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Product analyst vs data scientist

The differences between product analysts and data scientists can be seen in a few details. Each job has different responsibilities and duties. It typically takes 2-4 years to become both a product analyst and a data scientist. Additionally, a data scientist has an average salary of $106,104, which is higher than the $79,316 average annual salary of a product analyst.

The top three skills for a product analyst include tableau, data analysis and product management. The most important skills for a data scientist are python, data science, and visualization.

Product analyst vs data scientist overview

Product AnalystData Scientist
Yearly salary$79,316$106,104
Hourly rate$38.13$51.01
Growth rate11%16%
Number of jobs176,369106,973
Job satisfaction--
Most common degreeBachelor's Degree, 73%Bachelor's Degree, 51%
Average age4441
Years of experience44

What does a product analyst do?

A product analyst job utilizes data analysis software and notates trends in market research. Primarily, analysts project the costs of product development and marketing. They think of the possibilities for profit and sales and monitor the performance of products on the market to come up with a better product. Their responsibilities include company product evaluation, product understanding, and product rating reviews. Familiarity with Microsoft Office Suite, strong communication skills, and proficiency in database software is necessary for this job.

What does a data scientist do?

A Data Scientist analyzes information from multiple sources in order to gain maximum insight that can give the company a competitive advantage. They work in different domains, including manufacturing, healthcare, education, and finance.

Product analyst vs data scientist salary

Product analysts and data scientists have different pay scales, as shown below.

Product AnalystData Scientist
Average salary$79,316$106,104
Salary rangeBetween $56,000 And $111,000Between $75,000 And $148,000
Highest paying CitySeattle, WARichmond, CA
Highest paying stateWashingtonCalifornia
Best paying companyMetaThe Citadel
Best paying industryTechnologyStart-up

Differences between product analyst and data scientist education

There are a few differences between a product analyst and a data scientist in terms of educational background:

Product AnalystData Scientist
Most common degreeBachelor's Degree, 73%Bachelor's Degree, 51%
Most common majorBusinessComputer Science
Most common collegeUniversity of GeorgiaColumbia University in the City of New York

Product analyst vs data scientist demographics

Here are the differences between product analysts' and data scientists' demographics:

Product AnalystData Scientist
Average age4441
Gender ratioMale, 53.2% Female, 46.8%Male, 79.6% Female, 20.4%
Race ratioBlack or African American, 7.6% Unknown, 4.5% Hispanic or Latino, 8.5% Asian, 14.6% White, 64.6% American Indian and Alaska Native, 0.2%Black or African American, 4.2% Unknown, 5.4% Hispanic or Latino, 6.9% Asian, 18.8% White, 64.2% American Indian and Alaska Native, 0.6%
LGBT Percentage12%9%

Differences between product analyst and data scientist duties and responsibilities

Product analyst example responsibilities.

  • Manage CRM development teams in India.
  • Use the SoapUI tool to automate the API calls relate to the corresponding UI functions.
  • Document test procedures to ensure reliability and compliance with standards using JIRA to report and manage bugs.
  • Work as a team to manage a student-run portfolio by researching and debating various securities for the benefit of the fund.
  • Manage inventory and supervise site payroll and invoice processing.
  • Lead architecture, design and development of a customize financial analytics solution.
  • Show more

Data scientist example responsibilities.

  • Update, maintain, and manage regional CRM database and records for customers, vendors, and suppliers.
  • Configure and manage JobScope ERP system for a make-to-order/make-to-stock design and manufacturing environment.
  • Lead the analysis in SAS for data integration of mortality data using meta-analysis integration methods.
  • Implement a proximal stochastic gradient descent with a line search to fit a regularize logistic regression in Scala
  • Perform cross-validation-test on linear regression model of data using scikit-learn.
  • Develop python base statistical visualization to provide insights of fuzzy social media data.
  • Show more

Product analyst vs data scientist skills

Common product analyst skills
  • Tableau, 7%
  • Data Analysis, 6%
  • Product Management, 6%
  • Product Development, 5%
  • PowerPoint, 5%
  • Project Management, 5%
Common data scientist skills
  • Python, 13%
  • Data Science, 10%
  • Visualization, 5%
  • Java, 4%
  • Hadoop, 4%
  • Tableau, 3%

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