Introduction to MongoDB for Data Analytics

Introduction to MongoDB for Data Analytics, available at $59.99, has an average rating of 4.6, with 84 lectures, based on 171 reviews, and has 1341 subscribers.

You will learn about MONGODB BASICS: Create your own databases from scratch Learn MongoDB Architecture: databases, documents, collections, key-value pairs Use CRUD operations in the mongo shell DATABASE DESIGN: Use Embedded Documents to represent relationships in data Use various data structures to design a database for a blogging website case study DATA ANALYSIS Analyze real world datasets with projection, sorting, and filtering Use query operators to examine correlations between features in data This course is ideal for individuals who are Database Administrators or Data Scientists or Anyone who is interested in NoSQL databases or Data Analysts or Big Data Enthusiasts It is particularly useful for Database Administrators or Data Scientists or Anyone who is interested in NoSQL databases or Data Analysts or Big Data Enthusiasts.

Enroll now: Introduction to MongoDB for Data Analytics

Summary

Title: Introduction to MongoDB for Data Analytics

Price: $59.99

Average Rating: 4.6

Number of Lectures: 84

Number of Published Lectures: 80

Number of Curriculum Items: 84

Number of Published Curriculum Objects: 80

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

MONGODB BASICS:

Create your own databases from scratch

Learn MongoDB Architecture: databases, documents, collections, key-value pairs

Use CRUD operations in the mongo shell

DATABASE DESIGN:

Use Embedded Documents to represent relationships in data

Use various data structures to design a database for a blogging website case study

DATA ANALYSIS

Analyze real world datasets with projection, sorting, and filtering

Use query operators to examine correlations between features in data

Who Should Attend

Database Administrators

Data Scientists

Anyone who is interested in NoSQL databases

Data Analysts

Big Data Enthusiasts

Target Audiences

Database Administrators

Data Scientists

Anyone who is interested in NoSQL databases

Data Analysts

Big Data Enthusiasts

In this course, we’ll begin by covering the basics of MongoDB and the key differences between NoSQL and SQL to help you build an intuitive understanding of foundational concepts for the course. This will be followed by engaging practical exercises to help you understand how to use database operations in the Mongo Shell.

You’ll then move on to designing your own database to store data for a blogging website, exploring how different data structures can be best-suited to solve different problems depending on the use case.

Finally, we’ll move on to data analysis. You’ll use filtering, projection, sorting, and querying complex data structures to extract actionable insights from real world datasets. You’ll build these skills through a blogging website case study and then apply them on an E-Commerce data analysis challenge.

Course Curriculum

Chapter 1: Extras materials for you

Lecture 1: Ultimate MongoDB Cheat Sheet

Lecture 2: Data Analysis Case Study: Heart Disease

Lecture 3: Pymongo: Connecting MongoDB and Python

Chapter 2: Getting started with Databases – Intuition

Lecture 1: Introduction to Databases

Lecture 2: NoSQL vs. SQL

Lecture 3: EXTRA: Learning Paths

Lecture 4: Complete Course Curriculum

Lecture 5: Course Materials and Additional resources

Lecture 6: Cheatsheet! 7 NoSQL Advantages

Lecture 7: Updates on Udemy Reviews

Lecture 8: Vertical VS Horizontal Scaling

Lecture 9: Cheatsheet! Top 5 NoSQL Databases

Lecture 10: Introduction to CRUD operations

Chapter 3: Getting Started with Databases – Practical

Lecture 1: Installing MongoDB for Mac

Lecture 2: Installing MongoDB for Windows

Lecture 3: Stopping the MongoDB Shell and Server

Lecture 4: Creating a database/collection

Lecture 5: Inserting documents

Lecture 6: Introduction to queries (using find) part 1

Lecture 7: Introduction to queries (using find) part 2

Lecture 8: Updating documents

Lecture 9: Deleting documents

Chapter 4: Database Design – Intuition

Lecture 1: Introduction to Data types

Lecture 2: Primary Keys

Lecture 3: Establishing Relationships in Data

Lecture 4: Embedded Documents

Lecture 5: Case Study #1 Overview

Lecture 6: Embedded Documents vs Separate Collections

Chapter 5: Database Design – Practical

Lecture 1: Setting Up MongoDB Compass (Mac)

Lecture 2: Setting Up MongoDB Compass (Windows)

Lecture 3: Supplemental Materials

Lecture 4: Basic CRUD operations in Compass

Lecture 5: Setting up our user database

Lecture 6: Inserting user documents

Lecture 7: Posts Collection

Lecture 8: Inserting Multiple Posts

Lecture 9: Updating Post Documents

Lecture 10: Using Embedded Documents

Chapter 6: Data Analysis Intuition

Lecture 1: Quantitative vs. Categorical Data

Lecture 2: Analysis Techniques

Lecture 3: Using the Midpoint for Quantitative Data Exploration

Lecture 4: Extracting Insights: Exploration

Lecture 5: Extracting Insights: Correlations

Lecture 6: Case Study 1 Overview Part 1: Blog Posts

Chapter 7: Data Analysis Practical: Blog Case Study

Lecture 1: Preparing our Dataset

Lecture 2: Starting your MongoDB Server for Windows

Lecture 3: Supplemental Materials

Lecture 4: Exploring the Dataset: Part 1

Lecture 5: Exploring the Dataset: Part 2

Lecture 6: Exploring the Dataset: Part 3

Lecture 7: Exploring the Dataset: Part 4

Lecture 8: Exploring the Dataset: Part 5

Lecture 9: Exploring the Dataset: Part 6

Lecture 10: Identifying Correlations in Data: Part 1

Lecture 11: Identifying Correlations in Data: Part 2

Lecture 12: Reviewing Our Findings for Correlations

Lecture 13: Identifying Correlations in Data: Part 3

Lecture 14: Reviewing Additional Findings for Correlations

Lecture 15: More Queries with $or and $in

Lecture 16: Finding Correlations with $in

Chapter 8: Data Analysis: Blogging Case Study with Embedded Documents

Lecture 1: Prepare the Dataset

Lecture 2: Starting your MongoDB Server for Windows

Lecture 3: Supplemental Materials

Lecture 4: Querying Embedded Documents: Part 1

Lecture 5: Querying Embedded Documents: Part 2

Lecture 6: Querying Embedded Documents: Part 3

Lecture 7: Exploring the Dataset

Lecture 8: Reviewing our Findings for Exploration

Lecture 9: Identifying Correlations in Data

Lecture 10: Reviewing Our Findings for Correlations

Chapter 9: Data Analysis Practical: ECommerce Case Study

Lecture 1: Preparing the Dataset

Lecture 2: Starting your MongoDB Server for Windows

Lecture 3: Supplemental Materials

Lecture 4: Projecting Fields of Interest

Lecture 5: Exploring the Dataset

Lecture 6: Exploring the Dataset

Lecture 7: Reviewing our Findings for Exploration

Lecture 8: Identifying Correlations in Data: Part 1

Lecture 9: Identifying Correlations in Data: Part 2

Chapter 10: Congratulations!! Dont forget your Prize 馃檪

Lecture 1: Bonus: How To UNLOCK Top Salaries (Live Training)

Instructors

Brian Dowe
Full Stack Web Developer and Programming Instructor

SuperDataScience Team
Helping Data Scientists Succeed

Ligency Team
Helping Data Scientists Succeed

Rating Distribution

1 stars: 2 votes

2 stars: 4 votes

3 stars: 30 votes

4 stars: 53 votes

5 stars: 82 votes

Frequently Asked Questions

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