Giter Site home page Giter Site logo

pyspark-tutorial's Introduction

Python-and-Spark-for-Big-Data

Course Notebooks for Python and Spark for Big Data

Course Outline:

  • Course Introduction

    • Promo/Intro Video
    • Course Curriculum Overview
    • Introduction to Spark, RDDs, and Spark 2.0
  • Course Set-up

    • Set-up Overview
    • EC2 Installation Guide
    • Local Installation Guide with VirtualBox
    • Databricks Notebooks
    • Unix Command Line Basics and Jupyter Notebook Overview
  • Spark DataFrames

    • Spark DataFrames Section Introduction
    • Spark DataFrame Basics
    • Spark DataFrame Operations
    • Groupby and Aggregate Functions
    • Missing Data
    • Dates and Timestamps
  • Spark DataFrame Project

    • DataFrame Project Exercise
    • DataFrame Project Exercise Solutions
  • Machine Learning

    • Introduction to Machine Learning and ISLR
    • Machine Learning with Spark and Python and MLlib
    • Consulting Project Approach Overview
  • Linear Regression

    • Introduction to Linear Regression
    • Discussion on Data Transformations
    • Linear Regression with PySpark Example (Car Data)
    • Linear Regression Consulting Project (Housing Data)
    • Linear Regression Consulting Project Solution
  • Logistic Regression

    • Introduction to Logisitic Regression
    • Logistic Regression Example
    • Logistic Regression Consulting Project (Customer Churn)
    • Logistic Regression Consluting Project Solution
  • Tree Methods

    • Introduction to Tree Methods
    • Decision Tree and Random Forest Example
    • Random Forest Classification Consulting Project - Dog Food Data
    • RF Classification Consulting Project Solutions
    • RF Regression Project - (Facebook Data)
  • Clustering

    • Introduction to K-means Clustering
    • Clustering Example - Iris Dataset
    • Clustering Consulting Project - Customer Segmentation (Fake Data)
    • Clustering Consulting Project Solutions
  • Recommender System

    • Introduction to Recommender Systems and Collaborative Filtering
    • Code Along Project - MovieLens Dataset
    • Possible Consulting Project ? Company Service Reviews
  • Natural Language Processing

    • Introduction to Project/NLP/Naive Bayes Model
    • What are pipelines?
    • Code Along
  • Spark Streaming

    • Introduction to Spark Streaming
    • Spark Streaming Code-along!

pyspark-tutorial's People

Stargazers

 avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar

Watchers

 avatar

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    ๐Ÿ–– Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. ๐Ÿ“Š๐Ÿ“ˆ๐ŸŽ‰

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google โค๏ธ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.