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  • 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 PySparkMLlibPower 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
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  • Section 1 : Introduction 1 Lectures

    • Lecture 1 :
  • Section 2 : Setup and Installations 6 Lectures

    • 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

    • 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

    • 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

    • 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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