[New post] 10 Best Statistical Modeling Training Courses

Imran Abdul Ghani posted: "Without any doubt, statistical modeling is quite a tough subject and majority of the learners are always facing problems understanding this subject. To help you with statistical modeling, our team at takethiscourse.net has compiled a list of Best Statisti" New post on Take This Course 10 Best Statistical Modeling Training Courses by Imran Abdul Ghani Without any doubt, statistical modeling is quite a tough subject and majority of the learners are always facing problems understanding this subject. To help you with statistical modeling, our team at takethiscourse.net has compiled a list of Best Statistical Modeling Training Classes. With the help of courses available in this list, any learner can understand technicalities involved in statistical modeling. So let us take a look at this list. Statistical Modeling for Data Science Applications Specialization Statistical Inference and Modeling for High-throughput Experiments Applied Statistical Modeling for Data Analysis in R Fitting Statistical Models to Data with Python Basics of Statistical Inference and Modelling Using R Advanced Statistical Inference and Modelling Using R Predictive Modeling, Model Fitting, and Regression Analysis Data Analysis and Fundamental Statistics Data Analysis: Statistical Modeling and Computation in Applications Regression Analysis for Statistics & Machine Learning in R In order to help our readers in taking a knowledgeable learning decision, TakeThisCourse.net has introduced a metric to measure the effectiveness of an online course. Learn more about how we measure an online course effectiveness. Best Statistical Modeling Courses Here you can find the names and description of the Best Statistical Modeling Training. Statistical Modeling for Data Science Applications Specialization CU-Boulder via Coursera 4 months of effort required! In this specialization, what you will learn is to analyze and apply the tools of regression analysis correctly. The instructor will explain how you can make predictions in a given set of input variables. Next, you will learn to conduct experiments based on best practices in a step-by-step guide. Then you will get to understand how to use advanced statistical modeling techniques. This includes generalized linear and additive models and much more. The skills that you will gain upon completing this specialization are relevant to linear model R programming, statistical model, and regression. Statistical Inference and Modeling for High-throughput Experiments Harvard University via edX 4 weeks (2-4 hours weekly) of effort required! 56,174+ already enrolled! Course Type: Self Paced This course focuses on such techniques that are used to perform statistical inference on high throughout data. Starting the course, you will understand how to organize high throughput data. Then you will understand what multiple comparison problem is. After that, you will understand all about the family wide error rates. Similarly, you will understand all about the error rate control procedures. Going further, there will be detailed discussion on q-values and statistical modeling. In addition, you will understand the basics of Bayesian Statistics and hierarchical models. Moreover, you will understand what exploratory data analysis is and much more in this course. Applied Statistical Modeling for Data Analysis in R Minerva Singh via Udemy 8,736+ already enrolled! ★★★★★ (1,284 Ratings) This course has to be your complete guide to statistical data analysis and data visualization as well for practical applications in R. Starting the course, the instructor will talk about how you can analyze your data by applying different statistical techniques. Then you will learn to interpret the results of the statistical analysis. Similarly, you will get to develop a strong foundation in fundamental statistical concepts. After that, there will be detailed discussion on how to implement different statistical analysis in R and interpret different results. In addition, you will learn to carry out formalized hypothesis testing. Similarly, the instructor will explain how you can implement linear modeling techniques like multiple regression and GLMs and much more in this course. I can say this was an awesome course and is suitable for all those people out there who want to learn to use R for data analysis. The instructor Dr. Singh has done a great job in explaining the statistical concepts. I really appreciate the efforts that she has put into this course. she was highly determined and focused (Dr Marcia Hawk, ★★★★★). Fitting Statistical Models to Data with Python University of Michigan via Coursera 15 hours of effort required! 24,652+ already enrolled! ★★★★★ (567 Ratings) This is a type of course that will help you explore the different statistical interference techniques. In this course, you will first get an introduction to various statistical modeling techniques. Then you will explore linear regression, logistic regression, and generalized linear models. After that, the instructor will talk about the mixed effects and Bayesian inference techniques in detail. Then you will get to understand the different types of data sets and much more in this course. You might also be interested in free Ivy League Data Science courses at takethiscourse. This course had great statistical lessons to offer. When I took this course, I only thought to have learned all about ordinary least squares. But this course had details about different regression-type models was well. I can say taking this course has expanded my learning horizon. And yes this course can be recommended to all those interested (Walt T, ★★★★★). Basics of Statistical Inference and Modelling Using R University of Centerbury via edX 6 weeks (5-10 hours weekly) of effort required! Course Type: Self Paced Do you want to understand how a statistical method works? Or how you can implement it using R? In this course, you will learn to do that in the most engaging manner. The instructor will explain what sample and population is in detail. Then you will understand what sampling distribution is. After that, you will understand the parameter estimates and confidence intervals. Similarly, the instructor will explain everything there is about Central Limit Theorem. Then you will understand what hypothesis testing is. After that, you will understand what P-values, t-test, Chi-squared test is. You can also checkout Business Analytics Certification courses at our platform. Moving further, you will understand how to do exploratory data analysis and data visualization with the help of R. Next, you will understand how to do analysis of variance (ANOVA) and post-hoc tests with the help of R. Then you will learn to do multivariate analysis using linear regression. After that, you will learn all about the numerical methods and much more in this course. Advanced Statistical Inference and Modelling Using R University of Centerbury via edX 6 weeks (5-10 hours weekly) of effort required! Course Type: Self Paced Are you interested in extending your knowledge of linear regression? In this course you will understand where to use linear regression in detail. Starting the course you will learn to do exploratory data analysis using R. Then you will get to understand how to do multivariate analysis using generalized linear models. After that, you will understand what binary response is. Then the instructor will talk about Poisson counts (GLM). Going further, you will get to understand what nominal categorical response is. After that, you will get to understand what ordinal categorical response is. Going further, you will get a chance to understand what mixed effects linear regression models are. You will learn how to structure, assume, diagnose, and interpret them. Next, you will understand the basics of power analysis in detail. Then the instructor will give some thoughts on experimental design and missing data. Predictive Modeling, Model Fitting, and Regression Analysis UCI via Coursera 4 hours of effort required! ★★★★★ (27 Ratings) In this engaging course, you will get to explore the different approaches involved in predictive modeling. The instructor will talk about how a model can be either supervised or unsupervised. Then you will understand how a model can be fitted, trained, and then scored so as to apply data to address business objectives. In the end, you will get access to hands-on activity to develop a linear regression model. You can also find out Data Engineering courses here. I just want to thank the instructor for this amazing course. I was able to learn a lot of things relevant to predative modeling. The content was quite relevant and very engaging and is good for beginners (Divisha A, ★★★★★). Data Analysis and Fundamental Statistics via FutureLearn 4 weeks (4 hours weekly) of effort required! 358+ already enrolled! Taking this course will give you an introduction to basic data analysis functions in detail. You will understand the statistical models and how to deliver insights for your business. After that, you will learn the basics of excel and expand your knowledge. Then you will get to learn to tell stories using data. After that, you will develop such skills that can give you confidence to gain a competitive edge in the job market. Upon completing this course you would have gained a certain skill set in basic data analytics, excel, and statistics. Data Analysis: Statistical Modeling and Computation in Applications MIT via edX 16 weeks (10-15 hours weekly) of effort required! Course Type: Instructor - Paced This course will give you an introduction to the interplay between statistics and computation for the analysis of real data. Then you will understand how to perform statistical analysis on real data. After that, you will understand how to analyze networks and use measures to describe the importance of nodes. Next, you will learn all about model time series. Similarly, you will get a chance to understand how to use Gaussian processes to model environmental data. Finally, you will learn to communicate the analysis results more effectively. Regression Analysis for Statistics & Machine Learning in R Minerva Singh via Udemy 4,034+ already enrolled! ★★★★★ (498 Ratings) This course will help you learn complete hands-on regression analysis for statistical modeling and machine learning in R. Starting the course, you will be taught how to implement and infer ordinary least square (OLS) regression with the help of R. Then the instructor will talk about how to apply statistical and machine learning based regression models so as to deal with problems like multicollinearity. Similarly, you will get to understand how to carry out variable selection. After that, the instructor will talk about how to assess model accuracy using different techniques like cross-validation. Then you will learn to build machine learning based regression models and test their robustness in R. After that, you will learn about how machine learning models should be applied and when. Finally, you will learn to compare different machine learning algorithms for regression modeling. Taking this course has been a fantastic journey for me. The concepts were taught in a clear and simple manner and I can say all the exercises were quite interesting and kept me determined enough to complete the entire course in just a few weeks (Anonymous learner, ★★★★★). Final Thoguths Learning statistics modeling techniques has never been this convenient before as this list of Best Statistical Modeling Training Classes has top-quality high-rated courses that can help you learn from the comfort of your home. So take a look at this list of Best Statistical Modeling Training Classes and don't forget to stay safe, stay home, and never stop learning. Imran Abdul Ghani | October 28, 2021 at 6:39 am | Tags: MOOCs | Categories: Collections | URL: https://wp.me/p8YbFy-eMC Comment    See all comments Unsubscribe to no longer receive posts from Take This Course. 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[New post] Add Grpc to your Spring Application

Emmanouil Gkatziouras posted: "On the previous example we had a Java application spinning up an http server and upon this Java process operating a GRPC application. If you use frameworks like Spring you might wonder how you can achieve a Grpc and Spring integration. There are l" Respond to this post by replying above this line New post on Emmanouil Gkatziouras Add Grpc to your Spring Application by Emmanouil Gkatziouras On the previous example we had a Java application spinning up an http server and upon this Java process operating a GRPC application. If you use frameworks like Spring you might wonder how you can achieve a Grpc and Spring integration. There are libraries out there that do so, we shall use the grpc-spring-boot-starter from io.github.lognet. We shall start with the dependencies. We do need to import the gRPC generating plugins we used on the previous example. <dependencies> <dependency> <groupId>io.github.lognet</groupId> <artifactId>grpc-spring-boot-starter</artifactId> <version4.5.8</version> </dependency> </dependencies> <build> <extensions> <extension> <groupId>kr.motd.maven</groupId> <artifactId>os-maven-plugin</artifactId> <version>1.6.2</version> </extension> </extensions> <plugins> <plugin> <groupId>org.xolstice.maven.plugins</groupId> <artifactId>protobuf-maven-plugin</artifactId> <version>0.6.1</version> <configuration> <protocArtifact>com.google.protobuf:protoc:3.17.2:exe:${os.detected.classifier}</protocArtifact> <pluginId>grpc-java</pluginId> <pluginArtifact>io.grpc:protoc-gen-grpc-java:1.39.0:exe:${os.detected.classifier}</pluginArtifact> </configuration> <executions> <execution> <goals> <goal>compile</goal> <goal>compile-custom</goal> </goals> </execution> </executions> </plugin> </plugins> </build> What happens behind the scenes. Spring environment spins up gRPC Server starts Spring services annotated with @GRpcService are picked up and registered to the gRPC server Security and other filtering based components are integrated with the equivalent gRPC ServerInterceptor. So pretty much we expect that instead of controllers we shall have GRpcServices and ServerInterceptors for filters. Let's add the proto files. We shall use the same proto of the previous example. The location is src/main/proto/Order.proto and the contents would be syntax = "proto3" option java_multiple_files = true; option java_package = "com.egkatzioura.order.v1" service OrderService { rpc ExecuteOrder(OrderRequest) returns (OrderResponse) {}; } message OrderRequest { string email = 1; string product = 2; int32 amount = 3; } message OrderResponse { string info = 1; } As expected an mvn clean install will generate the gRPC classes. Now we should create the spring service. package com.gkatzioura.order.impl; import com.egkatzioura.order.v1.OrderRequest; import com.egkatzioura.order.v1.OrderResponse; import com.egkatzioura.order.v1.OrderServiceGrpc; import io.grpc.stub.StreamObserver; import org.lognet.springboot.grpc.GRpcService; @GRpcService public class OrderServiceImpl extends OrderServiceGrpc.OrderServiceImplBase{ @Override public void executeOrder(OrderRequest request, StreamObserver<OrderResponse> responseObserver) { OrderResponse response = OrderResponse.newBuilder() .setInfo("Hi "+request.getEmail()+", you order has been executed") .build(); responseObserver.onNext(response); responseObserver.onCompleted(); } } Also let's add the main class package com.gkatzioura.order; import org.springframework.boot.SpringApplication; import org.springframework.boot.autoconfigure.SpringBootApplication; @SpringBootApplication public class Application { public static void main(String[] args) { SpringApplication.run(Application.class, args); } } The Spring context is spun up, and the @GRpcService annotated services kick off. By default the port would be 6565 Let's run the same client that we run on the previous example. package com.gkatzioura.order; import com.egkatzioura.order.v1.Order; import com.egkatzioura.order.v1.OrderRequest; import com.egkatzioura.order.v1.OrderResponse; import com.egkatzioura.order.v1.OrderServiceGrpc; import io.grpc.ManagedChannel; import io.grpc.ManagedChannelBuilder; public class ApplicationClient { public static void main(String[] args) { ManagedChannel managedChannel = ManagedChannelBuilder.forAddress("localhost", 6565) .usePlaintext() .build(); OrderServiceGrpc.OrderServiceBlockingStub orderServiceBlockingStub = OrderServiceGrpc.newBlockingStub(managedChannel); OrderRequest orderRequest = OrderRequest.newBuilder() .setEmail("hello@word.com") .setProduct("no-name") .setAmount(3) .build(); OrderResponse orderResponse = orderServiceBlockingStub.executeOrder(orderRequest); System.out.println("Received response: "+orderResponse.getInfo()); managedChannel.shutdown(); } } The response is the one expected. We did connect to the server and got back a response. We did not have to manually register the services to the gRPC server, since spring did this one for us. You can find the code on github. This post is ad-supported ​ Emmanouil Gkatziouras | October 28, 2021 at 6:36 am | Categories: gRPC, Java, Spring, Spring Boot | URL: https://wp.me/p1EE3y-Vo Comment    See all comments    Like Unsubscribe to no longer receive posts from Emmanouil Gkatziouras. Change your email settings at Manage Subscriptions. Trouble clicking? Copy and paste this URL into your browser: http://egkatzioura.com/2021/10/28/add-grpc-to-your-spring-application/ Thanks for flying with WordPress.com