Big Data Analytics with PySpark + Power BI + MongoDB

Big Data Analytics with Predictive Modeling and Visualization with Power BI Desktop

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Last Updated: 2022-11-30 04:11:25 English Course Type: Self Learning

Power BI Data Visualization
PySpark Programming
Data Analysis
Data Transformation and Manipulation
Big Data Machine Learning
Geo Mapping with ArcMaps for Power BI
Geospatial Machine Learning
Creating Dashboards

Welcome to the Big Data Analytics with PySpark + Power BI + MongoDB course. In this course we will be creating a big data analytics pipeline, using big data technologies like PySpark, MLlib, Power BI and MongoDB.

We will be working with earthquake data, that we will transform into summary tables. We will then use these tables to train predictive models and predict future earthquakes. We will then analyze the data by building reports and dashboards in Power BI Desktop.

Power BI Desktop is a powerful data visualization tool that lets you build advanced queries, models and reports. With Power BI Desktop, you can connect to multiple data sources and combine them into a data model. This data model lets you build visuals, and dashboards that you can share as reports with other people in your organization.

MongoDB is a document-oriented NoSQL database, used for high volume data storage. It stores data in JSON like format called documents, and does not use row/column tables. The document model maps to the objects in your application code, making the data easy to work with.

You will learn how to create data processing pipelines using PySpark
You will learn machine learning with geospatial data using the Spark MLlib library
You will learn data analysis using PySpark, MongoDB and Power BI
You will learn how to manipulate, clean and transform data using PySpark dataframes
You will learn how to create Geo Maps using ArcMaps for Power BI
You will also learn how to create dashboards in Power BI

Basic Understanding of Python
Little or no understanding of GIS
Basic understanding of Programming concepts
Basic understanding of Data
Basic understanding of what Machine Learning is

Python Developers at any level
Data Engineers at any level
Developers at any level
Machine Learning engineers at any level
Data Scientists at any level
GIS Developers at any level
The curious mind

Section 1 : Introduction 1 Lectures 00:00:00
Lecture 1 :
Introduction Preview
Section 2 : Setup and Installations 6 Lectures 00:00:00
Lecture 1 :
Python Installations
Lecture 2 :
Installing Apache Spark
Lecture 3 :
Installing Java (Optional)
Lecture 4 :
Testing Apache Spark Installation
Lecture 5 :
Installing MongoDB
Lecture 6 :
Installing NoSQL Booster for MongoDB
Section 3 : Data Processing with PySpark and MongoDB 4 Lectures 00:00:00
Lecture 1 :
Integrating PySpark with Jupyter Notebook
Lecture 2 :
Data Extraction
Lecture 3 :
Data Transformation
Lecture 4 :
Loading Data into MongoDB
Section 4 : Machine Learning with PySpark and MongoDB 3 Lectures 00:00:00
Lecture 1 :
Data Pre-processing
Lecture 2 :
Building the Predictive Model
Lecture 3 :
Creating the Prediction Dataset
Section 5 : Building the Data Pipeline Scripts 3 Lectures 00:02:55
Lecture 1 :
Installing Visual Studio Code IDE
Lecture 2 :
Creating the PySpark ETL Script
Lecture 3 :
Creating the Machine Learning Script
Section 6 : Data Visualization with Power BI 9 Lectures 00:00:00
Lecture 1 :
Installing Power BI Desktop
Lecture 2 :
Installing MongoDB ODBC Drivers
Lecture 3 :
Creating a System DSN for MongoDB
Lecture 4 :
Loading the Data Sources
Lecture 5 :
Creating a Geo Map
Lecture 6 :
Creating a Table Plot
Lecture 7 :
Creating an Area Chart
Lecture 8 :
Creating a Bar Chart
Lecture 9 :
Creating a Doughnut Chart
Section 7 : Source Code and Notebook 1 Lectures
Lecture 1 :
Source Code and Notebook

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