At first glance, data analytics might seem to be a fancy word for data analysis — but while data analysis plays a prominent role in data analytics, there’s more to it than that. According to industry-leading IT mag CIO, data analytics is
“a discipline focused on extracting insights from data. It comprises the processes, tools and techniques of data analysis and management, including the collection, organization, and storage of data. The chief aim of data analytics is to apply statistical analysis and technologies on data to find trends and solve problems.”
There are four main types of data analytics that are important to keep in mind: descriptive, diagnostic, prescriptive, and predictive.
Descriptive data analytics
Descriptive data analytics involve the analysis of historical data to answer the question, “What happened?” In retail, companies employ descriptive data analytics for tasks like tracking inventory and sales.
Diagnostic data analytics
Diagnostic data analytics involves the analysis of data to answer the question, “Why did this happen?” In marketing, diagnostic data analytics can be employed to help understand why some campaigns are more successful than others.
Prescriptive data analytics
Prescriptive data analytics involve analyzing data to answer the question, “What should we do next?” In logistics, prescriptive data analytics are becoming more and more important as companies like Amazon seek to deliver goods to millions of customers as efficiently as possible.
Predictive data analytics
Predictive data analytics involve the analysis of data to answer the question, “What might happen in the future?” In manufacturing, companies frequently employ predictive data analytics to predict future buying patterns and manage their supply chains accordingly.
Data Analytics vs Data Science
There’s often confusion between data analytics and data science given the considerable overlap between the two. While there is no set of definitions that can catch all of the nuance of the relationship between the two — especially because job titles and data operations are rarely uniform or consistent between businesses and industries — for our purposes we will differentiate the two as follows:
Data analytics concerns the tools and techniques surrounding the process of data analysis. Often, but not always, data analytics professionals will use existing tools and techniques on existing databases.
Data science, on the other hand, concerns the development and deployment of new tools and techniques and new ways to gather data for analysis. The analytics involved in data science will thus likely be more advanced and will frequently involve machine learning. Oftentimes, data analysts and other data analytics professionals will employ methods and tools developed by data scientists.
If you are interested in data science, dive deeper with our articles on:
What kinds of jobs are out there for someone interested in data analytics?
We’ve covered the different types of data analytics, but who are the people getting it done? For the most part, jobs in data analytics will have the title “data analyst,” but there are some notable exceptions, including “business intelligence analyst,” “data engineer,” and “marketing analyst,” to name a few. Below, we’ll give an overview of each of these roles and provide some real-world examples.
Data analyst is an extremely broad title — so the day-to-days of two data analysts at different companies might look very different. Typically, however, a data analyst gathers data and readies it for analysis, and then analyzes it to yield business insights that the analyst will communicate to relevant stakeholders, often through data visualizations.
According to Salary.com, in the US the average data analyst salary is $81,719.
Business Intelligence Analyst
There’s a fair amount of overlap between a data analyst and a business intelligence analyst. Indeed, sometimes it just comes down to what title a company gives a position. But what sets a BI analyst apart? According to Indeed, a business intelligence analyst — or, sometimes just “business analyst” — is more narrowly focused than a data analyst on the metrics, sometimes called “key performance indicators” (KPIs), that can be used to evaluate a company’s performance. By definition, business intelligence focuses on the past, while the umbrella field of data analytics can include areas like prescriptive and predictive analytics that focus on future activity.
According to Salary.com, in the US the average business intelligence analyst salary is $84,803.
A far easier distinction to make is that between a data analyst and data engineer. While a data analyst is primarily focused on data analysis, with data collection, preparation, and storage being more ancillary to this analysis, a data engineer focuses squarely on efficiently transforming raw data for this analysis. In a sense, a data engineer is a specialist in one aspect of data analytics.
For their specialization, data engineers can demand higher compensation. According to Salary.com, in the US the average data engineer salary is $112,555.
Though the “marketing analyst” title can sometimes refer to professionals who focus on larger market trends, it will generally refer to a data analyst whose work contributes directly to a company’s marketing efforts. This often means analyzing how campaigns perform in particular markets or in particular media (i.e. print, app, web, etc.) using sources like Google Analytics.
According to Salary.com, in the US the average marketing analyst salary is $57,282.
Why a data analytics bootcamp?
If your goal is to get hired as a data analyst, business intelligence analyst, data engineer, marketing analyst, or one of the many other jobs out there in data analytics, a data analytics bootcamp has four chief advantages over a traditional degree program:
Shorter: The duration of a data analytics bootcamp is measured in months, not the years used to measure the length of most degree programs. This doesn’t mean that bootcamps are light on content: a lot of learning can be packed into those months.
More flexible: Because they are entirely online and often offer part-time options, bootcamps are perfect for those with existing professional or personal obligations who need some flexibility in how and when they study.
More affordable: Being shorter and online allows bootcamp providers to offer quality education at a fraction of the cost of a traditional degree program. Many providers also offer financing or income-share plans where you pay back a fraction of your income once you get a job. Some even offer a job guarantee, where you get your money back if you don’t land a job within a certain timeframe.
More practical: We aren’t saying that traditional degree programs don’t offer practical training — far from it — but with a shorter duration, data analytics bootcamps need to get right down to it, ensuring that every lesson provides technical skills and experience directly applicable on the job. Many boot camps also offer students an opportunity to put what they’ve learned into practice through a capstone project. But perhaps most importantly, data analytics bootcamps are solely focused on helping students land an entry-level job, and offer career services to help students do so.
Who are data analytics bootcamps for?
Seeing the chief advantages of bootcamps, you can start to see who a data analytics bootcamp might be right for, namely those with limited time and limited money who are looking to make a quick but lasting change in their career.
Boot camp attendees might have backgrounds in STEM, information technology (IT), or even the social sciences or humanities. They might already have a bachelor’s degree in a different field or might be experiencing post-secondary education for the first time.
Some programming experience or experience with statistics will typically help a student get more out of a data analytics bootcamp, but this is not essential. In fact, some programs even offer self-paced pre-course modules in these areas to help those with less experience hit the ground running.
Bootcamps vs. Certificate Programs
With so many options out there, it’s easy to get confused about the difference between data analytics certificate programs and data analytics bootcamps. The way we see it, all bootcamps are certificate programs — students who successfully complete one are awarded a certificate — but not all certificate programs are bootcamps. Bootcamps distinguish themselves by the relative length, rigor, and intensity of their curricula, plus their focus on preparation for an entry-level job and the career services they offer in service of this.
What will you learn in a data analytics bootcamp?
What exactly is contained in these curricula? A data analytics bootcamp attendee can expect instruction in the following areas:
Programming and other technical skills and tools, including SQL, Python, R, Tableau, SAS Enterprise Miner, and Excel
Statistical analysis, including linear regression and predictive modeling
Key data analytics processes, including data collection, data cleaning, data mining, and data visualization
Advanced concepts, including machine learning, data ethics, big data, and introductions to data science
Soft skills, including project management and decision-making
Many data analytics boot camps also offer extensive careers-guidance services, such as 1:1 mentoring, resume and portfolio help, employer information systems, and job leads.
What to look for in a data analytics bootcamp?
If you’ve decided that a data analytics bootcamp might be the best next step for your career, how do you know which will be right for you? Obviously, you want to ensure that the schedule and cost of the bootcamp works for your life and budget. Additionally, we suggest that you look for the following:
Data analytics bootcamps are almost only online — but there are a lot of different kinds of online study these days. While they will generally be more expensive, we recommend choosing a bootcamp that provides as much live instruction as possible. The more facetime you can get with a professor and your fellow students, the more opportunities you will have to clarify aspects of the instruction you are unsure about, gain valuable feedback, and stay excited and engaged.
Most reputable bootcamps will list their faculty on their websites. As you’re researching programs, be sure to pay attention to who exactly would be teaching you. What are their qualifications? How many years of experience do they have? Are they actually employed by the educational provider offering the bootcamp, or some third party? Will you have a chance to engage directly with them, or will you just be watching recordings?
Though information on student outcomes is not always readily available, it is an invaluable resource in determining if a bootcamp is worth the investment. As you’re researching, see if you can find out whether those who took the bootcamp in the past ended up getting jobs in the field, and if so, what the success rate was. If you can find this information, you might instead try to find testimonials from prior students, but keep in mind that if these testimonials were solicited by the educational provider, they might not be terribly valuable as to the overall experience of the program.
A bootcamp curriculum should be specific and jam-packed with practical, technical skills that you’ll be able to use on the job. At the same time, be wary of a data analytics course that promises to teach you dozens of different kinds of software and programming languages.
A successful bootcamp need only cover SQL, Python or R programming language, and Tableau to get someone up-and-running in data analytics. It’s also crucial that you come out of the bootcamp with something to show for it: a project you can use to start your portfolio. This will be an important proof-of-concept to show recruiters that you are able to analyze data and communicate your findings compellingly.
Data analytics boot camp tuition isn’t cheap: signing up for one is a big investment. It’s in your best interest to find a program that offers you some kind of guarantee that with their training you will be able to get a job offer in the field.
The job market can be tricky for even the best-qualified candidates. If a bootcamp offers careers services, such as 1:1 mentoring, resume and portfolio help, it’s a good sign that they are invested in your success.