“We are surrounded by data, but starved for insights.”
Jay Baer, Marketing and Customer Experience Expert
Companies across the globe have been struggling with this very concern of making sense of the humungous data that they generate. It is not the job of an entrepreneur to look into the data and summarize it. Experienced Data Science Professionals put in their exceptional data science skills to make sense of the huge number bank. Data Science is not just about data scientists, there are lot many roles that are offered under the bigger umbrella. Though, sometimes, many of these roles demand skills and prerequisites that are overlapping, thus creating confusion at times.
As the graph showcases, an immense rise in emerging data is expected by the year 2025, which is set to touch the 175ZB mark is a huge revelation. Over the span of three decades, business analytics as a discipline has experienced massive diversification as well. From manual computation with calculators to now rapidly changing landscape, data analytics has evolved. Alongside, two major trends in data science have been prevalent- diversified use of technology n a variety of sectors, and new technologies making analyzing and interpreting the vast amount of data possible for major business decisions. As per recent reports from the United States Bureau of Labor Statistics, the data science field is expounded to grow at a rate of 22% between 2020-2030. Data scientists and Data analysts are the two most common roles that are often confused and used interchangeably that deserve a clear line of bifurcation.
What is Data Analysis?
Data Analysis refers to detailed data research to find patterns that can direct businesses to useful insights for making key decisions. Such data analysis can enable us which product is most popular at a cloth shop as per different demographics. It also helps to indicate consumer preference on social media platforms and the what, when, and how of consumer choices.
What is Data Science?
Data science assists in finding valuable business insights from humungous data. It helps stakeholders make key future decisions that are well-informed and backed with critical data progressions.
SIMILARITIES
| DATA ANALYSTS | DATA SCIENTISTS |
| Their skillset involves impressive mathematical and statistical expertise, with programming languages knowledge- Python, R, SQL, HTML, JavaScript | The Data Scientist Job demands efficiency in mathematical and statistical skills, along with an understanding of programming languages-Python, R, SAS, Matlab, SQL, Hive, Scala |
| Spreadsheet tools (Excel) with Data visualization tools such as Tableau | Business acumen, with storytelling and data visualization. They are also equipped with knowledge of distributed computing frameworks- Hadoop and ML skills |
DIFFERENCES
| DATA ANALYSTS | DATA SCIENTISTS |
| Data analysts explore data from a single source such as a CRM system | Data scientists explore and examine data from multiple disconnected sources |
| They are not expected to possess hands-on ML experience or build statistical models | At the core of a Data Scientist’s Job is to build responsible statistical models and exemplary command over ML |
| They put analytical techniques to use at regular intervals and present timely reports | They deal with data frameworks and target automating tasks to solve complex business problems |
SKILLS:
Skills required by a data analyst and a data scientist may overlap, yet there is a significant difference. Both roles demand professionals equip themselves with basic mathematical knowledge, algorithms, communication, and software engineering skills. What sets a data scientist apart from a data analyst is the fact that the former possesses all the skills of a data analyst along with a strong grasp of data modeling, analytics, statistics, and computer science.
ROLES AND RESPONSIBILITIES:
A Data Analyst writes convention SQL queries to resolve business problems and analyze, and mine business data to identify any relation and form data patterns. They are also responsible for implementing new metrics for finding unclear parts of the business developments, designing and creating data reports, using data visualization, and applying statistical analysis to help stakeholders execute better decisions.
As a Data Scientist, you are responsible to clean, process, and organize data for analysis, and develop new analytical methods and ML models. Data storytelling and data visualization form a major part of their job responsibility.
EDUCATIONAL PREREQUISITES:
A data analyst is required to possess at least three years of prior work experience with a minimum bachelor’s degree or higher in the field. For a Data Scientist Job, a Data Science Professional must hold an advanced degree, master’s, or Ph.D.; with a background in engineering. This clearly shows that data scientists are more qualified as compared to data analysts. Further, these qualified professionals prefer equipping themselves with the best Data Science Certifications that elevate the chances of them being hired by the leaders in the global corporate world.
SALARY:
A senior data analyst with experience packs $65,812 annually, whereas a data scientist earns a hefty $144,481 per year globally. This sets the salary bar higher for a learned data scientist than a data analyst.
This is a clear indication that the demand for data analysts and data scientists is going to surge globally in the coming years as well, as depicted in the graphical presentation above by World Economic Forum. The future is definitely data science, yet you may consider a data analyst role, to begin with in the ever-evolving data science industry. For gaining strength in building your data science career, you must earn that extra kick to your basic qualification pack with a credible world-renowned Data Science Certification, to make your resume and skillset invincible. Begin today with a future-proof career vision!
