Aaron Young, who received his MS in data analytics from National University (NU), says enrolling in the program was one of the best things he has done for himself. The busy grad is currently a data analyst in the credit analytics department of Axos Bank and also runs his own analytics company, called Young Data Analytics. He’s always been a whiz with numbers and logic, and just “gets” the topic of data science. However, he wanted the benefit of an advanced degree to take his skill set and expertise to the next level. Of course, since he was a busy professional, he needed classes that would fit into his schedule. He also didn’t want to wait too long to get started on this phase of his academic and professional journey.
“I looked at one other school in California for an MS in data analytics,” says Aaron. “I would have had to wait an extra year to begin my studies, and it would have cost nearly $30-$50K more than National University. NU was an easy choice for me. I was able to start quickly, with very challenging classes, beginning every four weeks. I knew I would be 90% done with my degree before I could have even started classes at another university.”
Choosing NU was one analysis model that wasn’t too hard to figure out, he says. Additionally, Aaron says the education he received was a tremendously well-rounded and engaging experience. “I got the chance to partner up with analytically-minded people. I was able to network and collaborate with really, truly brilliant students. Every person I finished with in the MS in data analytics program was the cream of the crop. It was great to be in that atmosphere, all of us learning from each other,” he recalls.
So … What Is Data Analytics, Exactly?
Data analytics is a subset of data science (which includes a trifecta of applied statistics, computer programming, and domain knowledge). Data analytics entails looking at raw data from a source (such as a database), cleaning it and processing it, and transforming it into a form that can be easily understood by others.
From the flow of the data, an analyst can then draw conclusions, statistical patterns, and find meaning in those trends. One of the crucial parts of the job is taking all those numbers, statistics, and variables and presenting them in a way others in the company (who are not analysts) can relate to. Many times, an analyst will share their reports with stakeholders using charts, graphs, and other visuals.
Many sectors are using the power of data analytics to drive consumers to them, and there is an explosive growth of data science usage in the tourism, sports, banking, government, energy, healthcare, agriculture, education, entertainment, human resource management, and of course, online retail industries. More than ever, big business needs big data to help streamline its workflow, follow consumer interests, and target their markets effectively. Businesses large and small rely on the data knowledge they gather to hone in on their current customers, but also develop solutions and strategies for both the acquisition and retention of new business.
According to the data analytics giant SAS, the history of big data analytics is nothing new, but the way we look at it is. “The concept of big data has been around for years; most organizations now understand that if they capture all the data that streams into their businesses, they can apply analytics and get significant value from it. But even in the 1950s, decades before anyone uttered the term ‘big data,’ businesses were using basic analytics (essentially, numbers in a spreadsheet that were manually examined) to uncover insights and trends. The new benefits that big data analytics brings to the table, however, are speed and efficiency.”
What Does a Career in Data Analytics Look Like?
Aaron admits that his is a fast-paced, non-stop job, and he is constantly crunching numbers, working data, and finding solutions for questions and queries that his colleagues have. And, he wouldn’t change it for the world.
“I attend an analyst meeting in the morning at Axos Bank, which covers what the priorities of the bank are, and pertinent items or reports that we need to accomplish. From there, we pull specific details from the bank database using SQL programming. Data could include information such as commercial loan history, terms, and interest rates for a segment of clients. We are constantly busy building reports, but I love it,” says Aaron.
“For instance, we work with different data models to help determine a crediting algorithm. This helps influence things such as credit increases and understanding credit swings,” he says. The reports for these types of queries are then typically delivered to the bank’s CEO, bank governance, and the Securities and Exchange Commission (SEC).
At his sole proprietorship, Young Data Analytics, Aaron says the focus is more on small or family-owned businesses. Clients will ask Aaron to help them better understand their customers’ patterns and behaviors, in an effort to determine buying trends.
Taking a look at data models, habits, and demographics, Aaron can help a small business owner optimize and prioritize product lines so that they can, ultimately, give their customers the products and services they are looking for. Predictive analytics are utilized to process the data on hand currently, in order to forecast future spending and interest patterns. Artificial intelligence (AI), also covered in the curriculum of the MS of Data Analytics at NU, helps machines learn the habits of humans, to serve up the things they look for when they look for them. If you ever wondered, “How did Amazon know I was going to add that to my cart?” Well, you have your answer. It’s all in the data.
How The MS in Data Analytics Upped Aaron’s Game
“It has been invaluable to me. In addition to working with terrific professionals, what you learn is so valuable. The data science master really helps you think critically and teaches you what it means to be an efficient, critical, logical, progressive thinker. It sharpened my skills and mind exponentially. This degree takes you to the next level,” says Aaron.
Core topics in the MS in Data Analytics program include data modeling, mining, methods, and applications. Additionally, students learn to develop, implement, and maintain the hardware and software tools needed to make effective use of big data databases, including data marts and data warehouses. Classes also focus on machine learning and analytic programming. Online degrees are also offered, although the in-person environment appealed to Aaron the most.
“The in-person, intimate environment gets you direct feedback, and I liked that. I have to say, it was a very challenging course. At National University, they don’t go easy on you. You have to be very committed to it. If you stay on top of your studies, you will do well,” he says.
The culmination of this program is a three-month capstone project where real data from sponsoring organizations or publicly available data is used to solve specialized problems in analytical database design, programming, implementation, or optimization. Students use data files to help solve practical problems, which Aaron says, happens every day with data science.
“Statistics, data analytics, and data science aren’t just for data nerds,” he says. “It affects all sectors — locally, nationally, and globally, commercial and private. It’s big in the world economy. Many things are already automated, or use AI. This is going to have an indefinite place in our society.”
As a former Navy SEAL and police officer, Aaron says he’s used to structure and order. Almost every day while in the SEALs, he would run 10-15 miles, swim, and follow an extremely demanding regimen. His background in the service likely helped him to stay organized, strategize, and tackle the four-week courses in the accelerated pace of the MS in Data Analytics program.
“You start off right out of the gate fast, so you have to be prepared and have a real plan in place so that you can get the most out of it,” Aaron says.
Data Analytics Master’s Help You in This Growing, Competitive Job Market
A recent study done by IBM, called “The Quant Crunch” reported, “We project that by 2020 the number of positions for data and analytics talent in the United States will increase by 364,000 openings, to 2,720,000. In 2020, job openings for data scientists and similar advanced analytical roles will reach 61,799. This is a significant number, but it represents just 2% of the projected demand across all job roles requiring data and analytics skill.”
The study also mentions that the demand for data-driven decision makers, such as data-enabled marketing managers, will comprise one-third of the job market, with a whopping increase of 110,000 positions by 2020. Everyone, from marketers to C-level executives will need to grasp why data analytics is crucial for everything from finding your key target market to crafting a company’s digital strategy.
Roles in the data science industry have an average salary of $80,265-$105,909. Getting an MS of Data Analytics can increase your worth as a candidate in this field. Some data-centric occupations that are growing increasingly in-demand include:
Data Scientist: create sophisticated analytical models used to build new datasets and derive new insights from data.
Data Analysts: leverage data analysis and modeling techniques to solve problems and gather insight across functional domains.
Financial Analyst: utilize data and analytical models to inform specific functions and business decisions.
Chief Analytics Officer: oversee analytical operations and communicate insights to executives.
IT Project Manager: leverage data to inform strategic and operational decisions.
To be considered for either the data analytics master’s program on-campus or online degree, academic studies or work experience in areas such as math, statistics, or science are helpful prerequisites. This degree is appropriate for both experienced professionals as well as recent college graduates. While in the program you will:
- Evaluate data management methods and technologies.
- Construct data files using advanced statistical and data programming techniques.
- Design an analytic strategy to frame a potential issue and solution.
- Develop team skills to ethically research, develop, and evaluate analytic solutions to improve organizational performance.
- Analyze complex database queries for real-world analytical applications.
- Design medium to large data warehouses.
- Evaluate machine learning methods for advanced data mining.
Perks of the Data Analytics Master’s Program
“The cool thing about the data analytics master program is that during different intervals in the program, they would have librarians come in and explain different academic resources offered, such as paper editing services. One of my favorite aspects of the support received while at NU was the editing service. They were so good and helped during my capstone course. They teach you to not use long and lofty phrases, which enables others to retain and understand your message,” he says.
Another plus Aaron mentions is that the program guides students in perfecting their presentation technique in class, which helps hone public speaking skills — something he says uses daily throughout his professional life.
“You spend a good portion of your professional life in data analytics, relaying information to others who may not be as technically-minded. So, you have to present your findings clearly and visually, and in a way that helps others process your information.”
Working with a team to build knowledge-based solutions is at the heart of the curriculum for the data analytics master’s program. Aaron believes that it undoubtedly increased his level of expertise and challenged him every step of the way. “I loved it, without a doubt I recommend it.”
He has just a few words of advice for potential students looking to pursue an MS in data analytics. “Do a little bit every day; you can’t wait until the final week to get it together. I was in my textbook every day, underlining items and evaluating the information. Just remember to study daily … you can’t cram analytics!”
Degree Requirements for the National University MS in Data Science
To obtain the Master of Science in Data Science, you must complete at least 54 graduate units. A total of 13.5 quarter units of graduate credit may be granted for equivalent graduate work completed at another regionally accredited institution, as it applies to this degree.
To learn more about the Master of Science in Data Science or to request additional information, please visit our program page.