SGLearn@From 0 to 1 : Spark for Data Science with Python

SGLearn@From 0 to 1 : Spark for Data Science with Python, available at $129.99, has an average rating of 4.42, with 55 lectures, based on 6 reviews, and has 13 subscribers.

You will learn about Use Spark for a variety of analytics and Machine Learning tasks Implement complex algorithms like PageRank or Music Recommendations Work with a variety of datasets from Airline delays to Twitter, Web graphs, Social networks and Product Ratings Use all the different features and libraries of Spark : RDDs, Dataframes, Spark SQL, MLlib, Spark Streaming and GraphX This course is ideal for individuals who are Yep! Analysts who want to leverage Spark for analyzing interesting datasets or Yep! Data Scientists who want a single engine for analyzing and modelling data as well as productionizing it. or Yep! Engineers who want to use a distributed computing engine for batch or stream processing or both It is particularly useful for Yep! Analysts who want to leverage Spark for analyzing interesting datasets or Yep! Data Scientists who want a single engine for analyzing and modelling data as well as productionizing it. or Yep! Engineers who want to use a distributed computing engine for batch or stream processing or both.

Enroll now: SGLearn@From 0 to 1 : Spark for Data Science with Python

Summary

Title: SGLearn@From 0 to 1 : Spark for Data Science with Python

Price: $129.99

Average Rating: 4.42

Number of Lectures: 55

Number of Published Lectures: 55

Number of Curriculum Items: 55

Number of Published Curriculum Objects: 55

Original Price: S$144.98

Quality Status: approved

Status: Live

What You Will Learn

Use Spark for a variety of analytics and Machine Learning tasks
Implement complex algorithms like PageRank or Music Recommendations
Work with a variety of datasets from Airline delays to Twitter, Web graphs, Social networks and Product Ratings
Use all the different features and libraries of Spark : RDDs, Dataframes, Spark SQL, MLlib, Spark Streaming and GraphX

Who Should Attend

Yep! Analysts who want to leverage Spark for analyzing interesting datasets
Yep! Data Scientists who want a single engine for analyzing and modelling data as well as productionizing it.
Yep! Engineers who want to use a distributed computing engine for batch or stream processing or both

Target Audiences

Yep! Analysts who want to leverage Spark for analyzing interesting datasets
Yep! Data Scientists who want a single engine for analyzing and modelling data as well as productionizing it.
Yep! Engineers who want to use a distributed computing engine for batch or stream processing or both

Welcome to the SGLearn Series targeted at Singapore-based learnerspicking up new skillsets and competencies.

This course is an adaptation of the same course by Janani Ravi and the team and is specially produced in collaboration with Janani for Singaporean learners. If you are a Singaporean, you are eligible for the CITREP+ funding scheme, terms and conditions apply.

_____________�

Note from the team …�

Taught by a 4 person team including 2 Stanford-educated, ex-Googlers and 2 ex-Flipkart Lead Analysts. This team has decades of practical experience in working with Java and with billions of rows of data.

Get your data to fly using Spark for analytics, machine learning and data science�

Let’s parse that.

What’s Spark?�If you are an analyst or a data scientist, you’re used to having multiple systems for working with data. SQL, Python, R, Java, etc. With Spark, you have a single engine where you can explore and play with large amounts of data, run machine learning algorithms and then use the same system to productionize your code.

Analytics:�Using Spark and Python you can analyze and explore your data in an interactive environment with fast feedback. The course will show how to leverage the power of RDDs and Dataframes to manipulate data with ease.�

Machine Learning and Data Science :�Spark’s core functionality and built-in libraries make it easy to implement complex algorithms like Recommendations with very few lines of code. We’ll cover a variety of datasets and algorithms including PageRank, MapReduce and Graph datasets.

What’s Covered:

Lot’s of cool stuff ..

Music Recommendations using Alternating Least Squares and the Audioscrobbler dataset
Dataframes and Spark SQL to work with Twitter data
Using the PageRank algorithm with Google web graph dataset
Using Spark Streaming for stream processing�
Working with graph data using the Marvel Social network dataset�

.. and of course all the Spark basic and advanced features:�

Resilient Distributed Datasets, Transformations (map, filter, flatMap), Actions (reduce, aggregate)�
Pair RDDs , reduceByKey, combineByKey�
Broadcast and Accumulator variables�
Spark for MapReduce�
The Java API for Spark�
Spark SQL, Spark Streaming, MLlib and GraphFrames (GraphX for Python)�

Using discussion forums

Please use the discussion forums on this course to engage with other students and to help each other out. Unfortunately, much as we would like to, it is not possible for us at Loonycorn to respond to individual questions from students:-(

We’re super small and self-funded with only 2-3 people developing technical video content. Our mission is to make high-quality courses available at super low prices.

The only way to keep our prices this low is to *NOT offer additional technical support over email or in-person*. The truth is, direct support is hugely expensive and just does not scale.

We understand that this is not ideal and that a lot of students might benefit from this additional support. Hiring resources for additional support would make our offering much more expensive, thus defeating our original purpose.

It is a hard trade-off.

Thank you for your patience and understanding!

Course Curriculum

Chapter 1: You, This Course and Us

Lecture 1: You, This Course and Us

Chapter 2: Introduction to Spark

Lecture 1: What does Donald Rumsfeld have to do with data analysis?

Lecture 2: Why is Spark so cool?

Lecture 3: An introduction to RDDs – Resilient Distributed Datasets

Lecture 4: Built-in libraries for Spark

Lecture 5: Installing Spark

Lecture 6: The PySpark Shell

Lecture 7: Transformations and Actions

Lecture 8: See it in Action : Munging Airlines Data with PySpark – I

Lecture 9: [For Linux/Mac OS Shell Newbies] Path and other Environment Variables

Chapter 3: Resilient Distributed Datasets

Lecture 1: RDD Characteristics: Partitions and Immutability

Lecture 2: RDD Characteristics: Lineage, RDDs know where they came from

Lecture 3: What can you do with RDDs?

Lecture 4: Create your first RDD from a file

Lecture 5: Average distance travelled by a flight using map() and reduce() operations

Lecture 6: Get delayed flights using filter(), cache data using persist()

Lecture 7: Average flight delay in one-step using aggregate()

Lecture 8: Frequency histogram of delays using countByValue()

Lecture 9: See it in Action : Analyzing Airlines Data with PySpark – II

Chapter 4: Advanced RDDs: Pair Resilient Distributed Datasets

Lecture 1: Special Transformations and Actions

Lecture 2: Average delay per airport, use reduceByKey(), mapValues() and join()

Lecture 3: Average delay per airport in one step using combineByKey()

Lecture 4: Get the top airports by delay using sortBy()

Lecture 5: Lookup airport descriptions using lookup(), collectAsMap(), broadcast()

Lecture 6: See it in Action : Analyzing Airlines Data with PySpark – III

Chapter 5: Advanced Spark: Accumulators, Spark Submit, MapReduce , Behind The Scenes

Lecture 1: Get information from individual processing nodes using accumulators

Lecture 2: See it in Action : Using an Accumulator variable

Lecture 3: Long running programs using spark-submit

Lecture 4: See it in Action : Running a Python script with Spark-Submit

Lecture 5: Behind the scenes: What happens when a Spark script runs?

Lecture 6: Running MapReduce operations

Lecture 7: See it in Action : MapReduce with Spark

Chapter 6: Java and Spark

Lecture 1: The Java API and Function objects

Lecture 2: Pair RDDs in Java

Lecture 3: Running Java code

Lecture 4: Installing Maven

Lecture 5: See it in Action : Running a Spark Job with Java

Chapter 7: PageRank: Ranking Search Results

Lecture 1: What is PageRank?

Lecture 2: The PageRank algorithm

Lecture 3: Implement PageRank in Spark

Lecture 4: Join optimization in PageRank using Custom Partitioning

Lecture 5: See it Action : The PageRank algorithm using Spark

Chapter 8: Spark SQL

Lecture 1: Dataframes: RDDs + Tables

Lecture 2: See it in Action : Dataframes and Spark SQL

Chapter 9: MLlib in Spark: Build a recommendations engine

Lecture 1: Collaborative filtering algorithms

Lecture 2: Latent Factor Analysis with the Alternating Least Squares method

Lecture 3: Music recommendations using the Audioscrobbler dataset

Lecture 4: Implement code in Spark using MLlib

Chapter 10: Spark Streaming

Lecture 1: Introduction to streaming

Lecture 2: Implement stream processing in Spark using Dstreams

Lecture 3: Stateful transformations using sliding windows

Lecture 4: See it in Action : Spark Streaming

Chapter 11: Graph Libraries

Lecture 1: The Marvel social network using Graphs

Chapter 12: Interview with Singapore Expert

Lecture 1: Background of Expert

Lecture 2: Information and Communication Technology in Singapore

Instructors

DioPACT SG
SGLearn

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5 stars: 3 votes

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