Medicine

# M101P HOMEWORK 5.1

For this problem, we have used a subset of the data you previously used in zips. The documents look like this: In this assignment you will use the aggregation framework to find the most frequent author of comments on your blog. Different states might have the same city name. You will need to probably change your projection to send more info through than just that first character.

You will be using the zip code collection data set, which you will find in the ‘handouts’ link in this page. A city might have multiple zip codes. You must figure out the GPA that each student has achieved in a class and then average those numbers to get a class average. For example, to extract the first digit from the city field, you could write this query: This involves calculating an average for each student in each class of all non-quiz assessments and then averaging those numbers to get a class average. If you notice that while importing, there are a few duplicates fear not, this is expected and will not affect your answer.

In this assignment you will use the aggregation framework to find the most frequent author of comments on your blog. You m1001p need to probably change your projection to send more info through than just that first character.

Some students have three homework assignments, etc. A set of grades are loaded into the grades collection.

## MongoDB university m101p chapter 5 homework

The project operator can extract the first digit from any field. This is why there are only documents and zip codesand all of them are in New York, Connecticut, New Jersey, and California.

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Note that not all students in the same class have the same exact number of assessments. For this problem, we have used a subset of the data you previously used in zips. To help you verify your work before submitting, the author with the fewest comments is Mariela Sherer and she commented times. Try for your self.

For example, to extract the first character from the city field, you could write this pipeline:. In this problem you m011p calculate the number of people who live in a zip code in the US where the city starts with a digit.

When you mongoimport the data, you will probably see a few duplicate key errors; this is to be expected, and will not prevent the mongoimport from working. There is also an issue with some versions of MongoDB 3.

# MongoDB university mp chapter 5 homework – bigmongodb

You must figure out the GPA that each student has achieved in a class and then average those numbers to get a class average. In this assignment you will use the aggregation framework to find the most frequent author of comments on your blog.

To be clear, each student’s average includes only exams and homework grades.

For purposes of keeping the Hands On shell quick, we have used a subset of the data you homewok used in zips. Please round the m101 to a whole number. For example, to extract the first digit from the city field, you could write this query: Also, you will need a filtering step to get rid of all documents where the city does not start with the select set of initial characters.

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The answer for CT and NJ using this data set is Different states might have the same city name.

Start by downloading the handout zip file for this problem. The documents look like this: Don’t include their quiz scores in the calculation.

# Study IQ World: Week 5 : AGGREGATION FRAMEWORK : MP: MongoDB for Developers

You must figure out the GPA that each student has achieved in a class and then average those numbers to get a class average. Those students achieved a class average of This involves calculating an average for each student in each class of all non-quiz assessments and then averaging those numbers to get a class average. Choose the answer below. The answer for CT and NJ using this data set is Mm101p round the answer to a whole number.

We will take these are the prefered cities to live in chosen by this instructor, given is special affection to this set of characters! Now use the aggregation framework to calculate the author with the greatest number of comments.

You will be using the zip code collection data set, which you will find in the ‘handouts’ link in this page.