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 modeling is key to making 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.
It is crucial to evaluate the quality of the analyses that others present to you, considering how critical data-based decisions and opinions have become. There is more to statistics than just numbers and facts. Instead, it's a collection of knowledge and procedures that reliably let you learn from data.
Statistical modeling 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.
The statistical modeling 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 visualizations that aid data scientists in identifying relationships between variables and making predictions by applying statistical models to raw data.
Examples of common data sets for statistical analysis include census data, public health data, and social media data.
Data gathering is the foundation of statistical modeling. The data may come from the cloud, spreadsheets, databases, or other sources. There are two categories of statistical modeling methods used in data analysis. These are:
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.
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.
Statistics and machine learning differ primarily in their purposes. You can build ML models for predicting the future by making accurate predictions without explicit programming, while statistical models can explain the relationship between variables.
However, some statistical models are inaccurate because of their inability to 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.
Even though data scientists are usually responsible for developing algorithms and models, analysts may also use statistical models in their work from time to time. As a result, analysts seeking to excel should gain a solid grasp of the factors that contribute to the success of these models.
Companies and organizations are leveraging statistical modeling to make predictions based on data to keep pace with the explosive growth of machine learning and artificial intelligence. The following are some benefits of understanding statistical modeling.
A data analyst needs a comprehensive understanding of all the statistical models available. You should identify which model is most appropriate for your data and which model best addresses the question at hand.
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.
Most organizations 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 wants to know the main conclusions. There is a second group of people often interested in the granular details. These people often require a summary of your broad findings and an explanation of how you reached them.
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.
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.
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.
A master's degree in analytics is an effective way to gain these skills if you are interested in exploring statistical modeling techniques. However, not all analytics programs are created equal, so carefully making a choice is essential.
Choose programs that incorporate machine learning into the curriculum to align better your graduate school experience with your career goals as an analyst. Organizations will likely hire more and more data analysts who understand the underlying principles of these systems as this trend continues to develop.
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 can help to increase your salary potential.
By earning the Certificate in Applied Statistical Modeling, undergraduate students learn to apply statistics to their primary fields of study. Certificate programs complement diverse degree programs and appeal to students from all disciplines, including engineering, science, economics, mathematics, and many others.
The certificate program includes two courses on applied statistics, data mining, or machine learning, one course on the mathematical foundations of statistics, and three elective courses in statistics, machine learning, econometrics, etc.
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.
This course covers commonly used statistical inference methods for numerical and categorical data. You will learn how to set up and perform hypothesis ...
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Statistical Inference, Statistical Hypothesis Testing, R Programming
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.
This content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.