MongoDB for Data Wrangling: Aggregation

MongoDB for Data Wrangling: Aggregation

• Course Overview
• recognize the structure of aggregate operations in MongoDB
• use the $group operator to perform a computation using an accumulator operator
• use the $match operator to filter data in an aggregation query
• use the $project operator to specify fields in an aggregation query
• use the $limit and $sort operators in an aggregation pipeline
• use the $unwind operator to expand an array field
• use the $lookup operator to perform a join
• use createIndex to build an index on a collection
• use a geospacial index for a geoSearch operation
• implement a multi-stage aggregation pipeline

This Skillsoft Aspire course explores MongoDB, a cross-platform document-oriented database that has become a popular tool for data wrangling and data science. MongoDB is a NoSQL (not only structured query language) that uses Javascript Object Notation (JSON)- like documents with schemata. This course demonstrates reshaping, aggregating and summarizing documents in a MongoDB database, and gather, filter, modify, and query data, and to perform MongoDB actions related to data wrangling. Learners observe demonstrations of how to recognize the structure of aggregate operations in MongoDB; how to use the group operator to perform aggregate computations; and how to use limit and sort operators in an aggregations pipeline. Next, learn how to use the unwind operator to expand an array field in an aggregation, and how to use the Lookup operator to perform a joint operation between 2 collections in an aggregation, and how to use the index stats operator in an aggregation stage to view the statistics on the indexes. Finally, you will learn how to use a Geospatial Index for a geosearch operation.

MongoDB for Data Wrangling: Querying

MongoDB for Data Wrangling: Querying

• Course Overview
• configure and test PyMongo in a Python program
• work with MongoDB document structure
• perform create, read, update, and delete operations on a MongoDB document
• work with MongoDB document ObjectIDs and Timestamps
• use the find operation to select documents from a collection
• specify the fields to be returned from the find operation
• use the comparison query operators to match criteria
• apply the $exists and $type elements to a query
• use the $regex operator to query documents
• use the $size and $all operators to query array fields
• perform a text search query on string content
• use the mongoimport tool to import from JSON and CSV
• use the mongoexport tool to export data from MongoDB to JSON and CSV
• combine a number of different operators to get a result from MongoDB

This course explores how to use MongoDB, a cross-platform document-oriented database that has become a popular tool for data wrangling and data science. MongoDB is a NoSQL (not only structured query language) that uses JSON (Javascript Object Notation) like documents with schemata. One advantage of MongoDB is the flexibility of how it stores data. You will learn how to perform MongoDB actions related to data wrangling by using Python with the PyMongo library. You will learn how to perform basic CRUD (create, read, update, delete) operations on a Mongo DB document. Next, learn how to use the find operation to select documents from a collection, and to use query operators to match document criteria. You will learn how to select documents using a specified criterion, similar to a WHERE clause in an SQL statement. Finally, this course demonstrates how to use the mongoimport tool to import from JSON or CSV, and mongoexport to export data from a MongoDB collection to JSON or CSV (comma separated values).