Predictive Analytics Job Description

Predictive analytics enables organizations to use information from numerous raw data sets to predict future market trends. Predictive analytics professionals, who are in significant demand, play a major role in forecasting future trends, informing business decisions, and optimizing strategies across various industries.

Learn more about this growing field, including details on the skills you might need for success and information on the types of roles and industries that use this technique. 

What is predictive analytics?

Predictive analytics involves using data to predict future trends to aid business decisions. To identify these trends, predictive analytics uses a combination of machine learning and statistics, as well as other advanced mathematics.

The four types of analytics analysts typically use are hierarchically arranged as follows:

1. Descriptive analytics: Describes the past via historical analysis

2. Diagnostic analytics: Applies historical data to explain past events

3. Predictive analytics: Predicts trends based on past and current data patterns

4. Prescriptive analytics: Prescribes future decisions based on predictions

Predictive analytics is important in a variety of fields, including:

• Engineering

• Research science

• Business

• Economics

Types of predictive analytics roles

The use of predictive analytics is widespread. As such, a variety of predictive analytics roles exist:

Business intelligence analyst

Business intelligence analysts collect and interpret data to guide companies toward more profitable business decisions. These professionals generate reports forecasting important future data trends based on large amounts of past and current data.

Business intelligence analysts are not just good at gathering data. As a professional in this role, you must have good communication skills as a large part of your job will likely require you to effectively communicate your findings to relevant stakeholders, both within your company and outside of it. You must also manage the information workflow to guarantee that important analyses reach the relevant parties on time.

Important skills for business intelligence analysts include:

• Communication

• Critical thinking

• Creativity 

• Data mining and visualization

• Marketing 

• Machine learning

Although education requirements vary depending on industry, employer, and role specifics, business intelligence analysts usually hold a bachelor’s degree in business, economics, information science, or a related field. Some companies look for a master’s degree, ideally in business analytics. A Certified Business Analysis Professional (CBAP) certification from the International Institute of Business Analysis (IIBA) may also give you an advantage over otherwise similarly situated candidates.

The average annual salary for a business intelligence analyst is $103,099 [1].

Data scientist

Data scientists research and analyze data to help guide important business decisions, solve problems, and improve business operations. Your influence in making these decisions makes you a key player in your organization. Decisions based on relevant or expertly analyzed data will likely benefit your company.

Data scientists work at the intersection of:

• Business

• Communications

• Mathematics

• Technology

• Entertainment

• Finance

• Government

• Health care

• Utilities

Data scientists work with big data—larger data sets than they’re capable of analyzing with ordinary data analytics tools—including both structured and unstructured data:

• Structured data includes names, dates, and credit card information. It includes the subset of quantitative data, which is structured and numerical.

• Unstructured data includes text documents, social media posts, website content, and videos. It includes a subset of qualitative data, by which data points fit into categories.

To secure a role as a data scientist, consider earning a bachelor’s degree in computer science, computer engineering, or information technology. Boot camps, online courses, and certification programs can also provide you with further education on topics relevant to the field. The median annual data scientist salary is $120,027 [2].

Machine learning engineer

An interest in developing and perfecting the training technology that powers modern artificial intelligence (AI) applications might mean that a career as a machine learning engineer is a good option for you. Engineers in this field use AI to help provide sophisticated answers to business problems, to monitor and adjust project development processes, and, when necessary, to employ AI-based tasks such as:

• Data processing

• Data mining

• Speech recognition

• Robotic manipulation

Machine learning (ML) is a sub-discipline of AI in which programmers teach computers to learn via exposure to enormous amounts of data. Over time, an AI program learns to perform tasks more accurately. One major advantage is that certain ML techniques allow AI programs to train themselves autonomously after a certain point.

Machine learning engineers typically need a bachelor’s degree in computer science, information technology, software engineering, or a related field. You’ll want to concentrate on coursework relevant to machine learning principles. Consider classes with topics such as:

• Applied data science

• Data mining

• Machine learning foundations

• Statistical computing and modeling

The average annual machine learning engineer salary is $123,251 [3]. 

What are the skills required for predictive analytics?

To work in predictive analytics, you’ll need a variety of technical skills, including proficiency in and knowledge of the following:

• Data visualization techniques

• Machine learning 

• Microsoft Excel

• Structured query language (SQL)

• Programming languages (e.g., Python, R)

• Presentation skills

You’ll need experience with data analytics tools and platforms like Microsoft Azure, SAP Predictive Analytics, and Tableau. Familiarity with the following predictive analytics models will also be necessary:

• Classification

• Regression

• Clustering

• Time-series

Industry variations in the predictive analytics role

Companies in various industries and sectors use predictive analytics to influence data-driven decision-making. Examples include:

Finance

Employees in the financial and insurance sectors use predictive analytics technology to approve loans, assess credit risk, detect fraud, and predict market trading trends or weather events (to reduce insurance claims).

Health care

Health care providers use predictive analytics to monitor patient well-being. This includes monitoring sepsis, vital signs, and changes in a patient’s overall condition. Predictive analytics also assists in disease diagnosis and prognosis based on a patient’s historical health information.

Manufacturing

Predictive analytics helps manufacturers monitor equipment and predict defects, irregularities, and maintenance demands. This proactive approach to machinery breakdown optimizes production. 

Retail

Predictive analytics can forecast customer churn, which marketers can then attempt to avoid by communicating more closely with customers and using more personalized cross-selling strategies to retain them.

Learn more about predictive analytics with Coursera

Predictive analytics is a fascinating method whereby companies get the most out of data by using it to forecast future business trends. Predictive analytics professionals help organizations turn data into actionable insights.

Learn more about predictive analytics with Coursera. For a more in-depth study of the subject, earn a Tableau Business Intelligence Analyst Professional Certificate.