Strengthen your data analytics abilities through courses in Excel, SQL, and Tableau. Each class helps you transform information into meaningful business insights.
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 is a high-demand skill, globally and in nearly every industry. The amount of information that organizations must gather and understand is constantly increasing. The analytics industry strives to keep pace, finding new and better ways to produce insights and guidance. Work is always available for Data Analysts, whether as independent analysts, members of an analytics firm, or staff analysts within organizations. Analysts may apply their skills flexibly to varied clients or may specialize in fields like marketing, healthcare, finance, government, or sports statistics. Students in Virginia who are near larger cities like Richmond, Norfolk, Virginia Beach, or Washington, DC, can access in-person data analytics training. Even more options are available through live online training, including introductory classes, bootcamps, and professional training programs.
Information does not explain itself. Reading large databases is impossible, even after applying descriptive statistics. People need sophisticated analyses and visualizations to detect significant patterns, answer questions, and make useful predictions. Data analytics applies data science, statistics, and communication to derive meaning from raw information and apply this knowledge to decisions and actions. Given the increasingly large datasets that businesses, researchers, and governments must collect and manage, data analytics is crucial to interpret and act upon this information.
Data analytics is as old as the first methods used to describe and interpret number sets. Records were once collected and processed by hand, requiring physical storage, precise organizational methods, sophisticated formulas, and careful graphing techniques. Researchers developed early computers largely to improve such data-processing tasks. Now, Data Analysts use spreadsheet and database programs like Microsoft Excel and languages like Python, SQL, R, or SAS to gather, store, process, and display data. These technical tools do not replace older mathematical and scientific knowledge but rather automate and expand their processes to make large analyses practical. Data Analysts also use software like Tableau and Microsoft Power BI to produce graphic depictions and written reports that more effectively explain their findings.
Any field that handles significant amounts of information can benefit from data analytics. Data Analysts can be freelancers or employees of a data analytics firm, providing analyses and guidance for varied clients, or staff Analysts within businesses or other organizations. Often, analysts specialize in a particular industry, focusing on the data types, analyses, and visualizations used in that area. The industries that employ the most Data Analysts are finance, marketing, scientific research, and healthcare, but there are also Sports Analysts, Educational Analysts, Political Analysts, and Transportation Analysts, for just a few examples.
Scientific research is particularly data-driven, and every researcher—including students, academics, and non-profit investigators—needs data analysis training. Healthcare providers also use data analytics to confirm the effects of various interventions and determine the effectiveness of their care. Medical technologies, particularly imaging and diagnostic systems, are often direct applications of data analytics. Other technologies, including sensor systems, robotics, and artificial intelligence programs, are similarly built upon analytic algorithms.
Being able to analyze data effectively is useful for many other people, either professionally or for personal projects. Understanding data and analysis helps people translate and explain information and make better decisions. For example, all businesses, even small ones, gather data on sales and customers. Data analytics can find patterns in this data that help to identify and retain frequent buyers, spot problems, or suggest untapped markets. Data analytics training can also help a Business Manager understand the data and analyses provided by a consultant, vendor, or client. Similarly, a person trained in data analytics can better read and evaluate data products like poll results, investment information, or product performance reports. In particular, journalists and other writers benefit from data analysis to conduct investigations of available information and evaluate others’ reports.
Data science is the broad study of information: how data is collected, structured, and stored, plus the analysis and visualization methods of data analytics. This field explores the nature of data, starting with raw inputs like measurements, recorded actions, and surveys. It also discusses how data can be most effectively organized and retrieved, cleaned to remove irregular data points, and transformed for various uses. In data analytics classes, discussions of data science particularly address the usefulness and appropriateness of various analyses when applied to different kinds of data. An analysis used with the wrong type of data can give nonsensical or misleading conclusions. Finally, data science explains how choosing the right visualization methods can communicate analytic results more clearly and accurately.
The core of analytics is statistics, the mathematical processes that describe datasets and use them as samples to produce wider conclusions. Statistical processes include simple summaries like averages and more complex calculations like regression, factor, cohort, cluster, and time-series analyses. These analyses can reveal underlying relationships between data points and suggest causation between events. Data analytics students must be able to describe these analyses and perform them manually on small datasets so that they can choose appropriate methods and implement them correctly within larger, automated systems. Many data analysis classes use spreadsheet programs to demonstrate different statistical methods so that students can visualize processes and understand how they expand and combine into more sophisticated analyses.
The third major part of data analytics is communication: displaying and explaining the conclusions drawn from data and its analyses. Visualization methods convert data into images that quickly convey patterns and meanings. Static visualizations include simpler graphics like line, bar, and pie charts and more elaborate displays like scatterplots and heatmaps. Analyses may also be displayed in a dashboard interface, including updates and animations for changes over time. Data analytics students should learn many visualization forms, not only knowing how they are created but also understanding how to appropriately select images to accurately and successfully communicate their findings.
Most of an analyst’s work is performed with pre-written software packages and/or custom-coded programs. Nearly all data analytics classes feature at least one popular data management program such as Excel or Tableau, and some courses include multiple programs to give students versatility. Other courses teach the basics of data analysis in a programming language commonly used for this purpose such as Python or SQL. Learning to code allows analysts to build custom data collection, storage, analysis, and visualization tools. Professional training programs often include multiple programs and one or more languages, highlighting the features that make certain tools preferable for certain tasks. A given industry or employer might also prefer that their analysts be proficient with a specific tool.
These programs are easy to learn and use thanks to their graphical interface. They are also handy for many analysis and visualization tasks but struggle with larger datasets and more complex analyses.
These systems efficiently store and sort large quantities of data, often as part of a larger system providing data collection, analysis, and reporting functions. They take more study to master but offer enormous flexibility and power.
These programs and languages are primarily created for statistical processing. They structure, calculate, and report complex analyses with relative ease and high efficiency, even for huge datasets.
These comprehensive data systems offer multiple integrated services to support organizations’ analytic needs. They particularly emphasize data reporting, offering powerful visualization tools, easier sharing through varied methods like online dashboards and collaborative platforms, and automation and AI features.
These programs and languages are specialized to assist in data collection, gathering information from sources like surveys, forms, sensors, websites (aka ‘scraping’), or searches through larger databases (aka ‘data mining’).
Many general-purpose programming languages have found extensive use in data analysis, due to their flexibility, processing power, and prewritten code libraries that simplify analysis tasks. It is often more convenient to build an entire data system as a set of interlinked programs written in a single language, especially for unique datasets or analysis types. Programming analyses from scratch also helps students more thoroughly understand their underlying structures.
Some data analytics courses will include lessons on artificial intelligence applications, particularly machine learning algorithms. Machine learning systems use data analysis methods to collect and process information and then use their conclusions to guide subsequent actions, including further investigation. Machine learning and automated data-gathering programs can identify patterns and pursue analyses that might not occur to human analysts working alone. Machine learning is increasingly in-demand, used in areas like internet search, content generation, and market analysis.
Even with modern technical tools, data analytics is built upon fundamental mathematical procedures. Students intimidated by numerical principles and methods will have more difficulty understanding data analysis and learning its skills. Similarly, new students must learn enough data science to understand the collection and structure of data and how its properties guide effective analyses. In an introductory course, these abstract lessons are often taught using data management software and languages that implement their principles. Students less familiar with computation and coding will then need additional time to practice with these technologies.
An introductory data analytics class only familiarizes students with basic concepts and techniques and helps them interpret more complex analyses. To effectively use analytic techniques on real data, students need more comprehensive training, ideally beginning with a bootcamp or similarly intensive course. Such courses cover essential skills like cleaning and organizing large datasets, selecting and performing analyses based on inquiries, using multiple analytic tools, and creating visualizations and reports from their results. Specialized programs, like financial analytics, also address specific data types and methods within their fields. While this degree of study can give workers many useful analysis skills, it is still typically insufficient for most data analytics careers.
Professional data analytics training such as a certificate course or degree program spans even more analysis types, visualization methods, and technical tools. Students must master these skills to handle real-world analysis problems. Working data analysts must translate employers’ problems into full analysis projects including data collection and cleanup, well-chosen and accurate analyses, and effective reporting that provides clear answers. Advanced classes also cover challenging topics like predictive modeling, data mining, and machine learning, skills that further distinguish expert analysts from ordinary statisticians.
Outside observers and new students often think that the most challenging aspect of data analytics is its mathematics. Certainly, learning the principles and equations that underlie statistical analysis takes some study. Identifying the analyses that best apply to a given dataset and question also requires knowledge and experience. Complex analyses like multi-factor models can be difficult to structure and interpret correctly. However, persistent students soon learn that statistical methods are consistent and closely interrelated. Once they grasp common principles, the routine work of calculation can be left to software.
What remains difficult, even for experienced analysts, is dealing with the variety and noise of real, raw data. Clients’ information can come in wildly differing formats and can contain inconsistencies and irregularities that require extensive cleanup. At best, data cleanup takes time and care, but at worst, messy data can make analyses uncertain or useless.
Another hurdle for new analysts is the challenge of learning unfamiliar, multi-functional software tools like Tableau and Power BI and programming languages like SQL and Python. If students are not already technologically fluent, mastering these skills can take considerable time and thought. Even experienced analysts must continuously learn newer programs, updates, code libraries, and coding techniques to stay current and competitive.
Finally, visualizing and reporting analyses can be difficult to master. Not only must results be arranged in ways non-analysts can read and understand, but these reports must also explain exactly what employers need to know. Even if one’s underlying analyses are valid, a mishandled graph or description might yield useless or even misleading conclusions, if not guided by communication skills and experience.
Data analytics is not a single skill but an array of related abilities, including knowledge, techniques, and tools. Students first learn core concepts and methods for data collection, organization, analysis, and reporting, either in an introductory course or the first part of a longer study program. A one- or two-day class might show a student how to perform simple analyses in a single environment such as Microsoft Excel, Tableau, or Python. A longer tutorial, from three days to a week long, can give a firmer foundation and might introduce multiple tools.
Expanding on these basics takes more than a few class sessions. Students need to practice multiple analysis methods and work with larger, realistic datasets before they can consistently apply these techniques. Similarly, there are many visualization methods beyond simple charts and tables such as 3D models, maps, and infographics. A dedicated data analytics student should also build proficiency with multiple technical tools. To achieve consistent proficiency in data analytics, a student will need a minimum of two to three months of study and practice, possibly through a bootcamp, certificate course, or set of short courses. This is also a realistic minimum for an entry-level analytics position, longer for more specialized work like financial analysis.
Even this degree of ability is limited to a subset of techniques and tools. Full professional status as an independent analyst takes much longer to achieve. Certification programs, which train analysts to a verified standard, can last from four to six months. Degree programs that instill broader expertise will run even longer, from one to four years. Students will spend this additional time learning more sophisticated data collection, cleanup, modeling, and visualization techniques; practicing with larger, less orderly datasets; and learning additional technical tools, automation methods, and communication skills. Fully mastering these abilities takes even longer. Becoming a Senior Data Analyst—able to manage any analytic task, devise and conduct complete analysis projects, consult with clients, and direct and train junior analysts—takes around three to four years of continuous study and experience.
Since most data analysis work is done on a computer, online classes are a natural fit. Live online study conducted by video conferencing can offer nearly the same interaction as in-person attendance but allows students to work comfortably from the site of their choice, use their familiar computer, and avoid the costs and limitations of travel. By removing the need for a local provider, online study expands a student’s course options. Online classes also tend to be less expensive than in-person classes. Although students must supply their own hardware, online courses often offer free or discounted downloads for necessary software.
Still, some students find online study difficult. Sensory, attentional, or technical limitations can interfere with remote learning and may be avoided with in-person studies. In-person instruction also allows the fastest and clearest interactions with an instructor. As another advantage, most physical schools provide all the hardware, software, and training materials that students need. These benefits make in-person study necessary or preferable for some students.
Both online and in-person classes require adherence to a fixed schedule, making it challenging for students to balance work and family commitments. Live classes are also restricted by set topics and pace. An alternative, self-paced learning, eliminates these issues through pre-recorded video lessons and interactive materials, allowing students to study at their convenience and at their own pace. This format is cheaper due to the absence of a live instructor, but students may face delays in receiving feedback or support, as most self-paced courses provide only limited interaction through message boards and occasional chat, phone, or email support. These delays can slow progress and potentially raise costs for timed courses, hindering completion. Additionally, some students may struggle with self-guided study, requiring motivation from live classes, and pre-recorded courses may lack variety and up-to-date content.
Data analytics is a valuable, high-demand skill set. As a result, many free online resources offer to help people learn about data analytics and improve its various skills. These lessons come in a variety of formats, including videos, text, interactive websites, and downloadable programs and exercises. Video lectures are the easiest to access, available even on social media sites like YouTube. For example, Noble Desktop’s YouTube channel hosts a video series on Python, Data Science, and SQL.
A free subscription to several skill training websites—including Udemy, Coursera, CareerFoundry, and OpenLearn—grants access to many data analytics classes. Technical training sites, like Datacamp, Codecademy, and Springboard, also regularly feature free data analytics courses. Further, institutions like MIT, Harvard, and Yale and businesses like Google and Amazon host free courses to promote data analysis skills.
A survey of these resources soon reveals their relative value. Some free resources are convenient introductions, helping students understand data analysis principles and start using basic techniques. Free lessons can also familiarize students with technical tools like SQL and Tableau. Some free lessons teach specific analysis methods in-depth or give tips on better analyses or tool use. However, free lessons rarely reach the level of professional training courses. Video lessons are often disorganized, usually addressing single, scattered topics. Lessons on educational sites are better organized but are usually limited programs, demonstrations offered to encourage guests to purchase more complete training courses. Courses that do cover a fuller range of topics still provide limited examples and exercises; students must find sample data and practice on their own. Finally, few free courses include assistance of any kind, much less the fast, expert feedback provided by a live instructor. When working with free resources alone, students progress more slowly and with less certainty and can rarely achieve professional ability.
Learning more about data science can give data analytics students stronger grounding, a better understanding of how analyses work, and a broader skill set. Studying data science also increases a worker’s employability and potential income. Similarly, a deeper study of statistics will enable students to construct more detailed analyses and visualizations, read results better, and avoid incorrect conclusions. A third fundamental skill, and the one most often neglected by novice data analysis students, is communication. The most sophisticated and accurate technical work is useless if it does not answer a client’s questions and help resolve their problems. Data Analysts must not only provide useful visualizations and reports but also discuss a client’s needs and translate these concerns into appropriate analyses. Classes on business communication can help analysts conduct interviews more effectively, discuss their progress more clearly, and write better reports.
Additionally, data analytics uses a wide variety of technical tools, including software packages, general and specialized programming languages, and some web-based and mobile services. A given data analysis course might only focus on one such tool such as Excel or Power BI, or cover several tools, but a single study program can never address the full range of available tools. Students will benefit from studying other tools on their own, including statistical software like SAS and Matlab, database management systems like MySQL and MongoDB, data-specific languages like SQL and R, general programming languages like Python and Java, or general research systems like Jupyter Notebook. Certain analytic tools are designed for specialized work such as RapidMiner for data mining, Apache Spark for exceptionally large datasets, or Google Analytics for website monitoring. In some cases, an analyst may need to learn a specific tool for a particular task or employer. For example, some businesses prefer to use Amazon Web Services (AWS) Analytics Services for their data collection, storage, and analysis.
Data analytics is an industry in itself. Professional Data Analysts act as data-driven consultants for nearly every kind of business. Big Data firms handle volumes of information organizations cannot manage for themselves. Business intelligence, logistics, and expert data system providers provide sophisticated analyses that offer novel insights, strong predictions, and clear answers to complicated problems. Certain industries, though, need these services so frequently that they employ internal analysts, many specialized to better understand their chosen field. These industries include finance, marketing, sales, manufacturing, transportation, healthcare, scientific research, and government.
The data analytics industry includes businesses as diverse as the clients they serve. Self-employed Data Analysts act as independent consultants to address data-driven questions, often for small businesses that cannot hire a full-time analyst or consulting agency. Data analytics firms range from small local agencies to enormous, international corporations. Analytics firms often serve multiple clients, assist with large research projects, or provide long-term support, and can offer cutting-edge and computationally demanding approaches. Business intelligence (BI) and ‘Big Data’ companies in Virginia include cBEYONData (Lorton), RTS Labs (Glen Allen), Tellius LLC (Herndon), and Zoomdata (Reston).
Many individual analysts and most agencies specialize in particular industries, offering approaches and expertise specific to that field. Tailored analysis work is often offered alongside related services like consultation, software development, and risk management. For example, Telogical Systems in Tysons Corner, VA, specializes in data analysis and consulting for the telecom and cable industry.
Marketing agencies and departments connect businesses with customers through services like research, product design and testing, branding, advertising, social media management, and customer retention. Many of these functions need extensive data collection and analysis, so these businesses frequently employ Data Analysts and Market Analysts. Market analysis is also a common specialty for data analytics firms, particularly when helping entrepreneurs or businesses evaluate a potential product or sales campaign.
Market analysis overlaps with business intelligence (BI), which draws data from market research, sales, and investment to guide decisions on manufacturing, logistics, sales, employment, and facilities. Data analysts also assist manufacturers by studying efficiency and processing data for engineers and research and development teams.
Financial companies are another major employer of Data Analysts, particularly as Financial Analysts. The financial industry includes banks, credit unions, lenders, and similar customer-focused services; financial advisors, investment firms, and brokers; insurers; and financial technology (FinTech) providers. Financial Analysts may assist customers with investment decisions, help build FinTech products and services, or provide internal guidance to financial organizations. Self-employed Financial Analysts and consultants particularly need data analysis skills to yield valid recommendations from client records and market information. Traders and investors similarly benefit from data analytics training, both to perform their own investigations and to better understand third-party analyses.
Transportation providers, whether moving people or goods, benefit from analyses of data like travel routes, scheduling, vehicle performance, vehicle and facility capacity, road conditions, and expenses. Transportation and Logistics Analysts may perform spot analyses of problem areas, but they also design and maintain systems that track movement, detect inefficiencies, and identify areas for improvement. Analyses can also help transporters anticipate and respond to accidents, disasters, weather changes, and economic shifts. Organizations in this industry include airlines, shippers, railroads, public transit, and government transportation departments.
Nearly all research is built on data analytics. Scientists collect data through natural observation or experimental manipulation and produce data that can be queried to obtain new knowledge and predictions. Data analytics also explains how to clearly and validly explain these findings. Thus, most scientists study and practice some form of data analytics, though only some research programs handle large datasets that require sophisticated analysis tools. Still, an increasing number of scientific fields are finding uses for methods like data mining, machine learning algorithms, and meta-analyses (analyses that combine data from multiple studies). For example, DNA sequencing is a massive data procedure requiring numerous stages of collection, analysis, and comparison. Some research organizations even hire full-time Data Analysts to handle their data storage, processing, and reporting needs. These analysts may suggest new research designs and models that take fuller advantage of their skills. Data science, too, is a scientific field that includes data analytics and also explores the nature of data, especially the properties of real-world datasets, human awareness of information, and our behaviors toward it.
Government organizations, from federal agencies to local councils, need to collect and understand various kinds of information. The most obvious data sources are population statistics, but different agencies might need to explore public health, education, utilities, transportation, construction, crime, finance, economic, or housing data. If governments do not outsource these tasks, they must employ internal analysts. Data analysts also find regular employment with political parties, campaigns, and policy consultants, often examining polls and other public opinion data and producing reports on subjects under debate.
The healthcare industry includes hospitals, medical offices, home care providers, insurers, pharmaceutical companies, and medical device manufacturers. Patient care produces enormous amounts of data, which facilities, governments, and related businesses track to identify expenses, inefficiencies, effectiveness of treatments, and needs like staffing, training, supplies, and infrastructure. Data analysts also assist with medical research, including studies of the healthcare industry as a whole, investigations of diseases (e.g., epidemiology), and tests of specific treatments. Many data analytics firms specialize to serve the healthcare industry’s many needs. Analysts are also employed by companies developing healthcare technologies with embedded analytic functions such as medical imaging devices and expert diagnosis systems.
Data Analyst jobs are often flexible, generalist positions. These Data Analysts apply analytic techniques to varied datasets to address a wide range of needs. They may be self-employed or employees of a data analysis firm or data-driven business. Some Data Analysts are members of data management teams including Data Scientists, programmers, and consultants.
Data Analysts typically start at entry-level positions like Junior Data Analyst, managing data collection and storage, cleaning data for more senior Analysts, and building visualizations and reports from completed analyses. A Junior Data Analyst in Virginia starts at an average annual salary of $70,000. With a year or more of active experience, their annual salary can increase to around $80,000.
A full Data Analyst is usually defined as having three to five years of experience, a wider mastery of data analysis tools, and one or more certifications. Data Analysts often build and manage full analysis tasks as needed and may oversee the work of Junior Data Analysts. The average annual income for a Data Analyst in Virginia is $88,000, varying depending on experience and duties. Some companies have finer grades of experience like Data Analyst I, II, and III.
Senior Data Analysts typically have 10 or more years of experience and may supervise subordinate Analysts. A Senior Data Analyst in Virginia earns around $106,000 per year, with even higher salaries possible in certain industries or for positions with significant supervisory duties. These top positions may also be titled Data Analyst IV (or higher), Lead Data Analyst, or Principal Data Analyst.
Many data analytics jobs are titled according to their specific industry or field of inquiry. These positions have comparable analysis duties but require additional subject-specific knowledge and experience. Specialization also affects the types of data these workers handle, the analyses they perform, and their preferred visualizations. Specialized analysts may overlap as consultants, providing advice based on their analyses, especially when self-employed. As for general Data Analysts, salaries for specialized positions can vary widely depending on experience, specific duties, and employer. For example, an entry-level Financial Analyst at a local credit union will earn considerably less than a Senior Financial Analyst working for an international FinTech company.
Data Scientists often perform analysis work but have studied data more extensively and can address the subject more broadly. These positions require additional study, and their duties are often more complex and wide-ranging. Data Scientists have research and technical skills that enable them to investigate data closely and address problems that are not yet well understood. Data Scientists are often hired by organizations that need to understand new domains of information and create original solutions. They also assist in the development of new technologies for data management, analysis, and visualization, either in academia or as employees of tech development companies. Some Data Scientists work for organizations that manage massive amounts of data; in these roles, they may collaborate with or supervise one or more Data Analysts. Data Scientists in Virginia earn an average annual salary of $134,000.
Data Engineers are programmers and technical experts who work alongside Data Scientists and Data Analysts to create technological solutions for data work. Data Engineers are often employed by software and web developers building data-driven applications but may work in any industry that handles large quantities of data. Data Engineers create and maintain:
The average annual salary for a Data Engineer in Virginia is $135,000.
Another application of data analytics is in machine learning and artificial intelligence systems. These technologies gather, organize, and interpret extremely large datasets, using their analyses to choose actions, including the creation of novel analyses in response to new data and problems. Machine learning is increasingly in-demand, being used in areas like internet search, content generation, and market analysis. Machine Learning Engineers combine data analysis techniques with expertise in software development and adaptive algorithms. They often work with Data Scientists, Analysts, and Engineers to build expert systems that incorporate specific data types and models. Salaries for Machine Learning Engineers in Virginia average about $137,000 per year.
In partnership with Fullstack Academy, Virginia Tech’s Continuing and Professional Education division holds a live online Data Analytics Bootcamp. This bootcamp runs for 10 weeks on a full-time schedule or 26 weeks for part-time students, divided into several course units. The first unit teaches fundamental business analytics techniques in Excel. Students then progress through data collection and manipulation in SQL and AWS Glue, visualization methods in Tableau, data analytics in Python, applications of generative AI, and ethics in data analysis. These lessons include multiple workshops and projects, brought together in a portfolio-quality capstone project using all previous data tools. In addition to portfolio and career guidance and a certificate of completion, graduates receive preparation for the Tableau Desktop Specialist Certification exam.
The School of Professional & Continuing Studies at the University of Richmond, in partnership with edX, holds a 24-week Data Analytics Boot Camp as a part-time program combining live online sessions with self-paced practice work and projects. Starting with fundamental statistical techniques and Excel for data analysis, the course then focuses on technologies with high market demand like Python coding, database creation and management, front-end web development for data visualization, Tableau for business intelligence, big data analytics in Apache Hadoop, and machine learning systems. In addition to a live instructor’s guidance during classes, students can access up to one hour per week of 1-on-1 tutoring and receive support for study skills and career planning. In addition to a certificate of completion from the University of Richmond, graduates also receive access to alumni networking resources.
IT Dojo, located in Virginia Beach, offers both in-person and live online technology classes, specializing in organizational training for businesses and government agencies. Their catalog includes several courses for data analysts, particularly a series of classes on Tableau and Power BI. Students can start with an Introduction to Tableau | Tableau Quickstart, a one-day class that introduces Tableau, teaches its data handling functions, and then guides students through multiple visualization forms including interactive, web-ready dashboards. A longer course, Tableau Desktop Fundamentals, runs for two days and explores Tableau in more depth. With this background, students can progress to a more advanced, focused class, Advanced Tableau | Next-Level Data Visualization, Calculations, Analytics, Integration with Hadoop, R & More. This course also lasts two days, with half of its time spent on skill-building lab exercises and a short comprehensive project. A similar series of classes featuring Microsoft Power BI starts with Microsoft Power BI Data Analyst, a 3-day class, and progresses through three levels of classes focusing on different tasks in Power BI.
ONLC Training Centers offers in-person technical training at over 300 local classrooms, including several cities in Virginia. Rather than presenting a comprehensive data analytics course, they teach most technical tools used in analysis work, including Microsoft Excel and Power BI, Tableau, R, SAS, SQL, and Amazon Web Services. One of the most directly applicable courses, PL-300: Microsoft Power BI Data Analyst Course, is recommended for students with prior experience in data analysis and cloud computing. This three-day course connects these methods with Power BI, teaching how to collect, clean, transform, and model data in Power BI and then generate visualizations in multiple formats including online dashboards. The course also covers some advanced analytic techniques and the creation of complete workflows through Power BI, including data security and report publishing with Workspaces.
Students enrolled in live online classes from Certstaffix Training can attend from home or at their computer labs in Richmond, Norfolk, or Washington, DC. Certstaffix’s data analytics classes are arranged around specific tools including Microsoft Excel, Tableau, SPSS, SQL, and Python. For students already familiar with Excel, the most immediately usable data analysis skills will come from Excel—Data Analysis with PivotTables, a half-day class that demonstrates how to use Excel’s Pivot Table function to summarize and order large datasets to simplify analyses. Data analytics students planning to use SPSS, a statistical analysis program, can begin with Certstaffix’s prerequisite course, Introduction to SPSS, then progress through Introduction to Statistical Analysis using SPSS, Data Management & Manipulation using SPSS, and finally, Advanced Statistical Analysis using SPSS. Each class consists of two full-day online sessions which include hands-on exercises and teach SPSS as part of an integrated data analysis program. Similarly, Tableau Desktop, a data analysis program with prominent visualization features, is covered through Basic, Advanced, and Business Intelligence courses, each two sessions long. Students can instead book a combined Tableau Desktop Combo to cover all three courses in six days.
Noble Desktop offers live online classes for coding, design, and business skills. Their data analytics courses include a professional certificate program and several bootcamps teaching data analysis using specific technical tools. Noble’s Data Analysis Certificate program is a six-week course for full-time students or 24 weeks on a part-time, evening schedule. This program begins with a unit on Excel’s data analysis features and then covers the foundational concepts, techniques, and applications of data analytics. Next, students learn to code custom analyses in units equivalent to Noble Desktop’s Python for Data Science Bootcamp and SQL Bootcamp. Subsequent units teach automation methods, data visualization techniques, dashboard creation, and machine learning algorithms in Python. Finally, a unit comparable to Noble Desktop’s Tableau Bootcamp teaches analysis and visualization in this popular program. Certificate students can also enroll in a bonus course, Python for AI: Create AI Apps with Flask & OpenAI, which adds AI tools to their data analysis repertoire. In addition to their graduation certificate, students receive eight 1-on-1 mentoring sessions they can use to discuss difficult topics, polish their professional portfolio, or set career goals.
Noble Desktop’s short class, Data Analytics Foundations, introduces data analytics and explains its general principles and techniques using examples in Excel. This two-day class helps students explore this field and plan their further studies. Students who need to learn multiple data analysis tools for personal or professional use should take Noble’s Data Analytics Technologies Bootcamp. This two-week course combines multiple shorter courses on Excel, SQL, and Tableau, focusing on their combined use in data analysis work.
Organizations seek data analytics training to improve existing analysts’ skills or to add analysis techniques to their members’ repertoire. Noble Desktop can provide live data analytics training either privately, in custom classes held onsite or online, or through their online open enrollment. Organizations can customize their private classes, highlighting the skills they need most and even using their preferred datasets. Custom scheduling is also available for private training sessions. Alternatively, any of Noble Desktop’s regular online data analytics classes are available for open enrollment. Either way, clients can choose a basic introduction, an in-depth bootcamp, an advanced course, or a full certificate program in data analytics, or include data analytics within a longer course on data science or machine learning. If your members need differing schedules or training, you can purchase vouchers for Noble’s regular online classes, at a discount for bulk purchases. Then, you can distribute these vouchers and allow members to book the classes they need at their available times. All Noble Desktop classes include supplemental study materials and access to class recordings for later study and practice.
Contact Noble Desktop at corporate@nobledesktop.com for a free consultation to discuss your corporate training needs. A consultant can answer your questions, help you create the perfect custom training program, book class sessions, or sell bulk course vouchers.