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.
Master Python for data analysis, machine learning, and automation. Build predictive models, create dynamic dashboards, and unleash the power of data visualization. Launch your career in data science and Python engineering, equipped with Python, NumPy, Pandas, and Matplotlib.
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.
Master the industry-standard tools and programming languages used by Data Analysts, including Excel, SQL, and Tableau, in this comprehensive classroom training program. Gain hands-on experience working on real-world projects and learn to organize, analyze, summarize, and visualize data to present actionable insights.
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 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 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.
With the invention of computers, data science has become the go-to method for using data to extract meaningful insights. By combining statistical analysis, computer science, and domain expertise, Data Scientists use data to find patterns and solve various problems. This process involves collecting, processing, and analyzing large amounts of data to support decision-making. Even though data science became popularized in the late 20th century with the advancement of computing technology, data science has become a leading field in the modern world.
The idea of data science predates the invention of modern computers, as people first utilized statistical models to solve business and scientific problems. Now, data science has evolved to enable large-scale data processing. When the internet became a household utility in the 2000s, digital storage expanded exponentially, prompting organizations to realize the potential of harnessing vast amounts of data. This massive growth led to the development of specialized tools and techniques today, including machine learning and data visualization, that further cement data science as a transformative field in this modern time.
While a highly technical discipline, data science offers tools to tackle problems and uncover insights in unimaginable ways. Training in data science allows you to use these specialized tools in various aspects of your life. With training, you will learn how to use predictive modeling, which allows users to forecast outcomes based on historical data. In a professional setting, businesses can utilize data science to predict customer behavior, optimize supply chains, or identify market trends. For those who want to impact the environment, you can use data science to analyze environmental patterns like climate change or predict natural disasters. This type of statistical modeling can help societies better prepare for future events.
Yet, many individuals who start learning data science see its power outside of their professional domains. Data science allows you to quickly analyze your finances and identify trends to improve your financial standing over time. Fitness enthusiasts can use data science to track their workouts and progress over time to ensure they meet their weight loss or muscle gain goals. You can even use data science to help at the grocery store, as you can easily track your spending over time.
Beyond data science, data visualization tools make communicating and understanding complex data in statistical models easier. These visualizations may be presentations of interactive dashboards, depending on the type of data you want to communicate. While starting at a data spreadsheet may reveal patterns, data visualizations allow those patterns to come to life. It enables anyone to easily identify patterns and make more informed decisions, both professionally and personally. By implementing data science outside of professional uses, it becomes easy to see how it can transform daily routines and support you in making more informed personal choices.
Data science has various applications that can change your professional and personal life. Now that you know how data science can help you, you need to know how to start learning data science. The best and quickest way to ensure you learn everything you need to know is to enroll in a data science course. Instructors design data science classes to cover various topics. This gives students a well-rounded education for their careers after the course. That said, courses can vary in content depending on the school and the instructor. As you begin your search for the perfect data science course for you, there are several key skills you need to learn in the class.
As one of the main parts of data science, data analysis and visualization allow you to examine raw data, identify patterns, trends, and anomalies, and effectively communicate those findings. Visual tools like graphs, charts, and dashboards display these findings. Without these two skills, you are imputing data and hoping for information to pop out at you. With these two skills, you build the foundation for data science and ensure that data insights are actionable and accessible to a diverse audience.
While you may not think programming has a place within data science, you may not realize how versatile certain programming languages are. When dealing with large datasets, you may want to automate tasks or implement algorithms to simplify the process. By utilizing Python and R, you can create code that does these things for you. These flexible programming languages have multiple data manipulation and statistics libraries, making them ideal for data science. Enrolling in a course that covers these programming languages, you learn the tools to clean and preprocess data, perform complex calculations, and build scalable solutions. While it may seem difficult and unnecessary initially, having strong programming skills in your toolkit is indispensable for solving complex problems and exploring data science applications.
Databases are another key component of data science as they allow Data Scientists to store, organize, and retrieve vast amounts of information. When looking at courses, you will want to ensure they cover tools like SQL and NoSQL databases, as these allow professionals to interact with large datasets. These tools allow you to access the precise data needed for analysis whenever necessary. Database management is another foundational skill critical for maintaining data integrity and enabling seamless workflows, no matter what dataset you are looking through.
Machine Learning is a modern data science skill that enables systems to learn from collected data and make predictions without explicit programming. It has its place in almost every industry, and people encounter it daily without even realizing it. With machine learning, you will train algorithms on various datasets to recognize patterns, classify the information, or forecast trends. A strong foundation in this area allows data scientists to create intelligent systems capable of solving complex, dynamic problems.
While you will primarily be looking for courses that cover technical skills, it is important to note that they will cover more skills than those. When dealing with large datasets, you must know how to effectively problem-solve. At any moment, something could go wrong within the database, and you must devise a way to fix it. However, data science and problem-solving go beyond the personal level. At its core, data science was developed to solve problems. Whether it's optimizing a supply chain, identifying inefficiencies in a system, or uncovering insights to drive innovation, problem-solving is the ultimate goal. This skill involves critical thinking, creativity, and a systematic approach to breaking down challenges into manageable components. By learning how to approach problems, data scientists can apply their technical skills effectively, ensuring meaningful and impactful results across projects.
After looking at the skills most data science classes cover, you may wonder how difficult it is to learn data science. On the surface, data science dives into various disciplines, including mathematics, computer programming, and AI. Many people may look at these disciplines and think that data science will be too complicated to learn, as these areas are difficult to grasp. Of course, the difficulty level will depend on your learning goals, previous experience, and the depth of expertise you wish to achieve. You can become familiar with the core skills like data analysis, data visualization, and database management with consistent practice. These foundational skills are easier to grasp and work with consistently. Skills like programming and machine learning may take longer, and you may face more challenges when learning these skills. This does depend on your experience; if you have previous experience with programming, you will have an easier time learning Python or R, as you already have foundational knowledge in programming. However, any learning journey can become easier. When you enroll in a data science course, you gain access to data science professionals who can help you tackle the concepts you are struggling with.
Data science is not exempt from challenges that everyone must overcome. The skill includes built-in challenges because it involves learning programming and database management. When first learning data science, most students struggle with programming because it is learning an entirely new language. There are multiple layers when learning how to program; even those who just want to learn programming struggle when first starting. Luckily, data science uses some of the easier programming languages to learn. Python uses simple syntax and has a large community that offers support. R can prove to be more challenging; however, after learning one programming language, it becomes easier to learn more. Many students also struggle with the steep learning curve of certain topics, including machine learning and advanced data wrangling. Both of these skills involve learning algorithms and processes that are unique to them. Since there are several moving parts to each of these, it can become hard to remember each step or when best to use them.
Many students will break down the learning process into smaller, manageable steps to overcome each challenge. This allows them to focus on the more manageable foundational skills before jumping into the advanced topics. Some students even leverage other resources like courses, mentorship programs, or interactive projects that will provide efficient guidance while working through the difficult topic.
After looking at some of the challenges students face, it is reasonable how long it will take to learn data science. While data science has challenges, one can easily master the fundamentals in six months or less. Of course, as mentioned above, this depends on several factors. If you are transitioning from another data-based position, you might already know many of the tools and programs used in data science. You will also need to consider how you plan to learn these skills. If you want to enroll in a course, it can take several weeks to a few months to master the basics. However, this process could take several years if you opt for a self-guided education. If you utilize your free time to experiment with data science skills, you will quickly grow comfortable with them. While it is possible to quickly master the foundational skills of data science, it can still take several years to fully master the skill set, including programming and machine learning. If you want to jump into a new career soon or start implementing data science into your life immediately, enrolling in a bootcamp or certificate course is the best way to ensure you promptly learn the most important skills.
So, you've settled on learning data science through a course. That still leaves the door open to several different options. With the internet becoming ingrained in everyone's daily life, more training formats have become available in recent years. Your first thought may be of the traditional learning method of in-person training. With this method, you will meet with fellow students at a specific place and time to receive training. The instructor will take the class through the material and work with everyone individually to help them through difficult concepts. This traditional format allows for easy collaboration and immediate feedback. It also provides a structure that lets you stay focused and engaged with the content. Choosing this option may be best for those who have been out of a classroom for some time or have trouble concentrating when they are at home. It is also important to remember that in-person training does require you to commute to a specific location, which can cause inconveniences for those with a busy schedule or who live in areas without accessible programs.
Live online training eliminates the commute and opens the virtual door to many other remote classes. Students who opt for this learning format still receive the structure of a traditional in-person class without the commute. Students will still meet at a designated time and learn directly from their instructor; however, they can complete the course wherever it is most comfortable. They will still benefit from direct interaction with their instructor and classmates through a video conferencing tool. You need a stable internet connection to attend these classes. Some may struggle to concentrate outside a traditional classroom setting.
Your options don't stop there, though. You can also enroll in a course that allows you to work at your speed, and when you have the time. Asynchronous classes provide students with the most flexibility when it comes to learning. Students will gain access to pre-recorded lectures and materials when they enroll, and they can complete the modules in a way that fits into their schedule. If you have a busy schedule filled with personal or professional obligations, taking a self-paced learning opportunity may provide the ideal way to still learn data science. It is important to note that students who choose this option will learn independently and will not always have access to an instructor. You may need to answer your questions as they arise, or you may have to wait several days to weeks to receive a response from an instructor. You will also miss networking opportunities from the other two learning formats. Success in asynchronous learning often depends on strong self-discipline and seeking additional support when needed. Each format offers distinct advantages, so the best choice depends on individual learning styles, goals, and logistical needs.
While above, we discussed how you can enroll in a data science course, you may not be in a position that allows you to do so. That does not mean you must give up learning data science. Numerous free online resources are available for those curious about data science, offering a great way to explore foundational concepts and basic skills. For example, platforms like Noble Desktop's YouTube channel provide tutorials on topics ranging from data visualization to programming. These resources are excellent for gaining an introduction to the field or experimenting with specific tools. However, professional skill development in data science—particularly the advanced techniques and industry-specific knowledge required for a career—requires more than what free resources typically offer. Mastering data science involves structured learning, guided mentorship, and hands-on projects, best accessed through comprehensive training programs or courses. Free materials can supplement this learning journey, but building expertise often demands an investment in time and professional education.
Everyone knows that it is great to specialize in one skill; however, few jobs require you to perform just that one skill. Before looking at what jobs you want to pursue with your data science skill set, you may also consider learning other valuable skills to help your resume stand out against the crowd. One skill that most employers look for is strong communication skills. This is especially true with Data Scientists. When a large part of the position is explaining the patterns you have uncovered through datasets, you will need to effectively communicate those patterns with various audiences. Strong communication skills allow you to confidently discuss your work and translate your technical findings into actionable insights. Strong communication skills let you write clear reports and give great presentations. They also help you drive informed decisions.
While strong communication skills are vital in almost every position, you may also want to learn useful skills within a specific industry or field. If you already know which field you want to apply your data science skills to, taking some time to research skills in that area can benefit you in the long run. For instance, if you want to enter the healthcare field, you may spend some time familiarizing yourself with treatment planning, drug usage, or diseases. While not specific to data science, having a basic understanding of these topics can help you better understand the data you will be working with and develop better solutions based on the patterns you find. Similarly, in the finance industry, having familiarity with the stock market, risk analysis, and investment strategies can help you make more informed insights.
When entering any data science position, you will likely simultaneously work on several analysis projects. You may benefit from learning some project management skills. Doing so lets you plan, prioritize, and execute each project efficiently. These skills and technical expertise create a well-rounded data professional capable of delivering impactful results in various settings.
With a diverse economy like Charlotte, several industries call the city home. It is the second-largest banking center in the United States, making it a go-to place for financial professionals. However, this also makes it a destination for data science professionals. Every industry has started to depend on data science to drive efficiency, innovation, and decision-making. As the city continues to grow over 1% each year, the demand for data science professionals grows with it, as the use of data-driven insights helps shape the success of businesses and public services.
As the second-largest banking center in the United States, it only makes sense that one of the largest industries within Charlotte is the financial sector. While many of the professionals you find within this industry will include Financial Advisors, Accountants, and Financial Analysts, you will also find many individuals who work behind the scenes to help make the industry run. With major institutions such as Bank of America and Wells Fargo having a significant presence in the city, these banks need data science professionals to analyze the vast amounts of financial data. Data Scientists within the finance industry take data to help firms understand risk management, fraud detection, and investment strategies. They use machine learning algorithms to assess risks, forecast market trends, and optimize customer service.
Charlotte powers the nation as headquarters to Duke Energy, a utility titan. This southeastern hub drives America's electric grid from its perch atop the energy industry. Data science is indispensable for optimizing energy production, distribution, and consumption, particularly as the industry embraces more sustainable practices. Energy companies analyze consumption patterns through data-driven solutions, forecast energy demand, and improve grid management. Data science also plays a role in predictive maintenance for power plants, helping to minimize downtime and reduce costs.
Charlotte is home to the largest research park in the United States, and several medical research universities call the city home, making it a prime location to enter the healthcare industry. While most people think of doctors and nurses as the primary force in the healthcare industry, several players behind the scenes help drive innovation. Data science is transforming Charlotte's healthcare. It enables accurate diagnoses, improves patient outcomes, and optimizes hospital operations. Using machine learning algorithms developed by Data Scientists, doctors and other medical professionals can easily analyze patient data, predict health risks, and assist in decision-making. Also, data science helps healthcare groups streamline operations. It reduces wait times, improves resource use, and enhances patient care.
Charlotte's education is growing fast. Schools like the University of North Carolina at Charlotte and Central Piedmont Community College boost the city's economy and intellect. Data science is vital in education. It boosts student performance, optimizes teaching, and improves admin efficiency. By analyzing student data, educators can identify learning patterns, predict academic success, and tailor interventions to meet individual needs. In addition, educational institutions use data science to optimize campus operations, from resource allocation to course scheduling.
With almost every industry in Charlotte benefitting from data science, there are countless career opportunities for those with that skill set. No matter which industry you want to enter, there are various positions that you can pursue.
Data Scientists extract meaningful insights from complex datasets to help inform business decisions. They will use machine learning, statistical analysis, and data visualization to help identify patterns. They often develop predictive models, optimize processes, and create data-driven strategies that enhance operational efficiency. In Charlotte, a Data Scientist can earn an annual salary of around $120,000, depending on experience and industry.
Financial Analysts apply data science skills to analyze financial trends, forecast future market behavior, and help organizations make informed investment decisions. They utilize Excel, Python, and specialized financial modeling software to collect, clean, and analyze financial data. Their work involves producing reports, assessing risks, and providing insights that drive strategic financial planning. This role requires a strong grasp of finance and economics. In Charlotte, Financial Analysts typically earn around $80,000 annually, with salary variations depending on the specific sector and the complexity of the role.
Data Engineers craft the backbone of information flow from data pipelines to analysis-ready insights. They Architect systems to handle massive datasets, collaborating with scientists and analysts. Their expertise ensures seamless access to crucial data, empowering organizations to extract valuable knowledge efficiently and reliably. Data Engineers must be proficient in SQL, Python, and data architecture. They should also have experience with cloud tech and big data platforms like Hadoop or Spark. Data Engineers in Charlotte can expect to earn around $100,000, with salaries varying based on experience and the role's technical demands.
Machine Learning Engineers design, build, and deploy machine learning models. These models automate tasks, improve decisions, and enable predictions in organizations. In Charlotte, they often work in finance and healthcare, developing algorithms for credit scoring, fraud detection, patient risk assessment, and more. Machine Learning Engineers use skills in programming (Python or Java), data modeling, and algorithm design to build robust, scalable systems. In Charlotte, Machine Learning Engineers command salaries around $130,000. Industry and expertise shape exact figures, with top talent earning more in competitive sectors.
After looking into the types of classes you can enroll in and the industries and careers you can pursue, the next step is deciding on the training you want to enroll in. While the most effective way to develop the data science skills you need to excel in the position you want is to enroll in an in-person class, there are several courses available for you to choose from. For those in Charlotte or looking for online options, various schools offer courses tailored to different aspects of data science, from foundational programming to advanced analytics. To find more options, use Noble Desktop's Classes Near Me tool. It helps find in-person and live online data science classes that fit your schedule and career goals.
Noble Desktop offers several certificate programs, including the Data Science & AI Certificate, Python for Data Science Bootcamp, and ||CPN395||. These courses target beginners and intermediate students who want a structured, immersive experience. The Data Science & AI Certificate covers Python programming, machine learning, and predictive modeling, equipping students with the skills needed for AI and data science careers. The Python for Data Science Bootcamp focuses on foundational programming and data visualization techniques, perfect for those new to coding. These live online courses provide the flexibility of remote learning combined with the interactivity of in-person instruction, making them ideal for working professionals.
The Online Data Analytics Bootcamp from Wake Forest University is an excellent choice for students interested in an online program with a strong reputation. This program provides students with comprehensive training in analytics, including data cleaning, visualization, and statistical modeling. The curriculum includes real-world projects, which let students apply their skills to solve complex problems, helping them stand out in the job market. Wake Forest's program particularly appeals to those seeking a university-affiliated certificate without the need for in-person attendance.
New Horizons offers the Data Wrangling with Python course. This program teaches students how to handle, clean, and transform raw data into usable formats, a critical skill for anyone pursuing data science. By learning Python-based tools such as Pandas and NumPy, students gain the technical expertise needed to preprocess data for analysis. This class is especially valuable for beginners and intermediate students who want to strengthen their Python skills while mastering essential data-wrangling techniques.
UNC Charlotte's Data Analytics Boot Camp is a comprehensive program that prepares students for various data-related roles. The course includes training in Python, SQL, Tableau, and machine learning and instruction on analyzing and visualizing data effectively. Students work on real-world projects and develop portfolios they can present to potential employers. With its strong connection to Charlotte's thriving industries, this boot camp is well-suited for local students aiming to build professional networks and secure roles in the area.
If you already belong to a team of trained Data Scientists but are looking for a way to upskill or reskill your team, Noble Desktop has a way to do that. Noble Desktop provides corporate training solutions tailored to your team to help them develop essential skills in data science, programming, or any area they need training in. Companies can choose from various formats, including in-person training sessions held at their office or Noble Desktop's facilities and live online options that allow teams to learn together from anywhere in the world. The team customizes each training program to align with organizational goals and includes practical exercises relevant to real-world projects. This ensures that employees gain theoretical knowledge and hands-on experience to immediately apply to their roles.
Noble also offers discounted bulk vouchers for businesses seeking flexibility, allowing team members to attend regularly scheduled classes. These vouchers make it easy to accommodate varying schedules while still providing access to top-tier instruction. Whether it's a single department or an entire company looking to upskill, Noble's corporate training programs deliver impactful results. To explore customized training options or learn more about bulk voucher discounts, contact Noble Desktop.