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
EducationEnseigné 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
- Cuestionario de práctica: Course Logistics
- Reading: Setting Up Your Course Environment
- Vidéo: Aggregation Introduction
- Cuestionario de práctica: Aggregation Introduction
- Vidéo: The Concept of Pipelines
- Cuestionario de práctica: The Concept of Pipelines
- Vidéo: Aggregation Structure and Syntax
- Cuestionario de práctica: Aggregation Structure and Syntax
- Vidéo: $match: Filtering Documents
- Cuestionario de práctica: $match: Filtering Documents
- Vidéo: Using $project
- Cuestionario de práctica: Using $project
- Vidéo: Using Expressions
- Cuestionario de práctica: 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
- Cuestionario de práctica: 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
- Cuestionario de práctica: Intro to Week 2
- Vidéo: mongoimport
- Cuestionario de práctica: mongoimport
- Vidéo: Importance of Schema
- Cuestionario de práctica: Importance of Schema
- Vidéo: Exploring Schemas
- Cuestionario de práctica: Exploring Schemas
- Vidéo: Migrating Your Schema
- Cuestionario de práctica: Migrating Your Schema
- Vidéo: Views
- Cuestionario de práctica: Views
- Vidéo: Supplementing Schemas with Accumulators
- Vidéo: Tree-like Data in Individual Documents (Part 1)
- Vidéo: Tree-like Data in Individual Documents (Part 2)
- Cuestionario de práctica: 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
- Cuestionario de práctica: Intro to Week 3
- Vidéo: Calculation of Persons-Rho
- Vidéo: Intro: Associative Role Learning
- Cuestionario de práctica: Intro: Associative Role Learning
- Vidéo: Principal Component Analysis (Part 1)
- Vidéo: Principal Component Analysis (Part 2)
- Cuestionario de práctica: Principal Component Analysis
- Vidéo: Intro to Linear Regressions
- Cuestionario de práctica: Intro to Linear Regressions
- Vidéo: Linear Regressions with MongoDB
- Cuestionario de práctica: Linear Regressions with MongoDB
- Vidéo: Decision Trees Part 1
- Vidéo: Decision Trees Part 2
- Vidéo: Decision Trees Part 3
- Cuestionario de práctica: Decision Trees
- Vidéo: Intro to Clustering Algorithms
- Cuestionario de práctica: 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
Comment cela fonctionne
Trabajo del curso
Cada curso es como un libro de texto interactivo, con videos pregrabados, cuestionarios y proyectos.
Ayuda de tus compañeros
Conéctate con miles de estudiantes y debate ideas y materiales del curso, y obtén ayuda para dominar los conceptos.
Certificados
Obtén reconocimiento oficial por tu trabajo y comparte tu éxito con amigos, compañeros y empleadores.
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
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.
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AM
The first two weeks are a good introduction to Aggregation and what you can do with it. The third week is more focused on using Mongo in machine learning applications and teaches you less new Mongo content and isn't in depth enough to be a good introduction to machine learning, but is a good way to get familiar with using the tools you've been shown earlier in the course. Some of the exercises are a little unclear in their wording but the course administrators are quick to reply in the forums and they update the workbooks in response to feedback. Overall would recommend to anyone who will be using Mongo and wants to get an overview of it's capabilities and some practise putting it into action.