It’s everywhere — from sports and medicine to social media and marketing.
Your complete guide to degrees, skills, and schools.
How to get started now.
PLUS: Why being data literate matters — no matter your career.
Welcome to the World of Data Science!
Look around you — data is everywhere. Every time you stream a show, scroll through social media, use a maps app, or even shop online, data science is working behind the scenes to make your experience smoother, faster, and more personalized. It’s the hidden force shaping the world around us, and it’s growing at an unbelievable pace.
The amount of data we generate is exploding. In fact, more data has been created in the last two years than in all of human history before that. Every click, search, and swipe adds to an ever-growing pool of information. But raw data alone isn’t useful — it takes skilled professionals to collect, organize, and analyze it to unlock its potential. That’s where data scientists come in.
Data science is one of the most exciting and in-demand career fields today, offering opportunities in nearly every industry you can imagine. Whether you’re passionate about sports, entertainment, healthcare, finance, national security, or even conservation, data scientists are making a difference in all of these areas. They predict weather patterns, help doctors diagnose diseases earlier, power self-driving cars, and even improve video game experiences. If you like solving problems, finding patterns, and working with technology, a career in data science could be a perfect fit for you.
In this guide, you’ll explore the major sectors where data scientists work, learn about the different career paths available, and discover how you can start building the skills to join this rapidly growing field. Even if you don’t become a data scientist, understanding how data is used will give you a major advantage in almost any career.
So dive in, explore the possibilities, and see how data science is shaping the future — and maybe even your own!
Robert Black CEO Start Engineering
bblack@start-engineering.com
Data science is the process of using data to solve problems, make predictions, and find patterns. It combines math, statistics, computer science, and domain knowledge (knowledge of the specific field you’re working in). Think of it as detective work with data serving as clues. Data science “detectives” collect and analyze all sorts
of data — numbers, graphs, social media posts, and even pictures — to uncover hidden patterns, solve problems, and make smarter decisions. Their job is to turn messy piles of information into clear and useful insights, essentially turning data into decisions.
Data scientists use math, technology, and problem-solving skills to make sense of information and solve real-world problems. From predicting weather to recommending your next favorite show, data science is all around us!
Data scientists are the people who dive into big piles of data, make sense of it, and use it to solve real-world problems. Here’s what their jobs typically involve:
1. Collect Data
Data can come from anywhere — websites, apps, sensors, surveys, or social media.
EXAMPLE: Netflix collects data on what shows you watch, when you pause, or what you rate.
Data is often messy. Data scientists clean it up by fixing errors, removing duplicates, and organizing it.
EXAMPLE: A sports data scientist might remove incorrect player stats from a database.
This is where math and statistics come in. Data scientists look for patterns, trends, and insights.
EXAMPLE: Analyzing weather data to predict hurricanes.
Data scientists use programming and machine learning to build models that can predict outcomes or automate decisions.
EXAMPLE: Creating a model to recommend movies on Prime Video based on what you’ve watched before.
Data scientists make charts, graphs, and dashboards to share their findings in an easy-to-understand way.
EXAMPLE: Showing a map of wildfire risks based on temperature and wind data.
Ultimately, data scientists use what they learn to solve real-world challenges.
EXAMPLE: Helping hospitals predict which patients are at risk for certain illnesses.
Data science involves collecting, processing, analyzing, and interpreting large amounts of data to uncover patterns, trends, and insights. It combines aspects of statistics, computer science, domain knowledge, and machine learning.
Data comes from nearly every part of life — your phone, the internet, stores,
Every time you scroll, click, or post, you create data.
Examples:
⊲ Your Instagram likes or TikTok views.
⊲ Product reviews or YouTube comments.
⊲ What you search for on Google or watch on Netflix.
Special devices can measure and collect information about the world.
Examples: ⊲ Fitness trackers recording your steps and heart rate.
⊲ Weather stations measuring temperature, wind, and rainfall.
⊲ Smart home devices tracking energy usage (like a Nest thermostat).
When you shop, companies collect data to improve their services.
Examples: ⊲ Your purchase history at Amazon or Target.
⊲ Customer surveys about what you like or don’t like.
⊲ Loyalty programs (like Starbucks Rewards) tracking how often you visit.
The amount of data being created is growing exponentially. In fact, more data has
machines, and even nature.
Every photo, video, or message you share becomes data.
Examples:
⊲ Snapchat filters record how often they’re used.
⊲ Instagram tracks hashtags to find trends.
⊲ TikTok sees what videos you watch and how long you watch them.
Scientists collect data to study the world and solve problems.
Examples:
⊲ Satellites taking pictures of Earth to track climate change.
⊲ Ocean sensors measuring sea levels and temperatures.
⊲ Data from telescopes exploring outer space.
Many tools you use daily generate data without you realizing it.
Examples:
⊲ Your phone collects location data for maps.
⊲ Traffic cameras record how busy roads are.
⊲ Streaming services track what shows you binge-watch.
has been created in the last two years than in the entire history of the human race.
Data science helps AI learn. AI helps data science act faster. Together, they’re a powerful team.
Now that you know what data science is, let's learn how it is connected to artificial intelligence. AI is a tool that can learn from data and make decisions on its own. AI is powered by data science — it needs data to learn and improve. And the more it learns, the more it helps analyze data faster and make better predictions.
1. Collecting Data (Data Science)
• Your bank records every transaction: time, location, amount, and store name.
• It also tracks your usual spending habits (e.g., you always buy coffee in the morning, shop online at night, and rarely spend over $100 at once).
To understand how they work together, let's look at the example of banking and finance.
Let’s say you have a bank account and use a debit or credit card to buy things. Your bank collects data about your purchases — where you shop, how much you spend, and when you spend money. This is data science at work — gathering and analyzing financial data.
But what if someone steals your card and tries to use it? Your bank needs to detect fraud instantly to stop the thief from spending your money. That’s where AI comes in. Let's take a look at how the two work together to stop fraud from happening.
2. Finding Patterns (Data Science)
• Data scientists analyze this information to identify what "normal" spending looks like for you.
• They look for patterns: Do you usually shop in Chicago? Do you buy groceries every Sunday?
• Let’s say someone suddenly tries to buy a $1,000 laptop in another country using your card.
• AI instantly compares this transaction to your usual behavior and sees it’s very different.
• AI decides: “This looks suspicious!”
• The bank’s AI system immediately flags the transaction and might even block your card before the thief can spend more money.
• You get a text: "Did you just try to buy a laptop in China? Reply YES or NO."
The great thing is that AI keeps learning from millions of transactions every day. The more data it gets, the better it becomes at spotting fraud while avoiding false alarms.
So, how do you become a data scientist?
Most data scientists start with a bachelor’s degree in data science, statistics, or a related field, but more and more are taking a different path — beginning with an associate degree in data science, computer science, math, or business analytics. And here’s the cool part: you don’t even need a degree to get started! Certifications in data analytics or programming can help you land entry-level roles like junior data analyst or data technician, especially if you build strong coding skills. (See "Learn to code" on page 19.) Even better, you can start exploring data science in high school! Check out the pathways below to see how, and learn what the job titles mean on pages 14-15 of this career guide.
See page 16 for more info.
Entry-level positions:
Data Technician
Junior Data Analyst
Business Intelligence Analyst
Most employers require at least a degree, but some will hire individuals with strong skills in programming, statistics, and machine learning. See page 21 for more info.
Entry-level positions:
Data Technician
Data Analyst
Business Intelligence Analyst
While a 2-year degree can lead to entry-level positions, advancing to higher salary brackets often requires continuous learning, certifications, or further education. Many community colleges offer both an associate degree and a certification. See page 20 for more info.
Entry-level positions:
Data Scientist
Data Analyst
Business Intelligence Analyst
Data Architect
This is the most common path to a career in data science. About 34% of data scientists also hold a Master’s degree. See page 22 for more info.
Being a data scientist isn’t just about numbers — it’s about curiosity, problem-solving, teamwork, and communication. If you enjoy finding patterns, solving puzzles, and explaining things in creative ways, you might already have what it takes to be a great data scientist!
⊲ Loves to explore and ask “why?”
Example: You enjoy science experiments, digging into why certain variables change results.
⊲ Thinks logically and solves problems with data
Example: You analyze sports stats to figure out why your favorite team keeps losing.
⊲ Pays attention to details
Example: You double-check your math homework to make sure you didn’t make a tiny mistake that changes the whole answer.
⊲ Always learning new things
Example: You like trying out new apps, coding, or learning tricks to improve your gaming strategy.
⊲ Thinks about fairness and responsibility
Example: You question whether an AI-powered grading tool treats all students fairly.
⊲ Explains ideas in a way that others understand
Example: You break down a tough science concept for a friend who missed class.
⊲ Works well with others to solve problems
Example: You team up on a group project and make sure everyone’s ideas are included.
⊲ Sees the big picture
Example: You help decide the best way to spend your club’s budget by analyzing past expenses.
⊲ Understands how data helps people and businesses
Example: You track your school’s vending machine sales to suggest better snack choices.
⊲ Tells a story with data
Example: You make a graph to show how students’ study habits affect their test scores.
⊲ Manages time well to handle multiple tasks
Example: You juggle school, sports, and a part-time job while keeping up with deadlines.
Data Technician
Focuses on handling and maintaining data systems (i.e., patient records or sales data) often in a technical support or IT capacity.
Example: Sarah works as a Data Technician for a retail company. She manages the company’s database, ensuring all customer and sales data is correctly entered and maintained. When there are issues with data quality, she troubleshoots errors and keeps the system running smoothly so that the marketing and sales teams have reliable information to work with.
Entry-level position focused on data cleaning from various sources, visualization, and reporting.
Example: Alex is a Junior Data Analyst at a healthcare company. He spends most of his day cleaning data from different medical records, ensuring that patient information is accurate and complete. He also creates simple reports that show trends in patient care, helping the hospital’s leadership make better decisions about resources.
Business Intelligence (BI) Associate
Collects, analyzes, and visualizes data, creating dashboards and reports to track business performance and identify trends.
Example: Priya is a BI Associate for a transportation company. She gathers and analyzes data on delivery times and fuel usage, then creates dashboards that visualize this information. Her reports help management identify ways to improve delivery efficiency and reduce fuel costs.
Data Scientist
Uses statistical analysis, machine learning, and AI to extract insights from data. Builds predictive models and works on solving business problems.
Example: Leo is a Data Scientist at a financial institution. He uses machine learning to build predictive models that identify potential fraud in credit card transactions. His work helps the company prevent millions of dollars in fraud losses and ensures customer accounts stay secure.
science offers rewarding, stimulating opportunities at every career stage.
Data Analyst
Interprets data and creates reports, dashboards, and visualizations to support decision-making.
Example: Maria works as a Data Analyst for a sports team. She analyzes player performance data and creates visualizations that help coaches make informed decisions about strategies and lineups for upcoming games. Her insights have helped the team improve their win rate.
Designs plans for organizing and managing data so that it is stored in one place, easy to access, kept up to date, and protected from security risks.
Example: Ethan is a BI Analyst at an e-commerce company. He analyzes data on customer buying patterns and market trends to help the company decide which products to promote. His work helps the company increase sales and identify new market opportunities.
Data Architect
Designs plans for organizing and managing data so that it is stored in one place, easy to access, kept up to date, and protected from security risks.
Example: Jasmine is a Data Architect for a large healthcare network. She designs a system that integrates data from all the hospitals in the network, ensuring that patient records are accessible, up to date, and secure. Her system makes it easier for doctors to access important patient information during treatment.
While data scientists analyze data to find insights, data engineers build the systems that allow data to be collected, stored, and processed efficiently. Without data engineers, data scientists wouldn’t have clean, organized data to work with.
Data engineers focus on building data pipelines, databases, and infrastructure to ensure data is accessible and usable while data scientists use that data to build models, analyze trends, and make predictions.
Think of it like a restaurant: Data engineers design and build the kitchen with the latest, best gear for the chefs, or data scientists, to use in cooking up the ingredients, or data, into gourmet dishes full of satisfying insights and tasty predictions.
Data engineering is often taught in engineering schools, typically within computer science, software engineering, or information systems programs. Data engineering is about building largescale data systems. Many engineers who study computer science, electrical engineering, or IT move into data engineering careers.
How’re things sounding so far? If you’re starting to imagine yourself in a data science career, you’re not alone. It’s one of the fastest-growing fields out there, with opportunities in everything from business to healthcare to entertainment. And here’s the great part — you can start preparing for a future in data science right now, even in high school. Whether your school offers data science classes or not, there are lots of ways to start developing the skills and knowledge you’ll need.
More and more schools are offering introductory data science courses that cover programming, statistics, and data manipulation. Some states even allow these courses as an alternative to Algebra II. If your school has one, take advantage of it! Here are the best classes to include in your schedule:
Math (Algebra through Calculus) – Builds a strong foundation for data analysis and modeling.
Statistics & Probability – Critical for understanding data trends and making predictions.
Computer Science – Learning to code (especially Python or R) is key in data science. (See paragraph on page 19.)
Physics or Other Science Courses – Teaches analytical thinking and data collection skills.
Economics or Social Studies – Helps apply data analysis to real-world problems.
English & Communication Studies – Develops skills for explaining data findings clearly.
Advanced Placement (AP) and International Baccalaureate (IB) Courses – If your school offers AP or IB courses, consider taking:
• AP Statistics – A great introduction to data analysis.
• AP Calculus – Useful for understanding changes and trends in data.
• IB Mathematics – Offers a strong foundation in problem-solving and logic.
Did you know you could do that? Many colleges offer dual-credit programs, where you can take college-level courses that count toward both your high school and college graduation requirements. This is a great way to save time and money while getting early exposure to college coursework — or even studying subjects that might not be offered at your school.
If your school doesn’t have dual-credit options, don’t worry! Plenty of colleges have online programs that allow high school students to enroll in college courses. Even though you won’t be a fulltime college student yet, the credits you earn can be applied toward your future degree. Some colleges offer these courses through their adult learning or extension programs, and you might be able to take classes in data science, computer science, or math. If you’re interested, reach out to the office of the college’s registrar to find out about eligibility and how to enroll.
Crash Course Data Literacy Study Hall
HIgh School DataJam competition winners
1. Watch some fun educational videos about data science: Check out Crash Course Data Literacy Study Hall, a 15-video series from the University of Arizona that explores data literacy fundamentals. By the end of the course, you will be able to define foundational statistical concepts, explain methods for visualizing data, locate datasets, analyze data, and recognize ethical issues connected to data interpretation.
2. Take an online class or workshop: The website datascience4everyone.org has an enormous database of online classes at every level, and some are even free, like Trailhead from Salesforce. Another great option is IBM’s Data Science Foundations course on Coursera, which introduces core concepts like data visualization, Python programming, and machine learning — no experience required.
Contests help sharpen your analytical and critical thinking skills, your ability to solve problems, and your communication skills — key skills that data scientists use every day! Here are a few to check out:
If you’re excited about data science and looking for ways to build your skills, summer camps can be a fantastic opportunity. Many programs offer hands-on learning experiences in coding, data analysis, and problem-solving while also giving you a taste of college life. Some camps even include residential stays, where you’ll meet like-minded students and work with industry professionals or college professors.
1. Kaggle is an online platform for data science and machine learning enthusiasts. It provides a space for users to collaborate on projects and compete in real-world data science challenges. Look for competitions labeled “Getting Started,” such as: Titanic - Machine Learning from Disaster. These are designed for beginners and come with tutorials to guide you. Kaggle also has free courses that teach Python, data science, and machine learning.
2. The High School DataJam is an academic competition that teaches about the use of big data to answer a research question. Teams of 3-8 students work to formulate a research question, find publicly available data sets, analyze their data, make data visualizations, and present their findings to a panel of judges. Students learn skills pertaining to the scientific method, data analysis, and how to give scientific presentations.
3. American Statistical Association (ASA) Data Science
Competition is geared toward high school students and focuses on using real-world data to solve practical problems. Participants analyze datasets, develop insights, and present their findings in a structured report. It’s an excellent way for students to apply their data science skills in a meaningful way while gaining experience in statistical analysis and communication.
Attending a summer camp can help you strengthen your skills, setting you up for self-guided learning throughout the rest of the year. It also demonstrates to colleges that you are serious about data science and have gone beyond the classroom to pursue your passion. However, it’s important to note that some of these programs can be quite expensive. If cost is a concern, look for scholarships, financial aid, or free programs offered by universities and organizations.
If your school doesn’t offer coding classes, don’t worry — you can learn online for free. Check out platforms like: Code Academy, Alison.com, and the Khan Academy. Start with Python, one of the most important languages in data science. Other great options include R, JavaScript, Java, Go, C, and C++
Community colleges are an affordable option with hands-on experience prized by employers.
Data science is one of today’s fastest-growing fields, and a two-year program at a community college can be a great way to get started. It’s cost-effective, flexible, and focused on real-world skills — whether you want to launch a career quickly or continue on to a four-year degree.
Affordable and accessible
real-world skills you can use right away. That might include training in Excel, SQL, Python, or Tableau — tools used daily in data jobs.
At Sinclair Community College in Ohio, for example, students can earn an associate degree in data analytics while learning how to work with large datasets, create dashboards, and support business decision-making.
fully online Data Analytics program that covers everything from basic statistics to business intelligence.
Real-world learning
Community colleges offer lower tuition than four-year schools, and living at home can cut costs even more — making them an affordable way to begin your data science journey.
Build job-ready skills
Many two-year programs focus on
Similarly, Johnson County Community College in Kansas offers a Data Analytics certificate with hands-on experience in cleaning and analyzing data, creating visualizations, and presenting findings to an audience.
And at Coastline College in California, students can enroll in a
Internships, co-ops, and projectbased courses are key parts of many data programs at community colleges. At Wake Technical Community College in North Carolina, students work with local employers on real data projects that build their resumes and confidence. Some programs even partner with businesses to develop courses around their specific workforce needs — giving you a direct line to employment.
A stepping stone to a four-year degree
If your ultimate goal is a bachelor’s degree, many community college programs are designed to help you transfer smoothly. Associate of Science degrees often align with
four-year programs in computer science, statistics, or information systems. And increasingly, some community colleges offer four-year degrees of their own. For example, Miami Dade College in Florida offers a Bachelor of Science in Data Analytics, helping students transition from foundational to advanced coursework without changing schools.
Smaller class sizes, dedicated instructors, and accessible support services make community colleges welcoming places to learn. If you’re new to programming or nervous about math, you’ll find extra help and encouragement along the way. The strong focus on teaching — and the close connection to students — can help you grow your skills and your confidence at the same time.
Start building your portfolio now
Data science is a “show what you
know” field. That means employers often want to see what you’ve done, not just what you’ve studied. Many community colleges now include capstone projects in their data programs — giving you the chance to analyze real data, solve meaningful problems, and present your findings. Some students even publish work or enter competitions like the DataFest student challenge, where teams tackle large, messy datasets provided by real companies.
Before you begin, consider what kind of program fits your goals:
Associate of Science (AS) degrees include academic courses that prepare you to transfer to a four-year program. These degrees are a strong choice if you’re interested in more advanced roles in data science, computer science, or business analytics down the road.
Associate of Applied Science (AAS) degrees focus more on
practical, workforce-ready skills. These programs often include classes in databases, data visualization, and basic coding, getting you ready for entry-level jobs as data analysts or business intelligence specialists.
Certificates offer fast-track training in specific tools and techniques. Awarded by colleges after completing a focused set of courses, they’re a great option for students who want to build job-ready skills quickly and enter the workforce without committing to a full degree.
In addition to degrees and certificates from community colleges, students can earn industry-recognized certifications to boost their resumes. Some of the most popular for entry-level data roles include:
● Google Data Analytics Certificate (Coursera)
● Microsoft Certified: Data Analyst Associate (Power BI focus)
● IBM Data Analyst Professional Certificate
● CompTIA Data+
These certifications often focus on tools and techniques used in real jobs, and some can be completed in just a few months. Ask your instructors or career center about how to combine them with your college coursework.
A bachelor’s degree in data science is a great investment in your future.
Data science is powerful and exciting because it pulls together insights and methods from a wide range of fields. A strong data science education will blend math, statistics, and computer science with elements of business, economics, psychology, engineering, ethics, and the humanities. This mix of technical skill and critical thinking gives data scientists the highly valued ability to solve problems in virtually any industry.
But with so many directions available, your college journey will come with choices. In a four-year data science program, you can build a solid foundation that prepares you for a lifetime of learning and opportunity. To do that, you’ll need to find the right academic structure and choose the focus areas that align with your strengths and interests.
A bachelor’s degree in data science can be either a Bachelor of Science (BS) or a Bachelor of Arts (BA), and it might be housed in a school of computer science, math, business, or even social science. Each setup offers different strengths — some with more flexi-
bility, others with a deeper technical emphasis. What matters most is finding a program that supports your long-term goals, whether that means jumping into a data job right after graduation or continuing on to grad school in a specialized field like AI, bioinformatics, or economics. Schools across the country are now building data science programs to meet growing student demand and workforce need. And even if a school doesn’t offer a formal “data science” degree, you can often create your own path through a combination of math, programming, and applied courses. The examples below show how different schools are helping students make data science their own.
The basics: strong, versatile foundations
Some programs take a well-rounded, interdisciplinary approach to data science. These degrees provide a solid base in statistics, computer science, and data ethics, with room to explore different application areas. The Bachelor of Science in Data Science at University of
California, Irvine, for example, includes core courses in linear algebra, algorithms, and statistical modeling, along with electives that allow students to explore domains like public health or finance.
Likewise, Purdue University offers a flexible data science program that encourages students to combine their technical coursework with a “domain area” such as life sciences, political science, or marketing. This kind of structure is great for students who want to apply their data skills in a specific field — or who aren’t quite sure yet which direction they’ll take.
If the school you wish to attend doesn’t offer a dedicated data science major, look for a combination of classes in statistics, programming (Python or R), database systems, data visualization, and machine learning. Many students successfully build a strong foundation by pairing a math or computer science major with electives in applied analytics or domain-specific work.
Some four-year programs place a clear emphasis on preparing students for the job market right after college. These programs may offer internship pipelines, employer partnerships, or career coaching focused on data analytics roles in business, tech, or government.
At George Mason University, the BS in Computational and Data Sciences trains students in high-performance computing and simulation, with hands-on work us-
ing real-world datasets. The goal? To prepare students for entry-level jobs in research labs, policy think tanks, or data-driven businesses.
At the University of Texas at Austin, students can major in Data Science through the College of Natural Sciences, with an emphasis on programming, statistics, and data ethics. Strong industry ties, research opportunities, and career services help students transition smoothly into tech and business roles.
Specialized and strategic
Other programs combine core data science instruction with targeted training in a particular industry or problem area. These programs are ideal for students who already know where they want to apply their skills.
For example, Temple University offers a data science degree with tracks in computational analytics, bioinformatics, or business analytics, allowing students to tailor their experience.
Indiana University in Bloomington offers a Bachelor of Science in Data Science through its Luddy School of Informatics, Computing, and Engineering. Students can customize their degree by choosing from specialized tracks such as Data Systems, Data Science Design, Networks and Applied Data Science, or Biological and Health Data Science. This flexible structure allows students to focus on areas that match their career goals, whether they’re drawn to healthcare, design, or large-scale data networks.
The University of North Carolina at Charlotte will launch a Bach-
elor of Science in Sports Analytics in fall 2025. This degree combines core data science training with a focus on real-world applications in sports, including performance analysis, fan engagement, and business strategy.
These structured pathways help students gain both depth and breadth, often including access to expert faculty, labs, and real-world capstone projects.
Some schools approach data science as a platform for wide-ranging exploration. These programs attract curious, high-achieving stu-
dents who want to push boundaries or prepare for advanced study.
At the University of Florida, students can pursue a Bachelor of Science in Data Science that combines core training in statistics, mathematics, and computer science with electives in fields like business, public policy, and the natural sciences. The program encourages interdisciplinary exploration and hands-on research, preparing students for a wide range of career paths or graduate study in data-driven fields.
At the University of Virginia, students can pursue a BS in Data Science through the School of
Data Science, one of the first such schools in the country. The program blends statistics, computer science, and ethics, with a strong focus on real-world challenges and cross-disciplinary learning.
Make it your own
There’s no single formula for a great data science education — but there are lots of great options. As you explore colleges, think about how you like to learn and what kinds of problems you’re most interested in solving. Look closely at course listings, faculty backgrounds, research opportunities, and career support. And don’t worry if your dream school doesn’t have a “Data Science” degree program. With the right mix of math, computing, and application-focused courses, you can build your own data science degree — and shape a future that’s as versatile and dynamic as the field itself.
Add in certificates and certifications
Many four-year programs now offer certificates or help students earn industry-recognized certifications alongside their degree. While a certificate is awarded by a college after completing a specific set of courses in a subject area (like data visualization or machine learning), a certification is usually granted by an outside organization and often requires passing an exam to prove your skills. Both credentials can help you build specialized expertise and boost your resume before graduation. Some colleges even prepare students for external certifications like the Google Data
Analytics Certificate, Microsoft Power BI, or AWS Certified Data
Analytics. These options are great for showing employers you’re ready to work with real-world tools — and can give you a competitive edge in internships or job searches.
A data science degree opens doors to careers in nearly every industry. With the right mix of coursework and experience, you'll graduate ready to solve real-world problems — and shape a future powered by data.
Looking to gain real-world experience and earn while you learn? Data science apprenticeships offer a direct path from the classroom to the workplace. These programs combine hands-on training with academic learning and often include mentoring, certification, or even college credit. Whether you're still in school or just starting out, apprenticeships can help you build skills, grow your network, and boost your career prospects. Here are just a few to explore:
1. Google Apprenticeships
Google offers 18-month Data Analytics Apprenticeships for students pursuing or recently completing a data science degree. Apprentices receive structured training and work on real projects across Google teams. Locations: Atlanta, GA; Austin, TX; Chicago, IL; New York. NY.
2. NEW Apprenticeship — Data Analytics
This program starts with an 8-week pre-apprenticeship, followed by a 12-month, full-time apprenticeship that includes coaching and industry-aligned training. Participants can earn up to 27 college credits. Locations: Dallas, TX; Raleigh, NC; Indianapolis, IN; Hartford, CT; Tempe, AZ.
3. IBM Junior Data Scientist Apprenticeship
IBM’s 2-year program allows apprentices to work alongside seasoned data scientists on real business problems involving machine learning, coding, and data visualization. No degree required. Nationwide.
4. Accenture Apprenticeship Program
Accenture’s Data & AI Apprenticeship focuses on analytics, cloud technologies, and digital transformation. It offers full-time paid roles with mentorship, and a chance to convert to permanent positions. Locations vary by cohort; often includes major cities like Chicago, IL and San Antonio, TX.
TIP: These programs are competitive. Look early, check deadlines, and prepare your resume and LinkedIn profile.
Behind every medical breakthrough is data — helping save lives, speed up treatments, and make care more personal. DID YOU KNOW?
What if a doctor could spot a heart attack before it happens? Or find the perfect treatment for a patient, based on their unique
DNA? Thanks to data science, those “what if’s” are becoming real.By analyzing massive amounts of health data, scientists can now track disease outbreaks, person-
alize treatments, and speed up the discovery of new medicines. These breakthroughs are making healthcare smarter, faster, and more effective than ever before.
But behind every medical breakthrough is a data scientist — someone who works with numbers, patterns, and real-world health data to solve some of the biggest challenges in medicine. Here are some of the ways data science is helping doctors:
Imagine catching a disease before any symptoms appear. That’s the power of data science and predictive health monitoring. By spotting patterns in medical records, wearable data, and genetic information, doctors can identify risks early — and step in before a small problem becomes a serious one. At Biofourmis, engineers and scientists are developing wearable health monitors
that track vital signs in real time. These devices can alert doctors to early signs of illness, giving patients a better chance at fast, effective treatment. The Mayo Clinic uses AI-powered models to scan patient records for early warning signs of diseases like heart failure and diabetes — before patients even know something’s wrong. And DeepRhythmAI is analyzing long-term heart data to detect life-threatening irregular heartbeats, often more accurately than human doctors.
Medical imaging — like X-rays, MRIs, and CT scans — is critical for diagnosing illness, but even skilled doctors can miss subtle signs. Data science is helping improve accuracy by analyzing huge volumes of imaging data to spot early signs of disease that might otherwise be overlooked. The company Enlitic uses advanced AI tools to analyze scans with 70% greater accuracy and at lightning speed — up to 50,000 times faster than traditional methods. Hospitals are also turning to image analysis tools like Aidoc, which can quickly flag abnormalities in CT scans, such as strokes or brain hemorrhages, so doctors can act fast.
Creating new medicines usually takes years of research and billions of dollars. But data science is helping scientists cut that time down dramatically by analyzing biomedical data, clinical trial results, and medical literature at massive scale. The company Databricks helps pharmaceutical giants like
Regeneron analyze huge research datasets to pinpoint promising drug candidates faster. Scientists are also using predictive models to simulate how thousands of chemical compounds might behave in the body — saving time, money, and lab experiments. Another biotech company, Atomwise, uses AI to search through billions of molecules to find potential treatments for diseases like Ebola and multiple sclerosis.
Everyone’s different — so treatments should be, too. Data science helps doctors find the best option for each person. For example, SimBioSys created TumorSight Viz, a virtual 3D model of a tumor that doctors can use to test different treatments — before trying them on a real patient. And Tempus uses advanced data tools to match patients with the most effective treatments for their unique condition, using everything from DNA to electronic health records.
Doctors spend a lot of time documenting patient visits — essential for treatment and billing, but also time-consuming. Data science is helping automate this part of the job, giving doctors more time to focus on patients. Companies like Abridge, Ambience Healthcare and Nuance use natural language processing (a type of AI) to analyze doctor-patient conversations in real time and turn them into accurate medical records. These tools save hours each day and make records more consistent.
Data science helps stores predict trends, plan smarter layouts, improve experiences, and reduce waste.
Have you ever wondered how stores decide what to stock, why prices change online, or why that exact hoodie you looked at once keeps popping up everywhere? It’s not a coincidence — it’s data science at work.
through stores, they can fine-tune shelf arrangements and encourage impulse buys.
Clothing retailers like Zara and Uniqlo use foot traffic data and purchase patterns to adjust product displays and fitting room locations for a smoother, more engaging shopping experience. Some stores even use smart shopping carts and in-store sensors to make layout changes in real time.
Retailers use data analysis and machine learning to understand what customers want, when they want it, and how they shop. From planning store layouts to adjusting prices and recommending products, data science helps make shopping more personal, efficient, and sustainable.
If you’re into shopping trends, business strategy, or solving realworld problems with data, retail data science could be a perfect fit.
Store design isn’t just about making things look nice — it’s backed by data. Retailers use heat maps and traffic data to track how shoppers move through stores. This helps them place high-demand products in the right spots, increase sales, and avoid crowding in busy aisles. Kroger, one of the largest grocery chains in the U.S., works with its data science partner 84.51° to analyze shopper behavior and optimize store layouts. By understanding how customers move
Retailers don’t guess what to stock — they predict it. Using data from past sales, seasonal trends, and even weather forecasts, they figure out what products to order and when. Walmart and Target use predictive models to keep bestsellers in stock while avoiding waste from unsold items. Grocery stores use real-time data to manage fresh foods, helping reduce spoilage and keep shelves filled with what shoppers actually want.
Thanks to data science, shopping is more personal than ever. Retailers use recommendation systems that analyze what you’ve bought, what you’ve browsed, and what others like you are buying. Amazon
suggests products based on your history and reviews. Nike and Levi’s use past purchases to recommend your size and favorite styles.
Prices aren’t always fixed. Many companies use dynamic pricing
DID YOU KNOW?
Retailers can use your weather forecast to decide what to stock — like putting rain boots on sale before the storm hits.
adjusting prices based on demand, competition, time of day, or shopper behavior. Uber and Lyft use surge pricing to raise rates when demand is high. Online stores adjust prices using machine learning that looks at how many people are browsing a product, how many are buying, and how much inventory is left.
Customer service is changing fast, thanks to AI. Many companies now use data science to power chatbots, virtual assistants, and predictive help tools. They can be frustrating when all you want is to talk to a real person, but chatbots are improving
fast. Some are surprisingly helpful, while others still need work. H&M’s chatbot can help you build an outfit. Sephora’s assistant recommends beauty products based on your skin tone and preferences. And Amazon uses predictive analytics to spot shipping issues or offer refunds — before you even reach out.
In a world of growing risks, data science is helping security teams act smarter, faster, and more effectively.
Security threats are always changing. From cyberattacks to terrorism, governments need to stay ahead of danger to keep people safe. One of the most powerful tools they use today? You guessed it: data science. Every day, national security teams collect and analyze massive amounts of information — from satellite images and phone records to social media posts and financial transactions. Their goal is to spot patterns that
could reveal a threat before it happens. Without data science, sorting through all this information would be nearly impossible.
stop attacks before they cause serious damage. For instance, AI systems can analyze billions of online activities to identify patterns that suggest a hacking attempt. Agencies like the Cybersecurity and Infrastructure Security Agency (CISA) and the National Security Agency (NSA) use machine learning models to identify threats in real time — helping them stay a step ahead of cybercriminals.
Terrorist groups use technology to communicate, recruit, and plan attacks. To stop them, governments use data science to spot suspicious patterns in things like travel records, online activity, and financial transactions. Machine learning models scan thousands of international financial transactions daily, looking for unusual patterns that might indicate terrorist funding. Agencies like the Federal Bureau of Investigation (FBI) and the Central Intelligence Agency (CIA) also analyze social media to track dangerous conversations and stop threats before they turn into attacks.
respond to threats. Agencies like the Department of Defense (DoD) and the Defense Intelligence Agency (DIA) use data science every day to support national security and military operations.
National security isn’t just about stopping attacks — it’s also about helping people during disasters. Whether it’s hurricanes, wildfires, or pandemics, data science helps governments prepare and respond more effectively. Real-time data can predict the path of a hurricane or show where wildfire damage is most severe, helping emergency responders reach the right areas quickly. During the COVID-19 pandemic, data models helped track outbreaks and guide public health decisions. Agencies like Federal Emergency Management Agency (FEMA) and Centers for Disease Control and Prevention (CDC) rely on data to act fast in a crisis.
Hackers and cybercriminals try to break into government systems, steal sensitive information, and disrupt operations. Data science helps security teams monitor networks, detect suspicious behavior, and
Today’s military decisions rely heavily on data. From predicting battle outcomes to tracking enemy movements, data science plays a key role in modern warfare. Drones and satellites gather huge amounts of intelligence that help military leaders make smart decisions in real time. Predictive analytics helps identify the best strategies to protect important locations and
Keeping borders secure is another important job where data science plays a role. It helps detect illegal activities like human trafficking, drug smuggling, and organized crime. AI-powered facial recognition systems are used to spot fake identities. Predictive models scan shipping, travel, and border crossing data to flag suspicious activity before it becomes a bigger issue. Agencies such as U.S. Customs and Border Protection (CBP) and the Department of Homeland Security (DHS) depend on these tools to protect the nation’s borders.
What keeps your feed fresh and your games fair? Data science working behind the scenes.
DID YOU KNOW?
Spotify analyzes over 100 billion data points a day — just to help you find your next favorite song.
Every song you play, skip, or replay sends a signal. Music streaming services like Spotify collect all that data to learn your music taste — and then compare it with millions of other users to spot patterns and recommend songs you might love. For example, if you’re into chill
indie music, Spotify won’t just feed you the most popular tracks. It can also dig up deep cuts and hidden gems that match your vibe. The more you listen, the smarter it gets — building playlists that feel like they were made just for you. It’s not just listeners who benefit
from data — artists do, too. Musicians can see where their fans are located, which songs are most popular, and when people stop listening. This helps them decide what songs to promote, where to tour, and even what kind of music to make next. How cool is that?!
Each time you like, share, or comment on a post, you’re giving social media platforms a better picture of what interests you. They use that information to prioritize content that matches your behavior, filling your feed with posts you’re most likely to engage with. For example, if you start watching a lot of funny pet videos, TikTok will begin showing you more dog and cat clips. But if you suddenly get into basketball highlights, your feed will quickly adjust to focus on sports content instead.
Beyond recommendations, data science also plays a huge role in platform safety. Social media companies use advanced content filtering systems to analyze posts and detect inappropriate or harmful content before it spreads.
Whether you’re playing Fortnite, Call of Duty, or any other online game, data science is working behind the scenes to keep the experience smooth, competitive, and fair.
One major use is matchmaking: games analyze data like wins, reaction time, and play style to pair players with opponents of similar skill. In Fortnite, this helps prevent beginners from facing experts, mak-
ing matches more balanced and fun. Data science also adjusts difficulty in single-player games by detecting when players are struggling and offering subtle help, like slower enemies or extra hints.
And to keep things fair, developers use data to detect cheating by spotting suspicious behavior, allowing them to quickly remove rule-breakers.
Netflix doesn’t just guess what you might like — it analyzes millions of viewing habits to recommend shows and movies just for you. Every time you watch, pause, or skip a show, Netflix learns more about your preferences. That’s why your homepage looks completely different from someone else’s.
But it goes beyond recommendations. Data science also helps Netflix decide what shows to create. Take Wednesday, for example. Netflix greenlit the series after seeing strong interest in dark comedy, supernatural themes, and characters with a unique edge — trends revealed through user data. Instead of relying on guesswork, they used AI-driven insights to predict what would connect with audiences.
Even streaming quality is powered by data. If your Wi-Fi slows down, Netflix automatically adjusts the video resolution in real time so your show doesn’t stop mid-scene.
Every click tells a story. Data science is helping brands understand people, personalize experiences, and create campaigns that actually work.
Every time you scroll through Instagram, watch a YouTube ad, or see a brand trending on social media, data science is working behind the scenes. Companies no longer rely on guesswork to reach customers — they use data-driven insights to target the right people, at the right time, with the right message. From personalized ads to viral marketing campaigns, data scientists help businesses understand what customers want, what they’ll buy, and how to keep them coming back. If you’re interested in psychology, creativity, and numbers, a career in marketing data science could be for you!
data science. They analyze voter behavior to find out who’s most likely to respond to certain messages — and then tailor ads just for those groups.
Testing what works
Creating an ad is only the beginning. Marketers want to know what actually works — and that’s where A/B testing comes in. It’s like a science
How do companies know what customers are interested in? They study patterns — what people search for, which websites they visit, how they interact on social media, and even how long they watch certain videos.
Let’s say millions of people are talking about a new fashion trend online. Data scientists can track that buzz across posts, reviews, and hashtags to help brands stay ahead of the curve. That way, companies can create marketing campaigns that really connect with what people care about.
Even political campaigns use
experiment for advertising. Here’s how it works: They might test two versions of a social media post — one with a bold headline, and one with a funny image — to see which gets more engagement. The version that performs better helps guide future campaigns.
Social media and influencer marketing
Social media is a goldmine for marketing data. Every post, like, share,
and comment gives companies clues about what people are thinking and feeling. It’s not just about counting followers — it’s about understanding trends, moods, and opinions in real time.
For example, Nike might analyze Instagram comments, hashtags, and reactions to see how people feel about a new sneaker release. Are the comments excited? Are people tagging friends or sharing posts? If the buzz is positive, Nike DID YOU KNOW? The average person sees over 4,000 ads a day — and data science decides which ones you
might ramp up production. If the reaction is negative or mixed, they’ll adjust their messaging, product design, or pricing.
Influencers are also a huge part of the equation. Brands track which YouTube and TikTok creators generate the most engagement — likes, views, comments, and shares. A well-placed mention from the right influencer can introduce a product to millions of potential customers in a way that feels personal and authentic.
Ever wonder why you see an ad for sneakers right after searching for running shoes? That’s not a coincidence — it’s data science at work. Platforms like Google, Facebook, and TikTok use algorithms to track your browsing habits, search history, and online activity. Then they match you with ads that seem most relevant, based on what you’ve shown interest in.
But it doesn’t stop there. Marketing has become highly personal. If you get an email with a special deal that feels perfectly timed — like a coffee discount in the morning or a late-night sale — it was probably sent based on when you’re most likely to open it. Companies analyze what you’ve clicked on, bought, or browsed to tailor messages and product suggestions just for you.
Even the ads you see online are often picked through real-time data auctions, matching the right ad to the right person at the right time. It makes marketing more effective — and harder to ignore.
Technology meets nature: How data is helping defend Earth’s most vulnerable ecosystems.
In the heart of the Amazon rainforest, illegal loggers move in under the cover of night. But they’re not alone. Hidden in the trees, AI-powered acoustic sensors detect the whir of chainsaws and send alerts to rangers miles away. Within hours, the operation is shut down. This isn’t science fiction — it’s how data science is revolutionizing environmental conservation. From tracking endangered species to predicting natural disasters, con-
servationists rely on data analysis, satellite monitoring, and predictive models to protect wildlife, preserve forests, and fight climate change. If you care about the planet and love solving problems with technology, data-driven conservation could be your future.
Can data help save the last 50 Javan rhinos on Earth? Conservationists believe it can. Using GPS
collars, drones, and automated tracking tools, scientists monitor animal movements and spot signs of poaching before it’s too late.
The World Wildlife Fund uses camera trap data to identify elusive snow leopards in remote mountains. Wild Me’s pattern-recognition tech helps researchers track individual whale sharks using photos shared by citizen scientists. Every photo, GPS ping, or video clip becomes part of a growing database. That
data helps scientists understand animal behavior, plan safe migration routes, and design better protection strategies. One study followed a humpback whale on a 9,000-mile journey — leading to the discovery of new marine corridors now being considered for protection.
DID YOU KNOW?
environmental data from organizations like Trase and Dryad help map deforestation, monitor forest health, and track carbon capture. These insights guide reforestation efforts and shape global conservation policy.
sustainable fishing, and respond quickly to threats in our oceans.
Illegal logging and deforestation are destroying forests at an alarming rate — but data science is helping fight back. The Rainforest Connection uses real-time audio sensors to listen for chainsaws and other threats. When suspicious sounds are detected, alerts go out instantly to rangers on the ground. Meanwhile, satellite imagery and
What if we could hear the ocean’s warning signs? With data science, we can. Oceana’s global monitoring system uses satellite and ship-tracking data to detect illegal fishing activity. Underwater drones and ocean sensors help NOAA Fisheries study fish populations, track endangered species, and monitor coral reefs. This data helps scientists enforce marine protection laws, support
As extreme weather becomes more common, climate data is more important than ever. NASA builds models to predict sea-level rise, droughts, and dangerous storms before they happen. The Environmental Defense Fund’s MethaneSAT satellite tracks methane emissions from space, helping scientists spot pollution hotspots and take action before it’s too late. Whether it’s measuring rising temperatures or preparing for the next hurricane, climate forecasting helps communities stay safe — and helps us act fast to protect the Earth.
Behind every great team is great data, helping players improve, coaches plan, and fans connect.
In 2025, the sports industry is expected to generate about $117.9 billion, and data science is a big reason why. From game strategy to player health to fan engagement, data gives teams a powerful edge.
If you’re into both sports and numbers, a career in sports data science could put you right in the middle of the action — helping teams win, stay strong, and build deeper connections with their fans.
and past performances, data models forecast game results. If a key player is injured, machine learning tools can quickly recalculate the team’s chances of winning.
Companies like IBM and SAP build sports analytics tools that help teams, broadcasters, and fans better understand what’s happening — and what might happen next.
Ever wonder how sports analysts predict which team will win? Data science makes it possible. By analyzing player stats, team rosters,
DID YOU KNOW?
NBA games generate over a million data points. Data science helps teams turn all that action into smarter plays and winning strategies.
Athletes train hard — but with data, they can train smarter. Wearable devices and video analysis track players' movements, their heart rates, and even sleep patterns.
Shottracker
Coaches use this data to customize workouts and avoid injuries.
The Golden State Warriors, for example, use wearable tech from Catapult Sports to monitor how fast players run, how hard they land, and how fatigued they are. ShotTracker uses sensors in balls, on players, and around the court to capture every shot, pass, and movement — giving teams real-time insights to improve performance.
Predictive analytics helps teams plan for just about anything. By analyzing past games, weather conditions, and opponent behavior, coaches can simulate different scenarios and pick the best strategy.
For example, if a football team is heading into a game with freezing, snowy weather, coaches might study past performance data to see how their players handle the cold. If their starting quarterback struggles in those conditions, they might
shift the game plan — favoring running plays or short, safe passes instead. Tools like Tableau and Microsoft Azure help teams visualize this complex data and make smart, fast decisions.
Fans don’t just watch games — they live them. And thanks to data science, teams can make that experience more personal, more exciting, and more convenient.
Stadiums and teams track fan behavior — like ticket purchases, food orders, and past game attendance — to better understand what fans want. This data helps teams offer personalized deals, seat recommendations, and even exclusive content based on each fan’s preferences.
Apps are also making the in-stadium experience smoother. The Levi’s Stadium app, for example, lets 49ers fans order food for inseat delivery and see which concession or restroom lines are shortest.
Another platform, FanFood, works with stadiums across the country to allow mobile ordering and express pickup — so fans don’t miss a play. Even ticket prices are powered by data. With dynamic pricing, costs can rise or fall in real time depending on demand, opponent popularity, or even the weather. If a thunderstorm is in the forecast, ticket prices might drop. But if a major rival is in town, prices could go up.
Behind all of this, companies like Salesforce and KORE Software help teams gather and analyze data to create custom fan experiences — keeping stadiums full and fans coming back.
Broadcasts today are packed with real-time data insights that give fans a deeper understanding of the game. Amazon Web Services (AWS) has teamed up with the NFL to transform how fans watch Thursday Night Football. Using
real-time analytics, AWS provides live on-screen stats during games, giving viewers instant updates on things like passing yards, player speed, and third-down conversions. These statistical overlays help fans understand not just what’s happening — but why it’s happening.
But it doesn’t stop there. AWS uses advanced machine learning to deliver deeper insights, such as pressure alerts (when a quarterback is under stress), coverage identification (what kind of defense is being played), and defensive vulnerabilities (where the opposing team might be weak).
They even generate AI-driven predictions during the game — showing win probabilities and forecasting potential outcomes based on real-time events. It’s like having a sports analyst and data scientist in your living room, breaking the game down play by play.
Nobody wants a great game spoiled by a bad call. That’s where data and technology step in. The NBA Replay Center uses high-speed cameras and data analysis to review crucial plays — like whether a shot beat the buzzer or if a player stepped out of bounds.
Tennis tournaments like Wimbledon use Hawk-Eye technology to track the ball’s exact path, giving instant answers to in-or-out questions. The NFL has even experimented with microchips embedded in footballs to measure first-down distances, reducing reliance on traditional chain measurements.
The financial world runs on data — helping protect people’s money and power smarter decisions.
Imagine spotting fraud before it happens, predicting stock market moves, or helping small businesses grow — all with the help of data. That’s the reality of finance today. From major banks to fintech startups, data scientists are at the center of innovation, making the industry smarter, safer, and more personalized. Behind every fraud alert, investment algorithm, and personalized banking app is a data scientist — someone who combines math, tech, and creativity to solve big financial challenges. Here’s how data science is transforming the world of money.
Big names like Citibank, Capital One, JPMorgan Chase, and Wells Fargo rely on data science to protect customers and improve the way people bank.
Fraud detection is a huge focus. JPMorgan Chase uses predictive analytics to stop credit card fraud before it happens. Capital One’s AI assistant, Eno, watches for unusual spending and alerts customers right away.
Data also helps banks offer better services. Citibank analyzes patterns to predict when customers might leave and reaches out with improved offers. Banks are
also streamlining loans, speeding up fraud investigations, and offering personalized budgeting tools based on how you spend and save.
Even physical branches are getting smarter — Wells Fargo uses predictive models to track busy times, reducing wait times and improving service.
Investment giants like BlackRock, Goldman Sachs, and Morgan Stanley use data science to help people grow their wealth and avoid financial risks. BlackRock’s powerful platform, Aladdin, scans massive amounts of financial data to spot trends and help investors make smarter decisions. At Goldman Sachs, trading algorithms react to market changes in milliseconds. Morgan Stanley builds models that guide clients toward safer, more stable investments — even in unpredictable markets.
Some firms also use alternative data — like weather trends, satellite images, or social media sentiment — to make investment decisions faster and with more context.
And then there are robo-advisors, which take the human advisor completely out of the equation. Platforms like Wealthfront and Betterment use algorithms and machine
learning to build and manage investment portfolios automatically.
Here’s how it works: you answer a few questions online — about your financial goals, how much you want to invest, and how comfortable you are with risk. Based on your answers, the robo-advisor creates a custom investment plan and keeps it updated over time. It can automatically rebalance your portfolio and adjust for market changes—all powered by data, not people.
Companies like Visa, Mastercard, and PayPal process billions of payments every day — and data science is their security system.
VisaNet, Visa’s global system, scans transactions in real time to catch fraud without blocking legit purchases. Mastercard and PayPal use predictive models to detect unusual activity and stop scammers instantly. (See page 10 for more.)
But it’s not just about safety.
Companies like Stripe and Square help small businesses use sales data to track trends, manage inventory, and adjust pricing.
Even mobile payments benefit. Data helps optimize contactless checkouts, keep apps secure, and make the payment process faster and easier to use.
And when it comes to marketing, data makes things more personal. Mastercard’s Priceless Cities program studies customer habits to
help companies design better deals. PayPal recommends personalized offers based on spending trends — making online shopping feel a little more intuitive.
As AI and data science keep advancing, the financial world will only become more data-driven. From smarter banking to safer shopping, the future of money isn’t just digital — it’s powered by data.
DID YOU KNOW?
Data science is reshaping how we travel — cutting delays, easing traffic, and powering safer, smarter systems.
Whether you’re using Google Maps to find the fastest route, calling an Uber, or waiting for a delivery, data science is behind the scenes making it all work. From reducing traffic jams to powering self-driving cars, data helps people and goods move.
No one likes being stuck in traffic, and cities are using data to make roads work better.
Sensors, GPS, and traffic cameras track how vehicles move in real time. That data helps adjust traffic
lights to keep cars flowing and avoid bottlenecks. Apps like Google Maps and Waze use similar data to predict delays and reroute drivers around crashes or construction.
In some places, cities even adjust speed limits or open extra lanes based on traffic conditions. In Los
Angeles, for example, traffic signal systems analyze real-time data to improve travel times on the city’s busiest streets.
Late buses and crowded trains aren’t just annoying — they’re a problem data science is helping solve.
and prevent breakdowns. Smaller cities analyze rider data to send extra buses when demand is high, like during concerts or sports games.
Apps like Transit, Moovit, and Google Maps show exactly when your bus or train will arrive, so you’re not left waiting and guessing.
traffic lights, pedestrians, and road signs — and use that information to navigate safely without a human behind the wheel.
In the freight world, Torc Robotics is developing self-driving trucks to move goods across long distances. These autonomous big rigs reduce driver fatigue, increase safety, and improve delivery reliability.
Apps like Uber and Lyft rely on data science to match riders with drivers, predict demand, and choose the fastest routes. They even use machine learning to adjust prices based on traffic, time of day, and weather — ensuring there are always enough drivers on the road. It’s how ride-sharing stays fast and efficient.
Air travel is complex — and data science helps keep it running smoothly. Airlines use data to plan flight schedules, manage fuel use, predict delays, and improve the overall passenger experience. For example, predictive models help airlines anticipate weather-related disruptions or aircraft maintenance needs before they cause problems. Behind the scenes, data helps with crew scheduling, luggage tracking, and optimizing how planes are loaded to save fuel and time.
Transit systems use real-time location data to improve schedules and reduce delays. In New York City, the Metropolitan Transportation Authority (MTA ) uses predictive models to manage subway service
The future of driving might not involve any driving at all. Companies like Waymo and Aurora are building self-driving vehicles that learn from massive amounts of sensor, camera, and GPS data. These systems “see”
And when it comes to deliveries, companies like FedEx, UPS, and Amazon rely on data to map out the fastest and most efficient delivery routes. Predictive models help them avoid delays, cut fuel costs, and make sure packages arrive on time — even during the busiest seasons.
Thanks to data science, today’s factories think, learn, and improve — making products better, faster, and with less waste.
When you picture a factory, you might think of machines stamping out parts or workers on an assembly line. But today’s factories are much smarter than that. Behind the scenes, data science is helping them run faster, reduce waste, and catch problems before they happen. From cars and computers to sneakers and snacks, data is making manufacturing more efficient, more reliable, and more innovative.
repairs before production is disrupted. Some factories even send alerts straight to a technician’s phone so they can fix issues immediately. This keeps the production line moving — and avoids costly downtime.
Factories use data to work smarter, not harder. By analyzing how fast machines run, how workers move, and how much material is used, companies can find ways to speed things up and waste less.
When a machine breaks down in a factory, everything can come to a halt — and that gets expensive fast.
Unexpected equipment failures cost factories millions of dollars in lost production time and repairs. Instead of waiting for machines to break down, manufacturers now use data-driven predictive maintenance to analyze past performance and detect early signs of wear and tear before failures occur.
Car manufacturers such as General Motors and Honda collect machine sensor data on temperature, vibration, and pressure levels from their assembly lines. Analyzing these patterns allows them to identify potential failures in advance, making it possible to schedule
Boeing runs massive computer simulations to figure out the most efficient way to build airplanes. Car companies use data to fine-tune robot arms on the assembly line — so every motion counts.
Factories depend on a steady stream of materials from all over the world. If a shipment is late, production can stall. If there’s too much, it costs extra money to store.
Data science helps companies predict demand and manage the supply chain. Amazon uses billions of data points to figure out which products should be stored where. Ford and Toyota track shipping data to avoid delays and keep everything running smoothly.
Defective products are bad for business — and bad for safety. That’s why companies use data to catch flaws early and make sure only the
best products leave the factory. Intel and Samsung use high-speed data analytics to scan microchips for defects, ensuring only perfect products make it to market.
In the food industry, data scientists use predictive modeling to spot contamination risks before they reach store shelves. For example, Nestlé uses real-time data and sensors to monitor temperature, moisture, and processing steps in their factories — helping ensure food stays safe from start to finish.
In electronics manufacturing,
American companies like Cognex use smart cameras to check smartphones and circuit boards for problems. These cameras work super fast — scanning thousands of tiny parts every minute — and can catch mistakes that people might not see.
As you probably can guess, robots are a big part of modern manufacturing, from building cars to moving boxes in warehouses. But they can’t do it alone — data keeps them accurate, fast, and reliable.
At BMW, robots on the assembly line use real-time data to work more efficiently. They adjust their speed, movements, and energy use based on what’s happening around them — helping the factory save power and stay productive.
In Amazon’s warehouses, smart robots rely on constantly updated tracking data to find, move, and sort packages. These data-driven systems help the robots work quickly and accurately, reducing mistakes and making sure the right products get to the right places on time.
A single car has over 30,000 parts — and AI-powered machines inspect many of them for tiny defects people might miss.
From startups to global brands, businesses turn to these experts for smart strategies, sharp insights, and real results.
As you’ve seen throughout this guide, data analytics plays a huge role in helping businesses make smarter decisions, work more efficiently, and stay ahead of the competition. While many large companies have in-house analytics teams, there are times when they need outside help — especially when tackling complex problems or scaling up fast.
That’s where data science consultants come in. These professionals bring advanced tools, fresh perspectives, and deep expertise to the table. Whether it’s launching a new product, optimizing supply chains, or predicting future trends, consultants use data to unlock insights and deliver results. It’s a fast-growing field — valued at $9.74 billion in 2024 and projected to reach $33.36 billion by 2034.
Whether you see yourself working inside one company or advising many, consulting is a path full of opportunity, challenge, and impact.
Why do businesses hire outside experts?
Companies turn to data consultants for many reasons. Sometimes they need specialized skills — like machine learning or predictive modeling—that
go beyond the abilities of their internal teams. Other times, they need help managing massive datasets or building cloud-based systems that can scale as they grow.
For smaller businesses or those with changing needs, hiring outside experts can also be more affordable than building a full-time data team.
One of the best things about a career in data consulting is the variety. You might work on a shortterm project for a startup one month, then help a global company rethink its strategy the next. Over time, consultants often build deep expertise in specific industries or technologies, opening doors to leadership roles or even launching their own firms. The fast pace keeps things fresh — and keeps you learning.
Love the idea of working with massive amounts of data? Cloud platforms like Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP) help businesses store, process, and analyze data on a huge scale. Working in this space might mean managing
databases, building data pipelines, or using AI to uncover insights from complex datasets.
Big consulting firms like Accenture, Deloitte, and McKinsey & Company help businesses make smarter decisions using data. Accenture focuses on AI-driven business strategy. Deloitte applies predictive modeling to improve risk management and efficiency. McKinsey’s QuantumBlack division uses machine learning to boost performance. Even Mastercard has a consulting division that helps businesses with payment strategies, customer insights, and fraud detection.
If you love customer trends, branding, or digital media, marketing analytics might be your thing. Companies like Adobe Analytics and HubSpot help businesses understand what customers want and how to reach them. Data scientists here use trends, click data, and social media insights to craft smarter campaigns and personalized experiences.
Sometimes, the most powerful part of data science is helping others see what the data means. Tools like Tableau and Power BI turn complex numbers into visual stories — interactive dashboards, charts, and
DID YOU KNOW?
Some data consultants work with 10 or more industries a year — from fashion to finance to sports — all without changing jobs.
reports that drive better decisions. A career in data visualization is perfect for anyone who loves both creativity and clarity.
Businesses often pull data from many sources, and it’s not always compatible. That’s where integration tools come in. Companies like Fivetran and Mulesoft help businesses connect systems, sync data, and build reliable workflows. Careers in this area involve building tools called APIs — short for Application Programming Interfaces — that allow different software programs to talk to each other. These professionals also design the
systems that help information move quickly and securely between apps, websites, and devices.
Some companies do it all. Snowflake offers cloud-based tools for storing and analyzing data in real time. Databricks brings together data engineering and machine learning with its powerful Spark platform. And Alteryx makes it easier for non-technical users to analyze data — no coding required. These firms are great for those who want to work at the intersection of tech and strategy.
Want to build smarter systems that can learn and adapt? Companies like Palantir Technologies and H2O.ai specialize in applying AI to real-world problems — everything from fraud detection to medical research to national defense. If you enjoy automation, algorithms, and predictive modeling, this is an exciting field to explore.
Some data analytics companies specialize in specific industries. SAS provides analytics solutions tailored to healthcare, retail, and finance. Booz Allen Hamilton applies data science to support national security and defense, using analytics to track cyber threats, optimize logistics, and inform policy decisions. If you’re passionate about a particular industry, this kind of work can help you make a real impact.
From protecting privacy to building fair algorithms, data scientists must make choices that put people first.
How we gather, use, and share data raises big ethical questions — and can shape lives for better or worse.
You’ve likely heard the expression “with great power comes great responsibility.” This is definitely the case with data science. Think
about it. Who controls all the data? How do we make sure data is used responsibly? Is artificial intelligence (AI) always fair? If you’re thinking about a future in data science, it’s important to understand these challenges and how to use data ethically.
Every time you use an app, visit a website, or even walk past a security camera, data is being collected about you. Companies must ensure that personal information stays safe. Data breaches can expose sensitive information like passwords, financial details, and even medical records. Ethical companies follow privacy laws like GDPR (General Data Protection Regulation) and CCPA (California Consumer Privacy Act) to protect users. Users should have control over their own data, deciding what is shared and with whom. Hackers and cybercriminals constantly look for ways to access sen-
sitive data, making strong cybersecurity measures crucial. Companies must use encryption, regular security audits, and strong authentication processes to safeguard data. Governments and businesses need to invest in stronger security measures to protect individuals and organizations from data theft.
about how they collect and use data, ensuring that users have the choice to opt in or opt out.
People should also always know when their data is being collected and how it will be used. Ethical businesses explain data usage in clear, simple language, ensuring that consent is actively given, not assumed. Data should be used to improve services, not manipulate users. Ethical data scientists think about the impact of their work before building models or collecting user information.
Data integrity, fairness, and transparency
How do we know if data is accurate, reliable, and unbiased? Data integrity ensures that information is not tampered with or misused. Poor data quality leads to misleading conclusions, so data should be verified and cleaned before being used for analysis. Ethical data scientists ensure they are working with high-quality, trustworthy datasets.
no one understands. Companies should provide clear explanations of how their AI systems work and ensure people have access to information about how decisions that affect them — such as loan approvals or job screenings — are made.
Who is responsible when AI makes mistakes? If an algorithm unfairly denies someone a job, loan, or medical treatment, there should be a way to fix it. For example, hiring algorithms have been found to favor certain demographics over others, and facial recognition software has wrongly identified people, leading to false arrests. These real-world issues highlight why AI decisions must be reviewed and corrected when necessary. Governments and companies should set ethical guidelines for AI use, ensuring that decisions made by machine learning models can be audited and improved. Data scientists should take responsibility for the impact of their algorithms, ensuring that AI is used fairly and ethically.
Just because companies can collect data doesn’t always mean they should. Ethical data usage means collecting only what is necessary and using it in ways that benefit people rather than exploit them. Companies should be transparent
Data science should work for everyone, not just a few. AI and machine learning models should not discriminate based on race, gender, or socioeconomic status. Data should be diverse and representative to avoid biased results, and companies should test and adjust their algorithms to ensure fairness.
Transparency is also key — when AI and machine learning models make decisions, users should be able to understand how and why those decisions were made. Algorithms should not be “black boxes” that
At the end of the day, data scientists must make ethical choices — especially when working with sensitive data. Some companies now use ethics checklists to review the impact of new tools before launching them. Privacy, fairness, security, and transparency must always come first. If you want to make a real impact in this field, using data responsibly is one of the most important skills you can build.
You will find data science jobs nearly everywhere, but states like California, Texas, New York, Florida, and Pennsylvania have the most.
$108,020
Publisher: Robert F. Black
Creative Director: Stacie A. Harrison VP, Learning and Communications: Eric Iversen
Editors/Writers: Eric Iversen, Stacie A. Harrison © 2025 Start Engineering, LLC www.start-engineering.com
