Unlock the power of Python for data-driven decision-making as you master Python programming fundamentals and dive into data analysis. Acquire essential skills to clean and manipulate data, create insightful visualizations, and perform statistical analysis, all through hands-on projects with real-world datasets.
Learn to extract information from databases by writing SQL queries, joining tables, aggregating data, and filtering results. You'll learn PostgreSQL in this class, but the concepts apply equally to other databases such as SQL Server and MySQL.
Become proficient in Microsoft Excel and go from beginner to pro with our hands-on Excel Bootcamp. Learn functions, formulas, Pivot Tables, macros, and time-saving techniques to conquer all the key skills needed to excel in Excel.
Learn how to use Tableau, a powerful data visualization tool, to create interactive and aesthetically pleasing charts, graphs, and maps. This course will teach you how to connect to datasets, analyze and structure data, customize visualizations, and publish your work.
Learn how to use Power BI, a powerful data visualization tool, to create interactive and aesthetically pleasing charts, graphs, and maps. Learn how to connect to datasets, analyze and structure data, customize visualizations, and publish reports.
Learn the fundamentals of data analytics, including descriptive and inferential statistics, and how different industries and organizations use data for forecasting and decision-making. Explore statistical analysis and modeling, including statistical algorithms, theorems, and models.
Learn the fundamentals of machine learning, including regression analysis and classification algorithms, in this practical, hands-on course. Gain the skills needed to solve real-world problems using machine learning, with a focus on Python programming and data science libraries.
Automate and enhance the data analysis process with artificial intelligence (AI). In this course, you'll learn how to utilize AI tools to collect, preprocess, analyze, visualize, and interpret data without the need for extensive coding knowledge.
Learn to gather, manipulate, and analyze real-life data in this course, where you'll gain hands-on experience with Python's NumPy and Pandas libraries. Develop your data visualization skills using Matplotlib, Seaborn, Plotly, and Dash Enterprise, and complete real-life projects that can be deployed online.
Learn the essential skills needed to become a Data Analyst or Business Analyst, including data analysis, data visualization, and statistical analysis. Gain practical experience through real-world projects and prepare for a successful career in the field of data analytics.
Learn Python, SQL, automation, and machine learning to become a Data Scientist. Gain Python programming, data analysis, SQL querying, and predictive modeling skills. Perfect for beginners, this program prepares you for entry-level data science and Python engineering roles. Unlock high-paying job opportunities in the field of data science.
Learn Tableau for data visualization and become a certified Tableau Desktop Specialist in this hands-on certification course.
Learn to extract information from databases by writing SQL queries, joining tables, aggregating data, and filtering results. You'll learn PostgreSQL in this class, but the concepts apply equally to other databases such as SQL Server and MySQL.
Learn Power BI for data analysis & visualization and become a certified Power BI Data Analyst Associate in this hands-on certification course.
Boost your resume with a Microsoft Excel Certification. Our program offers three courses, private training, the Specialist Exam fee with free retake, lifetime access to Excel video courses, and free exam proctoring.
Data analytics, in simplest terms, is the collection, transformation, and organization of data to draw conclusions, make predictions, and inform business decision-making. Data is obtained and analyzed in three stages. Data collection includes creating a method of obtaining the information requested. Data can be collected using various techniques, like surveys or interviews. This information is stored until the second stage, when processing and organization begin. During the second stage, individuals review the data and ensure there are no errors or missing information; this is called cleaning the data. Finally, once the data is cleaned, the analysis moves on to the final stage of data analysis and visualization. The data is reviewed for patterns, insights, and trends during this final stage. Once analyzed, it is time to present the information visually to explain your findings. The process can be tedious and require a lot of time, but many feel accomplished when finding a solution to a problem or an answer to a question from the data collected.
Data analytics is not a new concept. The concept has been around since the 1950s, and the term was often used to discuss business trends. Many businesses use data analytics to make well-thought-out business decisions, but it is most commonly used in health care, retail, manufacturing, and banking. Data can be used to future-proof the business, improve productivity, mitigate risk, help stakeholders make better business decisions, and help organizations reach their goals. Simply put, data analytics is the process of analyzing data trends; there are four primary types of data analytics to consider when discussing this topic.
Each type of data analytics provides businesses with the additional insight they need to make the best decisions for their business success. While each has its own goal, some are interconnected and rely on each other to gather sufficient information.
Many people have chosen to get into data analytics because it is becoming increasingly in demand. The increased popularity of data analytics provides job security for many because of the growing demand. Additionally, learning data analytics offers many skills that can be transferred to numerous roles. Skills like programming languages and data visualization play critical roles in training. Popular programming languages, like Python, can be transferred to many areas outside data analytics, making learning them highly beneficial. Much like programming languages, knowing how to present data collected or visualize it in a digestible manner for varying audiences is another critical skill in data analytics. Presenting findings effectively helps businesses make the best decisions for themselves and, in turn, recognize the value of investing in data analytics. Not only are there technical skills needed to be successful in data analytics, but there are also soft skills like communication, organization, attention to detail, critical thinking, and creativity that are just as important.
Various jobs are available that can use the skills learned in a certification or course. Skills can also be used to improve or enhance customer experience with a company by identifying areas of opportunity to streamline processes and increase productivity.
Data analysis improves efficiency by reviewing data that provides insight into key business success drivers. Someone well-versed in data analysis is well-suited for specific risk management positions. Risk management helps businesses identify potential risks and develop strategies to decrease the chances of risks occurring. Finally, some roles can use analytics to help enhance the customer experience. Using business data, an analyst can find what strategies lead to the business’s success and how to improve existing products and services. Data analytics training can be used in various industries and roles, so investing in the training is worthwhile, even if you are still trying to figure out what to do with those skills.
Data analytics courses can range from beginner to advanced. Depending on how detailed the course is, participants will learn a certain amount in the class. However, some basics will likely be covered during a data analytics class to ensure students are knowledgeable and set up for success.
Statistics collects, analyzes, interprets, and presents numerical data. Statistics is a skill that translates well and can be used everywhere, not just in data. However, data analytics is heavy in statistics, meaning courses will provide students with various levels of detail on statistics. It helps extract meaningful data that can be used to draw conclusions. Statistics is also used to create probability distributions; this helps manage which statements help predict future events. Statistics is vital in data analytics because it allows analysts to understand what’s happening and provides insight for businesses and other clients. Statistical methods also help identify patterns and areas of improvement, which help develop strategies.
A large part of data analysis is data visualization. Data visualization represents data using charts, tables, infographics, plots, and graphics. Data visualization is imperative for data analytics because it showcases the work done. It is a great way to display ideas in a way that all decision-makers can understand. Some additional and standard visualization techniques include pie charts, area charts, scatter plots, and histograms. Most data analytics classes will likely cover the most common data visualization methods. Many courses will also teach students how to use Java and CSS, valuable tools in the data industry.
Programming is a large part of a Data Analyst’s day, so they must be proficient in programming languages. The most popular programming tools include Python, SQL, R, and JavaScript. Programming applications can help Data Analysts quickly create and manage data structures, which can be helpful when analyzing and manipulating complex data. While coding is not always a requirement for analysts, it sets them apart. While you can be well-versed in some programming languages, knowing Python and SQL can significantly benefit you.
Machine learning can benefit all spaces with the growth of AI and other programs. Machine learning also plays a pivotal role in data analytics as it grows in popularity. It is a branch of computer science that uses data and algorithms to enable AI and improve its accuracy. Machine learning and data analysis are essential because they help automate the data analysis process and improve workflow. It can also cut business costs, mitigate risks, and improve business structures overall. Data Analysts will still analyze the data, and machine learning will help provide a deeper insight into the data by inputting the information obtained into a program; the two work closely together and enhance the data analysis process. Knowledge of machine learning as a Data Analyst can help improve workflow and reduce the risk of human error that could arise with analyzing data.
Though it might not be explicitly taught, attention to detail is vital to a Data Analyst’s work. When covering the core topics such as programming, data visualization, statistics, and machine learning, there is a need to pay attention to detail. It can help prevent detrimental errors in your work, saving time and money. As you navigate various courses and learn new skills, you might pick up ways to ensure you pay attention to detail. This could look like making lists, reviewing your work, or contacting a colleague with questions.
Data analytics can be complex for some because it is highly technical, but it is not impossible to learn. How technical data analytics gets depends on how in-depth individuals are looking to dive into the field. When learning data analytics, it is essential to know that it includes reviewing data, understanding it, and making it understandable for others who might be less connected to the numbers. It can be less challenging for those with previous experience with statistics, computer programming, and experience creating various types of data visualizations. While it can be difficult, that does not mean learning is impossible. Focusing on the critical aspects of data analytics such as statistical analysis, can help decrease the potential challenges one would face.
The challenging parts of learning data analytics can depend on a person’s skill set when they enter the course, but generally, most find navigating the data difficult. Running a data analysis can yield a lot of information—some pertinent in the moment and others not as crucial at the time. Many find it challenging to manage the amount of data received while also determining what is useful and storing the data that is not needed. It can be overwhelming for most people. The best thing to do is to take your time and start with one piece of data at a time. As you work through the data piece by piece, you will know what needs to be used at the time. Storing data securely and keeping it for later projects is just as crucial as determining when and how to use what you have. With more experience, the feeling of being overwhelmed should decrease as you develop a system for how to best manage your data.
For those wondering how long it takes someone to learn data analytics, the simplest answer is: it depends. Coming into data analytics, the time it takes you to learn the basics can depend on how much knowledge you have before starting your program. Some individuals begin learning the ins and outs of data analytics with some understanding of data, statistics, or even problem-solving with numbers. This could be a significant advantage and decrease the time it takes to learn. When you start a program or explore data analytics, foundational education about the subject is essential. This means understanding what data analysis encompasses and learning the skills to make you competent in an entry-level position. This means understanding its core concepts, learning how to analyze data, and grasping how to explain your findings in a digestible way. Entry-level competence can take a few months as you become more comfortable and improve your skills. You can independently learn technical skills or take a professional certificate program to help advance. Feeling comfortable with various industries and programs can take a little longer and, sometimes, several years. This could involve taking additional courses or jobs in multiple fields that expose you to new methods. With practice and experience, depending on how frequently you use your skills, becoming a professional in data analytics can come easily and naturally. The key is to understand the basics and spend a lot of time practicing.
Nowadays, convenience is essential for anything, including learning opportunities. With the current increase in popularity of online classes, some are on board with taking classes this way, while others tend to stick with the tried and true method of in-person learning. When deciding between online learning, in-person, or even an asynchronous option, despite what might be popular right now, it’s important to consider which method supports your learning style and is realistic for you. First, let’s consider an asynchronous option or a self-paced-style course. Asynchronous classes allow for independent learning without being in a live course. Assignments typically still have assignment deadlines and a course end date, but there is no live instructor; lectures are usually pre-recorded. Asynchronous is ideal if you need flexibility and want to work at your own pace. Depending on how well you understand it, you can spend more or less time with a particular lesson. It can be challenging if you struggle with deadlines and procrastinate. An asynchronous course removes the additional level of accountability by having weekly classes, which can be difficult for someone who needs more time to focus or becomes easily distracted. You can still access your instructor via email or virtual office hours, so asynchronous does not mean you are entirely alone.
Live online classes are classes taught by a live instructor with classmates but in a virtual space. Online courses offer a great amount of flexibility because you can attend the class anywhere. There are no concerns about being stuck in traffic or out of town during class time; simply log in from anywhere and be ready to learn. Many people love this feature because it feels less restrictive of their time. Additionally, you save time because there is no commute. Online classes mean not worrying about parking and walking across campus to your next class. This saves you time and money. Finally, online classes are open to everyone, which means your classmates can be from anywhere, and this gives you the opportunity to broaden your network by meeting people outside of your city and potentially outside of your state. This global networking opportunity can make your learning experience more enriching and diverse.
On the other hand, online courses can make it difficult to network with classmates. Spending time online versus in a classroom takes away from some of the organic conversations that can take place with classmates and instructors in the downtime before class starts. For many, this is where networking and building community can happen. Also, taking a class online typically means you need the software and other tools necessary for the class. In-person classes can offer a lab with specific programs you might need already loaded on the computer. While purchasing the program you’re learning could be an investment, if you are taking numerous courses, this can become costly.
Finally, there’s the traditional in-person course. Many prefer in-person courses because of the structured environment they offer. Students know they are in the classroom at a particular time and have that time set aside and dedicated to learning. This structured environment can help you prepare and focus better because you have the expectation to be present in class. Also, in the classroom, you are less likely to be distracted by your phone, TV, or others living in the house. This format prevents multitasking during class time. Finally, during an in-person class, it can be easier to ask questions of the professor and your peers because you are all in the same room together. In-person classes can limit your flexibility because you are expected to be in class; this can be challenging for some. Also, in-person classes can be limiting. While online courses are available worldwide at a variety of locations, when selecting an in-person course, you have to consider the feasibility of your commute. Regardless of what you choose, there is a form of class that can accommodate the needs of every learner.
Data analytics training will cover a lot of topics, specifically technical skills. While all aspects are essential, some additional technical skills are necessary and can further your learning. You might not find a data analytics course that explicitly teaches everything you need to know about Microsoft Excel. Still, it is a program often used to help data analysts remain efficient. It might be worthwhile to take additional Excel training to increase your skills and improve your data analytics techniques. Another technical skill, statistical knowledge, can help you succeed as a Data Analyst. While how in-depth your knowledge needs to be depends on your role, there are options to expand your knowledge to ensure you are prepared for future jobs. If you have the time, taking additional courses in statistics can be precisely what you need to further your advancement.
Some soft skills are not necessarily taught in courses but can be learned along the way and are just as vital as technical skills. Improving your communication skills can help improve your work as a Data Analyst. Improving communication skills can happen while working on a team in an unrelated field. Consider any areas of opportunity or feedback you receive related to communication as ways to improve these skills. Extracting data is one thing, but clearly communicating findings to other, less technical team members is just as important. Paying attention to detail is another essential skill. As a Data Analyst, you must be able to find answers and notice things that others might miss. Attention to detail can help you limit mistakes and stay on top of the data. This could look like finding a method that works for you such as keeping track of projects or staying organized with a planner. Being organized and having a system is a great way to notice those details that are out of place and catch them before they cause further complications.
Many industries see the benefits of using data analytics to help their businesses succeed. It can be used to help predict trends, solve problems, and even easily display a company’s successes to the entire team. Data analytics can help companies in finance, marketing, and human resources. Essentially, any industry can benefit from data analytics, but some benefit more than others.
Healthcare
Chicago’s robust healthcare industry is one of the city’s largest employers, with major organizations such as Northwestern Medicine, Rush University Medical Center, University of Chicago Medicine, and Advocate Health Care leading the way. Data analytics plays a transformative role across this sector, helping hospitals and healthcare systems improve operational efficiency, patient care, and decision-making. By leveraging analytics tools, these institutions can track electronic health records, monitor patient outcomes, and streamline hospital resource allocation. For example, organizations like Walgreens Boots Alliance, headquartered in the Chicago area, use data analytics to analyze consumer health trends, optimize inventory, and personalize patient experiences. Through advanced analytics, Chicago’s healthcare providers continue to reduce costs, enhance patient satisfaction, and develop data-driven treatment models that improve overall public health.
Education
Chicago’s education sector—from its public school system (CPS) to higher education institutions such as DePaul University, University of Illinois Chicago (UIC), and Northwestern University—is increasingly relying on data analytics to improve student outcomes and guide policy. Educational data analysts help administrators and educators track student performance, assess teaching effectiveness, and identify areas where resources can make the most impact. By applying predictive analytics, schools and universities can forecast student success rates, enhance retention, and create data-backed initiatives that foster equity and access in education.
Finance
As one of the largest financial hubs in the U.S., Chicago’s finance sector—home to major institutions like CME Group, Morningstar, Northern Trust, JPMorgan Chase, and Citadel—depends heavily on data analytics to make strategic, data-informed decisions. Analysts in this field assess vast datasets to identify market trends, detect fraud, and forecast financial risks. Data analytics also helps banks and investment firms improve operational efficiency, tailor financial products to consumer needs, and gain a competitive edge in trading and asset management. As Chicago’s fintech scene expands, with companies like Enova International and Avant, the demand for finance professionals with strong data analytics expertise continues to rise.
Government Agencies
Government agencies in the Chicago area—including the City of Chicago’s Department of Transportation (CDOT), the Chicago Police Department, and the Illinois Department of Public Health (IDPH)—increasingly rely on data analytics to improve public safety, optimize services, and inform policy. Analytics helps these agencies monitor infrastructure performance, track public health data, and allocate resources more efficiently. For example, transportation planners use predictive data models to manage traffic flow and improve transit safety, while law enforcement agencies apply analytics to crime data to identify trends and prevent incidents. Through data-driven insights, Chicago’s government organizations are becoming more transparent, efficient, and responsive to community needs.
A Freelance Data Analyst works as an independent business owner in various industries, giving them freedom and flexibility. This position offers flexibility because freelance data analysts are not associated with just one company but have the freedom to move about and work with different companies at once or take multiple jobs simultaneously. Analysts typically have to find clients independently and handle their billing; they can also choose which clients they want to work with and manage their own time. Being a Freelance Data Analyst can be beneficial because you can be your own boss and enjoy variety in your work. Additionally, you have the freedom to work with clients all over, thanks to the flexibility of the role. Finding clients and maintaining a consistent workload can be challenging initially or during slow periods. It can also be problematic because working freelance typically means not receiving the standard benefits of a traditional job. Most freelance positions do not offer benefits like insurance, paid leave, or retirement plans. Working independently can also feel isolating for some. There are no coworkers to check in with, and you typically work from home. Training as a Freelance Data Analyst is similar to other roles. As you gain more experience and begin exploring various industries, your expertise can make it easier to market yourself for a wide range of jobs. The average salary for a Data Analyst in Chicago is about $80,000.
Database Administrators (DBAs) are responsible for maintaining, securing, and operating databases. While there are several types of DBAs, one of the most common roles is that of a database analyst. Database Analysts collect and analyze data to help improve database performance. Regardless of the type of DBA, it is essential to be familiar with different databases such as MySQL and MongoDB. As a DBA, you can expect to work directly with business executives for large corporations or individuals. Database Administrators, on average, make about $110,000 in Chicago.
Business Analysts examine a company’s data and procedures to identify areas of opportunity to improve efficiency and performance. Business Analysts work with various business areas to assist with management, reduce costs, and ensure all resources are used efficiently and effectively. Business Analysts need to be familiar with data, business operations, and how organizations function. They should also identify areas that can be evaluated and improved if needed. A Business Analyst, particularly one who focuses on data, must have a strong background in programming, research, and statistics. In Chicago, a Business Analyst can earn about $90,000 depending on skills, education, and experience.
A career as a Financial Analyst is in high demand. As a Financial Analyst, you work with businesses to analyze their finances. This could include analyzing historical results, making projections, or recommending what and when to sell. As a Financial Analyst, you can use Excel and apply ratios like gross margin, return on equity, earnings per share, and net margin, among others. These ratios can be used to compare their client’s performance against themselves and their competition. Financial Analysts work closely with business executives and play a pivotal role in decision-making. On average, a Chicago Financial Analyst can make about $79,000 a year.
A Python for Data Science Bootcamp is an excellent way to gain essential knowledge and master Python. Noble Desktop offers an online bootcamp to help students feel comfortable writing basic statements and expressions, working with lists, indexing, and more. The course is 30 hours, with most sessions occurring during the week.
Noble Desktop’s Data Science Certificate program helps students build a foundation in key areas such as Python programming, data collection, and data analysis. They gain hands-on experience using essential Python libraries for data manipulation and visualization, including Pandas, NumPy, and Matplotlib, and learn how to leverage Tableau to create clear and compelling data visualizations. The course also introduces students to machine learning fundamentals, teaching them how to design and apply algorithms to automate complex analytical tasks. By graduation, students will have a strong understanding of modern data science practices and be well-prepared for roles such as Data Analyst, Data Scientist, or Machine Learning Engineer.
The Data Analytics Technologies Bootcamp from Noble Desktop provides students with an in-depth, hands-on education in the essential tools and programming languages used in modern data analysis. Throughout the program, participants gain practical experience working with Excel, SQL, and Tableau, developing the skills needed to collect, clean, analyze, and visualize large datasets. The bootcamp emphasizes real-world, project-based learning, allowing students to apply analytical techniques to real business scenarios and strengthen their ability to make informed, data-driven decisions.
Planning corporate training for your team can be a great way to build relationships and ensure your team is aligned in their skills. Noble Desktop recognizes the impact corporate training can have on business, which is why they offer corporate training. It provides training in SQL, Python, Excel, data science, and other vital programs that will help expand your team’s data analytics knowledge. Noble has developed an excellent curriculum that can be customized to suit the needs of your business and employees. Not only is the training well-crafted, but the support materials are as well. During training, students will receive handouts, certification exam guides, and access to premium training videos to help solidify the knowledge taught in the training program.
Noble offers several options to support corporate training. First, corporations can choose live online or onsite training. With this option, your business can have a live instructor in-person or virtually train your team on location. This option includes flexible scheduling to accommodate business needs. If your team has varying roles, responsibilities, and availability, making it hard to secure a training date, Noble Desktop offers a group class voucher option. Interested employers would purchase vouchers at a discount, and employees would schedule the training independently. Management would choose which courses are available to employees. The final option is attending a private training on location at the Midtown Manhattan Noble Desktop location. After selecting the course that best fits company needs, employees would arrive for their training and have access to the modern computer lab at the training site. Noble Desktop can set up the training for you and provide you with the information needed to make this training option successful. Email Noble Desktop and start a free consultation with their team.
In the heart of the Midwest’s finance capital, there’s opportunity everywhere. From tech startups in Fulton Market to powerhouse Fortune 500 firms in the Loop, Chicago offers a virtually endless array of ways to level up your skills in tech, business, data, and design. The city thrives on bold ideas, making it the perfect place to grow a career that is not only lucrative but also makes a real impact. With expert instructors with decades of experience, you’ll learn more about the subjects you’re interested in, all while receiving hands-on training. These instructors live and breathe to explore and work within their field, meaning they don’t just teach; they mentor, guide, and connect you to the heart of Chicago’s many successful industries and direct you to the most meaningful path to help you achieve your goals.
Noble Desktop’s partner school, Practical Programming, has state-of-the-art facilities conveniently located in downtown Chicago’s iconic Hayworth Building at 29 E. Madison Street, Chicago, IL, 60602. It’s an easy-to-find location for students from across the city to attend. The campus is modern, equipped with the necessary tech and tools you may need, and provides resources like Wi-Fi, comfortable workstations, and expert instructors and staff ready to help you learn. Getting there is simple, whether you’re commuting from the Loop, River North, or surrounding areas. The school is easily accessible from the CTA trains and buses, with nearby stops for major lines like the Red, Blue, and Brown. If you’re driving, you can park nearby at various parking decks and metered spots within walking distance. The central location means you won’t have to stress about making it to class, and that your training sessions can fit into any schedule with ease.
Data analytics helps organizations interpret information, forecast outcomes, and make informed decisions. The skilled instructors at Noble Desktop work to teach students to use visualization tools and analytical methods that reflect real business practices in Chicago.
Henry Gonzalez is a certified Project Management Professional (PMP) with over 30 years of experience in the federal government, leading engineering, program management, and acquisition initiatives in naval warfare, air traffic management, and homeland security. A retired senior executive, Henry now serves as a part-time instructor in Chicago, where he teaches project and program management, leadership, and strategic planning to professionals of all kinds. He holds the highest level of federal certification one can hold for acquisition program management and systems engineering. In addition, Henry was part of the core team that updated the Project Management Institute’s Standard for Program Management. Henry is also a certified Balanced Scorecard Professional and holds a Bachelor of Science in Ocean Engineering from Florida Atlantic University, along with a dual Master of Science in Ocean Engineering and Ocean Systems Management from MIT.
Robert Simon is a highly accomplished financial management professional and educator with more than four decades of federal and private sector experience. As a Financial and Management Curricula Instructor at Graduate School USA since 2007, he teaches courses in financial management, federal travel regulations, and organizational leadership, both virtually and in person. Prior to his teaching career, he held senior roles at the General Services Administration, overseeing a six-state regional budget of approximately $100 million, and at the U.S. Treasury’s Financial Management Service, where he led national training initiatives on electronic banking. Robert’s background spans accounting, budgeting, and program analysis, holding certifications as a Certified Public Accountant and Certified Government Financial Manager.
Edward (Scott) Dillion is a seasoned financial expert with over three decades of experience in the Department of Defense, where he specialized in a range of financial management areas like budgeting and auditing, as well as performance-based management and cost analysis. He holds an MBA from Southern Illinois University and a B.A. in Business Administration from Grove City College. After retiring in 2014, Scott pivoted to a second career in education and consulting. He teaches project management and marketing at Graduate School USA and has also taught courses in federal budgeting. Additionally, Scott serves as a Subject Matter Expert (SME) for Management Concepts Inc., where he helps develop financial management course materials. He also has a lifetime membership with the American Society of Military Comptrollers, holds a Top Secret security clearance with the Department of Defense, and is a recognized Tier 2 instructor at Graduate School USA for his wealth of experience.