Data Scientist

Section 1: Career Overview

A Data Scientist collects information from many different sources and turns it into answers that help people make better decisions. You use programming, statistics, and math to find patterns that are too large or complicated to see by hand. You build prediction models, create charts and dashboards, test ideas with real data, and explain what the results mean to business leaders, engineers, healthcare professionals, or researchers. Many Data Scientists now also build, evaluate, and improve artificial intelligence and machine learning systems while checking that the results are accurate and trustworthy. You spend most of your time using computers, databases, cloud platforms, and programming tools instead of working with physical equipment. If you enjoy solving difficult problems, working independently for long periods, and discovering answers hidden inside large amounts of information, this career offers many opportunities in 2026 and beyond.

Section 2: Training Path

The most common pathway begins with earning a bachelor's degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, or a closely related quantitative field. Students typically learn probability, statistics, linear algebra, databases, programming, machine learning, and data visualization while completing projects using Python, SQL, Jupyter Notebook, Git, Tableau or Power BI, cloud platforms such as AWS, Microsoft Azure, or Google Cloud, and machine learning libraries including pandas, NumPy, scikit-learn, TensorFlow, or PyTorch. Employers increasingly expect applicants to graduate with a portfolio of completed analytical projects hosted on GitHub, documented reports, and experience cleaning, analyzing, modeling, and presenting real datasets. Many employers also value internships where students work with production databases, business intelligence tools, and collaborative software development environments.

Section 3: Schools Offering the Required Training

School Location Distance from ZIP Code 61615
Bradley University Peoria, Illinois 4.8 miles
Illinois State University Normal, Illinois 38.0 miles
Augustana College Rock Island, Illinois 68.9 miles
Orion Technical College Davenport, Iowa 71.7 miles
University of Illinois Urbana-Champaign Champaign, Illinois 86.5 miles
University of St Francis Joliet, Illinois 93.0 miles
Quincy University Quincy, Illinois 109.6 miles
Illinois Institute of Technology Chicago, Illinois 127.1 miles
DePaul University Chicago, Illinois 128.6 miles
Loras College Dubuque, Iowa 130.0 miles
Valparaiso University Valparaiso, Indiana 141.7 miles
Purdue University-Main Campus West Lafayette, Indiana 147.5 miles
University of Wisconsin-Whitewater Whitewater, Wisconsin 148.0 miles
University of Wisconsin-Parkside Flex Kenosha, Wisconsin 155.4 miles
Truman State University Kirksville, Missouri 160.5 miles

Section 4: Job Postings

Principal Data Scientist - AI Trainer

Employer: DataAnnotation

Location: Remote (Bethesda, MD)

Salary Range: $50 - $100 an hour

Senior Software Engineer / Data Scientist (Training Systems)

Employer: Leidos

Location: Chantilly, VA

Salary Range: $154,050 - $278,475 a year

Clinical Data Scientist

Employer: RedSail Technologies, LLC

Location: Spartanburg, SC (Hybrid)

Salary Range: $130,000 - $160,000 a year

Palantir Data Scientist

Employer: Accenture Federal Services

Location: Vandenberg AFB, CA

Salary Range: $86,400 - $176,200 a year

Data Scientist - AI Trainer

Employer: DataAnnotation

Location: Remote (Brooks, CA)

Salary Range: $50 - $100 an hour

Senior Data Scientist

Employer: Leidos

Location: Chantilly, VA

Salary Range: $154,050 - $278,475 a year

Senior Data Scientist (TS/SCI Cleared)

Employer: Johns Hopkins Applied Physics Laboratory (APL)

Location: Laurel, MD

Salary Range: $105,000 - $290,000 a year

Data Scientist - Predictive Analytics

Employer: McKesson

Location: Columbus, OH

Salary Range: $108,800 - $181,300 a year

Section 5: What Matters for Getting Hired

The strongest candidates follow the traditional bachelor's degree pathway while building a portfolio that proves they can solve real problems instead of simply completing classroom assignments. Employers in 2026 expect graduates to demonstrate projects using Python, SQL, Git, Jupyter Notebook, Tableau or Power BI, cloud platforms such as AWS, Azure, or Google Cloud, and machine learning frameworks commonly used in production. Completing internships where you clean messy datasets, write production-quality code, build predictive models, document your work, and present results to business teams carries significant weight because it demonstrates experience working with real data instead of textbook examples. Employers also value GitHub repositories, Kaggle competitions, research projects, capstone projects, and technical documentation that clearly explain your methods, assumptions, testing, and conclusions. Strong communication is important because Data Scientists regularly explain technical findings to managers, engineers, customers, and executives who may not have technical backgrounds.