Are you looking forward to expand your knowledge of performing data science operations in Spark? Or are you a data scientist who wants to understand how algorithms are implemented in Spark, or a newbie with minimal development experience and want to learn about Big Data analytics? If yes, then this course is ideal you. Let’s get on this data science journey together.
When people want a way to process Big Data at speed, Spark is invariably the solution. With its ease of development (in comparison to the relative complexity of Hadoop), it’s unsurprising that it’s becoming popular with data analysts and engineers everywhere. It is one of the most widely-used large-scale data processing engines and runs extremely fast.
The aim of the course is to make you comfortable and confident at performing real-time data processing using Spark.
What is included?
This course is meticulously designed and developed in order to empower you with all the right and relevant information on Spark. However, I want to highlight that the road ahead may be bumpy on occasions, and some topics may be more challenging than others, but I hope that you will embrace this opportunity and focus on the reward. Remember that throughout this course, we will add many powerful techniques to your arsenal that will help us solve the problems.
Let’s take a look at the learning journey. The course begins with the basics of Spark 2 and covers the core data processing framework and API, installation, and application development setup. Then, you’ll be introduced to the Spark programming model through real-world examples. Next, you’ll learn how to collect, clean, and visualize the data coming from Twitter with Spark streaming. Then, you will get acquainted with Spark machine learning algorithms and different machine learning techniques. You will also learn to apply statistical analysis and mining operations on your dataset. The course will give you ideas on how to perform analysis including graph processing. Finally, we will take up an end-to-end case study and apply all that we have learned so far.
By the end of the course, you should be able to put your learnings into practice for faster, slicker Big Data projects.
Why should I choose this course?
Packt courses are very carefully designed to make sure that they're delivering the best learning experience possible. This course is a blend of text, videos, code examples, and quizzes, which together makes your learning journey all the more exciting and truly rewarding. This helps you learn a range of topics at your own speed and also move towards your goal of learning the technology. We have prepared this course using extensive research and curation skills. Each section adds to the skills learned and helps you to achieve mastery of Spark.
This course is an amalgamation of sections that form a sequential flow of concepts covering a focused learning path presented in a modular manner. We have combined the best of the following Packt products:
Meet your expert instructors:
For this course, we have combined the best works of these extremely esteemed authors:
Eric Charles has 10 years of experience in the field of data science and is the founder of Datalayer, a social network for data scientists. He is passionate about using software and mathematics to help companies get insights from data.
Bikramaditya Singhal is a data scientist with about 7 years of industry experience. He is an expert in statistical analysis, predictive analytics, machine learning, Bitcoin, Blockchain, and programming in C, R, and Python. He has extensive experience in building scalable data analytics solutions in many industry sectors.
Srinivas Duvvuri is currently the senior vice president development, heading the development teams for fixed income suite of products at Broadridge Financial Solutions (India) Pvt Ltd. In addition, he also leads the Big Data and Data Science COE and is the principal member of the Broadridge India Technology Council.
Rajanarayanan Thottuvaikkatumana, Raj, is a seasoned technologist with more than 23 years of software development experience at various multinational companies. He has worked on various technologies including major databases, application development platforms, web technologies, and Big Data technologies.
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