Data science is defined as the combination of information science and data analysis, as well as statistics and computer science. This means that the mathematical formulas and theories which are central to understanding statistical analysis are the underpinnings of how we use data to make predictions and decisions. For example, statistical modeling is one of the primary methods of using quantitative tools for data analysis, and there are several statistical models which can be used to construct an argument based on past or historical data.
Models such as linear regression can be used to gauge the relationship between variables to make predictions about the behavior or outcome of a specific variable. One of the primary roles of linear regression within data analytics is acting as the foundation of statistical models and algorithms which are useful when developing predictive analytics and machine learning models. Data science students and professionals will benefit from learning linear regression (and other methods of statistical analysis) when they need to make decisions or recommendations for future events based on what is already known about a dataset.
What is Linear Regression?
Linear regression is a statistical model that uses an input and predictor variable (X) to determine a specific output variable (y). The equation for linear regression is “y = bo + b1x” where the X variable becomes a quantifiable predictor while the y variable returns a quantifiable outcome. By plotting a dataset on an X-y axis, most analysts use a linear regression model to visualize how close certain data points get to the line of best fit for the model, which is usually displayed as a line plotted in the center of the graph with the data points plotted around or on the line. Linear regression is a model that can be used to measure impact, as well as to make predictions about a dataset.
When measuring impact, linear regression shows the effect that one variable has on another, making it useful when developing a conditional argument, i.e., “If this, then that” offers an explanation for what will happen within a specific set of circumstances or after a particular action is taken. Within statistical modeling, linear regression is also applied as a method of learning more about the behavior of a sample, study, or numerical dataset by analyzing the impact of variable changes or making predictions about it. Depending on the field or industry, this could mean determining the growth or outcome of a sales strategy within marketing and advertising or even predicting the ebbs and flows of economic growth within society. There are many uses for linear regression in the world of data analytics.
The Role of Linear Regression in Data Analytics
In the realm of data analytics, linear regression is most commonly used for predictive and prescriptive analytics. Predictive analytics is focused on using data that is collected from the past and/or a particular time period to create a forecast or projection about what will happen in the future. Prescriptive analytics takes this data analysis a step further by allowing data scientists to also weigh the benefits and costs of different scenarios and business decisions to make the best possible decision with the data on hand.
For data scientists that rely on predictive and prescriptive analytics, linear regression makes it easier to plot the various scenarios and outcomes needed to make sound business decisions. In addition, linear regression can be used when developing machine learning models. As a statistical model that can be interpreted as an algorithm, linear regression is commonly used when working with automation and machine learning to run analyses on a dataset, as well as making predictions with that dataset. Using a combination of Python libraries, linear regression can be automated and used to validate a machine learning model during the development process.
Why Data Scientists Use Python for Linear Regression
There are several data science tools that allow data scientists to draw on linear regression models for predictions and machine learning, but many data scientists rely on the Python programming language when working with regression models. This is because Python has several data science libraries and packages that include tools and techniques for working with statistical models and algorithms for machine learning and predictive analytics. For example, both NumPy and scikit-learn are Python libraries that can be used for mathematical reasoning and statistical analysis.
Specifically, the NumPy library includes numerical computing tools that can be used to analyze any dataset imported into a Python environment, allowing the NumPy Python library to be used for predictive analytics. At the same time, data scientists using scikit-learn can use linear regression models for automation and machine learning. This is because scikit-learn includes a regression model as part of its extensive library, which can also be used to make predictions about a dataset. Using both of these libraries, data scientists can also learn to work with other statistical models and machine learning algorithms during the process of analyzing and visualizing a dataset.
Need to Learn More About Python and Statistical Modeling?
Programming with statistical models is the foundation of predictive analytics and machine learning and is common among open-source communities, making it easier to share models and training methods. This is why many data scientists and analysts use the Python programming language to automate machine learning models. As an open-source programming language known for its support community, Python offers several libraries and packages which curate all of the algorithms you would need for automating machine learning models.
Noble Desktop’s Data Science classes focus on teaching students how to use programming languages to develop statistical models and machine learning algorithms. Specifically, the Python for Data Science Bootcamp is an introduction to the fundamentals of Python for beginner data science students. By prioritizing Python’s data science libraries and the creation of data visualizations, this bootcamp offers an introduction to statistical models, algorithms, and recommendation systems.
Students who already have some training with Python and its libraries can take the Python Machine Learning Bootcamp, which includes more advanced instruction in using algorithms for automation and machine learning. These classes are also combined with the curriculum for the Python for Data Science and Machine Learning Bootcamp which introduces students to working with regression and other statistical models that form the basis of developing machine learning models.