Machine Learning vs. Statistics
Posted on: 01/03/2020
The term machine learning brings up images of robots, artificial intelligence, and flying cars, while statistics draws forth charts with bell curves and tracking of sports game outcomes. In reality, however, these two fields overlap significantly, as they both deal with the analysis of data. Statistical modeling and machine learning can even be applied to similar situations, and work together to provide solutions to a variety of questions.
Taking a closer look at what we consider machine learning vs statistics poses numerous considerations and understanding where machine learning picks up and statistics leaves off can be beneficial for professionals interested in advancing their data careers.
Related content: M.S. in Data Science vs. Applied Statistics: What’s the Difference?
What is Machine Learning?
Machine learning is the field that deals with creating algorithms that learn from data, so that programs and systems can accomplish tasks without an explicit set of programmed instructions— for example, image recognition technology often relies on machine learning algorithms that parse huge numbers of pictures, learning to identify objects and other features within those images over time and after analyzing large volumes of image data.
The field of machine learning started as a subarea of artificial intelligence research, but it has since evolved to become its own distinct branch within AI research and development.
What is Statistical Learning?
Where machine learning is a broad discipline that encompasses how computers can understand and “learn” from data, statistical learning focuses on taking raw data and turning it into actionable information, and it is the basis for machine learning algorithms.
Since statistical learning may be used to develop the underlying models that govern how a machine learning algorithm understands data, the two fields are very closely intertwined. One basic example of this in action is a linear regression algorithm, which is a type of machine learning algorithm that was developed based on the principles of statistics.
Knowledge of statistics is also critical when troubleshooting issues with machine learning algorithms as well as solving broader data analytics problems. For example, imagine if a machine learning algorithm is highly accurate in a test environment, but becomes less accurate when used on a real-world data set. Statistics expertise helps professionals understand why and how to address the underlying issue. Statistics knowledge also paves the way for a variety of data careers, ranging from marketing analysis to data science.
Machine learning vs. Statistics in the Real World
The use cases for machine learning span across many industries, but what generally makes a good machine learning problem is a matter of scale. Since machine learning algorithms learn from data, they can be used more effectively when there is a large volume of information available. For example, researchers can study the behavior of computer programs to identify likely instances of malware; however, researchers have access to billions of data points from sources like event logs and other security analysis tools. Analyzing this information manually would take decades, but machine learning can drastically cut down on the time it takes to parse this data and reach actionable conclusions.
Statistics for Machine Learning in Disease Research
The CDC and other health focused institutions also use machine learning to help predict and understand the way that diseases work, and to find ways to prevent the progression of diseases when they’re able.
The first stage of this work is usually done through statistical analysis, which is then built upon by implementing machine learning algorithms based on confirmed statistics.
An excellent example of this is the research done by the Elder Institute, that combined statistical modeling of diseases in animals, with machine learning; this allowed researchers to automate the identification, verification and sorting of new data. Machine learning is especially applicable in cases such as this where the volume of data continues to grow over time. The growing volume can aid in the training of the algorithms as the study goes on, allowing the algorithm to effectively become “smarter” and create new, more efficient or more accurate output.
Statistical Analysis and Social Media
Websites such as Facebook and various other social media platforms use statistical modeling to investigate the information gathered from users regarding demographics, engagement and reach, to understand how people connect through their platforms. In some cases, this information can be used to predict human behavior based off of the data that was generated by users.
Being able to understand what a certain set of actions means regarding a person’s likely political opinions, economic status or even simply age range, allows platforms to more carefully target their ads and features, helping them to drive revenue and expand their user base.
In addition, machine learning and statistics are increasingly being applied to customer service roles in relation to these platforms. Chatbots and machine learning systems are trained to respond to the most common user complaints and questions, allowing companies to focus their customer service agents on addressing complex or highly escalated cases. In this way, they can maintain fast response time to customer interactions, while making sure that high level requests are given the level of detail that will keep customers satisfied with the response.
Statistical Modeling and Software Development
Detailed statistics that look at bug reports can be used to inform how programs and platforms evolve over time in response to their user base. Products such as Debian based operating systems are developed with the intent to be free and open to the public, with the public receiving a product and the developer receiving massive inputs of data in return.
While this process does provide a large amount of data, it is not an environment that benefits from the use of machine learning, due to the highly variable nature of the reports, as well as the possibility of false reports due to user error.
What this does lend itself to, however, is statistical analysis of bugs as they relate to core functionality of the programs. As the study produces information about consistently unstable portions of the program, developers can prioritize their work and address the most common and most egregious defects.
When it comes down to it, the difference between statistics and machine learning is that machine learning encompasses the convergence of a variety of techniques and technologies that may include statistics and statistical modeling, whereas statistics focuses on using data to make predictions and create models for analysis.
While it is important to use statistics for machine learning to create more sophisticated algorithms, not every problem is a machine learning problem. For example, machine learning can help to automate data analysis, but not all data sets will be large enough to justify automation—in this case, statistics can still be used without machine learning to identify patterns and extract actionable information.
About University of Delaware’s online M.S. in Applied Statistics
The University of Delaware offers a 100% online M.S. in Applied Statistics (ASTAT) for data professionals interested in earning an advanced degree without interrupting the rest of their careers.
University of Delaware’s ASTAT master’s program provides students with opportunities to develop and apply their skills to current, real-world problems. Distance learners benefit from close relationships the University of Delaware maintains with large, locally based companies in numerous sectors, including the financial services, healthcare, chemical, pharmaceutical, technology and farming industries.
Our statistics courses also offer hands-on experience with popular statistical software packages such as SAS, enabling students to develop advanced proficiency in skills they will need to evolve their careers.
Expert statisticians from these and other organizations were recruited to develop and instruct case-study based courses specifically for the online ASTAT. These full-time faculty members aptly prepare students for jobs with a median base salary of $80,000.