Can you imagine a world without data? Yes? No? Well, whichever the case may be, data is invaluable. It has always been a key factor in basically everything that has to do with running the world. Today, most if not all result-driven activity demands a certain amount of data to be processed and of course, without data, the world might be stuck in the stone age. This is precisely why data science and data analytics jobs are high in demand — they’re undoubtedly two of the hottest jobs in tech. Yes, top companies are always on the lookout for seasoned professionals (in the field) who can work their magic and help them parse through large datasets to drive incredible results.
That said, if you find that you got a knack for decoding data coupled with an analytical mindset, you’ll certainly want to consider a career as a data scientist or data analyst. But what if you can’t really differentiate between data analytics and data science? Well, that’s where we come into play! In this article, we’ve taken our time to examine both careers and reveal exactly how they differ. And if you ever want to go deeper in the field (especially data analytics), we’ll recommend you sign up for the best data analytics training to ensure that your skills are up to par.
Now let’s get started with the differences (as it relates to your career).
Data Analytics
Responsibility of a Data Analyst
Although the responsibility of data analysts varies from industry to industry, their primary duty is to sift through data, identify trends and solve problems. They’re known to be professionals when it comes to analyzing large sets of data to proffer solution to the tangible needs of an organization. Essentially, data analysts can analyze data to figure out why marketing campaigns performed well in certain regions (and poorly in some), why sales weren’t good in certain quarters, how certain business decisions affect revenue and more. These professionals typically use statistical tools to not only interpret data sets but also to prepare reports that demystify relevant trends and patterns. And of course, the primary aim is to help businesses or organizations make strategic, result-driven decisions.
Data Analytics Background and Skills
When we talk of background, data analysts can be conversant with mathematics and statistics or just decide to take up the best data analytics training to hone their skills and set them up for a rewarding career in the field. The most important thing is for them to get a seasoned idea of exactly what it takes to make sound decisions with numbers.
As for the skills of data analysts, you should be looking at;
- Data modeling
- Data mining
- R or SAS
- SQL
- Database management and reporting
- Data analysis
- Statistical analysis
Data Science
Responsibility of a Data Scientist
This field is basically focused on revealing answers to things we do not know. In essence, data science is aimed at discovering actionable insights from large datasets. Experts in this field use coding, mathematical and statistical models (alongside other techniques) to interpret data that eventually provide answers or solutions to problems (including those that haven’t been noticed yet). At this point, it’s worth mentioning that data scientists can perform the duties of a data analyst. But the key difference between data analysts and data scientists is that the latter are pros in machine learning, advanced programming, and data modeling. With this in mind, one can easily tell that the field of data science generally demands a lot more work, qualifications and expertise. But of course, you can get started by enrolling for the best data analytics training course, become a pro and then figure out if you can try your hands on data science.
Data Scientist Background and Skills
The most common fields of study include mathematics and statistics, computer science and engineering. But besides having mathematical and statistical knowledge, data scientists are also known to have well-grounded experience and impressive hacking skills.
Some of the top skills of data scientists include;
- Programming
- Software engineering
- Data analysis
- Machine learning
- Data mining
- Statistics
- Linear algebra and calculus
- Data wrangling
Data Analysts and Data Scientists — Difference in Salary and Earning Potential
First off, the earning potential for both data analysts and data scientists are determined by expertise and level of experience. In other words, the job roles (as a data analyst or data scientist) you’re likely to get is highly dependent on the value you can offer to prospective employers.
So, what is the earning potential of data analysts? According to PayScale, the average annual salary for data analysts in the USA is $58,876. But as mentioned earlier, your earning potential will vary depending on experience alongside other factors. For example, data analysts with up to 10 years of experience can earn bigger salaries and even land higher-paying jobs along the way.
On the flip side, data scientists (who are generally more experienced than data analysts), can earn between $117,000 to $137,000 each year. Experts in the field of data science usually have an advanced graduate degree alongside a great skill set that makes them get better compensation for their work.
So there you have it! Data analysis or data science? Which works for you? Well, if you got a thing for programming, numbers, and statistics, then a data analytics role may be great for you. On the other hand, if you have a strong background in mathematics, statistics and computer science alongside a seasoned knowledge of the business world, pursuing a career in data science may be just right for you.