Get yourself trained on Hands on Google with this Online Training Hands on Google cloud platform(GCP) – Data Engineer.
Online Training Hands on Google cloud platform(GCP) – Data Engineer
This course is exclusively designed by NoTEZ to teach about GCP in most simplest way possible.Students who enrolled for our previous courses on GCP had requested more in the series and hence this course is live.If you havn’t enrolled for our Other courses, enroll today n start exploring more.This course is designed to give idea about Google’ s data engineer certification But Not limited to just that. This course will give you indepth practical knowledge on various components of GCP.Enroll today & explore moreCourse Overview Module 1- Introduction All about Google certification, Overview Data Engineer Certification, What is and why to use CLOUD? Module 2 – Hands on GCP Labs Module 3 Hadoop Introduction to,Hadoop, Hadoop-bigger picture, Hadoop- In detail, HIVE, HBASE,PIGModule 4- Compute Introduction to Computing, Google compute engine(GCE), Preemptible Virtual Machine, Google APP engine (G A E), Google container engine ,Kubernetes ( GKE),Comparison,Labs Module 5- Storage Introduction, Cloud Storage, BIGQUERY, Data Store, More on cloud storage, Working with cloud storage, Transfer service, Cloud SQL, Cloud SQL PROXY, Cloud Spanner, Hot Spotting, Data Types, Transactions , Staleness, Labs Module 6-Big Table Big Table Introduction, Columnar store, Denormalized Storage, CRUD Operations, Column families, Choice of BigTable Module 7-Datalab Module 8-Pub/Sub Module 9- Dataflow Module 10-BigQuery BigQuery Data Model, Querying & Viewing,Labs Module 11-Machine Learning & Tensorflow Introduction to Machine Learning, Typical usage of Mechine Learning, Types, The Mechine Learning block diagram, Deep learning & Neural Networks, Labels, Understanding Tenser Flow, Computational Graphs, Tensors, Linear regression , Placeholders & variables, Image processing in Tensor Flow, Image as tensors, M-NIST Introduction, K-nearest neighbors Algorithm, L1 distance, Steps in K- nearest neighbour implementation, Neural Networks in Real Time, Learning regression and learning XOR Linear Regression, Gradient descent, Logistic Regression, Logit, Activation function,Softmax, Cost function -Cross entropy,Labs Module 12-Operation & Security Stack driver, Stack driver Logging, Cloud Deployment Manager, Cloud Endpoints, Cloud IAM ,API keys, Cloud IAM- Extended, Labs,
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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.