Get yourself trained on From 0 to with this Online Training From 0 to 1 : Spark for Data Science with Python.
Online Training From 0 to 1 : Spark for Data Science with Python
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)
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As a society, we spend hundreds of billions of dollars measuring the return on our financial assets. Yet, at the same time, we still haven’t found convincing ways of measuring the return on our investments in developing people.
And I get it: If my bank account pays me 1% a year, I can measure it to the penny. We’ve been collectively trained to expect neat and precise ROI calculations on everything, so when it’s applied to something as seemingly squishy as how effectively people are learning in the workplace, the natural inclination is to throw up our hands and say it can’t be done. But we need to figure this out. In a world where skills beat capital, the winners and losers of the next 30 years will be determined by their ability to attract and develop great talent.
Fortunately, corporate learning & development (L&D), like most business functions, is evolving quickly. We can embrace some level of ambiguity and have rigor when measuring the ROI of learning. It just might look a little different than an M.B.A. would expect to see in an Excel model.