Predictive Analytics Job Description

Predictive analysis allows organizations to use information gleaned 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 in the field and information on the types of roles and industries that use this technique. 

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Tableau Business Intelligence Analyst

Launch your career in Data Analytics. Build in-demand skills and gain credentials to go from beginner to job-ready in 8 months or less. No degree or prior experience required.

4.7

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43,577 already enrolled

Beginner level

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Average time: 8 month(s)

Learn at your own pace

Skills you'll build:

Data Analysis, Presentation Skills, Reporting Skills, data visualizations, Tableau Software, Data Management, Data Manipulation, Tableau Data Analytics, Data Analysis Reporting, Tableau Public Platform, Data Insights, Data Visualization, Interactive Tables, Preprocess Data, Data Visualization Fundamentals, Data Restructuring, Business Analysis, Business Process Model, Requirements Elicitation, Business Requirements Documentation, Stakeholder Identification, Data Architecture, Data Governance, Data Sources, Data Analytics Lifecycle, Foundational Project Management, Data Literacy, Interactive Data Visualization, Spatial Analytics, Advanced Data Visualizations, Data storytelling, Interactive Dashboards, Data Presentations

What is predictive analytics?

Predictive analytics involves the use of 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 will also need to 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 specifics such as industry, employer, and role, business intelligence analysts usually hold a bachelor’s degree in areas like 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 $101,805 [1]. 

Data scientist

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

Data scientists work at the intersection of: 

• Business 

• Communications

• Mathematics

• Technology

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 values such as names, dates, and credit card information. It includes the subset quantitative data, which is structured and numerical. 

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

Data scientists work in all manner of industries and sectors, including: 

• Business

• Entertainment

• Finance

• Government

• Health care

• Utilities

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 $118,517 [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 plus 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

• Clustering

• Regression

• Time-series

Industry variations in the predictive analytics role

Companies in a variety of 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 their equipment and predict defects, irregularities, and maintenance demands. This helps optimize production via a proactive approach to machinery breakdown. 

Retail

Predictive analytics can forecast customer churn, which marketers can then attempt to avoid via closer communication with customers and 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. 

professional certificate

Tableau Business Intelligence Analyst

Launch your career in Data Analytics. Build in-demand skills and gain credentials to go from beginner to job-ready in 8 months or less. No degree or prior experience required.

4.7

(767 ratings)

43,577 already enrolled

Beginner level

Learn More

Average time: 8 month(s)

Learn at your own pace

Skills you'll build:

Data Analysis, Presentation Skills, Reporting Skills, data visualizations, Tableau Software, Data Management, Data Manipulation, Tableau Data Analytics, Data Analysis Reporting, Tableau Public Platform, Data Insights, Data Visualization, Interactive Tables, Preprocess Data, Data Visualization Fundamentals, Data Restructuring, Business Analysis, Business Process Model, Requirements Elicitation, Business Requirements Documentation, Stakeholder Identification, Data Architecture, Data Governance, Data Sources, Data Analytics Lifecycle, Foundational Project Management, Data Literacy, Interactive Data Visualization, Spatial Analytics, Advanced Data Visualizations, Data storytelling, Interactive Dashboards, Data Presentations