Key Takeaways
- Data science is an interdisciplinary field that blends computer science, statistics, and domain expertise to extract insights and solve complex problems using data.
- Relevant tools and techniques in data science include programming, machine learning, and data visualization, enabling professionals to analyze and extract insights from vast datasets.
- Data science is applied across industries like healthcare, finance, retail, technology, and more.
- Data science career opportunities are vast, with roles like data scientist, analyst, and machine learning engineer, among many other options.
With over 5 billion internet users worldwide, the amount of data being created every second is mind-blowing. Browsing online, shopping, streaming, and using social media—people in virtual spaces generate an endless flow of data. And that’s just one source—data also comes from sensors, machines, and countless other channels.
However, data, on its own, isn’t really information. It’s unstructured and, essentially, meaningless until it’s processed, analyzed, and transformed into insights—when data science is used.
What Is Data Science?
Data science is the practice of understanding data and using it to solve real-world problems. While the idea of analyzing information isn’t exactly something new, as people have been studying numbers and trends for centuries, what’s changed in recent years is the amount of data that we now have at our fingertips.
Thanks to the many advancements made in technology, computers now create massive volumes of data and, at the same time, give us the tools we need to process and understand all that data. Through a blend of computer science, statistics, and domain knowledge, data scientists can clean up data, combine different datasets, and then analyze the results.
The Data Science Lifecycle
The data science lifecycle is a series of stages, from the data’s initial creation or collection to its final use or preservation, that are needed for managing it. This lifecycle encompasses five primary stages:
Data collection
The first step includes gathering raw information by pulling data from surveys, sensors, websites, databases, or other sources. A company might collect customer feedback from online reviews to understand satisfaction levels, or wearable fitness devices might capture health metrics like steps taken and heart rate.
The focus is to collect as much relevant and accurate data as possible, as this serves as a foundation for all the following stages. Without good data at this stage, the rest of the process can easily fall apart.
Data cleaning and preparation
It rarely happens for data to be collected in a perfect, ready-to-use state. Therefore, data cleaning and preparation are needed in order to fix errors, remove duplicates, fill in missing details, and organize the information in a usable format.
For instance, if some fields in a dataset are blank or numbers are recorded incorrectly, they need to be corrected. This step is what helps ensure trustworthy results later on.
Exploratory data analysis (EDA)
Now comes the fun part—exploring the data to see what stories it has to tell. In this stage, analysts or data scientists use tools like charts, graphs, and statistics to look for patterns, trends, and relationships.
For example, EDA might reveal that sales spike during specific holidays or that a particular group of customers spends more than others.
Modeling and algorithms
The next step is creating models or algorithms that help data scientists further analyze and understand the data. These models might help predict future trends, automate processes, or even make real-time recommendations.
For example, a shopping website might use a recommendation system to suggest products based on what customers have previously purchased.
Deployment and monitoring
The final stage is about putting everything to work. The models and systems developed in the previous step are deployed in real-world scenarios where they can make a difference.
But it doesn’t stop there—deployment requires monitoring so that if something changes, like user behavior or market trends, the models stay relevant and effective.
What Does a Data Scientist Do?
With many data science careers to choose from, what a data scientist does can vary. However, generally, most data scientists share these core responsibilities:
- Extract, clean, explore, analyze, and present large datasets
- Collaborate with teams to create data-driven solutions
- Design and implement algorithms to analyze complex datasets
- Work with engineers to test, validate, and maintain models in production
- Perform ETL (extract, transform, load) operations to organize data
- Design, perform, and analyze tests to compare and improve outcomes
Essential Tools and Techniques in Data Science
Data science relies on various tools and techniques in order to work with the vast amounts of information available today. Professionals in this field must be skilled in a combination of technical, analytical, and computational methods.
Popular tools
When it comes to working with data, data scientists often turn to some widely used tools, including:
- Programming languages, such as Python, SQL, and R
- Data visualization tools, such as Power BI and Tableau
- Big data technologies, such as Hadoop and Spark
- Machine learning libraries, such as Scikit-learn and TensorFlow
- Statistical tools, such as SAS and MATLAB
- Data management tools, such as Apache Kafka and Snowflake
Key techniques
Armed with these tools and others, data scientists then use a variety of techniques to drive decisions. These include:
- Machine learning
- Predictive analytics
- Natural language processing (NLP)
- Data mining
- Data wrangling
- A/B testing
Data Science Across Industries
Two common questions people often have after learning about data science are “What is data science used for?” and “Where can it be applied?” The adaptability of data science to the unique challenges of different industries makes it an invaluable resource for establishments everywhere, including:
- To predict customer preferences and personalize shopping experiences in retail
- To aid patient care with insights, wearables, and predictive models in healthcare
- To power fraud detection, virtual assistants, and personalized financial services in finance
- To optimize routes, predict delays, and improve customer travel in transportation
- To streamline supply chains and analyze data for better operations and resource use in manufacturing and natural resources
- To track student progress and create tailored learning experiences in education
- To monitor energy consumption, enhance customer feedback, and increase efficiency in energy and utilities
- To provide personalized recommendations and content creation insights in entertainment and media
- To detect fraud, support disaster planning, and allocate resources efficiently in government and public services
- To optimize networks, predict outages, and improve service delivery in communications and technology
- To monitor crop health, predict weather, and optimize resource use for sustainability in agriculture
- To analyze guest preferences, optimize pricing, and craft personalized experiences in hospitality and tourism
Data Science vs. Related Fields
Since data science is a multidisciplinary field, it often overlaps with other fields. However, each has a distinct focus and role. Still, understanding these distinctions can help clarify how data science fits into the bigger picture.
Data science vs. data analytics
Data analytics focuses on reviewing past data when trying to find trends in data or answer specific questions. On the other hand, data science takes a significantly broader view since it also builds predictive models in order to analyze and work further with data.
For instance, while a data analyst might examine past sales to understand customer behavior, a data scientist uses that same data to develop models that forecast future trends or reveal hidden opportunities.
Data science vs. business analytics
Business analytics uses data to solve problems or make decisions directly related to business operations. In comparison, data science covers a broader range of applications and techniques, such as creating tools and models, like algorithms, that analyze data and make predictions.
Therefore, the difference between data science and business analytics is in their focus. While the former creates the model to work with data and extract insights, the latter takes that output and decides on actions that benefit businesses.
Data science vs. machine learning
Machine learning is an important part of data science, but the two are not the same. While data science provides the framework and insights, machine learning powers the automation and adaptability of these insights.
So, the main difference lies in the fact that data science is a broader field, whereas machine learning is a specialized area within it that focuses specifically on creating algorithms that allow computers to learn patterns from data and make predictions or decisions without being programmed for every task.
Data science vs. artificial intelligence
Artificial intelligence (AI) builds upon the work of data science, but it goes further in its capabilities. Data science focuses on processing and analyzing data to uncover insights, patterns, and useful knowledge.
AI takes these insights and applies them to create intelligent systems that can simulate human-like thinking and behavior. These systems can make decisions, solve problems, or perform tasks without direct human input. So, while data science discovers the knowledge, AI uses that knowledge to power intelligent decision-making systems.
Data science vs. data engineering
Data science and data engineering are also closely connected but focus on different aspects of working with data. Data engineers build systems that collect, organize, and store data. They also maintain these systems. Whereas data scientists use the data once it has been gathered and prepared.
For example, a data engineer would design a pipeline to gather customer data from an e-commerce site. Then, the data scientist would use that data to predict future shopping trends.
Data science vs. statistics
In a way, data science originated from statistics—it adopted its principles for analyzing data but expanded the scope with programming, machine learning, and other advanced tools.
Statistics still primarily focuses on analyzing numerical data to answer specific questions or identify trends. It is centered on tasks like calculating averages and probabilities as well as testing hypotheses. For instance, a statistician might determine the likelihood of a particular event happening based on past data. But then, a data scientist would take that probability, combine it with other tools, and create a model to predict future occurrences or automate decisions.
Challenges in Data Science
Data science is incredibly valuable. However, it requires a thoughtful approach and a strong attention to detail, especially when it comes to some of its challenges that not everyone can offer.
One of the major concerns is data privacy and ethics. There is so much personal information collected these days. Therefore, there are strict rules in place, like the General Data Protection Regulation (GDPR), to protect people’s privacy by requiring their personal data to be handled responsibly. However, this poses a challenge for those unprepared to manage data responsibly and prevent its misuse in their work.
Another challenge is data quality. There’s a common saying in computing that goes, “garbage in, garbage out”—if the data being analyzed is incomplete, incorrect, or biased, then the insights gained won’t be reliable either. There’s also model bias and fairness, which can have serious consequences. Models and algorithms are only as good as the data they’re trained on. If that data carries any kind of bias—whether it’s gender, race, or anything else—the model could end up reinforcing those biases.
Overcoming these challenges demands a high level of technical skills, ethical awareness, and a commitment to fairness and accuracy. It’s about finding ways to use data responsibly while delivering insights that truly make a difference.
Career Opportunities in Data Science
Data science is brimming with possibilities, offering a variety of career options that tap into its core skills. In this field, you’ll find roles like:
- Data scientist
- Data analyst
- Machine learning engineer
- Data engineer
- Business intelligence analyst
- Research scientist
- AI engineer
- Data science manager
- Quantitative analyst
- Data consultant
- Predictive analytics specialist
- Healthcare data analyst
- Marketing analyst
- Natural language processing engineer
- Computer vision engineer
All of these data science careers are within reach, provided you have the proper education to support your qualifications and build your expertise in the field.
At Syracuse University’s School of Information Studies (iSchool), students are offered a variety of programs that are thoughtfully crafted to keep pace with the fast-changing world of data science. If you’re just starting out, our Bachelor’s in Applied Data Analytics or our Data Analytics Minor are excellent choices for building a strong foundation in understanding and managing data.
For those looking to advance their expertise or change careers into data science, our Master’s in Applied Data Science equips graduates with insights into sophisticated techniques and applications. Whereas for those aiming to sharpen their focus without committing to a degree, our Certificate of Advanced Study in Data Science provides specialized training in this area.
Jeffrey Saltz, an associate professor at the iSchool and program director for the Master’s in Applied Human-Centered Artificial Intelligence, highlights the school’s dedication to staying at the forefront of innovation:
“The continuing enhancement of courses helps to ensure that the iSchool’s program is robust and comprehensive and can evolve as the field evolves.”
This forward-thinking approach is what sets the iSchool apart, as the goal is for students to not merely follow industry advancements but be the ones driving those advancements themselves.
Data Science: What Comes Next
Without data science, so many conveniences and advancements we take for granted—in healthcare, retail, transportation, finance, and many other industries—would fall apart.
The future of data science holds endless possibilities for those willing to put in the work. If that sounds like you, Syracuse University’s iSchool offers programs designed to equip you with all the skills needed to succeed. The next move is yours—explore what we have to offer and lead the charge in a world powered by data.
Frequently Asked Questions (FAQs)
What degree is required for a data scientist?
To start, a bachelor’s degree in data science, computer science, or a related area is often enough for many entry-level roles. However, a master’s can give you a competitive edge.
How long does it take to become a data scientist?
Typically, it takes 4–6 years to become a data scientist, considering undergraduate studies and optional further education or certifications.
Is data science a good career choice?
Absolutely—it’s in high demand, offers excellent earning potential, and provides opportunities across a range of industries.