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Answered: 3 Common Questions About Careers in Data Analytics

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Data can be incredibly powerful, but only when utilized effectively. Humans currently generate vast quantities of information capable of informing decision-making in spheres as different as healthcare and human resources. However, chances are that organizations will only leverage a fraction of the 181 zettabytes of global data experts predict will exist by 2025. Traditional statistical analysis methods were sufficient to analyze trends in relatively small static datasets. Today's data is vast in quantity, rapidly changing, and often unstructured, making it challenging to collect, curate, and analyze.

Organizations increasingly need analysts who can use more sophisticated methods applicable to more fluid and diverse data sets to provide actionable business insights. As a result, new data analyst roles (including specialist roles such as data engineer) are opening across industries, leading many professionals to ask: "Should I get a data analytics degree?" The challenge is that there is no correct answer–only the answer that's right for the individual. 

People study analytics and become data analysts for various reasons. Developing data analysis skills opens access to virtually any field. Data analysts work for healthcare networks, nonprofits, government agencies, and fast-growing tech startups. Demand for data analysts is also growing, and professionals in the field are well compensated. According to the U.S. Bureau of Labor Statistics (BLS), "statistician" (a job category encompassing data analyst and data scientist) is one of the fastest-growing occupations in the United States.

With leading-edge data analytics training, you can use data more effectively and become more competitive in evolving job markets. Even if you don't plan on becoming a data analyst or business analyst, data analytics skills–especially skills in areas such as machine learning and Python programming–can help you advance professionally. And a graduate-level data analytics degree such as Butler University's Master of Science in Data Analytics can help you qualify for more advanced data analytics jobs or leverage the power of data on your current career path. A wise first step is assessing how well your skill set aligns with the most in-demand data analytics skills.

Do I Have the Skills Data Analysts Use?

Data analysts use a mix of technical and soft competencies honed by years of experience and formal education. In online MS in Data Analytics programs such as Butler's, learners typically come from diverse academic and professional backgrounds and bring diverse skills to their classrooms. You don't need to have each of the below skills right now to earn a graduate data analytics degree or enter this field. Your desire to learn analytics is much more important.

Computer Programming

In the not-so-distant past, analysts didn't need programming skills, but Big Data changed that. Big Data work involves enormous data sets and complex databases that are challenging to analyze. "Data will not be handed over to you on a silver platter," writes logistics analyst Wilma Lapuz. "It is messy, incomplete, [unbalanced], humongous, and continuous." Thankfully, modern programming languages and libraries have revolutionized data analysts' ability to collect, clean, curate, and analyze massive, unstructured or raw data sets fairly quickly. 

Data analysts do not have to be coding wizards but should have proficiency in programming languages, including Python, SQL, and R–the three languages data professionals use most often, according to a 2021 report from Anaconda. A comprehensive master's in data analytics curriculum will cover these and other programming languages. In Butler's program, world-class instructors teach programming in the context of data analysis rather than for use in the broader data landscape or in entirely unrelated career paths.

Critical Reasoning

To identify which trends are significant and which are coincidental, data analysts must not only recognize patterns but also understand data in ways beyond the obvious. "While analyzing data strengthens critical thinking, critical thinking, in turn, helps data analysis," write researchers Kurt F. Reding and Carolyn Newman in Strategic Finance.

Through their studies of the topic, Reding and Newman found that data analysis is an optimal discipline in which to develop and utilize critical thinking skills. "[Analysts] continuously question what they see and hear and rigorously investigate new evidence coming to their attention, including any anomalies." 

The boundaries of critical reasoning as a competency aren't as clear-cut as in other skills for data analysts. However, having a curious and logical mind and a good eye for patterns is crucial in this discipline. Butler emphasizes critical thinking in the MS in Data Analytics curriculum, and students hone related abilities in online courses that address real-world issues in analytics.

Data Mining

Data mining encompasses the processes used to discover insights in large datasets, and it requires technical mastery of machine learning, predictive modeling, and statistics. Effective data mining helps organizations make better business decisions and create informed marketing, recruiting, sales, and customer engagement strategies. Insights gleaned from data can also help organizations mitigate the effects of fraud and identify other risks.

In Introduction to Data Mining, Butler's expert faculty teaches analytics master's candidates to apply techniques learned in class to modern and classic data sets and generate algorithms for supervised and unsupervised machine learning.

Machine Learning

Machine learning in analytics uses self-improving computer programs to manage and process massive amounts of data and automate model building. Advanced machine learning skills are becoming more common among data analysts because data sources are rarely static or straightforward. Gone are the days when one Excel sheet could power an analytics project for months. Today, machine learning algorithms collect and clean streaming data sources in real time, and humans train machine learning models to do some of the most laborious, time-consuming tasks in data analytics.

Predictive Modeling

The modern, technical nature of data analytics makes it possible for analysts to provide more insight than ever before into trends using historical data. In predictive analytics, analysts use statistics and modeling to diagram potential future state scenarios in specific functional areas such as consumer behavior or financial risk. 

There are several common predictive modeling techniques and tools in analytics, including linear regression, neural networks, and decision trees. Data analytics master's students at Butler learn to choose and create predictive models using regression and machine learning in the Advanced Analytics, Predictive Modeling, and Decision Making course. 

Predictive Modeling

Predictive modeling is one tool under the broader umbrella of predictive analytics. Predictive analytics refers to the overhead process of using predictive models and statistics to make predictions about a company's performance or the behavior of its customers. This is a crucial skill in business intelligence leveraging some of the most essential analytics skills, including machine learning and modeling, to support problem-solving and help stakeholders make better decisions. 

Visualization

Useful data means nothing if it doesn't communicate information in a way the people in charge of decision-making can understand. Data analysts draw insights from data, but that's not where their work ends. Once an analyst discovers trends in data, they often present their findings to audiences with varying degrees of technical aptitude. Communication skills come in handy here, as do visualization skills and the ability to use tools such as Tableau to create easy-to-digest diagrams, graphs, and other visual aids. Butler's master's in data analytics program focuses on the entire world of data, including presentation. Students learn to create interactive, high-quality data visualizations in the Visualization, Storytelling, and Ethics course. 

Do I Have to Work in Tech if I Become a Data Analyst?

One of the biggest misconceptions about data analysis is that most analysts work for major tech corporations such as Google or Facebook. In reality, trained data analysts work in organizations across industries. MS in Data Analytics graduates hold titles beyond analyst, including general and operations manager, database administrator and architect, information security analyst, operations research analyst, statistician, and management analyst. 

The data analytics master's program at Butler University offers concentration pathways in healthcare analytics and business analytics, letting students focus on the concepts, techniques, and tools that support their specific career goals. In healthcare, data analysts might work with researchers to improve immunotherapy methods and precision medicine for cancer patients by reviewing anonymized patient data. In retail, a data analyst might analyze customer interactions to create better online shopping experiences. And in other industries, data analysts support decision-making around pricing, staffing, operations, policy development, and more.

Will a Career in Data Analytics Pay Well?

Master of Science in Data Analytics graduates work in various industries and hold many titles, so data analyst salaries vary widely. A 2020 Burtch Works salary study found that the average wage in predictive analytics roles for professionals with master's degrees ranged from about $79,000 at the lowest level to $135,000 at the highest level. Managers in predictive analytics, who tend to have graduate degrees, earned between about $131,000 and $255,000. The latest Robert Half Salary Guide reports that experienced data analysts earn about $103,000. According to the BLS, database administrators and architects earn about $99,000, and data scientists and information security analysts often earn more. The substantial ROI of a data analytics master's–especially for those who want to advance into the highest levels of the field–is undeniable.

Answering the Question 'Should I Be a Data Analyst?'

When you're asking yourself, 'Is data analytics right for me?', envision the impact you want to make in your career and then consider how data expertise can help you achieve your goals. Investing in and benefitting from that expertise may be easier than you realize. You don't need to start with a bachelor's degree in statistics or computer science to launch a career in data analytics. And in 2022, there were more listings for data analysts on LinkedIn Jobs than jobs for data scientists. Investing in a program such as Butler's online MS in Data Analytics may also be necessary to grow in a data analytics career. Burtch Works found that 89 percent of data analysts with less than three years of experience have master's degrees.

The Master of Science in Data Analytics program at Butler University can give you the skills to make high-impact decisions with data and the credentials to excel in established and emerging data roles. Demand for data analysts will likely increase as the data generation rate increases, which suggests now is the best time to pursue data analytics training. Butler's data analytics master's curriculum consists of two foundational skills courses, five core courses, and four concentration courses delivered in a flexible online model. That means you can train to become an active participant in the data revolution without sacrificing income or opportunities for advancement in your current career.

Are you ready to make a difference with data? Learn more about Butler's MS in Data Analytics admissions process and tuition and financing, or apply online today.