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 and Scala 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.
Scala: Scala is a general purpose programming language - like Java or C++. It's functional programming nature and the availability of a REPL environment make it particularly suited for a distributed computing framework like Spark.
Analytics: Using Spark and Scala 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.
Scala Programming Constructs: Classes, Traits, First Class Functions, Closures, Currying, Case Classes
Lot's of cool stuff ..
.. and of course all the Spark basic and advanced features:
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 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!
He has a great categorization for insights in data, really!
There is a profound truth in here which data scientists and analysts have known for years.
Explore, investigate and find patterns in data. Build fully fledged, scalable productions system. All using the same environment.
RDDs are pretty magical, they are the core programming abstraction in Spark
Spark is even more powerful because of the packages that come with it. Spark SQL, Spark Streaming, MLlib and GraphX.
Let's get started by installing Spark. We'll also configure Spark to work with IPython Notebook
Start munging data using the Spark REPL environment.
We've learnt a little bit about how Spark and RDDs work. Let's see it in action!
Operations on data, transform data to extract information and then retrieve results.
RDDs are very intuitive to use. What are some of the characteristics that make RDDs performant, resilient and efficient?
Lazy evaluation of RDDs is possible because RDDs can reconstruct themselves. They know where they came from.
A quick overview of all operations and transformations on RDDs
Parse a CSV file, transform is using the map() operation, create Flight objects on the fly.
Use the flights dataset to get interesting insights.
Cache RDDs in memory to optimize operations using persist()
Use the aggregate() operation to calculate average flight delays in one step. Much more compact than map() and reduce().
This is surprisingly simple!
Pair RDDs are special types of RDDs where every record is a key value pair. All normal actions and transformations apply to these in addition to some special ones.
Pair RDDs are useful to get information on a per-key basis. Sales per city, delays per airport etc.
Instead of 3 steps use just one to get the average delay per airport.
Looking up airport descriptions in a pair RDD can be done in many ways, understand how each work.
Accumulators are special variables which allow the main driver program to collect information from nodes on which the actual processing takes place.
Spark is more than just the Read-Evaluate-Print Loop environment, it can run long running programs as well.
How does Spark submit jobs for distributed processing? How does the scheduler work? What does the cluster manager do? All this and more in this behind the scenes.
MapReduce is a powerful paradigm for distributed processing. Many tasks lend themselves well to this model and Spark has transformations which deal with this beautifully.
Pretend data is in a relational database using Dataframes. Dataframes are also RDDs, you get the best of both worlds!
This is a family of algorithms which give recommendations based on user data and user preferences.
One type of collaborative filtering algorithm is latent factor analysis. There is some math here but don't worry, MLlib takes care of all this for you.
Let's write a recommendation engine for music services
The code in Spark is surprisingly simple.
Spark can process streaming data in near real time using DStreams.
A script to parse logs in real time
Stateful transformations allow cumulative results across a stream using a sliding window.
Find the most well connected Marvel character using GraphX with Spark.
Loonycorn is us, Janani Ravi and Vitthal Srinivasan. Between us, we have studied at Stanford, been admitted to IIM Ahmedabad and have spent years working in tech, in the Bay Area, New York, Singapore and Bangalore.
Janani: 7 years at Google (New York, Singapore); Studied at Stanford; also worked at Flipkart and Microsoft
Vitthal: Also Google (Singapore) and studied at Stanford; Flipkart, Credit Suisse and INSEAD too
We think we might have hit upon a neat way of teaching complicated tech courses in a funny, practical, engaging way, which is why we are so excited to be here on Udemy!
We hope you will try our offerings, and think you'll like them :-)