Data is the new fuel of the 21st century, and one can harness data holds all the information and power in the world. Big Data is gaining prominence, especially with the digital revolution brought about the latest advancements in the field of information technology. As Pat Gelsinger, CEO of Intel, quoted, “Data is the new science. Big Data holds the answers.” As a result of big data’s growing use and significance in businesses and organizations, new career opportunities have emerged that include analyzing enormous data sets and data pools to uncover insightful business information that can help a company succeed. Two important career paths that have emerged because of the growing popularity of Big Data are data analyst and data science jobs, which are also one of the most in-demand as well as talked jobs in recent times.
Both the terms data analyst and data scientist are often thrown together in congruence with each other, which has led to confusion as to what the difference is between the two careers. This article will help you gain a comprehensive understanding of the differences between the two professions and bring you clarity as to which career path you want to pursue.
Although both data analysts and data scientists work primarily with data, the skills, and methodologies they employ to do so are radically different. So, if you are considering a career in big data and aren’t sure whether to become a data scientist or an analyst, it’s essential to know the distinctions between the two and what each position requires. Your career might be ruined by making a poor decision that is out of alignment with your aptitude and goals. For those who are starting out, there is a myriad of both traditional and online platforms that provide data analytics and data science courses. For example, the online platform of Simplilearn offers an excellent data science course that includes masterclasses, hackathons, and webinar sessions that covers the A to Z of data science concepts and help you forge a career in the field.
Let us now try to comprehend the differences between a data scientist and a data analyst in terms of their job roles, skills, educational requirement, and more.
Data analyst vs. data scientist: what it entails?
While both these branches deal primarily with data for business purposes, the difference lies in their approach.
Data analysis is the systematic collection and examination of raw data sets in order to extract meaningful information. The data analysis method encompasses a set of tools, processes, and technologies that are used to identify trends and patterns that can then be converted into actionable business insights by organizations.
Data analysts are specialists that analyze data to derive information. As a result, we can define data analysts as experts who use unstructured or raw data in conjunction with statistical analysis and data visualization software, as well as tools like Python, R, or SQL programming languages, to identify solutions to real-world business difficulties.
Data science can be defined as the process of developing strategies for collecting and preparing data for analysis. It encompasses processing and designing data models with a combination of math, statistics, programming languages like R and Python, artificial intelligence, and machine learning and integrating those models into functionalities.
Data scientists are professionals who develop strategies, tools, and techniques that assist data analysts in analyzing information to discover actionable insights buried within the data, which can then be used for an organization’s strategic planning and decision-making processes.
Data analyst vs. data scientist: what do they do?
Data analysts work with processing already-existing data sets and attempt to gather, organize, and analyze data through statistical analysis to unearth hidden actionable insights that may be used to address present issues. They concentrate on providing data in the most effective manner so that people within an organization may comprehend what the data signifies. They accomplish this by developing data reports and visualizations that make the data simpler for others to understand.
Simply put, data analysts use data to deduce answers to queries for which we do not yet have the answers.
Data scientists, on the other hand, work with large amounts of raw and structured data using a variety of techniques that combine computer science, predictive analytics, statistics, and machine learning to solve problems that have not yet been thought of. In other words, data scientists apply advanced analytical techniques to tackle the unknown and make predictions about future trends. They achieve this by designing predictive modeling processes and developing their own machine-learning algorithms to identify the right trends or patterns in the data. Their role can be thought of as an evolved or advanced version of that of a data analyst.
Data analyst vs. data scientist: Skills required
As mentioned earlier, although both disciplines deal with analyzing data to derive meaningful insights, the difference lies in the skills and techniques applied to tackle the data. Therefore, let us look at the different skillset you need to possess if you dream of becoming either one.
A data analyst should possess the following skills:
- Soft skills – Excellent written and verbal communication prowess, critical thinking, organizational and analytical skills, and business aptitude are needed for a data analyst.
- Technical skills- A data analyst must have a practical understanding of data mining, data frameworks, and machine learning methods.
- Data visualization tools- D3, Tableau, Power BI
- Statistics and foundational mathematical knowledge
- Microsoft Office tools- to effectively communicate the data interpretation so that others can understand, an excellent knowledge of spreadsheets like Microsoft Excel, Google Sheets, and others is needed.
As a data scientist, you must have highly advanced technical skills to delve deeper into the data and evaluate it from every possible angle to make future predictions. A data scientist must possess the following skills:
- Advanced mathematics, calculus, linear algebra, and statistics are required.
- Programming- A data scientist should be proficient in programming languages such as Python, R, Java, SAS, and SQL.
- Advanced technical skills- A data scientist should have experience and be able to work with data mining, creating linear model regressions, building data architectures, Machine learning models, artificial intelligence, data modeling, and other related tasks.
- Cloud computing knowledge
- Understanding of SQL and NoSQL databases
- Comprehension of web services and Big Data technology such as Hadoop, MySQL, TensorFlow, and Spark is required to be a data scientist.
Data analyst vs. data scientist: Educational requirements
Most data analyst positions require at least a bachelor’s degree in a data-related field such as mathematics, statistics, or computer science. Data scientists usually hold a master’s or doctoral degree in data science, information technology, mathematics, or statistics.
Although a basic education qualification of a degree in a related field is desired, alternative options such as enrolling in an online certification course or bootcamps that offer excellent bachelor/masters programs in data analytics or data science are available for beginners without a degree or previous experience who do not wish to go down the traditional learning route. These programs provide industry-standard certifications and provide you with the skills and foundational knowledge needed to land an entry-level position as a data analyst in less than six months of study. So. working as a data analyst first can be a good way to learn the craft and gain the experience needed to launch a career as a data scientist if you are just starting out in the field.