Guide
Data Analytics vs Business Intelligence vs Data Science
By Jess Feldman
Last updated on March 31, 2023
Data is driving business decisions in every industry, which means that companies need skilled business intelligence analysts, data analysts, and data scientists. But what exactly is the difference between these three data career paths? Aaron Gallant, a data expert and Curriculum Lead from Practicum, explains the differences and similarities between data analytics vs business intelligence vs data science, and the responsibilities and typical salaries associated with these data roles. Find out who’s hiring data professionals now, and how Practicum is helping students land data jobs with their data bootcamps.
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Business intelligence is the application of data analysis techniques to business information.
Business intelligence is similar to the other data specializations in terms of techniques, but it focuses on reporting, data visualization and storytelling, and dashboards — the sort of things that impact business decisions and inform strategy. When a company talks about being “data-driven,” they are probably talking about relying on business intelligence professionals to help inform their decisions.
Data analytics is the overall process of understanding data and deriving information from it.
Data analytics also supports decision-making and draws on a range of techniques, such as exploratory data analysis, hypothesis testing, and predictive analysis. It's similar to business intelligence because data analysts often use data visualization to inform decisions, but unlike business intelligence, data analytics goes deeper into the technical skills by using Python, performing forecasting, and automating some of the data analysis.
Data science lies at the intersection of statistics and computing to build predictive systems.
Both have been around for a while, but computing keeps advancing, which enables novel techniques in terms of statistics. Data science still means understanding your data first, so it shares some foundational technical skills with data analysis like loading, exploring, and cleaning data. Instead of focusing on either interacting with humans or building things to help human decisions directly, though, data scientists collaborate with software engineers to build scalable predictive systems.
Business intelligence, data analytics, and data science are all built on statistics. They all require a similar core understanding of data, distributions, and exploring data. Plus, they all use some kind of computational tools. BI doesn't use Python as much as data analytics or data science would, but nonetheless, you're still using a computer, writing scripts, and doing things to understand data.
Anyone working within the data field should have a basic understanding of wrangling, sorting, and cleaning data. Since BI Analysts and Data Analysts work more often with the business, marketing, or sales teams, they rely on tools for visualizations and predictions. Data Scientists are more focused on the technical aspect of data, so the tools they use rely more heavily on programming.
The 4 Essential Technical Tools of BI Analysts:
The 6 Essential Technical Tools of Data Analysts:
The 4 Essential Technical Tools of Data Scientists:
The 2 Non-Technical Skills of Data Professionals
Working in data isn’t all about your technical prowess! Since data affects many components of an organization, a good grasp of soft skills is important to succeed in your data career:
On the job as a BI Analyst, your primary responsibility is understanding the needs of the business and communicating results to your team. You may be interviewing stakeholders or applying business frameworks, like marketing funnels and cohort analysis, which are models to understand the behavior of a business quantitatively. Your role may include cleaning data and then building reports and dashboards to communicate findings to decision-makers at your organization.
💰Median starting salary of a BI Analyst, according to Glassdoor: $87,000
On the job as a Data Analyst, your primary responsibility is to analyze data to draw relevant conclusions and predictions for an organization. You may be cleaning data and then creating reports to share with your organization. As a Data Analyst, you will use Python and apply statistics to your data to forecast and predict future events. You may also design experiments and perform hypothesis testing.
💰Median starting salary of Practicum’s Data Analytics Bootcamp graduates: $65,500
On the job as a Data Scientist, your primary responsibility is to build and train sophisticated, predictive machine learning models on the data in order to create intelligent systems. You may be prototyping what can be done with the data, such as product recommendations, and then work with the engineering team to build those prototypes.
💰Median starting salary of Practicum’s Data Science Bootcamp graduates: $89,300
Which industries hire data professionals?
One of the great things about data-driven careers is that everyone has data, so potentially any industry has relevant openings! The most frequent data-intensive industries Practicum data graduates get hired for are finance, insurance, medical, government, commerce, and tech. I've also seen graduates get hired into logistics and agriculture.
Can you go from being a BI Analyst to a Data Analyst to a Data Scientist or vice versa?
There is not just one data career ladder. There is no real standardization across these data professions, so it can get blurry around job titles and descriptions. What you need to keep in mind is that these professions have transferable skills and individual careers are highly personal. You just need to have clear career goals and work toward them.
For example: You could start as a BI Analyst, but instead of working towards a career in data science (which means getting deeper into statistics and coding), you might go in the direction of product management or people management.
Traditionally, the data scientist job title is reserved for somebody with more experience, but it’s not the case with all employers these days. The thing about the tech industry is your job title and responsibilities aren't always going to map perfectly anyway, so you might end up being hired as a data analyst and find that you actually are doing things that are more like BI or data science.
What types of jobs have Practicum graduates been hired for?
Practicum graduates have go on to land jobs as:
Practicum offers a Data Analytics bootcamp, a BI Analytics bootcamp, and a Data Science bootcamp. Aaron, what is your advice for an applicant who’s interested in data and trying to determine which of these bootcamps is the best fit for their career goals?
Overall, Data Scientists and Data Analysts will use Python and some engineering tools, while business intelligence careers work with people and businesses and do less engineering.
Practicum’s data bootcamps vary in length, ranging from 5-10 months, which may also be a factor for someone deciding what bootcamp is right for them.
Do the data bootcamps at Practicum require previous technical experience or a degree to enroll?
We do not require a college degree nor any specific previous experience. All Practicum bootcamps are designed to get you hired in the tech industry. The data science program is the longest among data-focused bootcamps because it has the most ground to cover! Since data is everywhere, students can leverage their past experience to stand out in the job search.
Is there an ideal student for each of the data bootcamps?
If you are able to invest the time and focus, our programs are designed for you to succeed.
In data science we see other technical backgrounds, such as a Bachelor of Science degree, that didn’t pay off in the job market the way they desired, so they sought data science skills to elevate their career options. For example: Someone with prior medical experience can learn about data and end up with a special understanding of data in the medical space.
Lately, prospective BI students are often arriving with some exposure to the subject, like an entry-level role in a business that works with spreadsheets but not in a data-related way and they’re looking to strengthen those skills to steer their career in that direction.
Overall, do you need a college degree to work in data?
Definitely not. College can be a wonderful opportunity, but it's not for everyone and that's okay — today’s hiring managers know that! Traditional companies might require a degree and there are certain situations, like teaching, when a higher degree is required, but it's definitely not even close to a universal rule. You’ll see that positions for BI and data analytics will especially not require a college degree. You might see more data science listings that encourage college degrees, but many job listings will list desired education and include “or equivalent experience.”
Remember that job descriptions and job titles are really a company’s wish list made by a committee, so keep that in mind when looking for and applying to these data jobs. Even if you don’t have the exact experience they’re looking for, write a cover letter that positions your experience to argue why you should be considered and refer to your portfolio.
The best skills are evergreen — the skills and tools that stand the test of time — but we are operating with a living field and a constantly changing industry.
There's traditional databases, like MySQL and Postgres, and then there's data warehouses that have technical differences. For instance, you still use SQL with them, but they scale differently and they're getting more important as almost every industry has picked up data warehouses in some form or another in recent years.
Particularly with data science, we’re hearing about artificial intelligence (AI) — the large language models and other big deep learning models. These models are massive and cost at least tens of thousands of dollars worth of compute to even begin to train. This means that unless you work for a select few employers, you probably won’t be training these models all that often, but you should get familiar with approaches to sharing and adapting pre-trained machine learning models — all the things you can find on Hugging Face.
Data scientists need to know a technique called “knowledge distillation,” which means taking a large model and making it smaller, so you can use these massive models. They also need to understand transfer learning, which means taking a model that has been trained and training it just a bit more for your specific purpose
Find out more and read Practicum reviews on Course Report. This article was produced by the Course Report team in partnership with Practicum.
Jess Feldman is the Content Manager at Course Report. As a lifelong learner, Jess is passionate about education — She loves learning and sharing insights about tech bootcamps and career changes with the Course Report community. Jess received a M.F.A. in Writing from the University of New Hampshire and lives in southern Maine.
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