10 Courses and Certifications

Coursera Statistics Courses & Certifications

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Bayesian Statistics

Bayesian Statistics certification is a 7-week long programme where all the participants enrolling must devote approximately 34 hours of their time. This certification course is available on the Coursera platform but has been designed by Duke University. Also, this course is part 4 of 5 in the specialization of Statistics with R Specialization offering a free trial that is valid for 7 whole days. Whoever the interested participants must make sure that they have some kind of prior knowledge of what all is taught in the precious course parts of the specialization.

The Bayesian Statistics training will be responsible for introducing Bayesian comparisons related to means, credible regions, and Bayesian regression using multiple models to the participants. The course finally teaches all the applicants to learn the application of Bayesian methods to several practical issues and implementation in R programming. Apart from providing a certificate that is shareable, the candidates will be able to change the course timings with respect to their convenient schedules.

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7 Weeks
Intermediate
Free
Skills Covered:
Statistical skills R Programming
Certificate

Inferential Statistics

Inferential Statistics certification is a 5 weeks online course that belongs to the specialization of Statistics with R. This programme is course number 2 of 5 whose main focus is inferential statistics. The course belongs to a beginner level which only has a time requirement of 16 hours spread over the 5 weeks duration. Not only an end certificate is provided but also the study time can be reset at whatever time suits the candidates, 

Inferential Statistics training does help perform hypothesis testing, report the analysis of results by interpreting p-values, so that whatever is reported can be interpreted easily for the public or even the clients. Participants can learn to use the data examples that will help to report the estimates related to quantities that can express the uncertainty of interests. The participants can also learn the installation of R and RStudio software that is for the project in the final week of this course.

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5 Weeks
Beginner
4,115
Skills Covered:
Statistical skills R Programming
Advanced Linear Models for Data Science 1 Least Squares

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Certificate

Advanced Linear Models for Data Science 1 Least Squares

The Advanced Linear Models for Data Science 1: Least Squares course helps you develop a strong foundation of linear and regression modelling. Johns Hopkins University offers the programme, and your instructors will be subject-matter experts. It is the third part of the Advanced Statistics Specialisation by Coursera.  

Moreover, the course completion certificate that you will receive is shareable, after finishing the Advanced Linear Models for Data Science 1: Least Squares programme by Coursera. It will allow you to establish your relevant expertise in the field. Learn and develop fluency in the basics related to least squares and regression modelling, at your own pace.

In addition, the Advanced Linear Models for Data Science 1: Least Squares course features immersive content, developed around a self-paced model to align perfectly with your schedule. The training also provides adjustable deadlines. Furthermore, the online course allows you to quickly and efficiently start studying. You have to devote nearly 8 hours of learning time to the programme.

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3 Weeks
Expert
3,275
Skills Covered:
R Programming Knowledge of Applied statistics
Bayesian Statistics Mixture Models

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Certificate

Certification Course on Bayesian Statistics: Mixture Models

This certification course in Bayesian Statistics: Mixture Models by Coursera is a one stop place to learn the Bayesian Statistics and its Mixture Models along with its applications in the practical world. The course introduces one of the useful classes of statistics. This course is structured in a way to teach the most practical uses of Bayesian Statistics and its tools. It encourages candidates taking this course to practice and learn as the course progresses by not simply watching the videos but also constantly solving the Bayesian Statistics problems.

The course also utilises the industry software R in some places for solving the course problems. R is an easily available free statistical software that is used in m multiple industries of development, design and manufacturing, etc. The course lays out a basic tutorial to learn the software and teach the uses of it in Bayesian Statistics. Candidates are also encouraged to learn further about R for better advantages in their career.

The course is an intermediate level certificate course in Bayesian Statistics: Mixture Models taught by some of the expert faculty of the University of California Santa Cruz. For candidates who wish to take this course, it should be noted that prior knowledge of principles of estimation in maximum likelihood, Calculus based probability, and the Bayesian estimation is required. The course is a great way to take a step further in the statistics learning field and can provide you with the required edge in your career and the industry.

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3 Weeks
Intermediate
6,634
Skills Covered:
Statistical skills
Statistical Inference

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Certificate

Statistical Inference

3 Weeks
4,117
Statistics with SAS

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Certificate

Statistics with SAS

3 Weeks
Intermediate
Free
Certificate

Causal Inference 2

3 Weeks
Expert
Free
Probabilistic Graphical Models 1 Representation

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Certificate

Probabilistic Graphical Models 1: Representation

The Probabilistic Graphical Models 1: Representation course by Coursera is a part of the Probabilistic Graphical Models Specialization on the Coursera platform. Stanford University offers a certification course in association with Coursera.

The Probabilistic Graphical Models 1: Representation online course has a curriculum spread out over five weeks. The certification course will help you develop a deep understanding of the PGM framework by communicating perplex material with skill. Moreover, the online course primarily covers the fundamentals of Bayesian networks and Markov networks. 

Furthermore, the Probabilistic Graphical Models 1: Representation certification course promotes interaction and improvement with a comprehensive curriculum, intermittent assignments, and discussion boards. Candidates can self-assess using these mediums and grasp the course contents at their own pace. 

Finally, Coursera provides a certificate upon successful completion of the  Probabilistic Graphical Models 1: Representation course. The certificate can be added to the resume or shared on LinkedIn and other professional websites. Learners can also take a print out of the certificate.

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Expert
4,115
Skills Covered:
Machine learning
Probabilistic Graphical Models 2 Inference

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Certificate

Probabilistic Graphical Models 2: Inference

Probabilistic Graphical Models (PGMs) is considered to be a strong foundation for encrypting probability arrangement over composite domains. Hence, Probabilistic Graphical Models 2: Inference is drafted to address the questions related to probabilistic inference. This course depends upon the concepts from graph algorithms, probability theory, and machine learning. These concepts are the base for the advanced methods used in various applications like speech recognition, medical diagnosis, natural language processing, etc. These advanced methods are also essential tools in composing various machine learning problems.

In an order of three, Probabilistic Graphical Models 2: Inference is the second one. While the first course, focused on description, this course would address the question occurs in probabilistic inference or in other words use of PGM in answering the questions. PGM is described as a very high dimensional diffusion but its framework is drafted in such a way that it can answer the questions efficiently. The course introduces both approximate and accurate algorithms for the variety of inference tasks, and also talks over the application of each algorithm.

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Expert
4,115
Skills Covered:
Knowledge of Algorithms Knowledge of Monte Carlo Method

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