The answer is yes! Data is today’s buzzword, and for the most part, people wouldn’t have trouble defining data as the information we gather about the world which informs decision making. This process of analyzing and modelling this data occurs in two fields that are often used interchangeably: data science and data analysis.
With the continual growth and need of both data scientists and data analysts, it is important to understand the fundamental differences behind the two professions. This can be quite tricky because both analyst jobs essentially try to solve the same problems with regards to using data to ameliorate the world.
While the explosion of data analytics has seen increased specialization, to the point where the job market can seem reserved for only those with a degree in the field, it is important to understand that data analysis has been around longer than the 21st century. In fact, the root of data analysis holds skills common to fields like business, economics, accounting and even literature! This dynamic field involves expertise in the efficient collection of data, interpretation of a wide array of results, and providing key insight for employers.
To start defining the role of the analyst, it is important to understand that the explosion of data analysis could not have been possible without the innovations made in computer software, such as Excel and Stata. While data analysis has been around for a long time, it’s introduction into our mainstream lexicon was with science and banking. While scientists used analytical methods to aid in hypothesis testing, banking systems and businesses used it in order to analyze their yearly, monthly and quarterly data. The vital role data analysis plays in our daily lives is indisputable.
Being involved in data can often mean collaboration
The introduction of popular computer software into powerful sectors in government and financial markets in the late 20th century led to an explosion and innovation of statistics and data analytics. Analyzing data isn’t a skill that only scientists and businesses use today – on the contrary, the implementation of statistics can be found in schools, grocery stores, individual households, etc. Consulting data to make strategic decisions based off of previous financial, census, enrollment, and purchase history can help organizations and individuals evaluate and improve their industries, processes, or design.
Some examples of jobs in data analysis jobs are:
The history of data science can be found in its roots in the discipline of statistics and where 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 major examples of data science comes from the US, where IBM won a contract to collect, organize and digitize the information from the country’s social security users. The progression of computer systems has been vital for the progression and the subsequent specializations within data science and can be defined as the product of both statistics and computer science.
The role of the data scientist can be defined 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, such as business systems, banks, or governments. Innovations in big data normally come from designing new software or operational programs that automate functions – which can include anything from statistics software to the artificial intelligence in self-driving cars. The goal of the data scientist, however, can have many benefits not just to the individual studying within the field but often for the community which they are part of.
Whereas the role of the data analyst is to interpret statistics with regards to a wide variety of industries – research, business, communications – the data scientist covers fields pertaining to computer programming and engineering. Some of the most common jobs of the data scientist include:
Having analytical skills can help you in any situation
When looking for jobs in data science, the difference between both fields can lead to confusion and frustrating. It doesn’t help that many companies don’t know that there even is a difference between the two professions to begin with. This, coupled with the vast array of specializations that both data analysis and data science offer can be enough to deter people from either entering the job market or from choosing the right position.
Knowing how the two differ in terms of qualifications is also important for students who might be interested in studying data science and analysis as well. Knowing what kind of jobs are available after graduation can be a deciding factor in what type of undergraduate and graduate program to choose.
When looking at different specializations, take into account what kind of skills keep being repeated and look for those skills in the university programs you are deciding from. This can give you a clearer picture on whether a program is based more in 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 you interests.
|Skill||Data Science||Data Analyst|
|Exploratory data analysis||X||X|
|Visually representing data||X|
|Computer programming (R, Python)||X|
|Machine learning and deep learning||X|
Depending on how you use statistics, it can be very important to understand the differences between data science and data analysis. While many organizations often confound the two fields, data science and data analysis often have different objectives, salaries and recruitment processes. If you are interested in understanding the differences between the two, it is possible that you are seeking to start learning or improving your skills in either data science or data analysis.
University programs often have two separate programs for data science and data analysis, which will make it easier to identify which program is right for you. However, both fields offer a wide array of specializations, 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 involved in the theoretical computation behind data modelling.
Solve the world’s leading problems by studying statistics and data science
This program will involve courses that delve deeper into complex mathematical concepts such as linear algebra, calculus and probability. A data science program geared towards business, on the other hand, will include business or communications courses alongside computer science training.
Defining the differences between data science and data analysis will help you outline which courses interest you the most and what mix of subjects is right for you. With regards to the job market, knowing the differences between the two fields becomes equally as important. In their job descriptions, analyst jobs will usually communicate which type of degree they are looking for in a candidate.
A recruiter looking for a data analyst will most likely be looking 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 scientists that get jobs earn a Master’s degree, whereas data analysts have an easier time getting hired after attaining a Bachelor’s degree.
When searching for jobs in analyzing data, you should look closely at the job description to understand what duties and roles will be available to you. Often, businesses with little experience in the realm of data will include in their hiring description a mix of general skills that are passable for an entry-level job, but unsuitable for a more seasoned analyst.
As of now, the job market for both data scientists and data analysts is competitive but vast – so analysts in both fields will usually be able to choose between many different job offers. The important rule to stick to when preparing to search for or take a job in data science and data analysis is: differentiate and specify. The ideal job posting differentiates between the analytical skills they need from you in accordance to your interests, and on top of that gives specific examples of what roles you will take.
Understanding these crucial differences between the two disciplines will help you in your daily life. Being able to interpret what components of a certain situation pertains to data science or data analytics will help you keep up with current innovations in both fields. Developments in data science often do not have the same socio-political ramifications as those in data analysis. Another importance is one mentioned in the beginning. To elaborate, it is important in terms of keeping up with current events. Developments in data science often do not have the same socio-political ramifications as those in data analysis.