Whether you are a data analyst who needs to study statistical modeling or a student, you have come to the right place. In this article, we bring you the Best Statistical Modeling Courses to learn the fundamentals of quantitative and statistical modeling for analyses.
Before you dive right in, let’s explore the term ‘Statistical Modeling’ and why you should learn it.
Statistical Modeling = It is the process of using statistics to analyze data and make informed decisions for a company. It includes programming to obtain data from datasets, make predictions, and understand various situations. You can visualize results in charts, graphs, or other reports to get an overview of the data.
Why is it essential to learn statistical modeling?
Statistical Modeling can be used in various fields involving data analysis. It is mainly used to predict outcomes and make accurate decisions. The average Statistical Modeler salary in the United States is $83,044, but the salary range typically falls between $75,442 and $91,628, according to Salary.com.
As the demand for data scientists and analysts increases, companies seek people who can understand and apply statistical modeling to make predictions. According to the BLS, the employment growth for data scientists is expected to grow to 36% by 2031. Is it time to plunge in and add another skill to your resume? Take a chance now and review the compiled list of the 3 Best Statistical Modeling Courses Online.
Table of Content
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Best Statistical Modeling Courses, Certification, Tutorials, Training, Classes Online
- Best for R Statistical Modeling for Data Analysis with R [Udemy]
- Best for Data Science Statistical Modeling in Data Science [edX]
- Best for Data Analysis Statistical Modeling for Interpreting Data [Pluralsight]
Statistical Modeling for Data Analysis with R [Udemy]
Take a plunge to learn statistical modeling to analyze data with Udemy. This Statistical Modeling Course is a great way to acquire knowledge of statistical data analysis, where you use statistical tools to drive insights from qualitative data. It will help you save time in handling data analysis tasks.
Course Instructor
This Statistical Modeling in R Course will be taught by Minerva Singh, a bestselling Udemy and Data Scientist instructor. She has experience applying machine learning, data analysis, deep learning, and NLP using R and Python.
Pros & Cons
Pros
- High-quality and comprehensive
- Truly experienced instructor
- Access to theoretical and practical examples
Cons
- Need to be structured
Key Highlights & Learning Objectives
- This Applied Statistical Analysis Courses for Data Analysis is divided into 9 sections and offers 72 lectures to understand statistical modeling for practical applications in R.
- Learn to analyze their data by applying appropriate statistical techniques and be able to Interpret their statistical analysis results.
- Discover statistical techniques and fundamental statistical concepts.
- Able to apply different statistical analyses in R and create data visualizations to drive results.
- Implement linear modeling techniques such as multiple regressions and GLMs and work on advanced regression and multivariate analysis.
- Unlimited access to 40+ downloadable resources and 3 articles and receive a certificate of completion.
Who is it for?
This statistical modeling for data analysis with R courses is for those who have basic knowledge of R. If you need data analysis skills in your field, this is the perfect option. You will gain observational and statistical analysis skills to manage your projects and interpret the results upon completion.
Rating: 4.5/5
Students Enrolled: 13,783
Duration: 10 hours
Statistical Modeling in Data Science [edX]
This edX Statistical Modeling Course will teach traditional and cutting-edge methods, including supervised learning. You will primarily focus on two standard techniques: regression and classification methods. Also, this introduction to the statistical modeling training course will discuss other unsupervised learning methods, including principal components and clustering (k-means and hierarchical).
Course Instructor
James, Witten Hastie, and Tibsharini designed this Statistical Learning in R online course. The highly experienced instructors work as professors in the statistics and data science department at Stanford University.
Pros & Cons
Pros
- Free unlimited access
- Shareable certificate after completion
- Feedback on assignments and exams
Cons
- Lengthy course
- Require basic knowledge of ML
Key Highlights & Learning Objectives
- This edX Statistical Learning Course includes many topics in the list below.
- Learn about linear and polynomial regression, logistic regression, and linear discriminant analysis
- Work with cross-validation and the bootstrap, model selection, and regularization methods
- Develop knowledge about nonlinear models, splines, and generalized additive models;
- Discover other methods, such as tree-based methods, random forests and support vector machines
Who is it for?
This Statistical Modeling in Data Science Training is designed for beginners who understand statistics, linear algebra, and computing. It would be beneficial to understand R. After completing this course, you can gain a shareable certificate to showcase your skills to potential employers and share on your resume. Learn R programming languages with Geektonight, where you can find any suitable course.
Rating: 4.5/5
Students Enrolled: 97,738
Duration: 11 weeks 3–5 hours per week
Statistical Modeling for Interpreting Data [Pluralsight]
This Statistical Modeling with R Course will introduce you to the essential methods and concepts of statistics with R programming language. It will explain how to use statistical and data science models for any data-related problems. Interpreting Data using Statistical Models will teach how to fit statistical models to data. After that, you’ll discover how to test for relationships among data sets and use linear regression to make predictions.
Course Instructor
Creator and Instructor Fredrik Hallgren will guide you through this Pluralsight Statistical Modeling for Interpreting Data in R Course. He has a vast knowledge of machine learning and statistical programming. Also, Fredrik worked on researching trading models and developing software infrastructure.
Pros & Cons
Pros
- Well-explained and interactive
- Bite-sized lessons
- Complete expert-led course
Cons
- Only a 10-day free trial
Key Highlights & Learning Objectives
- This Statistical Modeling for Interpreting Data in R Tutorial is divided into 6 lessons, namely:
- Creating Statistical Models
- Fitting Statistical Models
- Implementing a Predictive Model
- Drawing Conclusions for Data
- Use Multi-variable Linear Regression
- Ensuring Predictive Accuracy
- Learn to create statistical models and understand how to recognize different data types.
- Work on fitting statistical models to data and use probability theory to choose the best estimate.
- Able to implement a predictive model including single-variable linear regression
- Understand how to draw conclusions from data with statistical testing and be able to differentiate between multiple groups.
- Discover critical methods such as multi-variable linear regression.
- Learn methods to predict errors in new data and improve prediction accuracy using linear regression.
Who is it for?
This beginner statistical modeling course is for professionals who want to upgrade their skills and learn statistical modeling. As a result of completing this course, you will be able to turn data into knowledge and gain valuable insight into the world around you.
Rating: 4.7/5
Students Enrolled: 24,682
Duration: 3 months, 12 hours/week
Frequently Asked Questions
What is statistical modeling?
Data science uses statistical modeling to analyze datasets. Statistical models are based on mathematical relationships between random and non-random variables. To make predictions based on raw data, data scientists can use statistical models to visualize relationships between variables. Statistical modeling analysis is commonly performed on census, public health, and social media data.
Is Statistical Modeling Difficult?
Statistical modeling is the process of choosing an appropriate statistical model for data and requires knowledge of relevant statistical analyses. Hence, it can be challenging for beginners to use statistical modeling for data analysis.
Why do we need statistical modeling?
Data analysts should learn statistical modeling to develop algorithms and models for data-related work. Companies and organizations are leveraging statistical modeling or data analysts who understand this topic to make predictions based on data to keep up with the trends in the market. Also, there is an explosive growth of machine learning and artificial intelligence that requires more people with statistical modeling skills.
Final Thought
Taking online Statistical Modeling Courses can help you to work on your interests and career goals. All the courses offer a pathway to build knowledge and skills required in specific fields, such as artificial intelligence, finance, machine learning, and more.
You must check the Best Data Science Courses Online for beginners who do not know data science. It would be beneficial for you to enhance your career. This article will help you to choose the right course. Thanks!
