Building Big Data Pipelines with PySpark + MongoDB + Bokeh

Build intelligent data pipelines with big data processing and machine learning technologies

What you'll learn:
PySpark Programming
Data Analysis
Python and Bokeh
Data Transformation and Manipulation
Data Visualization
Big Data Machine Learning
Geo Mapping
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 ​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

Who this course is for:
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

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