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  • PySpark Programming
  • Data Analysis
  • Python and Bokeh
  • Data Transformation and Manipulation
  • Data Visualization
  • Big Data Machine Learning
  • Geo Mapping
  • Geospatial Machine Learning
  • Creating Dashboards

Welcome to the ​Building Big Data Pipelines with PySpark & MongoDB & Bokeh​ course. In

this course we will be building an intelligent data pipeline using big data technologies like

Apache Spark and MongoDB.

 

We will be building an ETLP pipeline, ETLP stands for Extract Transform Load and Predict.

These are the different stages of the data pipeline that our data has to go through in order for it

to become useful at the end. Once the data has gone through this pipeline we will be able to

use it for building reports and dashboards for data analysis.

 

The data pipeline that we will build will comprise of data processing using PySpark, Predictive

modelling using Spark’s MLlib machine learning library, and data analysis using MongoDB and

Bokeh.

 

  • 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 Bokeh, inside of jupyter notebook

  • You will learn how to manipulate, clean and transform data using PySpark dataframes

  • You will learn basic Geo mapping

  • You will learn how to create dashboards

  • You will also learn how to create a lightweight server to serve Bokeh dashboards

  • 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
  • Developers at any level
  • Machine Learning engineers at any level
  • Data Scientists at any level
  • The curious mind
  • GIS Developers at any level
View More...
  • Section 1 : Introduction 1 Lectures 00:09:30

    • Lecture 1 :
  • Section 2 : Setup and Installations 7 Lectures 00:36:51

    • Lecture 1 :
    • Python Installation
    • Lecture 2 :
    • Installing Third Party Libraries
    • Lecture 3 :
    • Installing Apache Spark
    • Lecture 4 :
    • Installing Java (Optional)
    • Lecture 5 :
    • Testing Apache Spark Installation
    • Lecture 6 :
    • Installing MongoDB
    • Lecture 7 :
    • Installing NoSQL Booster for MongoDB
  • Section 3 : Data Processing with PySpark and MongoDB 4 Lectures 00:33:15

    • 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 00:20:05

    • Lecture 1 :
    • Data Pre-processing
    • Lecture 2 :
    • Building the Predictive Model
    • Lecture 3 :
    • Creating the Prediction Dataset
  • Section 5 : Data Visualization 5 Lectures 00:18:15

    • Lecture 1 :
    • Loading the Data Sources from MongoDB
    • Lecture 2 :
    • Creating a Map Plot
    • Lecture 3 :
    • Creating a Bar Chart
    • Lecture 4 :
    • Creating a Magnitude Plot
    • Lecture 5 :
    • Creating a Grid Plot
  • Section 6 : Building the Data Pipeline Scripts 4 Lectures 00:05:05

    • Lecture 1 :
    • Installing Visual Studio Code
    • Lecture 2 :
    • Creating the PySpark ETL Script
    • Lecture 3 :
    • Creating the Machine Learning Script
    • Lecture 4 :
    • Creating the Dashboard Server
  • Section 7 : Source Code and Notebook 1 Lectures

    • Lecture 1 :
    • Source Code and Notebook
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