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

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

Welcome to the

SGLearn Series

targeted at

Singapore-based learners

picking 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!