What Is Statistical Modelling?

The science of statistics is the study of how to learn from data. It helps you collect the right data, perform the correct analysis, and effectively present the results with statistical knowledge. Statistical modelling is key to scientific discoveries, data-driven decisions, and predictions.

By studying statistics, you can understand nearly any subject in-depth. Statistical analysts learn from data and navigate common issues while avoiding erroneous conclusions.

Evaluating the quality of the analyses others present to you is crucial, considering how critical data-based decisions and opinions have become. Statistics is more than just numbers and facts. Instead, it's a collection of knowledge and procedures that lets you learn from data reliably.

Statistical modelling helps you differentiate between reasonable and dubious conclusions based on quantitative evidence. Analyses and predictions made by statisticians are highly trustworthy. A statistician can help investigators avoid various analytical traps along the way.

What is statistical modelling?

The statistical modelling process is a way of applying statistical analysis to datasets in data science. The statistical model involves a mathematical relationship between random and non-random variables.

A statistical model can provide intuitive visualisations that aid data scientists in identifying relationships between variables and making predictions by applying statistical models to raw data. 

Census, public health, and social media data are common data sets for statistical analysis.

Statistical modelling techniques

Data gathering is the foundation of statistical modelling. The data may come from the cloud, spreadsheets, databases, or other sources. There are two categories of statistical modelling methods used in data analysis. These are:

Supervised learning

In the supervised learning model, the algorithm uses a labelled 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 modelling include:

• Regression model: A predictive model analyses 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 modelling.

• Classification model: An algorithm analyses and classifies large and complex data points. Common models include decision trees, Naive Bayes, the nearest neighbour, 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 favourable outcomes, and penalising activities that produce undesired effects.

Machine learning vs. statistical modelling

Statistics and machine learning (ML) differ primarily in their purposes. ML models can predict the future by making accurate predictions without explicit programming, while statistical models can explain the relationship between variables.

However, some statistical models need to be more accurate because they cannot capture complex relationships between data, even if they can predict. ML predictions are more accurate, but they are also more challenging to understand and explain.

In statistical models, probabilistic models for the data and variables are interpreted and identified, such as the effects of predictor variables. A statistical model establishes the magnitude and significance of relationships between variables and their scale. Models based on machine learning are more empirical.

Reasons for learning statistical modelling

Even though data scientists are usually responsible for developing algorithms and models, analysts may occasionally use statistical models. As a result, analysts seeking to excel should gain a solid grasp of the factors contributing to these models' success.

Companies and organisations are leveraging statistical modelling to make data-based predictions to keep pace with the explosive growth of machine learning and artificial intelligence. Understanding statistical modelling has some benefits.

Choosing models that meet your needs

A data analyst needs a comprehensive understanding of all the statistical models available. You should be able to identify which model is most appropriate for your data and which model best addresses the question at hand.

Improved 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 organising the collected information and removing 'bad or incomplete data' from the sample.

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

Enhanced communication skills

Most organisations require data analysts to present their findings to two different audiences. First, the business team is not interested in the details of your analysis but would like to know the main conclusions. A second group of people is often interested in the granular details. These people usually need a summary of your broad findings and an explanation of how you got them.

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

Job opportunities

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

With a proper background in statistics and maths, it is possible to optimise 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 modelling

You can use your prior experience in statistics and probability as a starting point for your journey into statistical modelling 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 modelling.

Degree program

A master's degree in analytics is an effective way to gain these skills and explore statistical modelling techniques. However, not all analytics programs are created equal, so carefully choosing is essential.

Choose programs incorporating 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, organisations will likely hire more and more data analysts who understand the underlying principles of these systems.

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

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 apply to various subject areas. Specific examples in the courses address agriculture, manufacturing, health care, banking, retail, and nonprofit.

Online courses

With free or paid online courses and classes in statistics, you can improve your skills and advance your career. You can also understand standard deviation, probability distributions, probability theory, ANOVA, and other statistical concepts.

Depending on your interests and needs, Coursera can help you learn statistical modelling in various ways. Some courses teach the basics of statistics, which can be helpful if you have no background in the subject.

So you want a job in statistical modelling?

Depending on your background and career goals, you may spend a year or more learning the skills you need for a job in data analytics.

If you have a mathematical mindset and are not afraid of coding, you can feel confident about taking your first steps toward becoming a data analyst.