Dan Ariely, a well-known behavioral economics expert, once said about big data: “Everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it.”
This concept applies to a great deal of data terminology. While many people toss around terms like “data science,” “data analysis,” “big data,” and “data mining,” even the experts have trouble defining them. Here, we focus on one of the more important distinctions as it relates to your career: the often-muddled difference between data analytics and data science.
Data Analytics vs. Data Science
While data analysts and data scientists both work with data, the main difference lies in what they do with it.
Data analysts examine large data sets to identify trends, develop charts, and create visual presentations to help businesses make more strategic decisions.
Data scientists, on the other hand, design and construct new processes for data modeling and production using prototypes, algorithms, predictive models, and custom analysis.
Working in Data Analytics
The responsibility of data analysts can vary across industries and companies, but fundamentally, data analysts utilize data to draw meaningful insights and solve problems. They analyze well-defined sets of data using an arsenal of different tools : e.g. why sales dropped in a certain quarter, why a marketing campaign fared better in certain regions,etc.
Data analysts have a range of fields and titles, including (but not limited to) database analyst, business analyst, market research analyst, sales analyst, financial analyst, marketing analyst, advertising analyst, customer success analyst, operations analyst, pricing analyst, and international strategy analyst. The best data analysts have both technical expertise and the ability to communicate quantitative findings to non-technical colleagues or clients.
Characteristics of Data Analysts
Data analysts can have a background in mathematics and statistics, or they can supplement a non-quantitative background by learning the tools needed to make decisions with numbers. Some data analysts choose to pursue an advanced degree, such as a master’s in analytics, in order to advance their careers.
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Skills and Tools
Top data analyst skills include data mining/data warehouse, data modeling, R or SAS, SQL, statistical analysis, database management & reporting, and data analysis.
Roles and Responsibilities
Data analysts are often responsible for designing and maintaining data systems and databases, using statistical tools to interpret data sets, and preparing reports that effectively communicate trends, patterns, and predictions based on relevant findings.
Working in Data Science
Data scientists, on the other hand, estimate the unknown by asking questions, writing algorithms, and building statistical models. The main difference between a data analyst and a data scientist is heavy coding. Data scientists can arrange undefined sets of data using multiple tools at the same time, and build their own automation systems and frameworks.
Choosing Between a Data Analytics and Data Science Career
Once you have a firm understanding of the differences between data analytics and data science—and can identify what each career entails—you can start evaluating which path is the right fit for you. To determine which path is best aligned with your personal and professional goals, you should consider three key factors.
1. Consider your personal background.
While data analysts and data scientists are similar in many ways, their differences are rooted in their professional and educational backgrounds.
As mentioned above, data analysts examine large data sets to identify trends, develop charts, and create visual presentations to help businesses make more strategic decisions. To align their education with these tasks, analysts typically pursue an undergraduate degree in a science, technology, engineering, or math (STEM) major, and sometimes even an advanced degree in analytics or a related field. They also seek out experience in math, science, programming, databases, modeling, and predictive analytics.
Data scientists, on the other hand, are more focused on designing and constructing new processes for data modeling and production. Because they use a variety of techniques like data mining and machine learning to comb through data, an advanced degree such as a master’s in data science is essential for professional advancement.
When considering which career path is right for you, it’s important to review these educational requirements. If you have already made the decision to invest in your career with an advanced degree, you will likely have the educational and experiential background to pursue either path. On the other hand, if you’re still in the process of deciding if going back to school is right for you, you may be more inclined to stick with a data analytics role, as employers are more likely to consider candidates without a master’s degree for these positions.
2. Consider your interests.
Are you excited by numbers and statistics, or do your passions extend into computer science and business?
Data analysts love numbers, statistics, and programming. As the gatekeepers for their organization’s data, they work almost exclusively in databases to uncover data points from complex and often disparate sources. Data analysts should also have a comprehensive understanding of the industry they work in. If this sounds like you, then a data analytics role may be the best professional fit for your interests.
Data scientists are required to have a blend of math, statistics, and computer science, as well as an interest in the business world.
Either way, understanding which career matches your personal interests will help you get a better idea of the kind of work that you’ll enjoy and likely excel at. Be sure to take the time and think through this part of the equation, as aligning your work with your interests can go a long way in keeping you satisfied in your career for years to come.
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3. Consider your desired salary and career path.
Different levels of experience are required for data scientists and data analysts, resulting in different levels of compensation for these roles.
Data analysts have an earning potential of between $83,750 and $142,500. Since these professionals work mainly in databases, however, they are able to increase their salaries by learning additional programming skills, such as R and Python.
However, data analysts with more than 10 years of experience often maximize their earning potential and move on to other jobs. Two common career moves ,after the acquisition of an advanced degree, include transitioning into a developer role or data scientist position.
Data scientists, who typically have a graduate degree, boast advanced skills, and are often more experienced, are considered more senior than data analysts. As such, they are often better compensated for their work.
The career trajectory for professionals in data science is positive as well, with many opportunities for advancement to senior roles such as data architect or data engineer.
Which data career is right for you?
Data analysts and data scientists have job titles that are deceptively similar given the many differences in role responsibilities, educational requirements, and career trajectory.
Once you have considered factors like your background, personal interests, and desired salary, you can decide which career is the right fit for you and get started on your path to success.
Data Scientist vs Data Analyst Salary
Here are some of the expected titles you can see as a data scientist that might have a significant change in salary as well:
Entry Level Data Scientist → Data Scientist → Senior Data Scientist
Lead Data Scientist — Data Science Manager — Data Science Director
In addition to these titles, there are also some seniority levels like I, II, and III.
Below, I will show the range of salaries by title with their respective years required or expected.
Keep in mind that these roles are based on a US average
- Average Overall Data Scientist →
- Average Entry-Level Data Scientist →
- Average Early-Career Data Scientist →
- Average Mid-Career Data Scientist →
- Average Experienced Data Scientist →
Data analysts can expect to see a big range in their salaries based on several factors. Some analysts might focus more on past analysis, or static data, where some analysts might focus on predictive modeling, more similar to that of a data scientist. Because of these differences per unique job, the salaries can increase significantly from early to late in your career, as well as from adding certain skills to your knowledge base.
If you see a data analyst job description that includes words/skills like:
- Python, R, SQL, programming, A/B Testing, and predictive modeling (etc.)
You can expect the position to pay more than if the job description is more focused around:
- Excel, PowerPoint/Google Slides, Tableau (etc.)
Let’s dive into the various seniority levels of data analysts and their respective salaries  :
- Average Overall Data Analyst →
- Average Entry-Level Data Analyst →
- Average Early-Career Data Analyst →
- Average Mid-Career Data Analyst →
- Average Experienced Data Analyst →
Here are some popular cities and some rural cities with their respective salary averages for mid-career data analysts:
- San Francisco → $98,627
- Austin → $70,485
- Los Angeles → $71,487
There is quite a range for the same seniority level of data analysts as we can see from above. Now let’s look at those same three cities and apply a filter that includes the various skills:
- San Francisco → $110,000 — “Data Modeling”
- Austin → $90,249 — “Python”
- Los Angeles → $81,951 — “Statistical Analysis”
These differences are huge. These are the same cities, with the same seniority level, but with an added skill that is closer to that of a data scientist or software engineer. With this information, you can see how important just a few changes in your experience can lead to a significant amount of more money or a bigger salary.
Knowing your worth is important, and getting a better sense of the skills that you do have now, as well as the skills that you could have, can make a huge difference in your salary. With that being said, the same seniority level of data analysts or data scientists could match one anothers’ salaries or exceed them, based on the skills that you have.
To summarize, here are some key takeaways of data science versus data analyst salaries:
* Average US data scientist salary $96,455 * Average US data analyst salary $61,754 * Data scientists can be more predictive, while data analysts can focus more on past/static data * Several factors contribute to salary, the most important most likely being seniority, city, and skills
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