What Is Statistical Modeling?

Statistics is one of the foundational disciplines when it comes to working with data—and statistical modeling refers to the different mathematical methodologies data analysts and data scientists use to interpret data. There are a variety of statistical models, and the one you apply to a dataset will depend on the question you're attempting to answer.

In this article, we'll dive more deeply into statistical modeling, the most commonly used techniques, and reasons why you should learn this important skill.

What is statistical modeling?

Statistical modeling is an important process in the field of data science. It involves identifying the best statistical model to identify a relationship in a given dataset, such as census data, public health data, or a company's user data. Think of statistical modeling as a framework. You'll use different frameworks to find different relationships within different datasets.

The statistical model involves a mathematical relationship between random and non-random variables, and can also provide intuitive visualizations that aid data scientists in identifying relationships between variables and making predictions. 

Statistical modeling techniques

Statistical modeling starts after you've gathered the necessary data to analyze. There are two categories of statistical modeling methods:

Supervised learning

In the supervised learning model, the algorithm uses a labeled data set for learning, with an answer key the algorithm uses to determine accuracy as it trains on the data. Supervised learning techniques in statistical modeling include:

• Regression model: A predictive model designed to analyze the relationship between independent and dependent variables. The most common regression models are logistical, polynomial, and linear. These models determine the relationship between variables, forecasting, and modeling.

• Classification model: An algorithm analyzes and classifies a large and complex set of data points. Common models include decision trees, Naive Bayes, the nearest neighbor, random forests, and neural networking models.

Unsupervised learning

In the unsupervised learning model, the algorithm is given unlabeled data and attempts to extract features and determine patterns independently. Clustering algorithms and association rules are examples of unsupervised learning. Here are two examples:

• K-means clustering: The algorithm combines a specified number of data points into specific groupings based on similarities.

• Reinforcement learning: This technique involves training the algorithm to iterate over many attempts using deep learning, rewarding moves that result in favorable outcomes, and penalizing activities that produce undesired effects.

Machine learning vs. statistical modeling

Statistics and machine learning (ML) models have different purposes, meaning data scientists tend to use them for different reasons.

ML models are computer programs that are used to recognize patterns in data or make predictions. After being trained on specific datasets, an ML model can make inferences and predictions based on new data. While ML predictions tend to be more accurate, they can also be more challenging to understand and explain.

Statistical models, on the other hand, are good at explaining the magnitude and significance of relationships between variables and their scale. Probabilistic models for the data and variables are interpreted and identified, such as the effects of predictor variables.

4 reasons for learning statistical modeling

Data has increasingly become an important foundation for many business decisions, making statistical modeling a key skill for those who work with data.

Even though data scientists are usually responsible for developing algorithms and models, data analysts may also occasionally use statistical models in their work. As a result, data analysts seeking to advance in their careers may benefit from gaining a solid grasp of the factors that contribute to the success of these models.

1. Choose models that meet your needs.

A data analyst needs a comprehensive understanding of all the statistical models available in order to identify the more important insights their dataset has to offer. You should identify which model is most appropriate for your data and which model best addresses the question at hand.

2. Improve data preparation for analysis.

Raw data is rarely ready for analysis. Data must be clean before conducting accurate and viable research. The cleanup process usually involves organizing the collected information and removing "bad or incomplete data" from the sample.

To build a good statistical model, you need to explore and understand the data. If the data is not good enough, you can't draw any meaningful inferences. Knowing how different statistical models work and how they leverage data will enable you to determine what data is most relevant to the questions you are trying to answer.

3. Enhance your communication skills.

Most organizations require data analysts to present their findings to two different audiences: technical and non-technical. Technical audiences will be interested in the granular details—these people often require a summary of your broad findings and an explanation of how you reached them. Non-technical audiences, on the other hand, may not always be interested in the details of your analysis, but they'll want to know the main takeaways.

An understanding of statistical modeling can help you communicate effectively with both audiences. You will generate better data visualizations and share complex ideas with non-analysts. You will create and explain those more granular details when necessary with a deeper understanding of how these models work on the backend.

4. Expand your job opportunities.

You'll find that statistical data analysis skills demand data science positions that will involve machine learning. They may ask you to solve some typical statistics problems during an interview.

With a proper background in statistics and math, it is possible to optimize linear regression models and understand how decision trees calculate impurity at each node. These are some of the top reasons machine learning needs statistics. Taking online courses on statistics can get you started.

Ways to learn statistical modeling

You can use your prior experience in statistics and probability as a starting point for your journey into statistical modeling if you have a background in these fields. Learn the basics of regression analysis and the relevant tools, and become comfortable interpreting analysis results. Explore some options below for learning statistical modeling.

Degree program

Students with a bachelor's degree in mathematics, computer science, or engineering and a firm understanding of statistical modeling are well-prepared to pursue a career in data science. Learning statistical modeling, algorithms, and machine learning to support various models is a strategic way to help to increase your salary potential.

A master's degree in analytics is also an effective way to gain these skills if you are interested in exploring statistical modeling techniques and wish to pivot into the field. Choose programs that incorporate machine learning into the curriculum to better align your graduate school experience with your career goals as an analyst. As this trend continues to develop, organizations will likely hire more and more data analysts who understand the underlying principles of these systems.

Certificate programs

Consider earning the SAS Statistical Business Analyst Professional Certificate. The program offers hands-on practices integrated throughout its three courses. Data examples are general enough to be applicable to a broad range of subject areas. Specific examples you will see in the courses address agriculture, manufacturing, health care, banking, retail, and nonprofit.

Online courses

You can improve your skills and advance your career with free or paid online courses and classes in statistics. Understand standard deviation, probability distributions, probability theory, ANOVA, and many other statistical concepts.

Depending on your interests and needs, Coursera can help you learn statistical modeling in various ways. In some courses, you'll learn the basics of statistics, which can be helpful if you have no background in the subject.

Learn more about statistical modeling on Coursera

Prepare for a career as a data scientist with the IBM Data Science Professional Certificate on Coursera. After learning to import and clean datasets, you'll learn how to build machine learning models. You can also accelerate your data insights with the Microsoft Copilot for Data Science Specialization, which provides you with hands-on experience with the AI tool Microsoft Copilot.