Quantitative Analyst vs Data Scientist

Everyone is talking about ‘big data’ these days, as well as ‘data science,’ and ‘data mining.’ There also is much said about the differences between quantitative analysts (or data analysts) and data scientists. This article hopes to set the record straight. Learn which occupation may be the best fit for you.

Quantitative Analytics vs. Data Science

Quantitative analysts and data scientists work with data. The major difference in their jobs is what they do with the data. A quantitative or data analyst studies large sets of data and identifies trends, develops data charges, and creates presentations visually to help companies make strategic decisions. (Northeastern.edu).

Data scientists design and build data modeling processes and production using algorithms, prototypes, predictive models, and custom analysis.

Working in Quantitative Analytics

The responsibility of quantitative analysts may vary across industries and companies. But fundamentally, quantitative analysts use data to reach meaningful insights and solve complex problems. They perform analysis of well-defined data sets with many fools to answer complex business needs:

  • Why sales have dropped in the last quarter
  • Why a sales campaign failed in certain states
  • How internal attrition affects revenue

Quantitative or data analysts have many fields and titles, such as database analyst, market research analyst, financial analyst or operations analyst. The best quantitative analysts have a high amount of technical skills but also can communicate quantitative findings to non-technical co-workers or clients.

Quantitative analysts usually have a heavy background in statistics and mathematics. They also can supplement a non-quantitative background by learning the analytical tools they need to make critical decisions with numbers.

Top quantitative analyst tools include:

  • Data mining
  • Data warehousing
  • Data modeling
  • R or SAS
  • SQL
  • Statistical analysis
  • Database management and reporting
  • Data analysis

Quantitative analysts usually are responsible for designing and maintaining databases and data systems with statistical tools to interpret large data sets. They also are responsible for writing reports that communicate patterns, trends, and predictions based on the latest findings.

Working in Data Science

Data scientists specialize in estimating what is unknown. They ask questions, write algorithms, and build statistical models. The major difference between a quantitative analyst and a data scientist is the amount of coding involved. Data scientists are able to arrange random, undefined data sets using several tools at the same time, and devise their own frameworks and automation systems.

The typical data scientist has heavy mathematical and statistical knowledge, hacking abilities, and detailed technical expertise. Skills necessary for data scientists are:

  • Machine learning
  • Software development
  • Hadoop
  • Java
  • Data mining and data warehousing
  • Data analysis
  • Python
  • Object-oriented programming

Data scientists are usually working on designing data modeling processes. They also create algorithms and predictive models to pull out information needed by a company to solve difficult data-driven problems.

A Quantitative Analyst or Data Scientist Career?

Now that you understand the differences between quantitative analytics and data science, you can determine which technical career is a better fit for you. To figure this out, consider your educational and professional background; personal interests; the career trajectory you desire.

Personal Background

While quantitative analysts and data scientists are similar, their major differences boil down to their educational and professional backgrounds.

Quantitative analysts look at large datasets to pinpoint trends, devise charts, and create presentations to help companies make better decisions on the strategic landscape. To align their education with these, analysts will usually get a bachelor’s degree in science, engineering, math, or engineering. Some may get a master’s degree in one of those areas. They also look for experience in science, math, programming, modeling, predictive analytics, and databases.

Data scientists are more interested in the construction and design of new processes for data production and modeling. Also, because they are using many sophisticated techniques to explore data, such as data mining and machine learning, having a master’s or Ph.D. is almost essential to advance. Experts say that data scientists are more technical and mathematical than quantitative analysts.

When thinking about which career is best for you, take a look at the above educational requirements first. If you already have a master’s degree in a suitable specialty, you may want to consider the data scientist path. But if you have only your bachelor’s, a quantitative analyst role may be more realistic at this time.

Consider Your Interests

Are you interested in statistics and numbers? Or are you interested in computer science and business more?

Quantitative and data analysts love statistics, numbers, and programming. They are the gatekeepers for the data at their organization. So they work almost all the time in databases to find data points from disparate and complex sources. Quantitative analysts also need to have a good understanding of the industry in which they work.

Data scientists should have a combination of mathematics, statistics, and computer science, as well as interest in and knowledge of the business world.

Either way, understanding which career better fits your interests will tell you the kind of work that you more likely to enjoy.

Career Trajectory

Different experience levels are necessary for data scientists and quantitative analysts, which results in different levels of compensation.

Quantitative analysts have a potential salary between $81,700 and $138,000. As these professionals are usually working in databases, they can boost their earnings by learning R and Python. (Roberhalf.com)

But quantitative analysts with at least 10 years of experience will maximize their salaries and move to other roles. You might eventually move into software development or data science. (Payscale.com)

Data scientists usually have a master’s or Ph.D. and are usually higher level than quantitative analysts. Data scientists in 2016 were found to have a salary range of $116,000 to $163,500. Many in data science eventually move into senior roles such as data engineer or data architect.

Which Is For You?

Quantitative analysts and data scientists have similar job titles, but they have different roles, educational requirements, and career paths.

Whichever path you choose, people with data-focused career interests are highly sought after in our job market. After you have considered the above factors, you should have a better idea of whether your career goals are more aligned with quantitative analysis or data science.