Data Science and Data Analytics are often tossed around interchangeably in tech blogs, job listings, and online posts and articles. However, they are not really the same thing, in fact, they are quite different.
If you want to pursue a career in business intelligence, risk analysis, and data science and management, an in-depth understanding of the difference between the roles of data scientists vs data analysts is a must.
Simply put, data scientists are professionals associated with a broad discipline involving the collection, processing, analysis, and interpretation of data. It also involves identifying current trends and predicting future ones to aid data-driven choices.
Data Analysts on the other hand are associated with a subset of data science – data analytics – that includes collecting, cleansing, and processing data for further study. It is used to spot patterns in data and facilitate the next steps like forecasting and strategizing.
Read on to understand the differences in the roles of data analysts vs data scientists in more detail.
What is Data Science?
Data science is a discipline that deals with studying, collecting, and analyzing large amounts of data to get insights into how the digital world functions. It allows businesses and organizations to position themselves better in a competitive environment and reach more potential consumers.
Data science is useful in understanding the current trends influencing the tastes and preferences of people and studying shifts in consumer behavior. It helps predict the future and develop data-driven business strategies.
Who is A Data Scientist?
Data Scientists are professionals who collect data, select and filter useful data for further study, analysis, and forecasting, and use statistical tools to look for patterns, trends, and interpretations.
They use their advanced skills in mathematics, statistics, programming languages, AI and ML, and data cleansing, processing, and management to create, test, modify, and update algorithms and build predictive models.
What is Data Analytics?
Data Analytics is both a field and a process. It includes cleansing, processing, and analyzing data to find possible patterns and identify trends. It is integral in decision-making processes, forecasting market trends, sales revenue, consumer behavior, and more.
It’s often considered a subset of the broader field of data science and focuses on descriptive and possibly predictive analysis of already existing data using statistical methods.
What Does a Data Analyst Do?
Data analysts are professionals skilled in data cleansing and visualization, basic programming languages, and statistical and mathematical tools. They fix any inaccuracies in data, look for patterns, trends, and possible interrelations, and prepare elaborate reports that would enable further strategizing.
They collaborate with IT and marketing teams and domain experts to make sure the reports are precise and high-quality and serve their diagnostic purposes.
Data Scientist Vs. Data Analyst – Understanding The Key Differences
While the job titles might sound similar or even overlapping, there are significant differences in the qualifications, skills required, complexity of tasks handled, and job roles of data scientists vs data analysts.
Education – You can become a Data Analyst after completing your bachelor’s degree in computer science, statistics, mathematics, and related fields. Data scientists additionally need a master’s degree in applied data sciences, or other relevant fields along with advanced certifications.
Skills – Data analysts need strong visualization, mathematical, and statistical skills, knowledge of programming languages, and expertise in Excel and SQL. Data Scientists require advanced skills in mathematics, statistics, AI and ML, programming, and algorithm development skills.
The focus of the role – Analyzing and making interpretations out of existing data to facilitate forecasting and predictive model building is the key focus of data analytics jobs. On the other hand, data science jobs focus on studying reports and inferences, developing algorithms to automate trend forecasts, and building predictive models.
Daily tasks – Data analysts identify patterns and trends in large data sets, prepare dashboards, and make detailed and insightful reports. These actionable reports are then utilized by Data scientists to develop AI-based automated systems, cyber security setups, and more.
Teams – Data Scientists work closely with data engineers and analytics teams whereas Data analysts collaborate with marketing, IT, and business teams to accurately collect and comprehend data.
Salary range – The average salary of a data analyst in India varies between ₹6,00,000 – ₹8,00,000; however, a data scientist can, on average, earn up to ₹11,00,000 – ₹13,00,000 and beyond per annum.
Conclusion
Understanding the key differences in the job profiles of data scientists vs data analysts is important to assess your skills and trace a career route map for yourself.
While Data analysis requires a basic to intermediate level of skills in computer science, statistics, and programming languages, data science requires advanced knowledge and skills for developing AI-powered automated predictive models and algorithms.
Enroll yourself in Data Science, computer science, statistics, mathematics, and related fields to access industry-standard training. Check out the Data science courses offered by AAFT to kickstart your career!
