Big Data for Data Engineers Specialization

Type:

Էլ-ուսուցում

Category:

Տվյալների ուսումնասիրություն/վերլուծություն

Language:

Անգլերեն

Location:

Online

Price:

Free

Date:

On request

Training Description:

This specialization is made for people working with data (either small or big). If you are a Data Analyst, Data Scientist, Data Engineer or Data Architect (or you want to become one) — don’t miss the opportunity to expand your knowledge and skills in the field of data engineering and data analysis on the large scale.

In four concise courses you will learn the basics of Hadoop, MapReduce, Spark, methods of offline data processing for warehousing, real-time data processing and large-scale machine learning. And Capstone project for you to build and deploy your own Big Data Service (make your portfolio even more competitive).

Over the course of the specialization, you will complete progressively harder programming assignments (mostly in Python). Make sure, you have some experience in it. This course will master your skills in designing solutions for common Big Data tasks:

- creating batch and real-time data processing pipelines,

- doing machine learning at scale,

- deploying machine learning models into a production environment — and much more!

Join some of best hands-on big data professionals, who know, their job inside-out, to learn the basics, as well as some tricks of the trade, from them.

Projects Overview

Are you ready to close the loop on your Big Data skills? Do you want to apply all your knowledge you got from the previous courses in practice? Finally, in the Capstone project, you will integrate all the knowledge acquired earlier to build a real application leveraging the power of Big Data.

You will be given a task to combine data from different sources of different types (static distributed dataset, streaming data, SQL or NoSQL storage). Combined, this data will be used to build a predictive model for a financial market (as an example). First, you design a system from scratch and share it with your peers to get valuable feedback. Second, you can make it public, so get ready to receive the feedback from your service users. Real-world experience without any 3D-glasses or mock interviews.