Data Analytics Classes & Bootcamps Near Me

Now more than ever, companies of all sizes can harness the power of data to guide their work. Data defines practically everything in the modern digital economy, from a user’s behavior on a site or social media platform to complicated macroeconomic trends. However, actually harnessing this data and drawing useful conclusions from it is much more complicated than it seems. In fact, without the field of study known as data analytics, making sense of the data that defines our world would be nearly impossible. Many fields and industries generate so much data in any given day, and human beings would be simply incapable of processing it all without the aid of a computer. For example, on an average day, the NASDAQ sees about 30,000,000 stocks exchanged. If you want to make sense of that volume of information, you’ll need specialized data analytics expertise, or you’ll need to hire someone who has specialized data analytics expertise.

Data analytics professionals are at the helm of organizations, making data manageable and useful. Combining knowledge in programming with statistics expertise and industry-specific knowledge, data analytics professionals help create the tools and processes for drawing valuable conclusions from immense datasets, often ones that are so large human beings wouldn’t be able to process all of the information. Since companies in every industry rely on such tools, professionals across the economy can learn new skills to specialize in data analytics. Even better is that data analytics expertise rests on fundamental programming languages, making the field easy to get into and fulfilling to master.

Why Learn Data Analytics?

Fortunately, getting into data analytics is easier than you may think. You’ll have to be familiar with three fields: programming, mathematics, and industry-specific knowledge. Let’s start with programming. You can start with a programming language like Python or R and gain the foundational skills needed to understand and clean data. Python makes wrangling, visualizing, and processing data a breeze. If you’re not working with large datasets, you can use Excel to clean and visualize your data. Powerful data visualization tools like Tableau and Power BI can also help you present your findings to non-technical clients. Knowing how to use data analysis tools like Excel, Tableau, and Power BI can make your journey to becoming an Analyst much easier and give you a range of capabilities to draw meaningful insights.

Next, you’ll have to learn the basics of probability and statistics to ensure the results you are seeing are statistically significant. As a Data Analyst, you’ll most likely compare performance metrics and work with percentages. Finally, if you are a subject matter expert (SME) in a specific industry, you probably already have the domain knowledge needed to succeed. If not, it’s okay. Once you start working, you’ll develop the knowledge and gain the business acumen related to your particular industry. Understanding business context is important because it helps provide context to your data and improve your data visualization and storytelling capabilities. As an Analyst, you’ll have to craft SQL queries to collect data for analysis. There might be times when your query could be off, but without business knowledge, catching mistakes may be difficult. It is important to remember that raw data usually creates more questions than answers. Your job as an Analyst is to interpret the heaps of data to find hidden insights and ensure you receive quality data. It is an exciting time to work with data, and it will help you develop and hone skills in graph theory, applied statistics, data visualization, and linear and numerical analysis.

How Does Learning Data Analytics Differ from Data Science?

As businesses continue to see the importance of and value in big data, they seek to leverage it to gain further actionable insights. Fields like data analytics and data science play an essential role in helping organizations get the most out of the information they collect. While data analytics and data science are interconnected disciplines, they differ in significant ways in terms of the approaches they employ and the results they produce. If you are tasked with studying your organization’s data, it’s essential to know the difference between these two disciplines and the role each can play to bring value to the table. 

Data science is a broad field that focuses on locating patterns and connections in large sets of structured or unstructured data that offer valuable insights. The driving force in the data science process is to find answers to questions we may not even know we don’t know. To do so, Data Scientists incorporate tools like predictive modeling, machine learning, computer science, advanced data modeling, and data visualization when exploring huge datasets so they can find solutions to a range of problems that may not even have been conceived of yet.

Data science plays a role for businesses of all types and sizes, from small tech startups to Fortune 50 companies.

On the other hand, data analytics is involved with the process of performing statistical analyses on existing sets of data. Data Analysts design methods that can be used to gather, process, and organize data to spot patterns or insights into current problems. They also concoct ways to present their data findings in a way that’s accessible and engaging to audience members, some of whom do not come from technical backgrounds. Data analysis focuses on finding answers to problems that a company or organization is currently facing or may anticipate encountering in the future. It strives to produce results that can yield immediate improvements in operations, reduce costs, or lead to better efficiency throughout the organization. Data analysis typically falls into four branches. Descriptive analysis strives to answer the question, “What happened?” Diagnostic analysis is more concerned with the “why” something occurred. Predictive analysis looks toward the future to provide answers into what may transpire then. Prescriptive analysis draws from the insights gathered in the other three forms of data analysis and uses them to provide recommendations on what should be done about these concerns.

Data Analytics Careers

As a field, data analytics is in extremely high demand today, particularly due to its applicability to data processing in any industry. As a result, professionals in the field can rest assured that their skills will remain in high demand across the country for the foreseeable future. Innovators in virtually every industry are realizing they can gain a significant competitive advantage over the rest of their field by hiring and retaining data analytic specialists to build models unique to their line of work. Whether you are working in marketing, investment banking, healthcare, entertainment, or advocacy (among hundreds of other examples), some aspect of your industry is likely reliant on data analytics to assist with making difficult or complex decisions.

Nationwide, the demand for data analysts manifests in a number of different data analytics-related job opportunities. For example, some data analytics professionals choose to specialize in advising businesses on their operations, which can pay anywhere from $70,000 to $80,000 per year. Other options include working as a Marketing Analyst, which focuses on using data to streamline marketing campaigns or working in a more data science-adjacent role. These positions tend to pay similarly well across the country, ranging from $65,000 to $80,000 a year on average.

Data analytics is an exciting, high-demand career field with excellent growth potential. Millions of new analyst positions are predicted for the foreseeable future, and Analysts can find themselves working in diverse fields such as healthcare, finance, education, agriculture, government, and entertainment. The Bureau of Labor Statistics reports that data-related careers are expected to grow over 35% year over year in the next decade, which is over 700% higher than the national average. Since almost all industries use data, it is common to see subject matter experts study data analytics to transition as Analysts working in their field laterally. Learning how to conduct data analytics can only help you navigate a world increasingly becoming more data-driven. It is more evident now than ever that people must learn how to comprehend and interpret data to stay competitive in the twenty-first century.

As a Data Analyst, your day-to-day work can be quite exciting. You will receive requests from different teams to support decision-makers through ad-hoc analyses, create data visualization dashboards, and write queries to wrangle data. Data Analysts are also responsible for providing feedback to data science teams on how their models perform and empowering non-technical teams with useful dashboards. Data Analysts could be considered the ears and eyes for data-driven organizations and recommend the best business decisions based on descriptive and diagnostic data.

Data analytics provides a whole new perspective in understanding how the world works. Besides the ability to think and perceive differently, it also offers the chance to transition within your career laterally. Many Data Analysts become Data Engineers, Data Architects, and Data Scientists. They can enjoy annual salaries well over $100,000. Some Data Analysts become freelance consultants and get to work for themselves, and some even get into education to train the next generation of data professionals. This field will continue to grow as society becomes more technologically dependent and the complexity of data increases.

How to Start a Data Analytics Career

If you’re looking to launch a career in data analytics, there are many ways to do so. Although there’s no one “right” path, there are a few useful steps you may wish to consider to ensure you’re on track to acquiring the necessary skills and training. 

First, you will need to study data analytics. Some opt to pursue a bachelor’s degree from a four-year college or university in data analytics or a closely related field. Since not all colleges offer data analytics degrees, you may instead opt to major in statistics, computer science, or math. In addition to acquiring a bachelor’s degree, some learners opt to continue with their data analytics studies into graduate school to obtain a more specialized skill set. Most master’s programs take approximately 12-15 months to complete and afford students a more focused course of study, one in which they can choose an area of focus in line with their professional goals. For example, some institutions allow students to focus on financial analytics or cybersecurity, and others offer training with specific tools like SQL, Tableau, or R. Another way to acquire hands-on training in data analytics for a career is to pursue a certificate study. These programs are much shorter and can usually be completed in just a few weeks or months. They provide focused, rigorous training in one or more aspects of data analytics such as data visualization, Python programming, and Excel. 

While education is a good start to launching a career in data analytics, it’s not usually enough for most employers. In order to be a competitive candidate, you’ll also need to demonstrate real-world experience working with data in various industries such as finance, government, or business. Showing employers that you have a firm grasp of data analytics’s role in an organization’s existing structure indicates that your knowledge extends beyond hypothetical data situations. It also lets them know you’re able to work under pressure.

Technical and creative training are also essential to pursuing a data analytics career. Employers look for candidates who can bring a level of expertise to their organization. If you can demonstrate that you already know computer programming languages like Python and SQL, that you know how to prepare and clean data, perform statistical analysis on it, and work with tools like Tableau and Excel to visualize data findings, you are likely to gain the attention of job recruiters. Additionally, creative skills like knowing how to tell data-driven stories can also help distinguish you from other applicants. So, too, can a strong portfolio. Professional data analytics portfolios showcase your skills and best work. They help employers know that you can gather reliable data from different sources and transform this information into accessible data visualizations.

Skills to Learn Alongside Data Analytics 

To succeed in the field of data analytics, it’s essential to have a range of hard and soft skills. Data Analysts must be clear communicators so they can convey complex data findings in a manner that can be understood by audiences at all levels of technical ability, including internal decision-makers, members of other teams at the organization, and clients. This not only requires a flare for creativity but also an eye for detail to create stunning data visualizations. Communication skills are also vital to being a successful Data Analyst. These professionals must be skilled at verbal and written communication since they will often be asked to write and present data reports. Storytelling skills also come in handy in data analytics because at the heart of any powerful data visualization is a story that tells the information being depicted visually. Additionally, Data Analysts often work with others within their organization, which means they must be able to collaborate as well. Critical thinking, time management, and innate curiosity are also useful skills for data professionals. 

In addition to possessing soft skills, Data Analysts must also have a working knowledge of relational databases and how to communicate with them using SQL to retrieve data. Statistical programming languages such as R and Python also are essential for cleaning, analyzing, and visualizing data. Since both are open-source, they are available for free. A background in machine learning can also be handy to learn alongside data analytics. It’s a branch of artificial intelligence in which you create algorithms designed to spot patterns in big data. Over time, these patterns become “smarter” and can then make more accurate predictions. Knowledge of Microsoft Excel spreadsheets is also useful since this app not only organizes data but can perform analysis and visualize the results. 

Most Data Analysts draw from a strong mathematical background, which can be acquired before or during the study of data analysis. Statistical training ensures these professionals are able to gather, analyze, interpret, and share data findings. Additionally, probability comes in handy when working with data because it can help ensure the data results you produce are trustworthy and as accurate as possible. One of the most closely related skills to data analysis is data visualization, which is often acquired simultaneously. Analysts work regularly to create a range of visual depictions of data findings such as graphs, maps, and charts. Some are basic and can be generated in Excel, whereas others are intricate and require extensive training in Tableau or other visualization software that is capable of generating data models, visualizations, dashboards, and business intelligence reports.

Is It Difficult to Learn Data Analytics?

Acquiring any new skill is a subjective process that largely depends on your previous experience and education. Data analytics is no different. The level of difficulty you may experience while learning to analyze data depends on your educational and professional background. If you have training working with Excel spreadsheets, coding languages like SQL and Python, or any prior training with databases and data visualization, learning data analysis may come much easier for you than if you lack one or all of these skills.

If you don’t come from a technical background or lack experience working with programming languages or data visualization software, there are a few steps you can take to acquire the skills you need more easily. Learning the ins and outs of the data analytics process is an excellent place to begin your journey. The first step is to formulate a question you will try to answer when working with the data. The clearer and more articulated this question is, the more useful it will be throughout the process. Next, you must locate the data you want to use. This involves choosing that which is most useful for your organization. Often, data comes from data warehouses, where information from multiple sources is stored in a central location. A detailed list of data assets kept in repositories is useful to streamline this part of the process. Next, you’ll want to work to understand the data you’ve accessed. Automated analytic features are useful for speeding up this process while maintaining accuracy.

After the data has been collected, it will likely be messy and has to be cleaned up. Preprocessing data is an involved process that entails deleting any outliers and encoding variables. Even though this part of the data analytics process takes a substantial amount of time, it’s vital because it ensures any models that are created based on data drawn from high-quality information are clean. AI and machine learning techniques like augmented analysis can be useful for assisting with data pre-processing.

Once you’ve gathered and cleaned the data, it’s time to apply various techniques and tools that can help you answer your initial question. The tools you select will likely depend on the question you pose and may involve statistical analysis, Python, Excel, or predictive analysis. The results you come up with at this stage will then need to be communicated with others. Some Analysts opt to visualize data using programs like Tableau, which help you communicate any essential findings that were uncovered during the analytics process. The visual representation you create can then be shared with any relevant stakeholders, either internal or external to your organization.

Ultimately, the best way to acquire data analytics training and master the entire process, from forming a question to presenting data results, is to enroll in a hands-on course.

How Long Does It Take to Learn Data Analytics?

Because the data analytics process requires knowing several related fields, it’s an involved process that can take time. You must be familiar with computer programming, spreadsheets, statistics, and data visualization skills. If you’re interested in acquiring basic proficiency in basic data analytics tools and concepts, most learners can do so in about three months. However, this estimate can vary based on several key factors. 

One important determinant of how long it will take you to learn data analytics is the method of learning you select. In-person and live online coursework can provide learners with hands-on training in just a few weeks or months, which can be applied professionally. Bootcamps and certificate programs typically cover a range of data analytics tools and skills such as SQL, Tableau, Excel, Python, and machine learning. Other learning options may take longer. Some students may opt to enroll in skills classes in a particular data analytics tool, which span several days and teach one aspect of the data analytics process such as working with Excel spreadsheets. Learners who opt to learn this way may need to parse together several courses to gain a baseline understanding of data analytics. Although the speed at which you learn data analytics will depend on the skills you already know such as whether you have a strong mathematical background or prior experience querying databases, this skill set takes time and practice to fully master. Some estimate that it may take learners three to four years to truly master the data analytics process.

Your Guide to In-Person and Virtual Data Analytics Training

In-Person Data Analytics Classes

Since data analytics is such a popular profession today, many different ways to learn the field’s basics and start a career foundation have appeared in recent years. However, the sheer variety available to learners can be dizzying at times, from in-person classes to virtual courses. To help students choose the best program for them, Noble Desktop has created a tool to search and compare data analytics classes near where they live and in the live online format.

Some learners prefer the traditional classroom environment of an in-person course. They may find that the dedicated classroom experience helps them focus or benefit from learning alongside a cohort of like-minded students. The presence of a live instructor can go a long way to ensure that participants meet their learning goals, particularly if they run into problems and need assistance or feedback. These courses tend to be taught primarily in major cities, meaning that students may need to commute a decent distance to attend an in-person course, if they can attend one at all.

Nationally, some of the biggest course providers include General Assembly and New Horizons, both of which offer data science-related courses for learners at various levels. For example, New Horizons’ data science training largely focuses on Microsoft Excel, a critical program that Data Analysts often use to sort data. At the same time, General Assembly hosts both introductory and advanced data analytics classes for learners. 

Virtual Data Analytics Classes

While in-person classes may be great for some students, they can be limiting for those looking to fit data analytics training into a busy schedule. Virtual courses offer unique benefits for these learners and are definitely worth considering. In addition to offering the same high-quality instruction that students typically expect from an in-person class, virtual courses allow learners to finish lessons from the comfort of their home or office. As a result, these classes are perfect for those wishing for a flexible and high-quality educational experience. Students still have access to a live instructor who can help them and provide feedback. Class sizes are small, ensuring that participants aren’t struggling to get their instructor’s attention. These courses tend to require students to provide their own hardware and software, though many service providers do offer set-up assistance for those who are new to online learning. 

As a leading online and in-person tech education provider for over three decades, Noble Desktop is proud to offer virtual data analytics classes suitable to learners of all time commitment levels. The highlight of these programs is the intensive classes offered by Noble Desktop, which include the Data Analytics Certificate, a comprehensive, career-focused training program designed to prepare novices for entry into the world of professional data analytics. In this program, students earn industry-standard tools and programming languages, including Excel, Tableau, SQL, and Python. Participants learn these tools and languages to organize databases, query datasets, and produce stunning visualizations from the queried data. This provides an opportunity for students to work with real-world datasets and practical, hands-on exercises to ensure that they’re comfortable with the applicability of their data science skills. Students also benefit from professional development services, including one-on-one career mentorship, designed to help assist with the stressful process of entering the job market. This course was created with new students looking to change careers in mind, so it is an immersive class that covers a lot of material. 

Those who want more targeted instruction, either because they already have a baseline knowledge of data analytics or because they’re looking to learn a specific skill to help them at their current job, should consider some of the shorter course offerings available through Noble. These include an Excel for Data Analytics course, which introduces students to the fundamentals of using Microsoft Excel to build searchable, manageable databases that will be utilized in more advanced data analysis projects. This class is a great entry point to data analysis for students with some Excel experience. More advanced learners may consider enrolling in the Python for Machine Learning course. In this class, students explore the basics of using Python to build computer programs that can read, interpret, and “learn” from datasets without the aid of a human operator. This is a newly developing field of computer science. Still, it is invaluable to Analysts, particularly those in sectors like finance and investment, who need to make incredibly time-sensitive decisions based on massive amounts of data. These courses allow students to learn specific skills that they feel will be most helpful for their unique situation, whether that means slowly learning introductory skills or picking up specific advanced training.

Flatiron School offers an immersive Data Science Bootcamp that aims to teach students fundamental data analysis principles, tools, and techniques. This course covers how to model data and build programs that use databases to forecast data trends, and work with tools for visualizing these trends and data points to help invested stakeholders make informed decisions. This bootcamp prepares students for jobs in the fields of data science and data analytics, making it an ideal course for students looking to build in-demand skills that will pay dividends in the long term. Class is available as a full-time or part-time training program, giving students added flexibility in their training. Regardless of which version a student chooses to enroll in, they receive professional career development and mentorship training. 

NYC Data Science Academy is a focused data science and analytics training center that offers a wide array of different course offerings for students looking to learn the ins and outs of the field. These classes include an Introductory Python programming course for students who have minimal experience working with computer programming languages. The content covered teaches participants the important foundational coding skills needed to really dive into complex data analytics problems. NYC Data Science Academy also offers advanced training options in more complex data analytic tools such as its Data Science with Python: Machine Learning course, which provides an overview of the process of automating data analytics work using Python-programmed machine learning applications. Students will receive practical instruction on building and training a machine learning model and learn how to judiciously use the programs in their daily business lives.

Interested learners who are looking for more information on the in-person or live online data analytics training programs available in their area should also consult Noble Desktop’s Classes Near Me.

Noble Desktop’s affiliate companies also provide data analytics training. For instance, NYIM Training’s data analytics classes include an SQL bootcamp, which teaches learners the programming skills needed to manage large databases. Meanwhile, NYC Career Centers’ data analytics classes cover programs including Excel and SPSS and many other vital skills that data professionals need to succeed.

Learning data analytics isn’t just for career professionals. High school students interested in the field can hone their skills through NextGen Bootcamp’s summer virtual data analytics classes for high schoolers.

Data Analytics Bootcamps

There are many different ways to study data analytics today, but only a few learning journeys provide students with the job skills they need to jump-start a career. These courses are commonly referred to as data analytics bootcamps and offer dozens to hundreds of hours of intensive data analytics training. While the time commitment needed to succeed in these courses is quite high, the payoff is especially worth it. In some cases, graduating from a bootcamp can even replace an equivalent college degree on a job seeker’s resume.

Learners interested in mastering their skills through a data analytics bootcamp have in-person and online varieties to choose from in many cities. For example, Noble Desktop’s Python for Data Science Bootcamp takes place over 30 hours to complete and covers everything from cleaning and combining data to generating valuable conclusions from a cleaned dataset. Other options include Thinkful’s four-month Data Analytics Immersion program, designed to help foster job skills in an intensive learning environment.

Self-Paced Data Analytics Classes 

If you’re a self-motivated learner looking for virtual data analytics course options, you may want to explore self-paced classes. Unlike in-person and live online coursework, which an instructor teaches in real time, self-paced classes are pre-recorded and placed online. You can access these classes at any time and from any location with a strong internet connection. On-demand data analytics classes are a flexible learning option if you have a busy work schedule or have other commitments such as family engagements or travel. Since all content was recorded earlier, you can opt to schedule your data analytics study for half an hour a day during your lunch break at work, over a long weekend, or for an hour at night three times a week. Not only do you get to decide when to study, but you also have control over lesson pacing. Whereas live training involves an instructor setting instruction pace, with on-demand material, you are in charge of how quickly you learn data analytics. This may mean breezing through a unit on Microsoft Excel if you already know how to create data visualizations using this app. It also may entail pausing, rewinding, or rewatching entire lessons to master more complex material such as working with machine learning.

Another benefit of on-demand data analytics courses is that they are widely available and largely affordable. Many top educators around the country offer coursework that covers core data analytics tools, skills, and programs. Udemy’s Complete Data Analytics Bootcamp, for example, offers over 20 hours of on-demand video content, 200-plus coding exercises, 16 articles, and more than 140 downloadable resources designed to help students learn a range of data analytics skills. This self-paced program teaches data types, preprocessing, data collection, Python, Pandas, NumPy, and data visualization. It is available for $130. Free content is also available from providers like FreeCodeCamp, which offers Data Analysis with Python. Students who enroll in this class have access to hundreds of online tutorials in addition to their coursework. Those enrolled explore the entire data analysis process, from reading and processing data by using Python libraries like Pandas and NumPy, as well as visualizing it with Matplotlib. If you’re simply looking for a beginner-friendly tutorial to explore data science basics, you can opt for Noble Desktop’s eighty-minute Get Started in Data Science YouTube video that provides an overview of the tools and skills Data Scientists use on a daily basis.

While on-demand data analytics training is a flexible and affordable way to acquire this skill set, it’s important to keep in mind that you won’t have access to an instructor or community of learners if you opt to learn this way. This can make it challenging for some students to ask questions or receive guidance on complex material. For this reason, some learners opt to begin their data analytics learning path with on-demand content. However, once they wish to learn more advanced skills for a work project or even to explore another career path, they may wish to enroll in a more rigorous and structured program such as a data analytics bootcamp. 

The Future of Data Analytics

The field of data analytics has been rapidly transforming for years, with the most radical evolution happening only in the last decade. Big data isn’t a new concept; even in the 1950s, before the term “big data” was coined, organizations relied on basic analytics methods like manually reviewing numbers on spreadsheets to spot insights and trends. Over the past decades, the field has transformed with the introduction of quicker and more efficient ways to examine information. Today, we rely on a range of methods and techniques that facilitate much faster data-based decision-making. Many of these techniques and transformative technologies continue to offer promising advancements that are likely to shape data analytics for years to come.

Explainable artificial intelligence is being used more and more to explain any drawbacks or benefits of a given model, as well as its potential for bias and how it may perform in a given situation. This helps identify times when organizational decisions were based on bad information. Machine learning, particularly deep learning, can help create a neural network built on huge data stores. It can also learn from the data structure, which allows it to spot anomalies and provide predictions. Continuous intelligence is an emerging technology in which real-time analytics are used to inform business operations. This technology compares new data to historical patterns to generate recommendations for courses of action. 

Another promising development in data analytics that will continue to inform the field’s evolution is the Internet of Things, which helps Data Analysts analyze vast volumes of data that are created by connected devices. This can help organizations attract new customers and optimize operations. Augmented analytics is also spurring the evolution of the field of data analytics. It draws from natural language processing and machine learning to automate insights. This helps organizations find solutions capable of handling huge datasets at scale, which can provide wider access to data insights. Even the types of data visualizations Data Analysts create are rapidly transforming. Long gone are the days when most Analysts create traditional dashboards or basic visual depictions of data. Self-service business intelligence tools offer cutting-edge capabilities that help Data Analysts create engaging visual representations of data with a focus on outcomes. In the future, it’s expected that data visualizations will continue to become more interactive and find new ways to frame insights such that audience members can connect emotionally to the content being depicted.