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 Raviand 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 bya 4 person team including 2Stanford-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 Sparkfor analytics, machine learning and data scienceLets parse that. What’s Spark?If you are an analyst or a data scientist, you’reused 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 includingPageRank, MapReduceand Graph datasets. What’s Covered: Lot’s of cool stuff. Music Recommendations using Alternating Least Squares and the Audioscrobbler datasetDataframes and Spark SQL to work with Twitter dataUsingthe PageRank algorithm with Google web graph datasetUsing Spark Streaming for stream processingWorking with graph data using theMarvel 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, combineByKeyBroadcast and Accumulator variablesSpark for MapReduceThe Java API for SparkSpark SQL, Spark Streaming, MLlib and GraphFrames (GraphX for Python)Using discussion forumsPlease 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!