Lookup Basics
- Vidéo: Entity Resolution with $lookup
Noté: Schemas and accumulators
Noté: Entity Resolution
SEMAINE 3
Machine Learning with MongoDB
This module is focused on demonstrating how MongoDB can be used in different machine learning workflows. You'll learn how to perform machine learning directly in MongoDB, how to prepare data for machine learning with MongoDB, and how to analyze data with MongoDB in preparation of doing machine learning in Python.
11 vidéos, 7 quiz pour s'exercer
- Practice Quiz: Intro to Week 3
- Vidéo: Calculation of Persons-Rho
- Vidéo: Intro: Associative Role Learning
- Practice Quiz: Intro: Associative Role Learning
- Vidéo: Principal Component Analysis (Part 1)
- Vidéo: Principal Component Analysis (Part 2)
- Practice Quiz: Principal Component Analysis
- Vidéo: Intro to Linear Regressions
- Practice Quiz: Intro to Linear Regressions
- Vidéo: Linear Regressions with MongoDB
- Practice Quiz: Linear Regressions with MongoDB
- Vidéo: Decision Trees Part 1
- Vidéo: Decision Trees Part 2
- Vidéo: Decision Trees Part 3
- Practice Quiz: Decision Trees
- Vidéo: Intro to Clustering Algorithms
- Practice Quiz: Intro to Clustering Algorithms
Noté: Pearsons-Rho
Noté: Associative Rules on Titanic Data Set
Noté: Linear regression on Titanic data set
Noté: Decision Trees on Titanic Data Set
Noté: K-means usage on Titanic dataset
FAQ
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Créateurs
MongoDB Inc.
MongoDB is the leading modern, general purpose database platform, designed to unleash the power of software and data for developers and the applications they build. Headquartered in New York, MongoDB has more than 4,300 customers in over 85 countries, including more than half of the global Fortune 100. The MongoDB database platform has been downloaded over 30 million times and there have been more than 730,000 MongoDB University registrations.
Tarification
| Acheter le cours | |
|---|---|
| Accéder au contenu du cours | Disponible |
| Accéder au contenu noté | Disponible |
| Recevoir une Note Finale | Disponible |
| Obtenir un Certificat de Cours partageable | Disponible |
Notation et examens
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BL
Overall a good course. Some of the labs could be more intuitive and in one (I gave some feedback in the comments) has IMHO so false datasource. I also think that especially in Week3 some labs have to much information given and the student only has to do some minor tasks.