While the future is often characterized as a distant, mysterious, and unknowable time, it is also understood that one of the greatest predictors of the future is a pattern of past behavior. One of the most compelling tools within the field of data science is predictive analytics. This is the process of using past data in order to create a forecast for future behavior or trends. Through the use of statistical analysis and the creation of models and visualizations, predictive analytics has gained popularity within multiple fields and industries that need to leverage information and data on individuals and institutions in order to make better decisions.
Predictive analytics is an essential skill for data scientists who not only want to work within the science and technology industries but any industry that is invested in data-driven decision making and insights. The breadth of predictive analytics, and the specificities of how this technique is applied across industries, may be a complex topic for many beginner data scientists to explore. The following article defines predictive analytics, how it is used within data science, and some of the ways that beginner data scientists can learn more about it.
What is Predictive Analytics?
Predictive analytics is the use of mathematical functions, statistical tools, and/or software in order to uncover insights within a dataset that can be used to make predictions about the future. Predictive analytics includes many data science methods, including data mining, predictive modeling, and machine learning, and acts as a series of steps that begins with analyzing the data and ends with making predictions based on that analysis. Beginning with data mining, data scientists can use data mining in order to search through a collection of data in order to find the emerging trends and patterns within that dataset. Then, predictive modeling can be used to generate a model of future behavior or patterns based on these past trends. Finally, these models can be deployed through the use of automation and machine learning, which focuses on the development of algorithms and artificial intelligence which use predictive analytics to make decisions.
Predictive Analytics in the Data Science Industry
Data scientists have different ways that predictive analytics can be used to make decisions about the future based on information and data from the past. This is because data scientists can use predictive analytics in order to forecast future trends through the recognition of patterns and themes within a dataset. Predictive analytics is especially useful in the industries of business and finance, government, healthcare, social media, and technology, as well as any field that focuses on consumer behavior, such as retail and manufacturing, as well as advertising and marketing. In each of these industries, there is a certain level of unpredictability and risk. Predictive analytics allows data science professionals to apply their analytical skills in a way that can improve decision-making in these industries.
For example, predictive analytics is used within business and finance in order to make decisions based on economic trends and the analysis of markets and/or an individual’s financial portfolio. Through collecting a history of how money moves in society, or through an individual’s accounts, businesses and financial institutions can predict how they want to invest. Predictive analytics is also used within the world of audience analysis and consumer insights in order to learn more about what consumers want and how they spend their money.
Through trend forecasting, predictive analytics and modeling can offer key insights about the type of advertising, products, and services that should be introduced to a market or niche. In addition, the social media and technology industries rely on predictive analytics in order to gather information and data that is required for the creation of the algorithms and machine learning models that run recommendation systems. Overall, predictive analytics allow data science professionals to respond to industry trends, make informed decisions, and learn more from past problems and performance.
How to Begin Using Predictive Analytics
One of the most important skills to learn when beginning to use predictive analytics is statistical analysis. Most predictive models are based on theories and equations which are common within mathematics and statistics, such as linear or logistic regression. By developing a firm foundation in using machine learning algorithms for data science and statistical analysis, you will be able to understand which models to use when analyzing a dataset. In addition, statistical analysis is a skill that can be employed using a variety of tools, whether or not you have access to more advanced data science software and analytic tools.
For beginner data scientists that have some knowledge of statistics, predictive analytics can also be explored while you are learning specific programming languages and data science tools. For data scientists that want to specialize in predictive analytics, both R and Python offer several features and libraries which specialize in data forecasting and machine learning. When using Python, libraries such as scikit-learn offer multiple open source and community-based predictive data analysis resources for beginners and industry professionals. Due to its reliance on statistical packages and data analytics tools, the R programming language can also offer a beginner-friendly introduction to predictive analytics.
In addition, working with and developing some knowledge of database management systems is another way to begin using predictive analytics on a dataset. For example, many relational database management systems allow you to use the SQL programming language for data mining and predictive modeling. So, if you have some background with SQL databases, you can use your own datasets, or a publicly available dataset, to explore predictive analytics within a database management system. Both R and Python are also compatible with several SQL databases and are primarily used to enhance the predictive analytic capabilities of these systems through data modeling and machine learning packages and programs.
Interested in learning more about Predictive Analytics?
The use of data to solve real-world problems thrives on predictive analytics and machine learning. Noble Desktop offers several Data Science classes which include instruction in various programming languages, as well as the methods of using those languages in order to analyze data, make predictions, and deploy machine learning models. The Data Science Certificate program offers beginner students and industry professionals the skills to work with Python and SQL to create machine learning models and query databases. The Python Data Science & Machine Learning Bootcamp includes hands-on instruction which teaches students how to use statistical analyses to create predictive models using Python. No matter what programming language or predictive models you are interested in, Noble Desktop has a series of classes for you!