Essential Predictive Analytics Skills

If you plan to use data to forecast future results, you are likely using predictive analytics. Because the value of the global predictive analytics market is expected to grow from $11.5 billion in 2023 to $61.9 billion by 2032 [1], you may want to consider enhancing your predictive analytics skill set. When it comes to predictive analytics, a few of the essential skills you may want to explore include statistical models, machine learning, and data preprocessing. 

Health care, finance, insurance, retail, and numerous other industries already apply predictive analytics. If working in predictive analytics sounds interesting, a career as a data scientist could be a good match for you. Professionals in this profession earn a median annual salary of $108,020 and can expect demand for their skills to grow at a much faster-than-average rate through 2033 [2].

Explore the fundamentals of predictive analytics and the technical, analytical, and communication skills necessary to use this process effectively.

Core competencies in predictive analytics

If you choose to increase your predictive analytics skills, you will probably want to start your journey by learning about the following core competencies: statistical models, machine learning techniques, and data preprocessing. Take a closer look at these three fundamental aspects of predictive analytics.

Understanding statistical models

The data science process of statistical modeling deals with the statistical analysis of the information in your data set. When you apply statistical modeling to raw data, you can create a visualization of that data, allowing you to spot connections between the variables and make predictions about future outcomes. When performing predictive analytics, you’re using statistics and modeling techniques to extract information from both historical and current data sets to predict trends and results that may occur in the future.

Familiarity with machine learning techniques

Predictive analytics require machine learning techniques. Machine learning is a type of artificial intelligence (AI) that uses computers to analyze massive data sets to identify patterns and then make predictions based on that information. Before the machine learning system can make these predictions, you need to train it to perform one or more of the following techniques:

• Regression: Learns from trials of input data and then, based on that knowledge, analyzes new data and makes numerical predictions. Predicting weather forecasts, property prices, and customer churn rates are all common use cases.

• Classification: This machine learning technique categorizes data fed to the system. Eventually, the computer can place each new piece of data into its proper category. It’s used for things like marketing and detecting fraud.

• Clustering: This technique groups the input data into clusters based on similar characteristics. It’s especially effective for unlabeled data in areas like image processing and biology.

• Neural networks: In terms of predictive analytics, if you’re searching for nonlinear relationships within data sets, this machine learning method, which is great at recognizing patterns, can help you.

Because predictive analytics uses machine learning algorithms to analyze data sets, identify patterns, and make predictions, you must understand how they work to help an organization make better decisions and avoid risk.

Knowledge of data preprocessing

Knowing how to preprocess raw data is important because you must prepare the data for the predictive model. Data preprocessing entails cleaning the data by removing anomalies, absent data points, and distant outliers. The aforementioned faults in the data can occur due to measurement or input errors.

Technical skills

If you’re working in predictive analytics, you’ll likely need certain technical skills, such as an applicable knowledge of programming languages and analytics software. Explore an in-depth explanation of these technical skills:

Proficiency in programming languages

An understanding of programming languages is typically helpful for a job requiring predictive analytics skills. If you want to pursue a career in data science, a field that uses predictive analytics often, you will most likely need to understand how to use the following programming languages: R, Python, SQL, Visual Basic for Applications (VBA), and Julia.

Familiarity with analytics software

Work to build a solid understanding of different types of predictive analytics software. These programs have a range of helpful functions, such as connecting data from separate sources, implementing data mining and statistical analysis for trend forecasting, and detecting patterns. Health care, finance, marketing, manufacturing, and many other sectors use this software. Some common types of predictive analytics software are Adobe Analytics, Microsoft’s Azure Machine Learning, SAP Analytics Cloud, Oracle Analytics Cloud, and IBM SPSS Modeler. 

Analytical skills

In predictive analytics, you may benefit from a solid set of analytical skills. These skills can help you effectively evaluate the performance of the models and interpret and explain the results.

Evaluating model performance

After developing a potential predictive analytics model, you need to evaluate whether it is capable of making accurate predictions. You can use a data set to train the machine learning algorithm and then evaluate the model’s efficacy by feeding it test data to determine whether it is functioning as intended. 

Interpreting and explaining results

If you’re working as a data scientist, for example, one of your responsibilities might be to interpret and explain the output produced by the predictive model. Your understanding of the model’s results and your ability to convey that information aid in the decision-making process for your organization. Two techniques for analyzing a model’s predictions include:

• Input-response analysis: Provide the model with various samples to better understand how it makes its predictions.

• Data visualization: Use charts and graphs to display the model’s predictions and gain insight into how it identifies relationships between data.

Communication skills

While working in predictive analytics, the ability to communicate the results of the predictive models to a non-technical audience is essential. Collaboration is also important because you’ll likely be working with cross-functional teams.

Explaining predictive model results to stakeholders

One way to communicate technical results to a non-technical audience is to develop your data visualization skills—using charts, graphs, and maps to communicate findings to colleagues, clients, and executives. By helping your audience comprehend the meaning of the results, you can more effectively make recommendations regarding business decisions.

Collaborating with cross-functional teams

Once the predictive model produces results, the data scientist can collaborate with other teams to interpret that information and identify insights. For example, in the health care industry, after the data scientist receives the output from the predictive model, they can collaborate with partners in both the operational and clinical departments to review the new insights and determine how to use that information to design interventions. 

How to gain more predictive analytics skills

You have several options to increase your predictive analytics skill set. Some universities now offer a bachelor’s degree in predictive analytics. If you have already earned a bachelor’s, you might consider pursuing a master’s degree in predictive analytics. If you want a non-degree option, other possibilities for gaining these skills are bootcamps, online courses, or a certificate program in predictive analytics. Because solid communication skills are important when working in predictive analytics, you might also consider taking a course such as Improving Communication Skills from the University of Pennsylvania.

What are the 4 steps in predictive analytics?

Now that you’ve sharpened your predictive analytics skills, you may be ready to put them into practice. The following process can help you begin: 

• Step one: Gather your data in one place. If you work at or run a business, your data may be siloed in several places. 

• Step two: Prepare the data by cleaning it. Typically, clean data will give you more accurate results.

• Step three: Construct your predictive model. Analytics software can help you create, test, and implement your model. You can even do this without a team of data scientists.

• Step four: Apply the model’s results to your business. If you work in marketing, you might share the insights with customer service or sales representatives to help them perform better at their jobs. You can even predict how customers might respond to marketing campaigns.

Develop your predictive analytics skills on Coursera

Data continues to become an important factor necessary for sound decision-making in business, making predictive analytics skills increasingly essential. Consider enhancing your skills or discovering more about predictive analytics with the Analytics for Decision Making Specialization offered by the University of Minnesota on Coursera. This Specialization covers topics including predictive analytics, predictive modeling, and forecasting. Another option is the Operations Analytics course from the University of Pennsylvania. In this course, you’ll explore operations management, data science, and predictive analytics.