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.
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Data analytics is the process of analyzing raw data to find trends and answer questions. Applicable in any industry, this process includes extensive sets of diverse data—structured, unstructured, and semi-structured—that are continuously generated at high speed and in high volumes.
Big data is typically measured in terabytes or petabytes, with one petabyte being equal to 1,000,000 gigabytes. One petabyte is the equivalent of 250,000 films, and the large datasets used in data analytics measure anywhere from hundreds of terabytes to millions of petabytes. (That’s a lot of data!)
A successful data analytics professional can help answer business questions related to historical trends, future predictions, and decision-making. Machine learning tools, massive datasets, and more affordable computing power have made data analytics techniques applicable in many industries, helping businesses draw meaningful conclusions from complex and varied data sources.
If you speak with a Data Scientist or Analyst, they’ll tell you that it’s more than just a potential profession. Data analytics is a hobby, a passion, one that can be applied in all areas of life. Noticing issues in your neighborhood? You could use urban data flows to predict and prevent infrastructural issues like potholes through a complex analysis of factors like traffic volume, pavement age, weather, and traffic accidents. Whether you’re on your local HOA board or presenting to your city council, your insights can help improve your community.
Or, you could use predictive analysis to gather information on various sports-related topics, allowing you to make strategic choices in your fantasy leagues based on current team and player strengths and weaknesses.
Data analytics is, at its core, the combination of noticing patterns along with a healthy understanding of math and statistics. These things exist in everything around you, so data analytics can apply to almost anything you can think of.
Data analytics courses can teach you about the data analytics process (including collecting, wrangling, cleaning, and visualizing data), different data roles, and data structures. Basic math will be covered as well, and for technical skills, you can expect to study programming languages like Python, database tools like Excel, and data visualization tools such as Tableau. By the end of a data analytics course, you should be competent and confident enough to begin or expand a career in data science and analytics.
The data analytics process is typically divided into six sequential parts:
A data type is the most basic and the most common classification of data, one where information is transmitted between the programmer and the compiler. The programmer informs the compiler about what type of data is to be stored and also tells it how much space it requires in the memory. A few basic examples are int and string, essentially any variable used in the code.
In contrast, a data structure is a collection of different forms and different types of data that has a set of specific operations that can be performed. A collection of data types, data structures are a way of organizing the items in terms of memory, and also a way to access each item through some defined logic. Examples of data structures are stacks, queues, linked lists, and binary trees.
The math requirements for a Data Analyst vary, but they typically include probability and statistics, calculus, and linear algebra. The foundation for optimization algorithms and deep learning techniques, calculus helps Data Scientists solve optimization problems, like finding the minimum or maximum of a function. Probability is critical for hypothesis testing and distributions like the Gaussian distribution, while statistical methods are used to analyze large datasets and interpret the results. Finally, linear algebra helps Data Scientists understand algorithms. While you don’t need to have complete mastery of each of these, it is helpful to have some knowledge of these specific math skills to succeed at data analytics.
Programming is essential for Data Analysts to manipulate, clean, and analyze data efficiently. Through programming languages like Python or R, Analysts can clean and preprocess data, handle missing values, and format it for analysis. Proficiency in these languages can be highly beneficial in performing advanced analysis and building custom data models.
Data visualization is a key part of data analytics and business intelligence that uses visual elements to represent data. It translates complex, high-volume, or numerical data into a visual representation that is easier to process. Data visualization tools improve and automate the visual communication process for accuracy and detail. You’ll use tools such as Tableau to create engaging and effective data visualizations that help communicate your findings to various stakeholders.
Given the field’s complexity and the diverse skill set required to excel, data analytics is considered somewhat challenging to learn, blending elements of statistics, mathematics, computer science, and specific industry knowledge. In data analytics, both the initial learning curve and the requirement for continuous learning are substantial, largely because of the breadth of the field and the continual advancement of technology. While data analytics comes with its challenges, it is ultimately a field that anyone can master. With the right amount of passion, dedication, and time, anyone can become an expert in data analytics.
The answer to this question may vary, depending on your current skill set. If you don’t have previous experience with programming or a background in mathematics, you may find mastery of these two subjects particularly challenging. Additionally, understanding the data often requires documenting data assets by keeping track of information such as every definition of columns in a data warehouse’s tables. Some parts of data analytics, like understanding data, may seem repetitive or time-consuming, but usually, there are tools to help streamline these processes.
Data analytics is a broad field; the time it takes to learn depends on several factors. You can master the basics of data analytics in less than a year, but your level of expertise will include various skills and practice in a particular role. You shouldn’t expect to become an advanced data pro in a matter of months.
The time required also depends on your existing knowledge and how much time you can dedicate each week. With full-time dedication (30-40 hours per week), you can become proficient in around three months, while part-time learners (15-20 hours per week) might take six months or more.
One factor to consider is the type of training you choose. Self-guided data analytics education can take much longer than a targeted bootcamp or certificate, so choosing the right course that fits your schedule is paramount to your success and efficiency.
There are several factors to consider when answering this question. First, you’ll want to consider what learning environment is best suited for you. Do you thrive in a collaborative environment where you can interact with your peers and receive in-the-moment feedback from your instructor? Or, do you prefer a more solitary learning environment, perhaps one where you can learn at your own speed?
You’ll also want to consider how much time you can dedicate to your education and what level of flexibility your schedule requires. Are you looking to immerse yourself in an intensive program? Do you work full-time and need your classes to be scheduled for evening and weekend hours?
In-person courses offer face-to-face interaction with both your fellow students and your expert instructor. This collaborative environment is available for both full-time and part-time classes and provides structure and accountability, two things that can help you succeed. Online classes may lack this in-person interaction, but they make up for it with increased flexibility. Live online classes still offer instant feedback from your instructor and interactions with other students, while on-demand virtual courses allow for the most flexibility since you can decide where and when you learn.
There are several other skills that you can start working on while studying data analytics. While you’ll most likely have the experience needed in mathematics and statistics, it can’t hurt to brush up on those two subjects. Additionally, programming languages like Python, R, Java, C, or Perl will be very useful.
Hadoop is the most popular Big Data framework. Most professional arenas will expect (or hope) that you have experience in Hadoop, so it can’t hurt to tackle it alongside data analytics. Likewise, SQL is the most common way of getting information from a database and updating it, so if you can master SQL as well, you’ll be a step ahead of your career competition.
Known as the Queen City, Charlotte is a thriving metropolis that blends Southern charm with modern innovation. Home to nearly 900,000 residents and a rapidly expanding economy, Charlotte’s key industries increasingly rely on data analytics to guide decision-making, improve efficiency, and drive growth. From manufacturing to finance, life sciences, and technology, data-driven insights are shaping the city’s future and creating a wealth of career opportunities for analytics professionals. Below are some of Charlotte’s top industries where data analytics plays a critical role.
Charlotte’s manufacturing sector, which includes over 3,500 companies and 145,000 employees, depends on data analytics to optimize production, reduce waste, and improve supply chain performance. Data professionals help manufacturers in industries like automotive, biomedical, plastics, and energy use predictive analytics and process modeling to increase efficiency and reduce costs. Given Charlotte’s strategic East Coast location and strong transportation infrastructure, analytics also supports logistics optimization—ensuring goods and materials move seamlessly across regional and national networks.
As the second-largest banking center in the U.S., Charlotte’s financial services industry has seen exponential growth—and data analytics is at the heart of its success. Major institutions such as Bank of America, Truist, and Wells Fargo use analytics for risk management, fraud detection, algorithmic trading, and personalized banking experiences. With more than 105,000 residents employed in finance, the demand for Data Analysts who can interpret complex financial data and uncover strategic insights continues to surge.
The life sciences sector, employing around 22,000 professionals and growing steadily, uses data analytics to drive research, innovation, and healthcare advancements. From clinical trial optimization to pharmaceutical supply chain analytics, data plays a pivotal role in ensuring precision and efficiency. Charlotte’s 60+ research and innovation centers and its strong network of higher education institutions provide the perfect environment for data professionals working at the intersection of healthcare and technology.
With over 67,000 employees in technology roles, Charlotte is rapidly emerging as a data and analytics powerhouse. Companies across industries rely on Data Scientists and Analysts to support cloud computing, automation, cybersecurity, and business intelligence initiatives. As organizations continue to expand their digital operations, data analytics enables them to identify trends, improve performance, and make smarter, faster business decisions—solidifying Charlotte’s status as a major tech and analytics hub in the Southeast.
There are several exciting career options available for both those who are just getting started in the field of data analytics and those looking for advancement. Each position will have a unique specialty or focus, so you’ll want to have a clear understanding of how you’d like to apply your data skills professionally.
Financial Analysts guide individual stakeholders, businesses, and companies on how best to invest money and resources to gain the maximum profit. They will analyze multiple factors like business environments, market trends, financial status of companies, expected outcomes of operations, and past financial data to make the most informed and calculated decision. In Charlotte, you can make $80,000 as a Financial Analyst, with the potential to earn much more as you gain experience.
Database Developers study database processes with the goal of updating them, improving their efficiency, and getting rid of inefficient coding. They keep track of how existing databases are performing and use code and web architecture to create data systems, analyze and maintain existing databases, and implement new user features. You can expect to make around $96,000 as a Database Developer, Administrator, or Architect in Charlotte.
Do you also have an interest in weather systems? Weather Analysts study atmospheric occurrences and analyze data pertaining to meteorological events to provide forecasts and reports. It’s important they have a sound understanding of math and statistics, and are also familiar with meteorology. Meteorologists and Atmospheric Scientists make $83,000 to $100,000 per year in Charlotte.
Are you a marketing or digital media wiz? You could become a Marketing Analyst and spend your time researching and optimizing campaigns. You may be required to use tools like Moz or Ahrefs, depending on your employer. A Marketing Analyst must also have a basic understanding of data analysis and using Google Analytics. You can earn $70,000 per year as a Marketing Analyst in Charlotte.
Offered by Noble Desktop, this in-depth course can take anywhere from four to 20 weeks to complete. You’ll learn all the skills needed to become a professional Data Scientist, like how to manipulate databases and perform data analysis, master the fundamentals of Python programming, and use Python’s main data science libraries to analyze data. You’ll also try your hand at creating machine learning models, making dashboards and visualizations, and deploying your projects online with GitHub.
Held live online, this course includes access to 1:1 mentoring, a free retake within one year, and a verified digital certificate upon completion. Small class sizes and live project-based training with industry experts help ensure you gain all the knowledge and experience needed to land your dream Data Scientist job.
This project-based course from Noble Desktop teaches you to gather, wrangle, analyze, and visualize data using various tools and technologies to help businesses in decision-making and strategy. You’ll work on multiple real-world projects to explore both predictive and prescriptive analytics and gain practical experience. Plus, you have access to 1:1 mentoring, a free retake, and setup assistance when you enroll in this course. Payment plans are available to make this certification more accessible.
If you’re looking to offer in-person or online training to your office or team, Noble Desktop can help. It offers a variety of corporate training programs, including in data analytics. You can order vouchers in bulk at a discount, choose which courses are available to employees, and let your employees choose their own schedule. Plus, you can choose to have your training hosted at a location of your choosing, live online, or at Noble Desktop’s Manhattan office.
With a top-notch customizable curriculum and instructors who are experts in their industries, Noble Desktop’s corporate training is designed to cater to your business or organization’s needs. For more information or to schedule a free consultation, you can reach out to Noble Desktop at corporate@nobledesktop.com.
Charlotte is a city full of opportunity for professionals looking to grow their skills in tech, design, business, data, and more. Known as a major hub for finance, energy, and technology, the Queen City is home to numerous banks, investment firms, and insurance companies, including Truist and Bank of America, as well as Duke Energy, the region’s leading electric company. With this in mind, it's no secret that Charlotte-based companies will hire data analytics professionals in droves to uncover important insights that can aid in organizational productivity and financial success. Learning data analysis in Charlotte enables you to make better choices, solve problems, and uncover meaningful patterns. Noble Desktop offers hands-on training with expert instructors who are not only willing, but excited, to help you take your skills to the next level. These instructors have decades of experience and know what it takes to succeed in the field that you wish to explore. All in all, you can learn how to enter the professional landscape and thrive in your role, no matter what that may be. In a city like Charlotte, the opportunities are abundant, so professional training is well worth the time and effort.
Henry Gonzalez is a certified Project Management Professional (PMP) and seasoned educator with over 30 years of experience working for the federal government as an Engineer, Manager, and Executive. During his time as an Executive, his main focus was program management of advanced technology programs in naval warfare, air traffic management, and homeland security. Now a retired senior official based in Charlotte, Gonzalez provides training in project and program management, leadership, and strategic planning to students and clients across sectors worldwide. He has achieved great heights in his career, obtaining the highest level of federal certification in acquisition program management and systems engineering. He also contributed to the Project Management Institute’s Standard for Program Management as a member of the original update team.
Based in Charlotte, Adrianna is a retired federal Human Resources (HR) Senior Leader with over 32 years of experience. She holds a Master’s Degree in Management Technology and Human Resource Management and is a certified Senior HR Professional by the Society for Human Resource Management. Throughout her career with the Department of Veterans Affairs, Adrianna worked everywhere from headquarters to regional offices, giving her ample experience with mentoring professionals all across the organization. She is passionate about sharing her knowledge with others, now working as an instructor to help others further their understanding of human resources and professional development.
James Campbell is an independent federal financial management consultant and seasoned instructor with over a decade of experience developing and teaching courses in finance and accounting at Graduate School USA, including subjects like budgeting and auditing. His career includes senior consulting roles with PricewaterhouseCoopers LLC and IBM Consulting Services. He’s also performed extensive audit and review work at the U.S. Government Accountability Office, where he served as an Assistant Director. James has also designed and updated numerous financial management and auditing classes, drawing on his expertise as a former CPA. His academic background also positions him to be an excellent instructor. He earned a Bachelor of Science in Accounting from East Tennessee University, an MPA from Georgia State University, and completed executive development training from George Washington University.