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Data drives business decisions in every industry, which means 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, data expert and training program manager at TripleTen, explains the differences and similarities between data analytics and business intelligence and data science, as well as the responsibilities and typical salaries associated with these data roles. Find out who’s hiring data professionals now and how TripleTen is helping students get data jobs with their data bootcamps.
Business Intelligence Systems And Data Analysis
Business intelligence is similar to other data specializations from a technical standpoint, but focuses on reporting, data visualization and storytelling, and dashboards—the kind of things that influence business decisions and inform strategy. When a company talks about being “data-driven,” they’re probably talking about relying on business intelligence professionals to help them make decisions.
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What is data analysis? Data analysis is the general process of understanding data and extracting information from it.
Data analysis also supports decision making and relies on various techniques such as exploratory data analysis, hypothesis testing and predictive analysis. It’s similar to business intelligence in that data analysts often use data visualization to make decisions, but unlike business intelligence, data analytics deepens technical skills using Python to make predictions and automate some of the data analysis.
What is data science? Data science is at the intersection of statistics and computing in building predictive systems.
Both have been around for a while, but computing is constantly improving, allowing new statistical methods to be used. Data science is still about understanding your data first, so it shares some key technical skills with data analysis, such as loading, exploring, and cleaning data. But instead of focusing on interacting with people or building things to help people make decisions directly, data scientists are working with software engineers to build scalable predictive systems.
Business Analytics And Data Analytics — What’s The Difference?
Business intelligence, data analysis and data science are based on statistics. They all require a similar basic understanding of data, distribution, and data exploration. In addition, they all use some kind of computational tools. BI doesn’t use Python as much as data analytics or data science would, but you’re still using a computer, writing scripts, and taking actions to make sense of the data.
Anyone working in the data field should have a basic understanding of data wrangling, sorting, and cleaning. Because BI analysts and data analysts work more often with business, marketing, or sales teams, they rely on visualization and forecasting tools. Data scientists focus more on the technical aspects of data, so the tools they use are more programming-oriented.
Working with data is not just about your technical skills! Because data affects many components of an organization, a good understanding of soft skills is essential to succeed in data:
As a BI analyst, your primary responsibility is to understand business needs and communicate results to your team. You may be surveying stakeholders or applying business systems such as marketing pipelines and cohort analysis, which are models that provide a quantitative understanding of company behavior. Your role may include cleaning data and creating reports and dashboards to provide insights to your organization’s decision makers.
What Is Business Intelligence In Data Analytics
As a data analyst, your primary responsibility is to analyze data to make relevant conclusions and predictions for the organization. Maybe you’re cleaning data and creating reports to share with your organization. As a data analyst, you will use Python and apply statistics to your data to predict and predict future events. You can also design experiments and perform hypothesis testing.
As a data scientist, your primary responsibility is to design and train sophisticated, predictive machine learning models based on data to build intelligent systems. Maybe you’re prototyping what can be done with the data, like product recommendations, and then you’ll work with the engineering team to build those prototypes.
One of the best things about a data-driven career is that everyone has data, so any industry can have relevant openings! The most common data-intensive industries where TripleTen data graduates are hired include finance, insurance, medical, government, commerce and technology. I have also seen graduates being employed in logistics and agriculture.
Can you go from BI analyst to data analyst to data scientist or vice versa?
Introduction To Business Intelligence And Data Analytics
There isn’t just one data career ladder. There is no real standardization in these data occupations, so job titles and descriptions can be vague. It is important to remember that these professions have transferable skills and individual careers are very personal. You just need to have clear career goals and strive to achieve them.
For example: you might start out as a BI analyst, but instead of pursuing a career in data science (which means delving into statistics and coding), you might go in the direction of product management or people management.
Traditionally, data scientist positions are reserved for individuals with more experience, but this is not the case for all employers these days. The tech industry is all about your job title, and the responsibilities still won’t always map perfectly, so you might be hired as a data analyst and find yourself actually doing BI or data science.
TripleTen offers a Data Analytics Bootcamp, a BI Analytics Bootcamp, and a Data Science Bootcamp. Aaron, what advice would you give to a candidate interested in data and trying to determine which of these camps best fits their career goals?
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In general, data scientists and data analysts will use Python and some engineering tools, while business intelligence careers will work with people and companies and less engineering.
TripleTen data bootcamps vary in length from 5 to 10 months, which can also be a factor in deciding which bootcamp is right for them.
We do not require a college degree or specific previous experience. All TripleTen 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 opportunities! With data everywhere, students can use their past experiences to differentiate themselves in the job search.
If you can invest the time and focus, our programs are designed for you to succeed.
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In data science, we see other technical backgrounds, such as a Bachelor of Science, not performing as well as they wanted in the job market, so they pursued data science skills to enhance their career opportunities. For example: someone with prior medical experience can learn about data and gain a specialized understanding of data in the medical space.
Recently, prospective BI students often arrive with some background in the subject, such as an entry-level role in a business that deals with spreadsheets but not data, and want to strengthen these skills to guide their careers. that direction.
Not really. College can be a great opportunity, but it’s not for everyone and that’s okay—today’s hiring managers know that! Traditional companies may require a degree, and there are certain situations, such as teaching, where an advanced degree is required, but it’s certainly not even close to a universal rule. You’ll find that BI and data analytics roles in particular won’t require a college degree. You may see more data science listings that promote college degrees, but many jobs will list education and “or equivalent experience” as desired.
Remember that job descriptions and job titles are actually a company wish list compiled by a committee, so keep that in mind when you search and apply for jobs in this data. Even if you don’t have the exact experience they’re looking for, write a cover letter outlining your experience and why you should be considered, and include your portfolio.
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The best skills are evergreen – skills and tools that stand the test of time – but we work in a vibrant and ever-changing industry.
There are traditional databases like MySQL and Postgres and data warehouses with technical differences. For example, you still use SQL with them, but they scale differently and become more important as almost every industry has adopted data warehouses in some form in recent years.
Especially when it comes to data science, we hear about artificial intelligence (AI) – big language models and other big deep learning models. These models are huge and cost at least tens of thousands of dollars to start training. This means that unless you work for a select few employers, you probably won’t be training these models very often, but you should familiarize yourself with ways to share and adapt pre-built machine learning models, all of which you can find on Hugging Face.
Data scientists need to be familiar with a technique called “knowledge distillation.”
Business Intelligence Vs. Data Analytics
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