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.
Improve your data analysis skills with our Excel for Data Analytics class. Learn essential functions, formulas, and advanced techniques like Pivot Tables and VLOOKUP. Gain hands-on experience and optimize your workflow for efficient data analysis.
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.
Gain practical SQL skills in this hands-on certification program, where you’ll learn to write queries, manage data, and analyze results using Microsoft SQL Server.
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 to clean, organize, and reshape data quickly with Power Query, a powerful tool built into Excel and Power BI. This hands-on course will teach you how to streamline your data preparation process for analysis and reporting.
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 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.
Gain practical skills in analyzing datasets, building predictive models, and creating AI-powered applications using Python and industry-leading tools.
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.
Prepare for the Microsoft Excel exam and become an Excel expert with our certification program. Learn formulas, functions, Pivot Tables, charts, and more through group classes, private training, and an exam with a free retake.
Data analytics, in simplest terms, is the collection, transformation, and organization of data to draw conclusions, make predictions, and inform business decision-making. Data is obtained and analyzed in three stages. Data collection includes creating a method of obtaining the information requested. Data can be collected using various techniques like surveys or interviews. This information is stored until the second stage, processing and organization begin. During the second stage, individuals review the data and ensure there are no errors or missing information; this is called cleaning the data. Finally, once data is cleaned, analysis moves on to the final stage of data analysis and visualization. The data is reviewed for patterns, insights, and trends during this final stage. Once analyzed, it is time to present the information visually to explain your findings. The process can be tedious and require a lot of time, but many feel accomplished when finding a solution to a problem or answer to a question from the data collected.
Data analytics is not a new concept. The concept has been around since the 1950s, and the term was often used to discuss business trends. Most businesses use data analytics to make well-thought-out business decisions, but it is most commonly used in health care, retail, manufacturing, and banking. Data can be used to future-proof the business, improve productivity, mitigate risk, help stakeholders make better business decisions, and help organizations reach their goals. Simply put, data analytics is the process of analyzing data trends; there are four primary types of data analytics to consider when discussing this topic.
Each type of data analytics provides businesses with the additional insight they need to make the best decisions for their business success. While each has its own goal, some are interconnected and rely on each other to gather sufficient information.
Many people have chosen to get into data analytics because it is becoming increasingly in-demand. The increased popularity of data analytics provides job security for many because of the growing demand. Additionally, learning data analytics offers many skills that can be transferred to numerous roles. Skills like programming languages and data visualization play critical roles in training. Popular programming languages, like Python, can be transferred to many areas outside data analytics, making learning them highly beneficial. Much like programming languages, knowing how to present data collected or visualize it in a digestible manner for varying audiences is another critical skill in data analytics. Presenting findings effectively helps businesses make the best decisions for themselves and, in turn, recognize the value of investing in data analytics. Not only are there technical skills needed to be successful in data analytics, but there are also soft skills like communication, organization, attention to detail, critical thinking, and creativity that are just as important.
Various jobs are available that can use the skills learned in a certification or course. Skills can also be used to improve or enhance customer experience with a company by identifying areas of opportunity to streamline processes and increase productivity.
Data analysis improves efficiency by reviewing data that provides insight into key business success drivers. Someone well-versed in data analysis is well-suited for specific Risk Management positions. Risk Management helps businesses identify potential risks and develop strategies to decrease the chances of risks occurring. Finally, some roles can use analytics to help enhance the customer experience. Using business data, an analyst can find what strategies lead to the business’s success and how to improve existing products and services. Data analytics training can be used in various industries and roles, so investing in the training is worthwhile, even if you are still trying to figure out what to do with those skills.
Data analytics courses can range from beginner to advanced. Depending on how detailed the course is, participants will learn a certain amount in the class. However, some basics will likely be covered during a data analytics class to ensure students are knowledgeable and set up for success.
Statistics collects, analyzes, interprets, and presents numerical data. Statistics is a skill that translates well and can be used everywhere, not just in data. However, data analytics is heavy in statistics, meaning courses will provide students with various levels of detail on statistics. It helps extract meaningful data that can be used to draw conclusions. Statistics is also used to create probability distributions; this helps manage which statements help predict future events. Statistics is vital in data analytics because it allows analysts to understand what's happening and provides insight for businesses and other clients. Statistical methods also help identify patterns and areas of improvement, which helps develop strategies.
A large part of data analysis is data visualization. Data visualization represents data using charts, tables, infographics, plots, and graphics. Data visualization is imperative for data analytics because it showcases the work done. It is a great way to display ideas in a way that all decision-makers can understand. Some additional and standard visualization techniques include pie charts, area charts, scatter plots, and histograms. Most data analytics classes will likely cover the most common data visualization methods. Many courses will also teach students how to use Java and CSS, valuable tools in the data industry.
Programming is a large part of a Data Analyst's day, so they must be proficient in programming languages. The most popular programming tools include Python, SQL, R, and JavaScript. Programming applications can help data analysts quickly create and manage data structures, which can be helpful when analyzing and manipulating complex data. While coding is not always a requirement for analysts, it sets them apart. While you can be well-versed in some programming languages, knowing Python and SQL can significantly benefit you.
Machine learning can benefit all spaces with the growth of AI and other programs. Machine learning also plays a pivotal role in data analytics as it grows in popularity. It is a branch of computer science that uses data and algorithms to enable AI and improve its accuracy. Machine learning and data analysis are essential because they help automate the data analysis process and improve workflow. It can also cut business costs, mitigate risks, and improve business structures overall. Data Analysts will still analyze the data, and machine learning will help provide a deeper insight into the data by inputting the information obtained into a program; the two work closely together and enhance the data analysis process. Knowledge of machine learning as a Data Analyst can help improve workflow and reduce the risk of human error that could arise with analyzing data.
Though it might not be explicitly taught, attention to detail is vital to a Data Analyst's work. When covering the core topics such as programming, data visualization, statistics, and machine learning, there is a need to pay attention to detail. It can help prevent detrimental errors in your work, saving time and money. As you navigate various courses and learn new skills, you might pick up ways to ensure you pay attention to detail. This could look like making lists, reviewing your work, or contacting a colleague with questions.
Data analytics can be complex for some because it is highly technical but not impossible to learn. How technical data analytics gets depends on how in-depth individuals are looking to dive into the field. When learning data analytics, it is essential to know that it includes reviewing data, understanding it, and making it understandable for others who might be less connected to the numbers. It can be less challenging for those with previous experience with statistics, computer programming, and experience creating various types of data visualizations. While it can be difficult, that does not mean learning is impossible. Focusing on the critical aspects of data analytics such as statistical analysis, can help decrease the potential challenges one would face.
The challenging parts of learning data analytics can depend on a person's skill set when they enter the course, but generally, most find navigating the data difficult. Running a data analysis can yield a lot of information—some pertinent in the moment and others not as crucial at the time. Many find it challenging to manage the amount of data received while also determining what is useful and storing the data that is not needed. It can be overwhelming for most people. The best thing to do is to take your time and start with one piece of data at a time. As you work through the data piece by piece, you will know what needs to be used at the time. Storing data securely and keeping it for later projects is just as crucial as determining when and how to use what you have. With more experience, the feeling of being overwhelmed should decrease as you develop a system for how to best manage your data.
When wondering how long it takes someone to learn data analytics, the simplest answer is: it depends. Coming into data analytics, the time it takes you to learn the basics can depend on how much knowledge you have before starting your program. Some individuals begin learning the ins and outs of data analytics with some understanding of data, statistics, or even problem-solving with numbers. This could be a significant advantage and decrease the time it takes to learn. When you start a program or explore data analytics, foundational education about the subject is essential. This means understanding what data analysis encompasses and learning the skills to make you competent in an entry-level position. This means understanding its core concepts, learning how to analyze data, and grasping how to explain your findings in a digestible way. Entry-level competence can take a few months as you become more comfortable and improve your skills. You can independently learn technical skills or take a professional certificate program to help advance. Feeling comfortable with various industries and programs can take a little longer and, sometimes, several years. This could involve taking additional courses or jobs in multiple fields that expose you to new methods. With practice and experience, depending on how frequently you use your skills, becoming a professional in data analytics can come easily and naturally. The key is to understand the basics and spend a lot of time practicing.
Nowadays, convenience is essential for anything, including learning opportunities. With the current increase in popularity of online classes, some are on board with taking classes this way, while others tend to stick with the tried and true method of in-person learning. When deciding between online learning, in-person, or even an asynchronous option, despite what might be popular right now, it’s important to consider which method supports your learning style and is realistic for you. First, let's consider an asynchronous option or a self-paced-style course. Asynchronous classes allow for independent learning without being in a live course. Assignments typically still have assignment deadlines and a course end date, but there is no live instructor; lectures are usually pre-recorded. Asynchronous is ideal if you need flexibility and want to work at your own pace. Depending on how well you understand it, you can spend more or less time with a particular lesson. It can be challenging if you struggle with deadlines and procrastinate. An asynchronous course removes the additional level of accountability by having weekly classes, which can be difficult for someone who needs more time to focus or becomes easily distracted. You can still access your instructor via email or virtual office hours, so asynchronous does not mean you are entirely alone.
Live online classes are classes taught by a live instructor with classmates but in a virtual space. Online courses offer a great amount of flexibility because you can attend the class anywhere. There are no concerns about being stuck in traffic or out of town during class time; simply log in from anywhere and be ready to learn. Many people love this feature because it feels less restrictive of your time. Additionally, you save time because there is no commute. Online classes mean not worrying about parking and walking across campus to your next class. This saves you time and money. Finally, online classes are open to everyone, which means your classmates can be from anywhere and give you the opportunity to broaden your network by meeting people outside of your city and potentially outside of your state. This global networking opportunity can make your learning experience more enriching and diverse.
On the other hand, online courses can make it difficult to network with classmates. Spending time online versus in a classroom takes away from some of the organic conversations that can take place with classmates and instructors in the downtime before class starts. For many, this is where networking and building community can happen. Also, taking a class online typically means you need the software and other tools necessary for the class. In-person classes can offer a lab with specific programs you might need already loaded on the computer. While purchasing the program you're learning could be an investment, if you are taking numerous courses, this can become costly.
Finally, there's the traditional in-person course. Many prefer in-person courses because of the structured environment they offer. Students know they are in the classroom at a particular time and have that time set aside and dedicated to learning. This structured environment can help you prepare and focus better because you have the expectation to be present in class. Also, in the classroom, you are less likely to be distracted by your phone, TV, others living in the house or wherever you are taking the course, and it prevents multitasking during class time. Finally, during an in-person class, it can be easier to ask questions of the professor and your peers because you are all in the same room together. In-person classes can limit your flexibility because you are expected to be in class; this can be challenging for some. Also, in-person classes can be limiting. While online courses are available worldwide at a variety of locations, when selecting an in-person course, you have to consider the feasibility of your commute. Regardless of what you choose, there is a form of class that can accommodate the needs of every learner.
There are many ways to access free course content online, and data analytics is no exception. There are options if you are looking for a course or certification. Coursera, for example, offers many free classes, including data science. For instance, they provide a free Data Analysis with Python course. The course is for beginners with some experience and is about 15 hours long. Noble Desktop offers accessible data analytics resources and tutorials. Noble’s resource page has over 100 articles and videos to help students learn new skills or refresh existing ones. Finally, Khan Academy offers accessible lessons for participants learning probability, statistics, and SQL. They provide mini lessons and even a practice test at the end to test your knowledge. Using free online resources can be great for additional practice, expanding your knowledge, and even deciding if you want to take your curiosity further with a particular topic.
Data analytics training will cover a lot of topics, specifically technical skills. While all aspects are essential, some additional technical skills are necessary and can further your learning. You might not find a data analytics course that explicitly teaches everything you need to know about Microsoft Excel. Still, it is a program often used to help data analysts remain efficient. It might be worthwhile to take additional Excel training to increase your skills and improve your data analytics techniques. Another technical skill, statistical knowledge, can help you succeed as a Data Analyst. While how in-depth your knowledge needs to be depends on your role, there are options to expand your knowledge to ensure you are prepared for future jobs. If you have the time, taking additional courses in statistics can be precisely what you need to further your advancement.
Some soft skills are not necessarily taught in courses but can be learned along the way and are just as vital as technical skills. Improving your communication skills can help improve your work as a Data Analyst. Improving communication skills can happen while working on a team in an unrelated field. Consider any areas of opportunity or feedback you receive related to communication as ways to improve these skills. Extracting data is one thing, but clearly communicating findings to other, less technical team members is just as important. Paying attention to detail is another essential skill. As a Data Analyst, you must be able to find answers and notice things that others might miss. Attention to detail can help you limit mistakes and stay on top of the data. This could look like finding a method that works for you such as keeping track of projects or staying organized with a planner. Being organized and having a system is a great way to notice those details that are out of place and catch them before they cause further complications.
Many industries see the benefits of using data analytics to help their businesses succeed. It can help predict trends, solve problems, and even easily display a company's successes to the entire team. Data analytics can help companies in finance, marketing, and human resources. Essentially, any industry can benefit from data analytics, but some benefit more than others.
The healthcare industry is a sector that consists of medical services, medical insurance, and healthcare providers. All systems are integrated in some way but can each stand independently. Data analytics in healthcare can be very beneficial in each sector. In healthcare, some refer to data analytics as health data analytics, and it uses quantitative and qualitative data to identify trends. It can show areas of opportunity to lower costs, evaluate practices, and assess various medical practitioners' performance. Using data analytics in healthcare can expand and improve vital areas such as improving the efficiency of hospital staff, increasing patient engagement and satisfaction, and redesigning treatment standards to benefit staff and patients. These changes can be made by analyzing survey data, tracking electronic medical records, and continuing research, generating more data.
Data analytics is a game-changer in the field of education, offering significant benefits to the entire system. By analyzing student data, it can provide compelling arguments for policy changes, inform educators, and facilitate informed decision-making. The application of data analytics in education involves using descriptive, diagnostic, and predictive analyses to enhance the understanding and utilization of the information obtained. This opens up a world of possibilities for the education sector, making it an exciting area to explore.
Finance encompasses many financial sectors. The finance industry may include banking, insurance, real estate, and consumer finance. It also covers accounting and investments. Finance professionals need to have a good grasp of data analytics. It can be used to improve internal operations, understand consumer needs, and identify financial trends. Data can be used to provide decision-makers with insight into which products to promote, key performance indicators, and even help with detecting fraud. Many financial industry analysts work with key leaders to help interpret the data. The need for a Finance Data Analyst will continue to grow as the finance industry expands.
Government agencies cover many areas, including national security, transportation, and criminal activity. There is a lot of ground to cover and a considerable need for analytics. With national security, for example, data analytics allows users to access large datasets. Data analytics in transportation can help improve public safety by monitoring roads, transport systems, and weather. There is room for all interest levels in data regarding the many government agencies that depend on the results. The information obtained from data directly influences how government funds are allocated to various agencies. As a Data Analyst, the work done will be vital in how soon issues are identified and how efficiently they can be resolved.
A Freelance Data Analyst works as an independent business owner in various industries, giving them freedom and flexibility. This position offers flexibility because freelance data analysts are not associated with just one company but have the freedom to move about and work with different companies at once or take multiple jobs simultaneously. Analysts typically have to find clients independently and handle their billing; they can also choose which clients they want to work with and manage their own time. Being a Freelance Data Analyst can be beneficial because you can be your own boss and enjoy variety in your work. Additionally, you have the freedom to work with clients all over, thanks to the flexibility of the role. Finding clients and maintaining a consistent workload can be challenging initially or during slow periods. It can also be problematic because working freelance typically means not receiving the standard benefits of a traditional job. Most freelance positions do not offer benefits like insurance, paid leave, or retirement plans. Working independently can also feel isolating for some. There are no coworkers to check in with, and you typically work from home. Training as a Freelance Data Analyst is similar to other roles. As you gain more experience and begin exploring various industries, your expertise can make it easier to market yourself for a wide range of jobs. The average salary for a Data Analyst in the United States is about $107,000.
Database Administrators (DBAs) are responsible for maintaining, securing, and operating databases. While there are several types of DBAs, one of the most common roles is that of a database analyst. Database Analysts collect and analyze data to help improve database performance. Regardless of the type of DBA, it is essential to be familiar with different databases such as MySQL and MongoDB. As a DBA, you can expect to work directly with business executives for large corporations or individuals. Database Administrators, on average, make about $100,000.
Business Analysts examine a company's data and procedures to identify areas of opportunity to improve efficiency and performance. Business Analysts work with various business areas to assist with management, reduce costs, and ensure all resources are used efficiently and effectively. Business Analysts need to be familiar with data, business operations, and how organizations function. They should also identify areas that can be evaluated and improved if needed. A Business Analyst, particularly one who focuses on data, must have a strong background in programming, research, and statistics. In the United States, a Business Analyst can earn about $105,000 depending on skills, education, and experience.
A career as a Financial Analyst is in high demand. As a Financial Analyst, you work with businesses to analyze their finances. This could include analyzing historical results, making projections, or recommending what and when to sell. As a Financial Analyst, you can use Excel and apply ratios like gross margin, return on equity, earnings per share, and net margin, among others. These ratios can be used to compare their client's performance against themselves and their competition. Financial Analysts work closely with business executives and play a pivotal role in decision-making. On average, a Financial Analyst can make about $91,000 a year.
The University of Illinois Chicago offers a Data Analytics Bootcamp program for students seeking an online full-time or part-time option. The program is designed for students pursuing high-quality education while balancing their lives. They offer flexible classes with live, online, and interactive class options or more independent structured options. At the end of the bootcamp, students will be prepared to take various certification exams, and they can choose to take them during the bootcamp or directly after they complete the program. The bootcamp is designed for beginners, and individuals with no data analytics experience are encouraged to apply.
Loyola University Chicago offers a plethora of course options that can propel your career forward. Their Business Data Analytics program, which includes a summer accelerated option, is a prime example. This 10-week online program, which can be completed from anywhere, requires students to finish five 2-week courses in 10 consecutive weeks to earn their certificate. The curriculum covers essential tools such as R, SQL, and Tableau, and equips students with various methodologies for modeling and analyzing data. The knowledge gained from this training can open doors to new career opportunities or help you excel in your current field, instilling a sense of hope and motivation.
The Flatiron School is based in Chicago but offers a global online campus, offering the same programs online as they offer on their in-person campus. Flatiron offers a deferred tuition program that allows students to defer paying their tuition until they are hired and earning at least $40,000. Their data science program is 15 weeks full-time or 40 weeks part-time. Part-time work can only be completed online, while in-person work offers both options. They offer various online courses also based out of the Flatiron School’s New York location to support a career as a Data Scientist and even as a Web Developer.
General Assembly offers a variety of courses for individuals looking to expand their knowledge in data analytics and other technology-based industries. For example, they offer a Data Analytics Bootcamp in-person for individuals looking to learn quickly and in detail about data analytics. The course lasts one full day and is designed for individuals interested in learning how to work with data and is great for beginners. Students should expect a full day of hands-on courses covering the essential programs needed to be successful in data analytics. If you want to expand your data analytics knowledge, consider General Assembly’s ||CPN834||: Learning the Language. There is no requirement for prior SQL knowledge, and this course will help students learn the basics of SQL and how to write queries.
A Python for Data Science Bootcamp is an excellent way to gain essential knowledge and master Python. Noble Desktop offers an online bootcamp to help students feel comfortable writing basic statements and expressions, working with lists, indexing, and more. The course is 30 hours, with most sessions occurring during the week. The Python for Data Science Bootcamp is also part of a certificate program, so this class could be the start of launching a new career. Noble Desktop’s Data Science certificate program is comprehensive and includes courses like the ||CPN764||. The program is four weeks full-time or twenty weeks part-time, covers nearly everything you need to know about Python, and consists of an ||CPN834||. This program is also offered online through Noble Desktop.
The University of Chicago Booth School of Business offers a six-week course taught by Chicago Booth professors aimed at helping individuals develop powerful data analytics strategies, understand how to effectively use the data to answer questions, and translate the data into actionable decisions for their customers. The Leading with Data and Analytics course offers group learning opportunities for 10 hours of on-demand learning, and students will complete a final project to integrate all the information they learned over the six weeks.
Planning corporate training for your team can be a great way to build relationships and ensure your team is aligned in their skills. Noble Desktop recognizes the impact corporate training can have on business, which is why they offer corporate training. They provide training in SQL, Python, Excel, data science, and other vital programs that will help expand your team's data analytics knowledge. They have developed an excellent curriculum that can be customized to suit the needs of your business and employees. Not only is the training well-crafted, but the support materials are as well. During training, students will receive handouts, certification exam guides, and access to premium training videos to help solidify the knowledge taught in the training program.
Noble offers several options to support corporate training. First, corporations can choose live online or onsite training. With this option, your business can have a live instructor in-person or virtually train your team on location. This option includes flexible scheduling to accommodate business needs. If your team has varying roles, responsibilities, and availability, making it hard to secure a training date, Noble Desktop offers a group class voucher option. Interested employers would purchase vouchers at a discount, and employees would schedule the training independently. Management would choose which courses are available to employees. The final option is attending a private training on location at the Midtown Manhattan Noble Desktop location. After selecting the course that best fits company needs, employees would arrive for their training and have access to the modern computer lab at the training site. Noble Desktop can set up the training for you and provide you with the information needed to make this training option successful. Email Noble Desktop and start a free consultation with their team.