In 2021, data scientists and data analysts will be two of the most sought-after and well-paid jobs. The World Economic Forum's Future of Jobs Report 2020 rated these professions #1 in terms of increased demand across industries, followed by AI and machine learning specialists and big data specialists.
While it is definitely true that data professionals are in high demand, the line between data analyst and data scientist employment is not always clear. Despite the fact that they both work with data, they do so in very different ways.
What are the responsibilities of data analysts and data scientists?
One of the most significant distinctions between data analysts and scientists is what they do with the data they collect.
Data analysts, for the most part, deal with structured data in order to address real-world business problems. They do it with technologies including SQL programming languages, data visualisation software, and statistical analysis, among others. Typical responsibilities of a data analyst could include the following:
● Involvement with organizational executives in order to establish informational requirements.
● Using both primary and secondary sources to gather information.
● Data cleaning and reorganization in preparation for analysis.
● Analyzing large data sets in order to identify trends and patterns that can be turned into useful information.
● To inform data-driven decisions, it is important to present findings in an understandable manner.
Data scientists, often, must cope with the unknown by employing more advanced data tools in order to create predictions about what will happen in the future. Their own machine learning algorithms may be automated, and they may create predictive modelling procedures that can handle both organized and unstructured data. This position is often seen as a more advanced variant of the data analyst position. Some of the jobs that you might encounter on a daily basis include:
● Obtaining, cleaning, and processing raw data are all important tasks.
● Predictive models and machine learning methods for mining large data sets are being developed.
● Tools and techniques for monitoring and analyzing data accuracy are being developed.
● The development of data visualisation tools, dashboards, and reports is underway.
● Creating computer programs to automate the collecting and processing of data.
What are the responsibilities of data analysts and data scientists?
Working in the field of data analytics
Despite the fact that the responsibilities of data analysts differ among industries and firms, data analysts are basically responsible for using data to draw useful insights and solve problems. They employ a variety of technologies to analyze well-defined sets of data in order to meet specific business objectives, such as why sales dropped in a given quarter, why a marketing campaign fared better in different places, and how internal attrition affects revenue, to name a few.
There are many different fields and titles for data analysts, including (but not limited to) database analyst, business analyst, market research analyst, sales analyst, financier, marketing analyst, advertising analyst, customer success manager (also known as a customer success manager), operations analyst, pricing analyst, and international strategy analyst. The most effective data analysts possess both technical understanding and the ability to explain quantitative conclusions to non-technical colleagues or clients in plain language.
Working in the field of data science
Statistical models are constructed by asking questions, creating algorithms, and constructing statistical models, whereas data scientists estimate the unknown. The most significant distinction between a data analyst and a data scientist is the amount of coding required. Data scientists are able to organize large sets of data utilizing various technologies at the same time, as well as create their own automated systems and frameworks from the ground up.
Differences in data science and analytics educational requirements
To qualify for most data analyst positions, you must have at least an undergraduate degree in an area such as mathematics, statistics, computer science, or financial analysis. Data scientists (and many advanced data analysts) typically have a master's or doctoral degree in a discipline like data science, information technology, mathematics, statistics, or a mix of the above.
While a degree has traditionally been the most common route to a career in data, some new opportunities are becoming available for those who do not have a degree or previous work experience.A Google or IBM Professional Certificate in Data Analytics can be completed in less than six months. You will have the skills essential for an entry-level position as a data analyst once you have completed the certificate program. As soon as you complete the Google Certificate, you'll gain access to a hiring consortium that includes more than 130 businesses.
If you're just starting out, working as a data analyst can be a great way to get your foot in the door for a career as a data scientist.
Data skills for scientists and analysts
Despite the fact that they are both concerned with data, data scientists and data analysts employ slightly different sets of skills and technologies. Many of the skills needed for data science are drawn from those needed for data analysis.
Data analyst- Foundational math, statistics; Basic fluency in R, Python, SQL; SAS, Excel, business intelligence software; Analytical thinking, data visualization.
Data scientist- Advanced statistics, predictive analytics; Advanced object-oriented programming; Hadoop, MySQL, TensorFlow, Spark; Machine learning, data modeling.
Making a Decision Between a Data Analytics and a Data Science Profession
Once you have a clear knowledge of the distinctions between data analytics and data science—as well as a clear idea of what each career entails—you can begin analysing which path is the best fit for your skills and interests. When deciding which path is most aligned with your personal and professional goals, there are three crucial factors to consider.
1. Take in account your own personal history.
While data analysts and data scientists have a lot in common, they are distinguished by their professional and educational backgrounds.
As previously said, data analysts evaluate massive data sets in order to discover trends, construct charts, and create visual presentations that may be used to assist businesses in making better strategic decisions. Analysts often pursue an undergraduate degree in a science, technology, engineering, or mathematics discipline, and perhaps an advanced degree in analytics or a similar field, in order to better prepare them for their jobs. They are also looking for candidates who have prior math, science, programming, databases, modelling, and predictive analytics experience.
Data scientists, on the other hand, are primarily concerned with the design and construction of new methods for the modelling and production of large amounts of data. Because they employ a range of ways to sift through data, such as data mining and machine learning, advanced degrees in data science, such as a master's degree in data science, are required for professional progress.
Data scientists must have a much more technical and mathematical background [than data analysts] and, as a result, a much broader understanding of computer science.
It's critical to consider the educational requirements for each career path before deciding which is best for you. In the event that you have already made the decision to further your education and enhance your job with an advanced degree, you will most likely have the educational and professional experience to pursue either path. Alternatively, if you're still contemplating whether or not to return to school, you may be more motivated to stay in a data analytics position, as companies who hire for these positions are more likely to consider individuals who do not have a master's degree.
In any case, considering your existing and desired levels of education and expertise should aid in narrowing your choices.
In the event that you decide to pursue a graduate degree to jumpstart your career, make sure to choose a school that will assist you in achieving your objectives. The emphasis on experiential learning in programmes allows students to obtain the skills and hands-on experience that they need to be successful in the profession.
2. Take into consideration your personal hobbies.
If your interests lie in numbers and statistics or if your interests extend to computer science and business, this is the place for you.
Numbers, statistics, and programming are all things that data analysts enjoy. Since they are the gatekeepers for their organization's data, they spend practically all of their time working in databases, uncovering information from complicated and frequently diverse sources. Additionally, according to Schedlbauer, data analysts should have a thorough awareness of the industry in which they operate. If this describes you, a career in data analytics may be the ideal professional fit for your interests and qualifications.
To succeed as a data scientist, you must have a strong background in mathematics, statistics, and computer science, as well as an interest in and knowledge of the business sector. If this description more closely matches your educational and professional background, a position as a data scientist may be the best fit for you, as described above.
In either case, knowing which job path corresponds to your particular preferences can help you obtain a better notion of the type of work that you'll like and, more importantly, thrive at in the future. Take the time to consider this aspect of the equation carefully, as matching your work with your hobbies can go a long way toward ensuring that you remain content in your job for many years to come.
3. Consider your desired pay and career progression.
As a result of these variances, data scientists and data analysts require different degrees of expertise, resulting in varied amounts of salary for both occupations.
Data analysts spend the majority of their time working with databases, they may be able to boost their earnings by learning new programming abilities, such as R and Python.
Nevertheless, according to PayScale, data analysts with more than ten years of experience frequently maximise their earning potential and move on to other positions.
Data scientists are considered to be more senior than data analysts because they often hold a doctorate degree, possess advanced abilities, and have more years of professional experience.
Therefore, they are frequently paid more for their work than other workers.
Data scientists have a bright future ahead of them, with many opportunities to advance to senior jobs such as data architect or data engineer.
Conclusion
Which is the best option for you?
Given the considerable variances in function responsibilities, training requirements, and career trajectory, data analysts and data scientists have deceptively similar job titles.
You may pick which career is the best fit for you and get started on your route to success once you've examined aspects like your background, personal interests, and desired compensation.
All things being equal, I anticipate a data scientist to add more value than a traditional analyst. Things, on the other hand, are rarely equal. The value of a high-level data scientist in the wrong environment is zero, whereas the value of a self-taught data analyst in the correct context is immeasurable.
