Data Science is one of the most in-demand career choices in the present times. From food to finance, every industry is exploring new ways to use data and improvise strategies to reach out to potential customers. Data Scientists play a significant role in this. Hence it has also triggered the demand for Data Science professionals.
Today, several platforms are providing Data Science course online. These courses aim to equip individuals with all the tools used in Data Science and help them implement the same in real-world.
While we talk about Data Scientists, it is important to mention here about Quantitative Analyst. The profile may be confusing for some, but they also have an integral role in business similar to Data Scientist. In this blog, we will focus on the work profile of a Quantitative Analyst vs. Data Scientist.
Exploring more on Quantitative Analyst vs Data Scientist
Quantitative Analysts and Data Scientists both deal with the data and use statistical tools to make an informed decisions and resolve complex problems. But when it comes to their job roles, there is a line of difference between them. Let’s start exploring more on Quantitative Analyst vs Data Scientist.
Quantitative Analysts- They are also known as “analysts.” These professionals use mathematics, statistics and computer science to analyse data and delve deeper into its hidden meaning. Thereafter, they use this data to make informed financial and business decisions.
The core work of Quantitative Analyst is prominent in the financial industry to analyse the market trend and develop financial models to help predict future performance. They also work in the insurance, marketing and health care sector.
Data Scientist- The Data Scientists are experts in statistical analysis. They use several Data Science tools and technologies like Machine Learning to analyse and interpret the data to resolve complex problems. Their prime focus is to extract insights from the large volume of data.
The role of the Data Scientist is omnipresent. Some common industries that actively hire data scientists are finance, healthcare, technology and retail. Data Scientists are also responsible for deriving and implementing data-driven solutions to business issues.
Overall, while both Quantitative Analysts and Data Scientists use data and statistical analysis to solve complex problems. But Quantitative Analysts tend to focus more on financial or business decisions. In contrast, Data Scientists may work on various problems across various industries.
What Does a Quantitative Analyst Do?
As mentioned above, Quantitative Analysts have a prominent job role in the financial industry. Their prime focus is analyzing market trends and developing financial models to help predict future performance. However, their role is not just limited to the finance industry; they also have an active role in the insurance, marketing and healthcare sector.
Specific tasks that a Quantitative Analyst performs include:
Analyzing financial data: They work with a large volume of financial data, such as market data, economic indicators, and company financial statements. They also use various tools and techniques like statistical analysis and Machine Learning to analyze this data and extract insights.
Developing financial models: The role of Quantitative Analysts is prominent in the finance industry. They work on developing financial models to help predict future market trends, asset prices, or other financial outcomes.
Providing advice: They also play the role of a financial advisor. They assist in decision-making by providing useful insights from financial data. This might include recommending investments, providing risk management strategies, or suggesting changes to business operations.
Managing risk: Risk management is another role a Quantitative Analyst must perform. They identify and analyse data, identify potential risks, and develop strategies to overcome them.
Skills required to become a Quantitative Analyst
For an individual to become a successful Quantitative Analyst, they need to nurture certain skill sets like:
Mathematical skills: Quantitative Analysts adopt several mathematical and statistical techniques to derive insights from the data. Thus promoting more informed decision-making. And so, strong mathematic skills in calculus, algebra, and probability are paramount.
Programming skills: Knowledge of programming languages is a must if one wishes to excel as a Quantitative Analyst. Some commonly used programming languages are Python or R. These languages help in precise data analysis and the development of financial models.
Data analysis skills: Working with large volumes of data requires analytical skills, enabling Quantitative Analysts to extract insights and identify patterns in the data.
In addition, to technical skill expertise, Quantitative Analysts also need to master certain non-technical skills that help them excel in this domain. These include:
Problem-solving skills: In addition to expertise and technical skills, a quantitative analyst should also be skilful in resolving complex problems using data and statistical analysis. They should have a critical thinking approach that can help them easily resolve complex problems.
Communication skills: Good communication skill is helpful in all profiles. As a quantitative analysis, you must also explain your findings to the clients and decision-makers. With effective communication skills, the task becomes easier and seamless.
To become a successful Quantitative Analyst, it is important to have a strong foundation in math and programming and excellent problem-solving and communication skills.
What Does a Data Scientist Do?
A Data Scientist is a professional who works with data using statistical and mathematical tools. They work with large and complex data sets, using various tools and techniques that help them interpret the data. They are responsible for designing and implementing data-driven solutions to business problems.
Specific tasks that a Data Scientist perform include:
Analyzing and interpreting data: One of the prominent roles of a Data Scientist is to analyse and interpret the data using different tools and mathematical techniques. They also incorporate Machine Learning technology to understand patterns in the data.
Developing predictive models: Data scientist also develops predictive models that help forecast future trends and patterns. These models are usually based on Machine Learning algorithms and other statistical techniques.
Communicating findings: In addition to working with their core technical team, Data Scientists also have to work with the management. They need to communicate their findings and recommendations to the stakeholders, managers and clients. This is done via reports, presentations or Excel sheets.
Collaborating with other team members: Data scientists have to work together as a team and collaborate with other professionals, like Data Engineers or analysts, to design and implement their data-driven solutions.
Overall, the role of a Data Scientist is to use data and statistical analysis to extract insights and solve complex problems in various industries.
Skills required to become a Data Scientist
A Data Scientist must develop and nurture different skill sets to excel professionally. As a part of their professional journey, Data Scientists must hone their technical and non-technical skills. These are highlighted below:
Mathematical skills: A Data Scientist uses advanced mathematical and statistical techniques to analyse data and derive useful insights. Hence they must have proficiency in different mathematical formulas and concepts like calculus, algebra and probability.
Programming skills: Similar to Quantitative Analysts, Data Scientists also need expertise in programming languages like Python and R. These are used to develop predictive models.
Data analysis skills: A Data Scientist has to work with a large volume of complex data. Hence having strong analytical skills and visual interpretation expertise is paramount.
Machine Learning skills: To develop different models, a Data Scientist has to use a Machine Learning algorithm. Hence, expertise in the concepts of Machine Learning and its application is also imperative.
Communication skills: As mentioned above, Data Scientist is responsible for communicating their findings to fellow members, managers and clients. Hence, good communication skills will ensure the effective dissemination of information and easier comprehension.
To become a successful Data Scientist, it is important to have a strong foundation in math and programming and excellent problem-solving and communication skills. In addition, a strong understanding of Machine Learning concepts and techniques is increasingly important in this field.
Salary of a Quantitative Analysts and Data Scientists
As per Indeed.com, people working as Quantitative Analysts draw an average salary of $152,817 per annum. Glassdoor lists the average salary of a Quantitative Analyst at $109,437 per annum. Comparatively, a Data Scientists annual salary of $97222 per annum.
The growth prospects in both these domains are impeccable. However, one must acquire the right skill sets and certification to gain a competitive edge. With more experience the Quantitative Analysts and Data Scientists increases, have greater prospects of professional, excellence and pay scale.
What are Data Scientist Eligibility Criteria?
Anyone wishing to pursue a career in Data Science can enrol for the Data Science certification course. The certification course incorporates all the core concepts of Data Science and its practical implementation. However, to enrol for the certification programmes, one must have at least graduated in mathematics, computer science, or a relevant field. The eligibility criteria for the Data Science certification course may vary from one organisation to another. However, you can enrol for the Data Science Bootcamps and workshops when you have cleared your class 12th. Platforms like Pickl.AI have created a range of Data Science certificate courses.
Wrapping it up !!!
The above discussion would have given you an insight into Quantitative Analyst vs Data and Scientist job roles. If you are inclined to become a professional Data Scientist, this is the right time to start your learning journey.
Data Science remains a prominent point of discussion for individuals from a professional perspective. The growth opportunities in this domain are unparalleled. Hence if you are willing to excel as a Data Scientist, this is the right time to enrol for the data signed certificate course.