Discover the Best Data Analytics Classes Near Me
Data analytics is an emerging field wherein tech professionals use data to get insights and make recommendations. Data Analysts and other tech professionals process and visualize data using technologies like Python, Tableau, or Google Analytics.
While there are many reasons someone may want to learn data analytics, here are some of the most popular:
- Data analytics is a field that includes data analysis. Whereas data analysis helps people understand what has happened, the analytics process can provide insights that lead to predicting the future.
- Insights gained through data analytics can help businesses create marketing strategies, streamline operations, and improve the customer journey at every touchpoint.
- Data analytics jobs are on the rise. For every company with a social media presence—that is, for every viable organization—the need for data analytics professionals is urgent, if not already fulfilled.
Whether you plan to work as a Data Analyst, Business Intelligence (BI) Analyst, or Marketing Analyst, consider making data analytics an essential part of your training.
Best Data Analytics Classes & Schools
When it comes to course providers for data analytics training, the field is wide open. Some students look for business intelligence tools, others look for a financial analysis education, and others look for data analytics training as part of a broader data science curriculum.
You can sign up for data analytics training through CourseHorse. Before you choose a class, consider your level of expertise and availability. Some of the best programs are immersive bootcamps or certificate programs, whether at the beginner, intermediate, or advanced level. How and where you plan to use the knowledge and skills you gain will influence your decision.
Business intelligence training is one possible area to begin your search. With the advent of artificial intelligence and technology like ChatGPT, positions like Data Analyst, Business Analyst, and Marketing Intelligence Analyst have become increasingly common. To prepare for these types of roles, consider the following training options:
- Noble Desktop offers numerous bootcamps and certificate programs covering data science and analysis, machine learning, and artificial intelligence, to name a few. Their analytics courses include the comprehensive Data Analytics Certificate. This certificate program is open to beginners and covers numerous topics, including Python, SQL, and data visualization with Tableau.
- Learning Tree International provides training in multiple data visualization tools for data analysis. Their Tableau Visual Analytics course covers interactive visualizations for business intelligence, including advanced visualizations. Learning Tree also offers an Introduction to Power BI Training program for those who want to learn Power BI from Microsoft.
- Developer Bootcamp offers targeted training on the development side of tech, including software, web, and mobile application development. Their SQL Server Business Intelligence Developer Bootcamp is one such course. It covers SQL, database management tools, and reporting and analysis servers. Check course descriptions for more details, including prerequisite information.
- The American Management Association International offers a range of educational products and services. One of their classes is Advanced Tools & Techniques for Data Analysis, a high-level live online program for business pros. This two-day course covers topics like Tableau, Google OpenRefine, and ANOVA. While the seminar is advanced, there are no statistics or programming prerequisites.
- NetCom Learning - NetCom Learning provides training and certification programs for businesses, governmental agencies, and other organizations and individuals. Their Microsoft Power BI Data Analyst is a live online course for those who want to use Power Bi to unlock data insights. The class is appropriate for students 16 and over who plan to get Power BI certification. See course descriptions for more details, including prerequisite information.
If you plan to get your data analytics training as part of a broader data science curriculum, consider that many Data Analysts and Business Analysts get comprehensive training through a bootcamp or certificate program.
Data science programs vary, but the most in-depth courses provide in-person or live online education through teleconferencing. Participants in these immersive courses typically prepare for entry-level data science or Python engineering careers. While some employers require a degree to qualify for a position, the urgent demand for data analytics pros means some of these data science bootcamps are all a company needs to see on a resume to hire a candidate.
Like data analytics certificate programs, data science programs typically cover Python in detail. Using Python data science libraries and frameworks is essential for Data Scientists at any level. With today's emphasis on artificial intelligence, machine learning also often plays a key role in training. Add data visualization using advanced Python libraries like Plotly and Dash, and you have a recipe for data science success.
The following providers offer courses that can meet the needs of data science trainees:
- Besides its Data Analytics Certificate program, Noble Desktop also offers a comparable Data Science Certificate. This comprehensive course includes hands-on training and mentoring from expert instructors. Python plays a significant role in data science and data analytics, and the Data Science Certificate includes bootcamps for Python for automation, data visualization, interactive dashboards, and machine learning (ML). The course also covers SQL, NumPy, Pandas, and Matplotlib. The Data Science Certificate is open to beginners.
- The Flatiron School is another provider that offers data science training. Their Data Science course is a 15-week in-depth training program for those looking to be Data Scientists, Machine Learning Engineers, or other data positions. While there are no prerequisites, beginner-level applicants should take Flatiron's pre-work course if needed. Check course listings for more information.
A final area of specialization for data analytics pros is that of financial technology, also known as FinTech. This field includes everything from investment advisory services to cryptocurrency and the blockchain. Business Analysts, Data Engineers, and Project Managers can all find roles in FinTech.
FinTech refers to services, products, and companies that provide either or both. FinTech services range from platforms like Venmo to investment advisory services like those provided by brokerage firms like Charles Schwab or Fidelity. If you have ever paid online for something, you used some form of FinTech.
Many companies do not differentiate FinTech products from services. For example, consumers often refer to a payment platform like PayPal as a product or a service. Technically, the app is a product, and its function is a service. However, further complicating matters is that many companies are themselves considered FinTechs. Examples include Stripe, Coinbase, TransUnion, Klarna, and Affirm. These firms provide online shopping, credit reporting, digital currency trading, and other financial services.
If you want to work in FinTech, the field is wide open. Data analytics careers in this industry include roles like Data Scientist, Data Analyst, Big Data Architect, and Power BI Developer. You can start learning FinTech, and even qualify for an entry-level position, by taking an in-depth FinTech bootcamp or certificate program.
- Practical Programming is one among many course providers who offer a FinTech Bootcamp. This certificate program covers Python for finance, machine learning, and algorithmic training, among other topics. Some participants take this course to train for a Financial Analyst position, while others start their data science educational journey here.
- Noble Desktop offers a FinTech Bootcamp in-person in New York City, or live online from anywhere. The course includes training in analyzing financial data, creating data visualizations, and advanced querying techniques. Graduates receive a verified certificate of completion and can retake the course for up to a year at no additional charge.
Industries That Use Data Analytics
The data analytics field continues to explode. Public and private organizations increasingly need to analyze data for multiple reasons, from marketing plans to machine learning algorithms.
Because so many entities use data analytics, it can be challenging to collate industry-specific information. Marketing Analysts may use data differently than Financial Analysts, whereas Data Scientists have an approach different from either.
According to Analytics Insight, the following are the Top 10 Industries Using Big Data Applications:
- Ecommerce
- Education
- Media & Communication
- Healthcare
- Gaming
- Financial Institutions
- Manufacturing & Natural Resources
- Insurance
- Human Resource Management
- Energy
The sheer diversity of sectors shows the need for data analytics in all industries, not just those listed above. Governmental agencies, non-profits, and small businesses can all use data analytics to provide actionable insights to key stakeholders—whether in the C-suite or on the campaign trail.
To better understand the need for data analytics, consider some of the top industries hiring data professionals. Business intelligence, finance, and the sharing economy all benefit from data analytics:
- Business Intelligence (BI) - Multinational organizations like Alphabet (Google, YouTube) and Meta (Facebook, Instagram) use data analytics and machine learning (ML) algorithms to get data about customer behavior. Data Analysts at large companies like these may be called Marketing Analysts, BI Analysts, or some other title.
- Finance - This industry falls into the BFSI category: banking, financial services, and insurance. All these organizations provide financial products of some type and the data they need can be sorted with ML algorithms to predict future behavior on the part of customers, clients, or patients, in the case of medical insurance. Top roles include titles like Financial Analyst, Investment Advisor, and Stock Analyst. Additional keywords to search for in this field include revenue, securities, assets, liabilities, and portfolio.
- Sharing Economy Services - Uber, Lyft, and eBay are all examples of shared economy services. While you might think mainly of the consumer side of these companies, they do have direct reports who work for them full-time. Titles for data analytics pros in these companies typically do not deviate from the standard—Data Analysts, Data Scientists, and Data Engineers are examples.
Data Analytics Jobs & Salaries
According to the U.S. Bureau of Labor Statistics (BLS), data pros in the United States have an excellent employment outlook. Among the top roles for data analytics are Data Scientists, Information Security Analysts, Management Analysts, Market Research Analysts, and Operations Research Analysts.
- Data Scientists - The BLS estimates that the growth rate for Data Scientists will be as high as 36% between 2021 and 2031. This rate is much faster than average. (By contrast, the BLS projects an average growth rate of around five percent for all occupations over the next decade). Average salaries for Data Scientists run about $127,000 annually.
- Information Security Analysts - The BLS Information Security Analyst category covers a range of positions related to systems security. Alternate titles or similar roles may include IT Security Analyst, Cybersecurity Analyst, or Network & Systems Security Analyst, among others. The projected growth for these positions over the next decade is comparable to that of Data Scientists, around 35%. The average annual pay for Information Security Analysts is around $90,000.
- Management Analysts - While not as robust as the anticipated growth for Data Scientists or Information Security Analysts, the growth rate for Management Analysts is still double the national average: around 11% over the next ten years, according to the BLS. These professionals can work in numerous industries, and salaries may vary accordingly. The estimated total pay for Management Analysts is around $80,000 annually.
- Market Research Analysts - Unlike either Data Analysts or Data Scientists, Market Research Analysts may work primarily on the marketing side of the ledger rather than data science. However, their increasing use of data analytics makes them more valued than ever in organizations large and small. According to the BLS, Market Research Analysts can expect a 19% growth rate over the next decade, nearly four times the national average. Annual estimated salaries for these professionals average $71,000.
- Operations Research Analysts - Positions with titles like Operations Research Analyst vary like those of Information Security Analysts. The BLS projects a 23% growth rate from 2021-2031 for these data pros—higher than Market Research Analysts but lower than Data Scientists or Information Security Analysts. Their yearly salary across all industries averages around $91,000, higher in large corporations. When seeking job requirements for Operations Research Analyst roles, consider alternate titles like Data Analyst, Systems Analyst, or Business Intelligence (BI) Analyst.
These national averages represent median salaries for all areas. The lower and higher ends of ranges can vary substantially from the median figure. For example, the median total estimated pay of $91,000 for Operations Research Analysts falls in the middle of a range from $58,000 to $145,000. However, this doesn't mean a $58,000 per year job is low pay or a $145,000 per year position is unusually high. The geographic area often plays a role as significant as that of company size or budget.
Consider the discrepancy between an area like New York City or Los Angeles and smaller metropolitan areas in the Midwest or Southwest. For example, in Brownsville, Texas, the average annual salary for an Operations Research Analyst is around $89,000. Travel a few hours west to San Francisco, however, and the average yearly compensation for Operations Research Analysts jumps to $103,000.
Always remember that salary is one of many factors when planning a career move. The local climate, number of job opportunities, and cost of living are a few of the most essential. A Data Analyst position in San Francisco may pay better than one in Brownsville, but the cost of living is substantially higher. On the other hand, if cultural activities and climate are more important to you, the many features of a city like San Francisco may outweigh what Brownsville offers.
Consider another top metropolitan area, New York City. While the cost of living in NYC may not fall below the national average, salaries for most New York professionals remain above national averages, particularly in fields like data analytics. Like San Francisco, climate, cultural activities, and housing may also fall into the plus or minus categories.
Finally, industry or sector is essential when determining where and how to search for data-centered positions. Job opportunities in sales and marketing will abound in some geographic areas but less in others. Some areas urgently need cybersecurity and IT roles in multiple industries, while others offer only a few. And while every industry needs management staff, most companies prefer to hire employees experienced in their sector or industry. Train thoroughly in the right field, and you can look forward to an exciting career in data analytics.
What Will I Need to Learn Data Analytics?
If you plan to study data analytics, you'll need several skills that can apply to multiple fields. You might learn them separately or as part of a data analytics bootcamp or certificate program. Consider the following essential skills for data analytics professionals:
- Python - The Python programming language holds the unique distinction as one of the most popular programming languages in the world. It's also essential in web development, data science, and data analytics. If you plan to work in a data-centered environment, Python is one of the programming languages you'll need to master.
- Excel - Most people who have office work experience have at least some familiarity with Microsoft Excel. While you might eventually substitute Python for Excel tasks, learning to create formulas and data visualizations with Excel is essential for many data analytics roles.
- SQL - Structured Query Language (SQL) is crucial for numerous programming roles, from Data Analysts to Web Developers. You may need SQL to alter, retrieve, manipulate, and analyze data. If you don't learn SQL in a separate course, consider learning it as part of a broader data analytics curriculum.
- Tableau - Tableau is essential for data visualization in numerous positions across nearly every industry and sector. While prevalent in data analytics roles, Tableau has become so popular that some tech pros specialize as Tableau Developers or Tableau Administrators. If you want to gain skills and tools for a Data Analyst career, consider Tableau your first choice for data visualization.
- Power BI - Like Tableau, Power BI is essential for many tech roles. The key to its importance is that Power BI is a suite of Microsoft products and services. Unless you know you won't be working in a Microsoft environment, you should learn both Tableau and Power BI.
For more about the pros and cons of Power BI and Tableau, check out some of the many articles on the topic, like this one from Noble Desktop.
Some products and services are freely available through a limited trial or at a low entry point. For example, you can download Python source code, installers, and documentation for free. SQL is also open source, and Tableau and Power BI offer free trials. Excel and other Office tools are available on a subscription basis, but you may already have access through a work account.
As for other materials, most course providers offer them for in-person classes. In a rapidly changing field like data analytics, course materials can become outdated quickly, so getting the most up-to-date information is essential. An in-person certificate program typically provides the computer and software required, and graduates may get recorded videos of the live sessions, plus the option to retake the class for up to a year at no additional charge.
Is it Difficult to Learn Data Analytics?
While mastering data analytics may be challenging, it’s ultimately possible if you are devoted to learning it. Training through an immersive bootcamp or certificate program is often the best way to get an in-depth education in a concentrated timeframe. If you come from a background in mathematics, like probability and statistics, that can also be an advantage.
Consider enrolling in a certificate program if you are a beginner to qualify for many entry-level positions in a few months or weeks. You can always get additional training, whether on-the-job or after business hours if the company requires it. They might even pay for your education, and complementary skills—Python, SQL, and data visualization tools like Power BI and Tableau—can all be easy to master. If you want to become a Data Analyst, Data Scientist, or Business Analyst, consider taking on the challenge of learning data analytics as one of your essential career goals.
