Best Data Analytics Bootcamps Near Me: Find Bootcamps & Certificates

Key Insights

Data Analysts utilize programming languages like Python and SQL along with Microsoft Excel and visualization tools such as Tableau and Power BI to organize, analyze, and present large datasets effectively.

Expertise in data analytics opens career opportunities in numerous sectors including finance, public policy, retail, media streaming platforms, social media companies, nonprofit organizations, and government agencies.

While Data Analysts primarily identify current trends from existing data, Data Scientists often perform predictive analysis and develop the underlying infrastructure and applications that Data Analysts use.

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Due to how much of our lives involves a direct connection to the internet, human beings generate an unfathomably large amount of data daily. Globally, Forbes estimated in 2018 that we were generating 2.5 quintillion bytes of data (more than 2.5 trillion trillions) daily. That number has only grown in the last half-decade. This data is extremely useful – but only if someone can turn it into business insights. That’s where the Data Analyst comes in. Data analytics is the programming and analytical skills required to make sense of this massive amount of data. Data analysis allows businesses to target their customers with precision, innovate based on trends, cut operating costs, and strategically solve problems. Big retailers, media streaming platforms, social media, and banks are all using data analytics to drive their industry-leading businesses.

Data analysis is also used by institutions and firms in the public sector. Government agencies use data analytics to guide policy-making decisions, political campaigns use large amounts of data to make a targeted platform, and campaign decisions and demographic information are important in virtually every industry. Advocacy organizations and nonprofits use data analytics to track support for their causes, build donor bases, and make policy recommendations.

Why Learn Data Analytics?

Careful data analysis is becoming a major part of almost every industry and institution. Companies are looking for ways to use the data they collect to inform their business decisions and build long-term financial plans. Owing to this, data analytics is an in-demand set of skills and experts in the field can find work in virtually any industry. If you are interested in working with data to help businesses, government agencies, and even nonprofits make more informed decisions about their long-term plans, learning data analytics is a good way to turn that interest into a career.

Data analytics can also be a valuable tool for aspiring entrepreneurs and investors to learn. While large firms can hire data analysis experts, smaller businesses and private investors may want to learn the skills themselves to help them make more informed decisions about the direction they take with their business or investment portfolio. Learning data visualization skills can also be useful for entrepreneurs hoping to convince investors and stakeholders that they have a project worth investing in.

Learning data analytics can also lay the foundation for further programming and data science training to help students work on more elaborate and cutting-edge projects. For example, machine learning is a significant development in data science, which is the process of building programs that analyze and interpret data independently. Commonly used in content recommendation algorithms like those that suggest content on streaming services, this is a nascent technology that offers a wide range of new possibilities for skilled Data Analysts to take advantage of. Learning how to use these tools can give you a leg up professionally and set you up for work on cutting-edge projects that can reshape the way industries operate.

Data Analytics Tools

The most important tools for Data Analysts are the complicated databases they use to collect, store, and query the data they will analyze. These programs are often built using programming languages like Python or SQL, so most successful Data Analysts will have some background in computer programming. These relational databases let professionals organize datasets and find patterns and relationships that might otherwise escape an untrained observer. Students will learn how to use SQL and Python to write complex queries that help them sift through the data to determine what is and isn’t vital to their project, and they will learn how to use Python to automate certain elements of data collection and organization, making it even easier to find what you are looking for quickly.

Data Analysts will also need to learn how to use tools like Microsoft Excel to construct their databases. While most people don’t think of it as such, Excel is a powerful and versatile database-building tool that professional Data Analysts need to know how to use. It is vital to ensure that the data entered into a database is clean, accurate, and well-categorized. Excel is an important tool for organizing and practically using the datasets produced once a database is queried. Excel plays an important role in virtually every step of the analytics process. Students enrolled in a data science bootcamp will likely learn how to use its more advanced features.

Finally, data is useless if you don’t know how to present your findings to stakeholders. Therefore, data analytics training programs tend to teach students how to use data visualization applications like Tableau or Microsoft Power BI. These programs allow users to take raw data (or the carefully curated data they have queried from databases) and transform it into visually compelling, persuasive graphs, charts, and maps. This process is essential to letting Data Analysts communicate their findings with non-specialist audiences and present those audiences with useful insights drawn from their data.

Data Analytics or Data Science

While closely connected, Data Analysts and Data Scientists are not interchangeable. Many Data Analysts will eventually go on to find work as Data Scientists, but the job responsibilities differ. Analysts work with smaller pools of existing data to isolate trends and make decisions based on existing knowledge. Data Scientists tend to make future forecasts based on the existing trends observed by Data Analysts. Data Scientists also tend to have a more direct hand in building the programs and applications that Data Analysts use to fulfill their job responsibilities. For example, a Data Analyst may teach a machine learning application to perform a specific job function, so they will need to learn how to write specific learning algorithms. By contrast, a Data Scientist is more likely to build the infrastructure that allows that machine learning application to function, requiring a broader programming knowledge base.

Data Analytics Industries

At this point, it would likely be easier to list off all of the industries that don’t employ data analytics experts to improve their workflow and efficiency. Almost all businesses collect huge amounts of consumer data and use that data to analyze trends, develop strategies, and predict market forces. Data has become such an important part of business that billion dollar companies and firms exist solely to collect this data from users so that it can be analyzed for actionable insights. Business Analysts are employed in virtually every industry to find new and clever ways that data can be utilized to help businesses find new customers, improve their standing in the marketplace, or invest in their long-term growth.

Beyond consumer businesses, other aspects of the financial sector rely upon Data Analysts to help guide their decision-making processes. Market Managers and Investment Bankers can’t rely upon gut feelings and instinct when they are moving millions of dollars around and the stock market moves too quickly to make informed decisions without the aid of some kind of data analytics technology. Almost all modern investment portfolios and strategies are backed by huge amounts of data pointing managers and investors in the right direction. Thus, specialized Investment Analysts are an important part of ensuring that investment strategies and decisions pay long-term dividends.

Data analytics is also beginning to crop up in unlikely places, since virtually every industry is realizing that data can be good for something if you know how to interpret it. Doctors and medical researchers are using data analytics and machine learning algorithms to make more informed diagnostic decisions and better predict potential long-term health problems in patience. Political campaigns are analyzing voter data down to a street-by-street level to determine the best way to spend their resources to mobilize their constituency. Sports franchises are hiring massive teams of data analytics experts to gain incremental advantages over their competition based on observable data trends. No matter what kind of work you want to do, learning data analytics skills can open a wide range of career opportunities.

Data Analytics Careers

Data analytics is considered an entry-level data science career. Typically, Data Analysts focus on business intelligence (BI). BI is essential for modern businesses. Data analytics skills can make your current job more efficient or provide a gateway to a new data career. The average Data Analyst makes over $70,000 a year on average. Many Analysts have the opportunity for year-end bonuses and other special benefits, depending on their industry.

Data Analysts are in-demand and job opportunities for this career path are only projected to continue rising. The industry with the most demand for data analysts includes government agencies, businesses, healthcare, supply chain management, and sports management.

Why Learn Data Analytics at a Bootcamp

A data analytics bootcamp is perfect for someone who wants to upskill or a career changer who is looking for an entry-level position in a different field. Data analytics offers a fantastic entry point for professionals who have never worked in data before. A data analytics bootcamp will teach you the fundamentals of Python and its most popular libraries, how to clean and combine data, how to glean insights, and how to visualize data. The most common tools for data analysts are Microsoft Excel and SQL.

Beginner-friendly Classes

One of the biggest advantages offered by bootcamps is that, despite being immersive career-focused courses, they are incredibly beginner-friendly. Python and SQL are easily approachable for students without prior programming experience. Almost all data analytics bootcamps provide students with basic programming instruction in writing, reading, compiling, and debugging their code. Students will be introduced to important programming tools, like Git and Visual Studio Code, and by the end of the class, they will be proficient programmers. These courses also assume students have yet to gain experience with tools like Excel or Tableau, meaning that students without experience with data analysis can learn these in-demand career skills.

Career-focused Training

Another major advantage of learning data analytics in a dedicated bootcamp is that these courses are tailored for students looking for a new career as Data Analysts. This means that the classes are designed to prepare students to enter the workforce immediately after they graduate from the program. This means that bootcamps will teach students practical data analysis skills using real-world datasets and interactive, hands-on lessons designed to prepare them for the kind of data analysis tasks they will encounter in the professional world. Many programs also provide students with professional development resources to ensure that they succeed when it is time to enter the job market. These resources vary from program to program, but they commonly include portfolio-development seminars, guest speakers with industry experience, and one-on-one career mentorship sessions.

Flexible Offerings

Bootcamps offer students a flexible learning environment that allows them to find the exact course that fits their learning goals and interests. Since data analytics is a fairly varied field, students may be interested in emphasizing certain aspects of their training such as Python skills or machine learning algorithms. Since there are so many bootcamps available for students, they can explore the syllabi of these classes to find ones that fit the learning outcomes they want to meet.

In addition to having flexible curricula, students can also find bootcamps that fit their scheduling needs. While many bootcamps offer full-time instruction options, with students meeting in a classroom during the workday, many bootcamps offer flexible part-time options for students to learn on the weekends or during a few evenings a week. This flexibility is especially important for students with work or family commitments that limit their time to learn new career skills.

Beyond scheduling concerns, students will be able to find bootcamps that offer in-person classes in dedicated training centers or online in private digital classrooms. In-person training is ideal for students looking for face-to-face instruction from experts in the field of data analytics and who want to benefit from the advantages of a classroom space such as a distraction-free learning environment, and the networking opportunities afforded by working alongside a cohort of classmates. The drawback to these classes is that students will be limited in their options since they can only enroll in classes they can physically attend. Students looking to cast a wider net regarding training options can enroll in a live online program. These classes let students learn in real-time from live instructors without having to leave their homes. The drawback is that students will be more responsible for maintaining their tech set-up and ensuring they aren’t too distracted during their lessons.

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Data Analytics Topics

Excel

Excel is more than just a tool for creating databases. It can also be used to analyze data. Excel can organize data using filters, sorting, and conditional formatting. Charts, pivot tables, and other visualizations can then be created from the assembled data sets. Users can also download and install the Analysis ToolPak, which provides additional data analysis tools specifically for financial and engineering data.

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SQL

SQL stands for Structured Query Language. Data analysts rely on the query functions of SQL to analyze massive quantities of data using relational models. SQL defines three distinct types of data, including predefined, constructed, and user-defined types. Standard SQL commands such as “Create,” “Update,” “Insert,” “Delete,” “Select,” and “Drop” can be used to do almost anything a data analyst needs to do with data. SQL powers many database management systems, such as Oracle, Access, and Microsoft SQL Server.

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Tableau

Tableau is an interactive data visualization tool that produces cloud databases, query relational databases, online analytical processing cubes, and spreadsheets. It offers even novice users the ability to change or transform the raw set of data. Using the Tableau Desktop, data scientists can create rich, interactive data visualizations, deploy data drilling and mining tools, and provide in-depth analysis that uncovers hidden patterns. Tableau’s intuitive, drag-and-drop interface makes it easy to get started.

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Python Data Science

Python is a powerful, general-purpose programming language with broad applications for data analysis and the creation of data visualizations. With Python, users can create scatter plots, line plots, histograms, and other plots to visualize data and facilitate analysis. Matplotlib is Python’s most widely used 2D plotting library, used for the creation of such data visualizations. The Python programming community offers many free code blocks to make the creation of visualizations easy and intuitive.

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Power BI

Power BI is a collection of software that works together to convert multiple sources of data into usable information. Using the Power BI Desktop, the Power BI service, and the Power BI mobile apps, businesses can analyze data, create visualizations, and make sense of it all. Leaders can use these critical insights to forecast, plan, and make informed decisions.

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Data Science

Data science draws from multiple disciplines to understand data. Combining machine learning, data analysis, statistics and domain knowledge, data science has been referred to as the “fourth paradigm of science”. Using algorithms and other statistical methods, data scientists can uncover knowledge and insights from structured and unstructured data that might otherwise go unnoticed.

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Data Analytics Certificate Learn The Skills Guarantee™

Key Insights

Learn More About Data Analytics Bootcamps

Why Learn Data Analytics?

Data Analytics Tools

Data Analytics or Data Science

Data Analytics Industries

Data Analytics Careers

Why Learn Data Analytics at a Bootcamp

Beginner-friendly Classes

Career-focused Training

Flexible Offerings

Data Analytics Corporate & Onsite Training Noble Desktop

Data Analytics Bootcamp Map & Top Locations

75 Bootcamps Available Live Online

35 Bootcamps Available in NYC

12 Bootcamps Available in Chicago

10 Bootcamps Available in Los Angeles

10 Bootcamps Available in Dallas

8 Bootcamps Available in San Diego

7 Bootcamps Available in Denver

7 Bootcamps Available in Houston

7 Bootcamps Available in Phoenix

7 Bootcamps Available in Washington, D.C.

7 Bootcamps Available in Atlanta

6 Bootcamps Available in San Francisco

6 Bootcamps Available in Seattle

5 Bootcamps Available in Charlotte

5 Bootcamps Available in Boston

5 Bootcamps Available in Minneapolis

5 Bootcamps Available in Miami

4 Bootcamps Available in Austin

4 Bootcamps Available in Orlando

4 Bootcamps Available in St. Louis

4 Bootcamps Available in Tampa

3 Bootcamps Available in NJ

3 Bootcamps Available in Virginia

1 Bootcamp Available in Philadelphia

Careers & Salaries Related to Data Analytics

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Data Analytics Topics

Excel

SQL

Tableau

Python Data Science

Power BI

Data Science

Data Analytics
Corporate & Onsite Training