Data science, or the use of scientific methods and algorithms to extract meaningful insights from large data sets, has seen incredible industry growth over the past several years. In fact, according to data from the United States Bureau of Labor Statistics, the job outlook for data scientists is projected to grow by 34% between 2024 and 2034 — which is much faster than the national average for all occupations.
As the demand for knowledgeable and skilled data scientists continues to grow, now could be an excellent time to break into the field with an online Bachelor's in Data Science and Artificial Intelligence from Champlain College Online (CCO).
Could a career in data science be right for you? Read on to learn more about what data science entails, its many applications across industries, and what you can expect to learn in a Data Science and AI degree program at CCO.
Understanding Data Science
So, what is data science? Let’s break down this loaded question a bit more.
Today, more data is being generated and collected than ever. In fact, it's hard to even comprehend the sheer amount of data being created as you read this sentence. Approximately 402.74 million terabytes of data are generated each day — a number that's only expected to increase in the future.
Of course, raw data on its own isn't particularly useful. Only once data is compiled and analyzed is it possible to extract meaningful insights from it. This is where the data science field comes in.
Data science uses a wide range of scientific methods, algorithms, systems, and techniques to make sense of raw data. Through the use of data analysis tools, visualization tools, predictive analytics, and other proven strategies, data scientists can use data to pinpoint trends and even predict future projections.
With so many potential applications, data science is now used across nearly every imaginable industry. In finance and banking, for example, it may be used for fraud detection. In healthcare, it can be used to create personalized treatment plans. Meanwhile, retailers rely on data science to forecast demand for their products and engage in dynamic pricing strategies. In other words, you'd be hard-pressed to find an industry where data science isn't used to improve operational efficiency, mitigate risk, and even predict the future.
Why Data Science Matters Today
With so much data being generated and collected, data science is arguably more important today than ever before — especially for businesses looking to differentiate themselves from their competition and set themselves up for long-term success.
More specifically, data science is an effective driver of business growth and strategy because it helps companies better understand their customers while optimizing operations. Meanwhile, many businesses rely on data insights to support decision-making and even drive innovation.
Core Components of Data Science
Data science is a broad and complex field, but it can typically be broken down into a handful of core components that range from the early stages of data collection to data visualization and communication with stakeholders.
Data Collection and Management
There are many ways in which raw data may be collected, ranging from automated collection methods to more interactive methods (such as surveys and focus groups). From there, a reliable system for storing and managing data (such as a database) must be in place.
Data Cleaning and Preparation
Raw data isn't often ready for analysis right away. First, it needs to go through a cleaning/preparation process. During this important phase, duplicates may need to be deleted, errors may need to be corrected, and formatting needs to be made consistent.
Exploratory Data Analysis (EDA)
The initial process of examining a dataset is known as EDA. During this phase, data scientists may use a combination of visualizations and statistics to discover relationships between variables, pinpoint trends, and detect outliers. This information may be crucial in choosing the correct statistical modeling or machine learning models for the next step.
Statistical Modeling and Machine Learning
Next, special algorithms or machine learning models may be applied to existing data sets to extract more meaningful and specific insights. In some cases, machine learning models may even be used to make predictions about future data trends.
Data Visualization and Communication
Ultimately, data scientists are tasked with communicating their findings to stakeholders and other decision-makers. However, because these stakeholders often aren't data science experts themselves, they need to find ways to communicate findings in ways that are easily accessible and understandable. This is where data visualization tools in the form of charts, tables, and graphs can really come in handy.
The Data Science Process: From Question to Insight
As with any science, there's a specific process data scientists need to follow to extract valuable insights from raw data sets. And while the process may vary slightly from one context to the next, we've broken down the most common steps below.
Defining the Problem
First, somebody needs to define the problem that the data will be used to solve. Often, this step is not completed by a data scientist, but by a business stakeholder or decision-maker. From there, a data scientist may need to translate the problem into a data science goal.
Acquiring and Preparing the Data
Next, it's time to acquire and prepare the data that will be used to solve the problem. Data may be gathered from many sources, including existing databases, before it is cleaned and prepared for analysis.
Exploring and Modeling
Data scientists may then apply exploratory data analysis and modeling techniques to extract insights from data as they relate to the problem at-hand. This may include pinpointing trends and patterns, and uncovering potential relationships between data points.
Interpreting and Communicating Results
Once a data scientist has successfully solved the initial problem, they must share their findings with the initial stakeholders or decision-makers. This requires the data scientist to present visualizations and communicate findings in a way that even a non-technical audience can easily understand.
Deployment and Continuous Improvement
Even after a question is answered or a problem is solved, data scientists must be committed to continuous improvement in their work. This means carefully monitoring and tracking the performance of different models and algorithms to make changes as needed.
Careers in Data Science and Related Fields
So, what kinds of jobs might you pursue if you're interested in data science? Learn about some of the most popular careers in data science and related fields below, including common job responsibilities and settings.
Data Scientist
When tackling the question of “What is data science?” it’s worth noting the exact responsibilities of the data scientist. In simplest terms, a data scientist is a professional who uses specialized tools and techniques to gain insights from raw and complex data sets. These professionals are responsible for everything from collecting and preparing data for analysis to applying models/algorithms and communicating their findings to relevant stakeholders.
Data Analyst
Compared to a data scientist, a data analyst tends to be more focused on descriptive analytics, or interpreting data to better understand historical trends and patterns. They use many of the same tools as data scientists, but are less involved in predictive modeling and prescriptive analytics.
Machine Learning Engineer
Meanwhile, machine learning engineers are responsible for designing, building, and maintaining the ML systems that data scientists use to make projections and predictions. Professionals in this field need to have extensive experience in coding/programming, system design, and machine learning and AI frameworks.
Business Intelligence (BI) Analyst or Developer
Similar to data analysts, BI analysts and developers are typically hired directly by companies and organizations to better understand historical trends and inform future decision-making. Professionals in this role may be responsible for data collection/preparation, analysis, interpretation, and making specific recommendations to stakeholders.
Data Engineer
Finally, a data engineer plays an important role in data science by building and maintaining the systems that collect, store, and prepare data for analysis. These professionals need extensive programming experience and are also experts in the use of relational databases, cloud platforms, and other big data tools.
How Data Science Intersects with Artificial Intelligence
As artificial intelligence (AI) continues to evolve and improve at an incredible rate, its applications in data science have also become clear. Today, AI can be used to improve data science processes by:
- Automating time-consuming data tasks (such as data cleaning/scrubbing and sorting).
- Enabling more advanced analytics, including prescriptive analytics for businesses.
- Using machine learning (ML) to build models that actually learn from existing data.
In many ways, data science and artificial intelligence are becoming inextricably linked — and as machine learning models continue to advance, the need for AI is only likely to increase. With this in mind, if you have plans to get into data science as a career, you may also want to focus your formal education and training on AI and ML.
How Champlain College Online’s BS in Data Science & Artificial Intelligence Prepares You
CCO's online Bachelor of Data Science and Artificial Intelligence is designed to prepare aspiring data scientists to excel in today's data-driven world, with essential coursework in topics like:
- Computer Science
- Mathematics
- AI methodologies
- Data management
- Machine learning
- Programming and algorithm development
Meanwhile, the online format of this program makes it ideal for working professionals and those with other obligations to work at their own pace. In fact, with just two courses completed per term, students can expect to earn their degrees in as little as 40 months.
Along the way, students in CCO's Data Science & AI program enjoy ongoing support and guidance from our experienced and knowledgeable computer science faculty, led by Department Chair Richard Pickering. With career-focused coursework, expert guidance, and plenty of opportunities for hands-on learning, students can enter the field knowing that their skills are aligned with the changing demands of the data science workforce.
Take the Next Step
As more data is being collected and used than ever before, the need for knowledgeable and skilled data scientists is only likely to increase. Meanwhile, as data science and artificial intelligence become inextricably linked, professionals with experience in both fields may be better prepared to tackle the emerging challenges and opportunities of machine learning and data-driven models.
CCO's online Bachelor of Science in Data Science and Artificial Intelligence is specifically designed with these emerging trends and dynamics in mind. With a career-focused curriculum and a flexible online format, you can work towards your degree without putting other parts of your life on hold.
Get in touch to request more information about CCO's Data Science and AI program today. Eager to get started? Complete your online application.
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