By: The Capella Editorial Team with Melissa Zgola, EdD, Program Director, Information Technology and Computer Science
Reading Time: 12 minutes
We’re living in an age of big data. Thanks to rapid advances in technology, companies are sitting on more information than they can handle. But raw information only matters if you know how to turn it into something tangible. Data analytics skills may help you pivot careers, strengthen your impact in your current role or launch your own business. And acquiring these skills is open to anyone. Whether you’re a student or a working professional, you can pursue an analytics degree with an online program designed for busy people. In this article, you’ll find out what data analytics really means, explore types of career opportunities in data analytics and discover how Capella University’s online degree programs are helping professionals chase their dreams despite a busy schedule.
Data analytics is the science of collecting, organizing and analyzing large data sets to identify patterns and draw conclusions. It’s used in many different industries to support important initiatives and help guide critical decisions.
The process of data analytics is split into four key components:
Data analytics is important because it helps organizations make smarter decisions. It touches every aspect of a business.
By processing huge pools of raw data, data analysts provide teams with a better understanding of what’s happening within their organization. They look at trends and behaviors to see what’s working and what’s not. They then use that information to guide strategy, which can, for example, lead to a business outmaneuvering its competitors, or a healthcare organization better serving its patients.
“For example, let’s say that the executives of a national fast food chain are looking to add a new fresh food item to their menu,” explains Melissa Zgola, program director for the master’s-level IT programs in Capella University’s School of Business, Technology and Health Care Administration.
“A data analyst could collect and examine data on all fast food purchases over the past 5 years. In doing so, they could identify trends and patterns in the data that would suggest the most popular fast food items being sold by competitors.”
This analysis can help executives make decisions about the best new food item for the menu. If examined a bit further, the data could also help executives choose specialty food items for different demographic regions.
These kinds of insights make data analysts invaluable to any organization. The strategic decisions they enable often result in improved efficiency and performance. Analytics can help streamline processes, cut back on waste and focus resources where they matter most.
By understanding customer behaviors and seasonal trends, organizations can also better anticipate risk and, therefore, reduce it.
Analytics can be applied to data security, marketing, healthcare, finance, education and everything in between. That’s why data analysts are so valued. It’s also why there are so many types of data analytics and varied potential career opportunities in this field.
Advances in data science and technology move fast. Yet, generally speaking, most tools and data analysis processes fall into one of four categories: descriptive analytics, diagnostic analytics, predictive analytics or prescriptive analytics.
Descriptive analytics tell you about what’s happened. Data scientists use descriptive analytics tools to dig into historical data to uncover patterns or customer trends that can guide future strategy.
For example, an online retailer would use descriptive statistics to look at last quarter’s data. With this information, the team would be able to see how many social media visitors came to their site, which pages were viewed most and how many users completed a purchase.
This information could then be used to help the business optimize how its website is presented or remove pages that aren’t adding any value.
While descriptive analytics explains what happened, diagnostic analytics digs deeper to uncover why it happened. This helps organizations identify the root cause behind events or results, and see how each action led to a particular outcome.
Let’s say you work on a brand marketing team. You can use diagnostic analytics to determine why website conversions dropped last month by comparing traffic sources and analyzing marketing campaign performance among social media users.
By digging into the data, you might be able to find and fix a broken checkout page that’s been stopping social media users from hitting “buy.”
Predictive analytics tell you what may happen in the future. This type of analysis relies heavily on deep learning and AI tools to pool together huge data lakes involving historical data, industry patterns and even macroeconomic data to conduct statistical analyses that forecast trends.
That sounds a little complicated, but the concept is simple. For example, a cybersecurity team might pool historical data on cyberattacks like phishing attempts or login anomalies, and use predictive analytics tools to identify these digital threats before they pose a bigger problem.
The team would then better understand which behaviors may lead to breaches, and then patch vulnerabilities accordingly.
Prescriptive analytics tell us what the best course of action might be in a given situation. By analyzing what’s happened and why, prescriptive analysis tools can recommend actions or strategies to help an organization achieve its goals.
Maybe you run a taxi company and want to optimize your driver experience and increase revenue by reducing waiting times between fares.
Using prescriptive data analysis, you could look at current demand, driver locations, traffic patterns and historical ride data to recommend optimal driver deployment zones. Prescriptive analytics could also provide the most efficient routes to reduce fuel costs and wait times.
Because data and business analytics enable companies to optimize their behaviors and operations, they’re becoming fundamental, especially to startups that need to achieve more with fewer resources.
The terms “business analytics” and “data analytics” are often used interchangeably throughout the industry. That’s because they both collect and analyze data to make an organization more efficient and better at making effective decisions.
Business analytics focuses much more on an enterprise’s internal functions, processes, operations and overall architecture. It’s the practice of enabling change by identifying business needs and determining solutions to those business problems.
Business analysts work across all levels of an organization. They focus on strategic planning, translating project requirements and developing policy to support an organization’s ongoing technology and process maintenance.
Data analytics focuses on the process of collecting, cleaning, analyzing, reporting and presenting data.
Data analysts break down the data and take the necessary steps involved in converting raw and messy information into clean and usable knowledge. They focus on tools and statistical methodology, often working with management to translate data, interpret results and test hypotheses.
A business analyst might look at past performance to predict how a company will do in the future. A data analyst, on the other hand, compares the company to its competitors, identifying trends and patterns that could impede or encourage success.
While business analytics and data analytics share similarities, the reach of data analytics extends far beyond business. From healthcare to education to technology, organizations across industries rely on data analysts to uncover insights and guide decision-making. This broad applicability is one reason data professionals have the opportunity to pursue career opportunities in many different industries.
Because data analytics touches on so many industries and business areas, the roles and career opportunities for data scientists are incredibly varied.
By moving into data analysis, you could end up in a role* as a:
*These are examples intended to serve as a general guide. Some positions may prefer or even require previous experience, licensure, certifications and/or other designations along with a degree. Because many factors determine what position an individual may attain, Capella cannot guarantee that a graduate will secure any specific job title, a promotion, salary increase or other career outcome. We encourage you to research requirements for your job target and career goals.
This list isn’t exhaustive. With AI and deep learning continuously creating potential new opportunities, exciting professional paths are emerging that could lead to you having an impact in currently unknown areas of development.
To be a successful data analyst, you’ll need to develop a range of specialist skills. A fundamental knowledge of technology, programming and statistics is necessary for someone to succeed in this field. You also need to be familiar with AI and deep learning tools. This requires advanced training.
But soft skills are equally important, and you may already have some key traits that could help you build a strong career in data analytics. These are just a few of them:
Don’t worry if you aren’t naturally strong in all of these areas. You could acquire these skills through training and education, for example, by getting an online degree in data analytics.
If you’re serious about a career in data analytics, one way to start your journey is by pursuing an advanced degree at a university.
By choosing university study, you’ll benefit from a comprehensive curriculum. For example, here at Capella University, the data analytics programs are designed to help you gain skills that are valuable in the field.
Capella University’s Master of Science in Analytics prepares professionals to work with data, transform it into solutions for real-world problems and clearly communicate their insights.
Throughout the program, students develop skills in:
Additionally, students strengthen their collaboration, communication, presentation and negotiation skills.
The data analyst virtual internship was developed with industry leader SAS so students may pursue the role of a data analyst working for a major healthcare system. In each course, students address real-world problems specific to health care.
From cleaning and analyzing large data sets to selecting tools and presenting insights through impactful visualizations, students work through interactive projects that mirror real analytics challenges.
“Creativity is an important attribute for any successful data scientist, as they are often asked to address complex problems in unique and creative ways,” says Dr. Zgola.
“This is also an attribute that employers look for in their potential candidates. Throughout the program, students will encounter spontaneous real-time analytics exercises that call for inventive strategies and solutions.”
These impromptu assessments are designed to help develop students’ creativity and offer experience working on today’s most current and relevant analytics puzzles.
When you earn a degree from an accredited institution, it signals to employers and peers that your education meets recognized academic standards. Accreditation ensures that the university has been evaluated by an independent agency and meets rigorous criteria for quality, curriculum and faculty.
Capella University is accredited by the Higher Learning Commission. You can be confident your degree represents a credible, career-relevant education designed to help you reach your goals.
As a Capella student, you could earn a Statistical Analysis System (SAS) certification along with your degree.
The coursework provides the skills and knowledge needed to take exams for four different SAS certifications, plus vouchers are provided to take the exams for no additional cost:
Capella also offers eligible students certification exam vouchers from CompTIA® and Cisco®.
If you’re keen to develop specialist data analytics knowledge, you should consider studying data analytics. Pursuing a degree in analytics can power a professional move, help you start your own business, or enable you to make more meaningful contributions at the job you do now.
“Analytics is such a diverse field. People are joining the field from a large number of different areas and backgrounds. We are seeing self-motivated and curious individuals who are analytical by nature and have an interest in helping guide strategic decisions for organizations,” explains Dr. Melissa Zgola, program director at Capella University’s School of Business, Technology and Health Care Administration. “Our students typically enjoy the investigative nature of analyzing data and the creative process involved in identifying innovative solutions for organizations.”
Through advanced study, data analytics graduates develop the specialized skills employers are looking for. Our dedicated Career Development Center offers one-on-one career coaching and networking to help you make the most of your competency-based learning. That’s why so many students have turned to Capella’s master’s programs to gain the skills to pursue new career opportunities.
We can help. Data analytics offers potential opportunities across nearly every industry.
Building advanced skills through a trusted degree program may help you professionally shape the future with data-driven decisions.
Capella University’s online Master of Science in Analytics gives you the flexibility, credibility, and hands-on experience to take your skills further. Start exploring how Capella can help you pursue data analytics today.
Data analysts gather and organize data and transform it into clear insights. It’s their job to identify patterns and trends, and then communicate their findings to help businesses solve problems and make confident, data-driven decisions.
Examples of data analytics include using historic customer data to forecast seasonal demand or looking at user behavior to identify opportunities to enhance conversion or remove points of friction.
Data analytics can be used in education to improve teaching, learning, and student outcomes within digital education platforms. Ed-tech examples include tracking how learners interact with online courses and using that information to make education more personalized and effective.
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