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    Big Data Analytics With Pyspark + Tableau Desktop + Mongodb

    Posted By: ELK1nG
    Big Data Analytics With Pyspark + Tableau Desktop + Mongodb

    Big Data Analytics With Pyspark + Tableau Desktop + Mongodb
    Last updated 2/2020
    MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
    Language: English | Size: 1.37 GB | Duration: 4h 18m

    Integrating Big Data Processing tools with Predictive Modeling and Visualization with Tableau Desktop

    What you'll learn

    Tableau Data Visualization

    PySpark Programming

    Data Analysis

    Data Transformation and Manipulation

    Big Data Machine Learning

    Geo Mapping with Tableau

    Geospatial Machine Learning

    Creating Dashboards

    Requirements

    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

    Description

    Welcome to the Big Data Analytics  with PySpark + Tableau Desktop + MongoDB course. In this course we will be creating a big data analytics solution using big data technologies like PySpark for ETL,  MLlib for Machine Learning as well as Tableau for Data Visualization and for building Dashboards.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 Tableau Desktop.Tableau Desktop is a powerful data visualization tool used for big data analysis and visualization. It allows for data blending, real-time analysis and collaboration of data. No programming is needed for Tableau Desktop, which makes it a very easy and powerful tool to create dashboards apps and reports.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 PySparkYou will learn machine learning with geospatial data using the Spark MLlib libraryYou will learn data analysis using PySpark, MongoDB and TableauYou will learn how to manipulate, clean and transform data using PySpark dataframesYou will learn how to create Geo Maps in Tableau DesktopYou will also learn how to create dashboards in Tableau Desktop

    Overview

    Section 1: Introduction

    Lecture 1 Introduction

    Section 2: Setup and Installations

    Lecture 2 Python Installation

    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

    Lecture 8 Integrating PySpark with Jupyter Notebook

    Lecture 9 Data Extraction

    Lecture 10 Data Transformation

    Lecture 11 Loading Data into MongoDB

    Section 4: Machine Learning with PySpark and MLlib

    Lecture 12 Data Pre-processing

    Lecture 13 Building the Predictive Model

    Lecture 14 Creating the Prediction Dataset

    Section 5: Creating the Data Pipeline Scripts

    Lecture 15 Installing Visual Studio Code

    Lecture 16 Creating the PySpark ETL Script

    Lecture 17 Creating the Machine Learning Script

    Section 6: Tableau Data Visualization

    Lecture 18 Installing Tableau

    Lecture 19 Installing MongoDB ODBC Drivers

    Lecture 20 Creating a System DSN for MongoDB

    Lecture 21 Loading the Data Sources

    Lecture 22 Creating a Geo Map

    Lecture 23 Creating a Bar Chart

    Lecture 24 Creating a Magnitude Chart

    Lecture 25 Creating a Table Plot

    Lecture 26 Creating a Dashboard

    Section 7: Source Code and Notebook

    Lecture 27 Source Code and Notebook

    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