10 Best Statistical Modeling Training Courses

Buckle up and master statistical modeling with the help of our Top Statistical Modeling Courses. These courses will equip you with the skills required to analyze and interpret complex data effectively.

Key Takeaways

• An opportunity to explore key statistical modeling techniques.
• Learn to apply skills in real-world scenarios.
• Suited best for data professionals and statisticians.

Where can I find the best training in statistical modeling?

The best statistical modeling training can be found online. This training offers deep dives into techniques like regression and multivariate analysis. This training is tailored specifically for data analysts and researchers.

• 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

Statistical Modeling Courses Evaluation & Selection Criteria

Our team carefully curated this list of statistical modeling training courses. It is based on factors like instructor expertise and reputation, course comprehensiveness, and positive learner feedback. Our aim is to make sure that learners get practical and applicable knowledge that is backed by real-world applications.

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.

Discover various Training Options available to deepen your understanding of Data Modeling concepts.

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.

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.

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.

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.

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.