Data Engineering, Big Data, and Machine Learning on GCP Specialization

Start Date: 02/23/2020

Course Type: Specialization Course

Course Link:

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About Course

This online specialization provides participants a hands-on introduction to designing and building data pipelines on Google Cloud Platform. Through a combination of presentations, demos, and hand-on labs, participants will learn how to design data processing systems, build end-to-end data pipelines, analyze data and derive insights. The course covers structured, unstructured, and streaming data. This course teaches the following skills: • Design and build data pipelines on Google Cloud Platform • Lift and shift your existing Hadoop workloads to the Cloud using Cloud Dataproc. • Process batch and streaming data by implementing autoscaling data pipelines on Cloud Dataflow • Manage your data Pipelines with Data Fusion and Cloud Composer. • Derive business insights from extremely large datasets using Google BigQuery • Learn how to use pre-built ML APIs on unstructured data and build different kinds of ML models using BigQuery ML. • Enable instant insights from streaming data This class is intended for developers who are responsible for: • Extracting, Loading, Transforming, cleaning, and validating data • Designing pipelines and architectures for data processing • Integrating analytics and machine learning capabilities into data pipelines • Querying datasets, visualizing query results and creating reports >>> By enrolling in this specialization you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: <<<

Course Syllabus

Google Cloud Platform Big Data and Machine Learning Fundamentals
Modernizing Data Lakes and Data Warehouses with GCP
Building Batch Data Pipelines on GCP
Building Resilient Streaming Analytics Systems on GCP

Deep Learning Specialization on Coursera

Course Introduction

Data Engineering on Google Cloud Platform. Launch your career in Data Engineering. Deliver business value with big data and machine learning. Data Engineering, Big Data, and Machine Learning on GCP Specialization This course is a continuation of the course Data Engineering, Big Data, and Machine Learning on GCP Specialization. In this course, you will continue to apply the knowledge you’ve acquired to solve some of the most important Data Science and Machine Learning problems related to Network Infrastructures. This is the third and final course in the Data Engineering, Big Data, and Machine Learning Specialization. Data Engineering, Big Data, and Machine Learning Specialization Overview Data Engineering, Big Data, and Machine Learning Specialization Overview: Understanding Networks Data Engineering, Big Data, and Machine Learning Specialization Overview: Representing Data Data Engineering, Big Data, and Machine Learning Specialization Overview: Basic Formulas and Problems Data Engineering, Big Data, and Machine Learning Specialization Overview: Learning Linear Regression Models Data Engineering, Big Data, and Machine Learning Specialization Overview: ➜Training & Evaluation Plans Data Processing, Regularization, and Spreadsheets Welcome to Spreadsheet, Data and Spreadsheet! In this course, you will learn the basic features of Excel, and how to use Excel’s Spreadsheet functions to create data. You will also learn about Excel’s statistics functions, how to use Excel’s FFT functions to perform regular expressions searches, and how to use Excel’s Data Modeling

Course Tag

Tensorflow Bigquery Bigtable Dataflow

Related Wiki Topic

Article Example
Big data Multidimensional big data can also be represented as tensors, which can be more efficiently handled by tensor-based computation, such as multilinear subspace learning. Additional technologies being applied to big data include massively parallel-processing (MPP) databases, search-based applications, data mining, distributed file systems, distributed databases, cloud-based infrastructure (applications, storage and computing resources) and the Internet.
Big data The Workshops on Algorithms for Modern Massive Data Sets (MMDS) bring together computer scientists, statisticians, mathematicians, and data analysis practitioners to discuss algorithmic challenges of big data.
Big data In the provocative article "Critical Questions for Big Data", the authors title big data a part of mythology: "large data sets offer a higher form of intelligence and knowledge [...], with the aura of truth, objectivity, and accuracy". Users of big data are often "lost in the sheer volume of numbers", and "working with Big Data is still subjective, and what it quantifies does not necessarily have a closer claim on objective truth". Recent developments in BI domain, such as pro-active reporting especially target improvements in usability of big data, through automated filtering of non-useful data and correlations.
Data mining It also is a buzzword and is frequently applied to any form of large-scale data or information processing (collection, extraction, warehousing, analysis, and statistics) as well as any application of computer decision support system, including artificial intelligence, machine learning, and business intelligence. The book "Data mining: Practical machine learning tools and techniques with Java" (which covers mostly machine learning material) was originally to be named just "Practical machine learning", and the term "data mining" was only added for marketing reasons. Often the more general terms ("large scale") "data analysis" and "analytics" – or, when referring to actual methods, "artificial intelligence" and "machine learning" – are more appropriate.
Big data Encrypted search and cluster formation in big data was demonstrated in March 2014 at the American Society of Engineering Education. Gautam Siwach engaged at "Tackling the challenges of Big Data" by MIT Computer Science and Artificial Intelligence Laboratory and Dr. Amir Esmailpour at UNH Research Group investigated the key features of big data as formation of clusters and their interconnections. They focused on the security of big data and the actual orientation of the term towards the presence of different type of data in an encrypted form at cloud interface by providing the raw definitions and real time examples within the technology. Moreover, they proposed an approach for identifying the encoding technique to advance towards an expedited search over encrypted text leading to the security enhancements in big data.
Big data Based on TCS 2013 Global Trend Study, improvements in supply planning and product quality provide the greatest benefit of big data for manufacturing. Big data provides an infrastructure for transparency in manufacturing industry, which is the ability to unravel uncertainties such as inconsistent component performance and availability. Predictive manufacturing as an applicable approach toward near-zero downtime and transparency requires vast amount of data and advanced prediction tools for a systematic process of data into useful information. A conceptual framework of predictive manufacturing begins with data acquisition where different type of sensory data is available to acquire such as acoustics, vibration, pressure, current, voltage and controller data. Vast amount of sensory data in addition to historical data construct the big data in manufacturing. The generated big data acts as the input into predictive tools and preventive strategies such as Prognostics and Health Management (PHM).
Big data Relational database management systems and desktop statistics- and visualization-packages often have difficulty handling big data. The work may require "massively parallel software running on tens, hundreds, or even thousands of servers". What counts as "big data" varies depending on the capabilities of the users and their tools, and expanding capabilities make big data a moving target. "For some organizations, facing hundreds of gigabytes of data for the first time may trigger a need to reconsider data management options. For others, it may take tens or hundreds of terabytes before data size becomes a significant consideration."
Data stream mining Data stream mining can be considered a subfield of data mining, machine learning, and knowledge discovery.
Data-driven learning Data-driven learning is a learning approach (and in particular, an approach to learning foreign languages) in which learning is driven by research-like access to linguistic data.
Big data Furthermore, big data analytics results are only as good as the model on which they are predicated. In an example, big data took part in attempting to predict the results of the 2016 U.S. Presidential Election with varying degrees of success. Forbes predicted "If you believe in "Big Data" analytics, it’s time to begin planning for a Hillary Clinton presidency and all that entails.".
Big data Big data often poses the same challenges as small data; and adding more data does not solve problems of bias, but may emphasize other problems. In particular data sources such as Twitter are not representative of the overall population, and results drawn from such sources may then lead to wrong conclusions. Google Translate—which is based on big data statistical analysis of text—does a good job at translating web pages. However, results from specialized domains may be dramatically skewed.
Big data Big data analysis is often shallow compared to analysis of smaller data sets. In many big data projects, there is no large data analysis happening, but the challenge is the extract, transform, load part of data preprocessing.
Big data Big data can be used to improve training and understanding competitors, using sport sensors. It is also possible to predict winners in a match using big data analytics.
Data lineage Big data analytics is the process of examining large data sets to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful business information. They apply machine learning algorithms etc. to the data which transform the data. Due to the humongous size of the data, there could be unknown features in the data, possibly even outliers. It is pretty difficult for a data scientist to actually debug an unexpected result.
Big data MIKE2.0 is an open approach to information management that acknowledges the need for revisions due to big data implications identified in an article titled "Big Data Solution Offering". The methodology addresses handling big data in terms of useful permutations of data sources, complexity in interrelationships, and difficulty in deleting (or modifying) individual records.
Machine learning Machine learning and data mining often employ the same methods and overlap significantly, but while machine learning focuses on prediction, based on "known" properties learned from the training data, data mining focuses on the discovery of (previously) "unknown" properties in the data (this is the analysis step of Knowledge Discovery in Databases). Data mining uses many machine learning methods, but with different goals; on the other hand, machine learning also employs data mining methods as "unsupervised learning" or as a preprocessing step to improve learner accuracy. Much of the confusion between these two research communities (which do often have separate conferences and separate journals, ECML PKDD being a major exception) comes from the basic assumptions they work with: in machine learning, performance is usually evaluated with respect to the ability to "reproduce known" knowledge, while in Knowledge Discovery and Data Mining (KDD) the key task is the discovery of previously "unknown" knowledge. Evaluated with respect to known knowledge, an uninformed (unsupervised) method will easily be outperformed by other supervised methods, while in a typical KDD task, supervised methods cannot be used due to the unavailability of training data.
Big data Big data usually includes data sets with sizes beyond the ability of commonly used software tools to capture, curate, manage, and process data within a tolerable elapsed time. Big data "size" is a constantly moving target, ranging from a few dozen terabytes to many petabytes of data.
Big data Based on the data, engineers and data analysts decide whether adjustments should be made in order to win a race. Besides, using big data, race teams try to predict the time they will finish the race beforehand, based on simulations using data collected over the season.
Big data Especially since 2015, big data has come to prominence within Business Operations as a tool to help employees work more efficiently and streamline the collection and distribution of Information Technology (IT). The use of big data to resolve IT and data collection issues within an enterprise is called IT Operations Analytics (ITOA). By applying big data principles into the concepts of machine intelligence and deep computing, IT departments can predict potential issues and move to provide solutions before the problems even happen. In this time, ITOA businesses were also beginning to play a major role in systems management by offering platforms that brought individual data silos together and generated insights from the whole of the system rather than from isolated pockets of data.
Clinical Data, Inc Designed on big data and cloud computing platforms, Clindata claims that it "compresses clinical trial duration by several months, drives significant cost savings, and improves patient safety through predictive analytics and machine learning algorithms."