Course Logistics and Prerequisites

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En provenance du cours de MongoDB Inc.

MongoDB Aggregation Framework

6 notes

MongoDB Inc.

MongoDB Aggregation Framework

6 notes

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.

À partir de la leçon

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.

Course Logistics and Prerequisites2:07

Aggregation Introduction4:20

The Concept of Pipelines2:04

Aggregation Structure and Syntax3:48

$match: Filtering Documents3:21

Using $project5:58

Using Expressions3:14

Cursor Like Methods Stages: Part 15:03

Cursor Like Methods Stages: Part 25:15

The $group Stage5:56

Using Accumulator Expressions5:14

The $lookup Stage4:25

$graphLookup Stage6:21

Rencontrer les enseignants

Nathan Leniz
Curriculum Engineer
Education
Kirby Kohlmorgen
Senior Curriculum Engineer
Education

In this video, we'll discuss

the necessary prerequisites you should have before taking this course,

what the course work is going to look like,

and finally we'll discuss what software is necessary to complete the coursework.

Let's begin with the prerequisites.

For beginning, it is expected that you have

a basic knowledge of mongoDB and you're familiar with Python,

the language of choice throughout this specialization.

Specific knowledge of the library that is used later in the course is not required,

as we will provide the basics for understanding,

though we will not delve too deeply into the art and science

of tuning hyper parameters during the machine learning portion.

Next, let's talk about the work you'll be doing.

Throughout this course, you're going to be watching lesson videos

that explain different mongoDB and data science topics.

Many of these lessons will be followed by an ungraded quiz

to test your understanding of the content in the lesson video.

Additionally, you'll be asked to perform

practical exercises by downloadable Jupyter notebooks.

These will be graded exercises that count towards your grade in the course.

Finally, let's discuss the necessary software

you'll need to complete those graded notebook exercises.

We'll be using Python 3 for our Jupyter notebooks and for the course.

We recommend that you install Python 3 in Jupyter by Anaconda.

Moreover, Anaconda makes it very easy to have separate Python environments.

This will come in handy as you install different Python libraries by Pip.

Throughout this course, we'll be using mongoDB Atlas,

the easiest way to run mongoDB in a cloud,

for many of the lesson exercises.

Sometimes you'll be connecting directly to our own Atlas cluster,

and other times you'll be connecting to your Atlas cluster.

To that end, you'll need to make sure you can connect to Atlas,

so make sure there are no firewalls or network restrictions preventing you from doing so.

Additionally, you'll need to create a mongoDB Atlas cluster.

If that sounded like a lot, have no fear.

We have lessons to walk you through all of this.

And those are the logistics for the course. Best of luck.

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