Many ask if data science and data analytics are the same things; the answer is yes! Data is today’s buzzword and not just in tech circles. Today, most people know data as the information we gather about the world which informs our decision-making.
This process of analysing and modelling data occurs in two distinct career fields that are often conflated.
Data science and data analysis are continual growth sectors and the need for data scientists and data analysts is urgent. But we have to understand the fundamental differences between the two professions.
This can be tricky because both positions try to solve the same problems with data.
Defining Data Analyst
These days, everything runs on data analytics so it's no surprise that more employers want to hire only those with a degree in the field. But the demand for increased specialisation aside, data analysis has been around longer than since the 21st century.
In fact, the root of data analysis holds skills common to fields like business, economics, accounting and even literature.
This field involves expertise in collecting data, interpreting results, and providing key insight for clients and employers. Their work is now much easier thanks to software like Excel, Stata and database applications like Access.
We became familiar with data analysis and all it does through greater exposure to science and banking practices. For instance, scientists use analytical methods to test hypotheses, while banks and businesses use them to analyse their yearly, monthly and quarterly data.
During the late 20th Century, computers and, in particular, software became ubiquitous in government offices and financial markets. From then on, statistics and analytics jobs enjoyed a surge of growth and innovation.
Some new jobs titled included Business Intelligence, Business Analyst, and Research Analyst. Accounting has long been a standard business and government feature but this field also enjoyed a software-induced boost.
Analysing data isn’t a skill that only scientists and businesses use today. On the contrary, schools, grocery stores, individual households and others all rely on statistics. In fact, t would be hard to argue against the fact that data analysis plays a vital role in our daily lives.
Some concerns consult data to make strategic decisions. The data they rely on might be past financial performance, census counts, university enrollment, and consumers' purchasing histories.
These data can help organisations and individuals evaluate and improve their industries, processes, or designs.
Data Science Jobs
Data science has its roots in statistics but it branches off from the discipline. Data science as we know it today would not have been possible without major advancements in technology.
One of the first data science breakthroughs happened in the US. The technology firm International Business Machines (IBM) won a government contract to collect, organise and digitise the country’s social security users' information.
Computer systems development was vital for the progression and the subsequent specialisations within data science. We might define this field as the product of statistics and computer science. Consequently, a beginner data scientist will usually enrol for one online statistics course or the other to grasp important statistical concepts.
We may further define the role of the data scientist as the collection, cleaning, modelling, and processing of big data. This data is usually a mass of unstructured information from a wide array of courses. It might come from business systems, banks, or governments.
Accordingly, job titles include Business Analyst, Systems Analyst and Senior Analyst. A Developer and a Programmer would manage the technical aspects.
Innovations in big data come from designing new software or operational programs that automate functions. These can include anything from statistics software to artificial intelligence in self-driving cars. The data analyst's role is to interpret statistics within an industry – research, business or communications.
The data scientist covers the fields pertaining to computer programming and engineering.
Data Science vs Data Analytics
When looking for jobs in data science, data science vs data analytics can be frustrating and lead to confusion. It doesn't help that many companies don't know there's a difference between the two professions. This, coupled with the vast array of specialisations that both data analysis and data science offer can be enough to scare people from these fields. They might not be keen to study them and they might not choose the right position once they enter the job market.
Knowing how the two differ in terms of qualifications is also important for students interested in either a data analytics or data science course. The kind of jobs available to them after graduation can be a deciding factor in what type of undergraduate and graduate program to choose. When looking at different specialisations, take into account the skills job adverts look for and search for those skills in university programs.
This can give you a clearer picture of whether a program is based more on mathematics, business, computer science, and more. The following chart describes the most common skills required of data scientists and data analysts both in school and in the job market.
Taking a look at these skills can help you decide which track matches more with your interests.
| Skill | Data Science | Data Analyst |
|---|---|---|
| Exploratory data analysis | X | X |
| Data cleaning | X | X |
| Visually representing data | X | |
| Computer programming (R, Python) | X | |
| Machine learning and deep learning | X | |
| Hadoop | X | |
| Excel, SPSS | X | |
| SQL | X | X |
Remember the Differences
The differences in data science vs data analytics inform how one might use statistics because many organisations confound the two fields. Data science vs data analytics have different tasks, salaries and recruitment processes. If these differences interest you, there's a chance you want to learn or improve your skills in either data science or data analysis.
Universities often have separate courses for data science and data analysis. That will make it easier for you to recognise which course is right for you. But both fields offer many areas of specialisation, which is why it is vital to outline which parts of each discipline interest you.
For example, mathematical statistics is less concerned with data modelling and more focused on the theoretical computation behind data modelling. This program will involve at least one data science course that delves deeper into complex mathematical concepts like linear algebra, calculus and probability.
A data science program geared towards business, on the other hand, will include a business or communications course alongside computer science training.
Defining the differences between data science and data analysis will help you outline which courses interest you the most. Also, you'll be better able to choose what mix of subjects is right for you. With regard to the job market, knowing the differences between the two fields becomes equally important.
For instance, the role of the data scientist delivers benefits to the individual studying as well as the professional community they become a part of.
In their job descriptions, companies looking to hire analysts will usually communicate which type of degree they want applicants to have. A recruiter looking for a data analyst will most likely want to hire someone with a mathematics, statistics, or business degree, with a focus on analytics.
Data science, in contrast, would most likely require a data science or computer science degree with a focus on statistics.
Around 88% of data scientist hires have a Master’s degree but data analysts have an easier time hiring in after attaining a Bachelor’s degree. When searching for jobs in analysing data, you should look closely at the job description and the duties and roles required of you.
Often, businesses unfamiliar with data's inherent possibilities will draft their hiring description to include a mix of general skills suitable for an entry-level job. They might not list duties and skills tailored to a more seasoned analyst.
As of now, the job market for both data scientists and data analysts is competitive but vast. Analysts in both fields may choose the most suitable positions from among different job offers. 'Differentiate and specify' is the important rule when preparing to search for or take a job in data science and data analysis.
The ideal job posting distinguishes between the analytical and other skills they need from you in accordance to your interests. It should also give you specific examples of what roles you will take.
Understanding these crucial differences between the two disciplines will help you find the career path you want. Being able to interpret which job components pertain to data science or data analytics will help you keep up with current innovations in both fields.
As professionals and citizens, we should be socially and politically aware, and we should be civically engaged. Few would think that developments in data science would have the same socio-political ramifications as those in data analysis.
That's because analysing data points to trends that help us keep up with current events.
When learning about data science vs data analytics, future scientists and analysts come to understand the importance of data. This information, collected bit by bit, reveals hidden patterns in our lives and environments.
They dictate what we consume and how much, and possibly what the next big consumer event will be. When you study data science or data analysis, that notion becomes a lot less ominous.
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