Learn More About Data Science Classes in Washington, D.C.
Data science is an interdisciplinary field of study that involves applying various scientific processes, methods, algorithms, and techniques to large volumes of data with the goal of unearthing patterns and locating meaningful insights. This information is then used to help organizations make informed business decisions. This robust field is a meeting point of disciplines like information science, statistics, data analysis, and computer science. Data Scientists work with structured and unstructured data and create predictive models with the help of machine learning algorithms.
The term “data science” originated in 1974 when Peter Naur coined it as a synonym for computer science. Although the origin of the phrase is modern, the field predates its naming. Major figures have helped data science transform from early manual computing tasks into its current version, which draws from AI and other cutting-edge technologies. Over 180 years ago, Ada Lovelace was the first person to conceive of how a set of annotations could perform calculations and become the first computer program. Other major players such as Alan Turing, John Tukey, and Karen Jones have helped this field transform into its current state. Today, Data Scientists employ the data science lifecycle’s five stages when encountering volumes of data. The first stage involves collecting data. Stage two pertains to data maintenance, including warehousing, cleansing, and processing. In the third stage, Data Scientists process the data, which entails mining, modeling, and summarizing the information. The fourth stage of the life cycle involves various types of data analysis. In the final stage, the findings from the prior four stages are conveyed in an accessible format such as a data report or a chart.
What Can You Do with Data Science Training?
Because data science requires extensive knowledge of a range of fields and skills, including advanced mathematics, data analysis, and statistics, those with a background in data science have many high-paying career options across locations and fields. Additionally, those who are interested in pursuing a profession in which they’re presented with unique challenges to solve will thrive as Data Scientists. Each day presents new data challenges that require expertise to solve. Not only is data science currently in demand, but positions in this field are expected to increase by 28% by 2026, which is well above the national projected job growth in other professions. To keep up with the demand for qualified Data Scientists, most who are employed in this field can expect to make a six-figure salary. Additionally, they often have the flexibility to relocate from one city to another or even work remotely for their employer. This flexibility makes data science an appealing career path for those who are interested in fully exploring data trends.
Since data science plays an integral role in all industries in which data is collected, those with a background in data science can apply their curiosity to many fields long after their formal education concludes. Data science has applications in creating search engines like Bing, Yahoo, and Google. This field also has real-time applications in the transportation industry, helping driverless cars manage training data. Within finance, Data Scientists rely on data science to automate the process of calculating the risk of loss so they can make important organizational decisions based on this information. Data science is the tool that helps e-commerce websites like Amazon improve users’ experience by offering them personalized recommendations. Even those who work in healthcare apply data science knowledge when performing tasks like creating predictive models for diagnosing disease, analyzing medical images, powering virtual medical bots, and detecting tumors.
What Will I Learn in a Data Science Class?
When deciding whether to enroll in a data science course, you may wonder what skills you’ll encounter during training. Although the content of each program varies depending on the educational provider, the course’s duration, and its level of difficulty, most data science programs offer participants training in core skills and programming languages they’ll need when pursuing a data science career. The following sections will take a closer look at several data science skills and tools you will likely encounter during your studies, including machine learning, data analysis, SQL, Python, and critical thinking.
Machine Learning
When you enroll in a data science course, you’ll likely be introduced to machine learning. Machine learning is a form of data analysis in which analytical model building is automated. This branch of computer science and artificial intelligence involves using algorithms and data to mimic the learning patterns of a human, which becomes more accurate the more it learns. Eventually, the system can learn from data, spot patterns, and perform decision-making, all with minimal human intervention. Data Scientists learn machine learning to continue to build and train their data models to offer increasingly accurate, real-time predictions.
SQL
SQL, which is short for structured query language, is a programming language that Data Scientists use to store information on relational databases and communicate with the data that’s kept there. SQL is considered relatively easy to learn because it utilizes common English keywords for statements. In relational databases, information is stored in rows and columns in tables; each row and column represents a specific data attribute and its relationships between data values. By using SQL statements, data professionals can perform tasks like searching, retrieving, updating, storing, or removing information from databases. SQL has been the premier data manipulation language since the 1970s. It’s a popular programming language that’s used in many types of applications. Companies like Instagram, Amazon, Netflix, and LinkedIn use this language for data modification, extraction, and management, and to perform data analysis. One of the main benefits of learning SQL is that it integrates with various other programming languages, like Java.
Python
Python is an object-oriented, interpreted programming language that’s commonly used to analyze data, automate tasks, and construct software and websites. This general-purpose language has a simple, readable syntax, which makes it relatively easy to learn. Data Scientists rely on this versatile language for tasks like natural language processing, data analysis and visualization, image processing, machine learning, computer vision, and deep learning. This language’s many packages and libraries, such as Pandas, NumPy, and Matplotlib, are considered staple tools in the data science lifecycle.
Data Analysis
When you enroll in a data science class, another skill you’re likely to encounter is data analysis. Data analysis involves gathering, organizing, cleaning, and studying data so that important insights can be taken from the information. When analyzing data, Data Scientists are attempting to figure out what the data is trying to communicate and use this information to make decisions. Data analysis is an important aspect of the data-driven world. With it, organizations can get the most out of the data they collect, which can lead to more sound decisions and optimized processes. Those with the skills to transform raw data into meaningful insight have a competitive edge over others because they have the power to spot promising opportunities, reduce risk, and improve overall performance.
Critical Thinking
In addition to the hard skills you’ll acquire when studying data science, you’ll also have the opportunity to develop soft skills through your coursework. One such soft skill is critical thinking. In order for a Data Scientist to be able to fully and accurately analyze a problem, they must be as objective as possible to correctly frame questions and evaluate whether their findings are useful in helping their organization progress toward a viable course of action. These professionals must be able to objectively analyze data in the interpretation phase before they offer an opinion on their findings. This requires deeply examining the source of the data, studying the problem from every angle possible, and maintaining a curious mindset, else bias will creep into the final output.
How Hard Is It to Learn Data Science?
The difficulty associated with learning data science is largely objective. While some learners approach their data science studies drawing from prior training with Python, SQL, data analytics, or machine learning, others may lack this knowledge base and have to start from scratch when acquiring data training. While some learners may be able to learn fundamental data science concepts in as little as six months, it can take others several years to acquire expertise in this field.
One of the contributing components to the difficulty of learning data science is that it requires expertise in a range of skills, including industry-specific knowledge pertinent to the sector in which it will be applied. Data science skills and tools vary from healthcare-related positions to financial roles, which means it’s imperative for Data Scientists to have more than just a solid background in data science; they must also be familiar with the most current best practices and software used in their industry of focus.
What Are the Most Challenging Parts of Learning Data Science?
One of the most challenging parts of learning data science is keeping up with this field’s rapid, constant evolution. To succeed as a Data Scientist, you must stay on top of current innovations and industry-specific tools. Over the past decade, this field has transformed significantly due to innovations like machine learning, artificial intelligence, and machine learning. Aspiring Data Scientists must stay current with these innovations and be prepared to embrace future changes. Another challenge to studying data science is that each field in which data science is used has its own unique tools. It’s the responsibility of the Data Scientists to learn the tools needed in their field, whether it’s Tableau or Microsoft Power BI. Additionally, to succeed in data science, it’s essential to be well-versed in handling data. This requires extensive training in data mining and analysis, as well as how to communicate data findings to relevant stakeholders.
How Long Does It Take to Learn Data Science?
Data science is a broad, encompassing field, which means that the length of time required to learn it is dependent on a host of factors. Those who already come from a programming-heavy background or have prior experience with data, AI, or machine learning techniques may find it easier to learn data science than those who are new to these fields. Most individuals can learn data science basics in between six months and one year, depending on their educational background. However, to become truly proficient in data science will likely require several years of study and real-world training.
When studying data science, it’s important to consider that the training you’ll require is dependent on the field in which you hope to apply your skills. If you’re interested in pursuing a junior position as a Python Developer or a Data Analyst, you may only need to train for a few months to learn the basics. Enrolling in a live bootcamp or certificate in data science, such as that which is offered by Noble Desktop, is an excellent way to expedite your learning process and ensure that you’re acquiring the skills needed to pursue a data career. These programs take just several weeks or months to complete and offer a more structured and hands-on learning environment than self-guided data science coursework.
Should I Learn Data Science in Person or Online?
One important decision you’ll need to make when you enroll in a data science class is whether to study in person or online. Both training options have their unique strengths and considerations. In-person data science programs are available for beginners as well as those who already have prior experience in data science and want to master advanced techniques. During in-person coursework, students have the chance to connect with an expert instructor who has industry experience in data science. Classrooms provide learners with computers that have the most current software and tools for data science already installed. These classes are also a great way to connect with other learners on a similar educational path, which can be a valuable way to network and benefit from a sense of community while you study. When opting for in-person training, it’s important to remember that these classes require students to commute to and from class for regularly scheduled meetings. This training format, therefore, may not be possible for those who don’t have access to reliable transportation or who live in remote areas.
An excellent alternative to in-person training is live online study. Just like in the classroom setting, participants in live online data science classes can take advantage of the same learning benefits as those who study in person. All learners have access to a live instructor who’s leading the class in real-time and is available to field questions, clarify difficult concepts, and even share your screen (with permission) if you need additional help. Live online classes take place using a teleconferencing platform like Zoom, which means that all coursework can be completed remotely without ever having to commute to a training facility. If you’re interested in pursuing live online data science coursework, keep in mind that you’ll still need to attend classes that occur during regularly scheduled meetings. This might mean that you will need to take time off from work or adjust your schedule accordingly to make it to meetings.
The most flexible training option available in data science is on-demand coursework. On-demand content, such as tutorials, videos, or lessons, is pre-recorded and placed online. Unlike live online or in-person data science programs, which take place in real-time, on-demand content is asynchronous, which means that students can opt for when to complete their lessons. This type of training also provides students with control over their learning pace. Whereas classes that meet in real-time are guided by an instructor who decides how much time to spend on each skill or lesson, self-paced content allows the learner to decide when to pause lessons to take notes, rewind them to revisit complicated material, or even rewatch them completely to ensure maximum retention. Asynchronous data science training can be completed from any location with a stable internet connection. Learners can opt to fully immerse themselves in a Python for data science program over the course of a weekend or devote fifteen minutes each night after work to learning core SQL skills. Another appeal of on-demand content is cost. Since the content is pre-recorded, it’s often a much more affordable learning alternative than live training. However, it’s important to consider that no instructor will be present in this type of data science training. Data science is a broad, interdisciplinary field that requires mastering a range of related skills and tools. Some learners may find it challenging to fully grasp complex data science concepts in the asynchronous format since they will be on their own to find answers to questions. For this reason, some students opt to start their data science learning journey with on-demand content but ultimately switch to live coursework, such as a bootcamp or certificate program, to learn more advanced data science skills.
What Should I Learn Alongside Data Science?
If you’re interested in learning data science, you may wonder what skills are related that you can learn at the same time. Most individuals who pursue data science rely on a set of related skills, including computer science, mathematics, statistics, and information technology. Ensuring you have a solid background in mathematics, including knowledge of algebra, calculus, and probability, is a good place to start. Aspiring Data Scientists who don’t have a strong programming background or who are new to working with data may also wish to brush up on commonly used data science languages and tools like Python, SQL, and relational databases. Object-oriented programming languages such as Java, C, or Python are integral to many data science career paths. Python is considered a good “first language” in programming to acquire because it’s relatively easy to learn and ubiquitous in data science. Since Data Scientists typically have to use structured query language (SQL) to write queries to communicate with relational databases and retrieve the information contained in them, learners also often find it useful to study this language alongside data science.
Since both data analytics and data science prepare students to work with large volumes of data, these two disciplines are often taught alongside one another. Data analytics involves gathering, organizing, transforming, and evaluating data so that conclusions can be drawn about this information and predictions can be offered that will drive the organization’s decision-making process. While both data science and data analytics require managing data to derive insights, these fields differ from one another in several ways. Data science entails working with data to create models capable of predicting various outcomes, whereas data analytics is more concerned with how past data can be leveraged to shed insight into current decisions.
Washington, D.C. Industries That Use Data Science
Data science is still one of the most in-demand skill sets across industries and professional roles. The US Bureau of Labor Statistics predicts that approximately 11.5 million jobs will be opened in data science by 2026. Those who have a background in gleaning insights from big data can put their skills to use in many sectors in Washington, D.C.. The following sections will briefly explore how data science is used in popular local industries like cybersecurity, healthcare, and telecommunication, as well as its range of applications for nonprofit career paths.
Cybersecurity
According to Cyber, Washington, D.C. was ranked the best city in the US for cybersecurity jobs in 2021. Those with a background in cybersecurity are hired to protect networks, programs, and systems from a range of digital attacks. These threats attempt to access, alter, and destroy sensitive information, disrupt business processes, or use ransomware to extort funds from users. Cybersecurity professionals with a background in data science are hired to safeguard systems from future threats. They analyze security logs, which store details about login attempts, system errors, file transfers, and network activity, and create machine learning models capable of detecting threats in real-time. The nation’s capital offers competitive pay rates and ample job opportunities for those who have a background in cybersecurity because many top government agencies are headquartered in the city.
Healthcare
Another popular industry in which data science skills are currently in demand in Washington, D.C. is healthcare. The city and surrounding area are home to 16 hospitals and medical centers, as well as a range of research centers like the National Institutes of Health. Within the healthcare sector, data is an essential component of medicine. Nearly one-third of the global data volume is related to health information. This data includes e-health records, information from clinical trials, and disease registries. Those who have data science training can put their skills to use in the city’s healthcare industry by identifying anomalies in patient data or designing wearable health devices. Additionally, Data Scientists assist with disease prevention, predictive analytics and modeling, pharmaceutical or vaccine development, and digital security-related tasks.
Nonprofits
There are currently over 15,000 nonprofits located in Washington, D.C.. The city’s nonprofit sector employs more than 258,000 residents. With the growing importance of big data over the past several years, nonprofits have been increasingly turning to data science to streamline operations and ensure they have the greatest impact possible. Data Scientists who work in the nonprofit sector create predictive models capable of spotting data patterns that may have otherwise gone undetected. With the help of machine learning models, nonprofits can study past data to make predictions about how many people may require food assistance in a month. This can help with resource or budget allocations. Those who work in the city’s nonprofit sector also turn to data science to evaluate which campaigns are connecting most with their target audience and which can be discontinued. Data science insights also shed light on how a nonprofit can improve its website to ensure users remain engaged and potential donors can easily navigate their space to make charitable donations.
Telecommunication
Telecommunication is a broad term that describes how information is exchanged over vast distances using a transmitter and receiver. Communications infrastructures and information-transmitting technologies like the internet, cell phones, fiber optics, and satellites are just a few examples of telecommunication. This sector creates huge amounts of data every minute, including information on call details, customer information, and network information. It’s up to Data Scientists in D.C. to manage that information. Because telecom companies must handle huge amounts of data every day, they turn to Data Scientists to provide a comprehensive view of customer profiles, traffic and usage patterns, locations, and real-time analytics updates.
Data Science Job Titles and Salaries in Washington, D.C.
If you live in Washington, D.C. and are interested in a data science career, many professional options are available with competitive salaries. The following sections will take a closer look at several popular career paths in data that are currently available in D.C., as well as the basic job descriptions and corresponding salary ranges for each role.
Data Scientist
Data Scientists are hired to gather, analyze, and interpret data to assist their organization with its decision-making process. These professionals create algorithms that tell computers what to do and develop machine learning models. Data Scientists who work in the D.C. area earn approximately $144,000-$154,000 a year.
Data Analyst
In Washington, D.C., Data Analysts are hired to study datasets to provide answers to questions or solve problems. These professionals are hired to work in a range of industries within the city, from government roles to healthcare to criminal justice. The insights they discover are presented to internal or external stakeholders, like those in senior leadership roles, to help inform the organization’s decision-making process. Those who focus on data analytics in D.C. make a yearly salary of about $78,000-$88,000.
Data Architect
Data Architects are IT professionals who study and evaluate their organization’s data infrastructure. Their daily tasks involve database planning and devising methods to better manage and store data for users. They also develop procedures to make sure data is accessible and kept secure. Because of how many organizations rely on data, Data Architects are in demand in D.C. in many industries, including finance, healthcare, entertainment, and IT. D.C.-based Data Architects can expect to make around $156,000-$166,000 annually.
Machine Learning Engineers
In Washington, D.C., Machine Learning Engineers are hired to perform tasks like researching and crafting the AI systems that are used for machine learning. They draw from a background in math, statistics, computer programming, model validation, data processing, and data visualization. Machine Learning Engineers in the city make a yearly salary of $135,000-$145,000.
Data Science Classes from Noble Desktop
Noble Desktop’s Data Science Certificate program is a great option to consider if you’re looking to learn how to analyze and manipulate data and gain the training needed to pursue an entry-level data science or Python engineering role. Participants in this hands-on, live online course explore Python’s data science libraries, learn how to clean data, read and write database queries, and automate repetitive tasks with Python. Instruction is provided on how to create machine learning models and evaluate their performance. By the end of this comprehensive program, you’ll be familiar with how to use NumPy, Matplotlib, Seaborn, and Dash Enterprise to create and present dashboards and data visualizations. In addition to comprehensive live instruction, all learners receive six one-on-one mentoring sessions that can be used for professional development purposes or to revisit complex class material. Tuition includes a free course retake for a full year.
Noble Desktop also offers a Python for Data Science Bootcamp. This is a great option to consider if you’re interested in acquiring hands-on training in how Python can be used for data science to manipulate databases and analyze data. In this bootcamp program, you’ll have the chance to work with core data science libraries like NumPy and Pandas for data analysis. Instruction is provided on how to use scikit-learn and other machine-learning packages to create predictive models. By the end of this program, all participants will be able to use Python to automate repetitive tasks, including formatting, updating, and aggregating data. This class is intended for those who want to apply for entry-level Python engineering or data science careers. All participants also receive a supplemental one-on-one mentoring session in which they can revisit complicated class material or ask professional development-related questions.
Data Science Corporate Training
Would those at your workplace benefit from learning more about data science? If so, let Noble Desktop help. Noble can provide corporate and online data science training for D.C. residents. Instruction is available on topics like Python machine learning, SQL, automation, and data science. Coursework can be provided onsite at your location or in the live online training format using a teleconferencing platform like Zoom. Study options can also be customized to ensure that your employees get the most out of their training and that their skill gaps are met. If you’re interested in providing your workers with the data science skills needed to stand out from other data professionals in D.C., Noble’s expert-led instruction is a great learning option. Training can be scheduled on weekends or at night to accommodate your employees’ working schedules. Additionally, you can enroll those at your workplace in open-enrollment data science courses. Vouchers can be purchased in bulk and for a discounted price.
To find out more about the exciting data science training options available from Noble Desktop, don’t hesitate to email Noble today to learn more.
Learn From Noble Desktop’s Experienced Data Science Instructors in Washington, D.C.
As the nation’s capital, Washington, D.C., is a hub for government, public service, and major organizations, which means there’s ample opportunity for professional development. If you’re looking to expand your knowledge in the field of tech, data, business, or design, you can explore training options available to both individuals and federal professionals. Classes are right in the heart of the city, offering knowledgeable instructors with decades of combined training and a wide range of specializations. From their real-world experience in the field, these instructors provide valuable insights that help students get comfortable while learning the material. Not to mention, the skills they develop in the classroom are practical and transferable, meaning students can put their newfound knowledge right to work.
Training with Noble Desktop’s team is easy with the modern facilities located at 600 Maryland Avenue SW, Washington, D.C., 20024. It’s mere steps away from L’Enfant Plaza and offers an array of benefits, like a professional yet welcoming environment and a modern workspace. Getting there from anywhere in D.C. is straightforward, whether by Metro, bus, or car. L’Enfant Plaza is served by multiple Metro lines, and there are several bus routes that stop nearby. Those driving can also access parking garages that are easily within walking distance.
Data science enables agencies and organizations in Washington, D.C. to analyze information, forecast trends, and make informed decisions. Students at Noble Desktop will get to learn statistics, coding, and machine learning from instructors who connect theory to practical, analytical tasks. Learning at this school also offers a wide range of professional development opportunities, with classes led by expert instructors who can help you acquire skills that get you ready for the real world.
Brian Simms
Brian is an educator and training leader passionate about helping professionals grow in fields like project management and AI. He designs adaptive learning programs that combine instructor-led sessions, live online experiences, and self-paced study, making training practical and within reach for anyone. Brian’s work also centers heavily around AI integration, particularly in training situations to show organizations how easy it can be applied to the workplace, showing ways to tackle real-world problems, enhance leadership, streamline projects, and support better decision-making. Beyond teaching, he has also developed curricula and led training on a massive scale. Brian’s blend of talents and interests makes him well-positioned to train others and help them navigate the complexities of the subject matter.
Clarissa Corbin
With more than 25 years of experience, Clarissa is a seasoned corporate trainer, Project Manager, and Business Consultant and has helped professionals and organizations alike achieve tangible results around the world. Her work has taken her to amazing places around the world, from China to Africa, training over 10,000 participants in leadership, project management, business analysis, and emerging technologies. Clarissa has worked with teams at NASA, Microsoft, Citibank, and FEMA, just to name a few, and is known for designing interactive yet practical sessions where her listeners can apply their skills immediately. At Graduate School USA, she played a pivotal role and contributed to numerous programs, including the Managing for Results course. She’s a quality instructor, focusing on subjects like project management, AI, and even Adobe, and has a heavy commitment to professionalism, innovation, and student success.
Michelle Proctor
Michele’s professional record in Human Resources, organizational development, and leadership training has been acquired over 25 years in the field. She is a Business Strategist and HR innovator who has helped public, private, federal, and higher education organizations on their path to success. In particular, Michele excels in organizational assessment, project and performance management, workforce development, change management, conflict resolution, and AI initiatives. She prides herself on guiding individuals to leverage their strengths while fulfilling their professional lives. Her consulting work includes leading the DC Courts HR Division’s Five-Year Strategic Plan and Workforce Development Program, “Fulfilling Our Future.” She’s also held senior executive positions at Howard University, the Air Line Pilots Association, and Executive Transitions International. She’s currently working as an adjunct faculty at Graduate School USA and Anne Arundel Community College.
Alan Zucker
Alan Zucker brings real-world expertise from both the federal government and Fortune 100 companies to the classroom. With more than 25 years of professional work in project management, he makes for an ideal instructor. He has managed the project execution organization of 175 professionals, led high-visibility strategic initiatives, and delivered thousands of successful projects. His education began at George Washington University, where he earned his Bachelor’s, before progressing to earn a Master's in Economics from the University of Maryland. Moreover, he holds a wide variety of certifications, such as the Project Management Professional Certification (PMP), PMI-ACP, Disciplined Agile Coach, SAFe Program Consultant, Certified Scrum Professional, and Agile Leadership Academy Trainer. Alan is an active member of the project management community, having served as a keynote speaker, frequent industry commentator, and author of nearly 150 articles on project management.
Tashea Coates
Tashea is a Human Resources Executive and Federal Consultant, holding over 23 years of experience across multiple federal agencies, including the Departments of Homeland Security, Justice, Treasury, State, and Health and Human Services. Known for her ability to strategize and take the lead, Tahsea has transformed HR policies to align with mission strategies and outcomes, influencing government-wide procedures such as onboarding and pay equity. Tashea is a staunch advocate for ethical leadership, inclusivity, and organizational success. In addition, she prides herself on her authenticity and purpose-driven spirit, which translates into her work as an educator as well. In addition to teaching subjects like AI. She’s an author and entrepreneur, and is always looking for ways to help organizations and individuals alike.
Charles Byrd
With a strong foundation in federal human resources management and legal counseling, Charles is a highly knowledgeable Employee and Labor Relations Consultant in Washington, D.C. He began his education at Loyola University, earning his B.A. in Business Administration and Political Science, before graduating from the University of Baltimore School of Law. Throughout his career, Charles has guided agencies through complex HR and labor-related challenges, managed HR programs, and represented organizations in legal proceedings. He has also designed and delivered specialized training in human resources, project management, and marketing. Charles’ professional background has led to numerous accolades and awards, showcasing his impact in the field.
David E. McCullin
Dr. David E. McCullin, better known as Dr. Mac, specializes in strategic communication, homeland security, data analytics, and accurate decision-making. Prior to his work at Graduate School USA, Dr. Mac obtained his Bachelor’s in Engineering, his Master’s in National Security and Strategic Studies, and a Doctorate in Management of Complex Adaptive Systems. He served 24 years in the U.S. Army, including 13 in Special Operations. He later worked at the Department of Homeland Security as an Intelligence and Infrastructure Security Analyst for nearly a decade. Through his education and solid professional career, he has been able to teach students a variety of important skills and has also developed innovative learning tools and games that help improve decision-making and analytical thinking skills.
Natalya Bah
As a part-time instructor at Graduate School USA for over 15 years, Natalya’s expertise cannot go unnoticed. She’s an educator and has developed curricula for the school, including the Change Management Workshops and several project management courses. Not to mention, she’s also served as a learning coach, facilitator, and instructor for government leadership programs and has developed the Define and Achieve Your Goals Process. Natalya earned her Master’s in Project Management and her Project Management Professional (PMP) Certification, making her a well-qualified and effective educator, project manager, coach, and consultant.
Alan McCain
Alan is a retired combat veteran of the U.S. Air Force and Navy with over 30 years of experience in federal and commercial budgeting, auditing, programming, operations, supply chain management, and IT acquisitions. He holds an MBA from George Washington University and a Teaching Certification from Harvard’s Bok Center for Teaching and Learning. Throughout his career across federal, state, and local government agencies, Alan has worked for numerous departments, such as the Department of State and Defense, as well as the Office of the Mayor of D.C. Alan is also an accomplished consultant and business strategist, having aided in the development of organizational projects at Lockheed Martin and PwC, to name a few. His background and lengthy career make him an ideal instructor for those looking to learn more about finance, accounting, and project management.
Derk Mattocks
As a skilled instructor and business leader, Derk Mattocks possesses the skills and career experience to bolster his credibility. He gained his B.S. in Organization Management from Nyack College and his Master’s in Leadership and Military Installation Management, as well as an MBA in Financial Management and Analysis from the University of Maryland. He is a licensed Certified Advanced Professional Business Coach and trained mediator, with the certification to facilitate the “Five Practices of Leadership” workshops. Derk has also served as a Senior Advisor and Instructor for the U.S. Army. Overall, he has the credentials to serve as an accomplished and well-versed instructor, often covering topics related to project management and marketing.
Melanie Dooley
Melanie is a federal acquisition and contracting expert with over 30 years of professional experience in Washington, D.C., spanning both government and education sectors. She has served as the Vice President of Acquisition Policy at SAIC and as the Managing Editor of the Federal Contracts Report at Bloomberg BNA. She’s currently an instructor at Graduate School USA, often teaching classes related to the marketing realm. She’s a Certified Professional Contracts Manager (CPCM) and a Fellow of the National Contract Management Association, positioning her as a person with incredible leadership skills and knowledge of her field. She earned her MBA from Georgetown University and is known as a clear, trusted instructor who consistently challenges students to take their skills to the next level.
