Data Scientist vs Data Analyst. Which is Right for You

Decide by viewing the responsibilities, qualifications, skills, and salary of the two.

Christopher Zita
Feb 12 · 4 min read
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Photo from Unsplash by Charles Rabada

In 2020 it was estimated that the number of data-based job listings was approximately 2,720,000. What’s impressive is that this number grew over 700% from the year before.

Data has become the driving force for businesses, and the two prominent data roles that can be found at any company is a data analyst and data scientist.

If you’re interested in data and creating models you’ve probably heard about the two and were confused yourself. The issue is that the line that separates them has become blurred and even experienced data scientists/analysts get confused about the different roles.

In this article, I’m going to try to make that line a little more clear by going over the responsibilities, qualifications, skills, and salary of a data scientist versus a data analyst.

See what it takes to become a data scientist vs. a data analyst, and make a decision on which one is right for you.

Responsibilities

The main responsibilities differ based on how each position utilizes the data.

  • Discover opportunities by identifying trends: Collect and analyze data to garner actionable insights that are then shared with the company.
  • Develop analytical methods and machine learning models: To discover those insights they utilize machine learning for optimizing predictive models, and forecasting patterns.
  • Data cleaning: This is what nearly 80% of the job truly is. It’s pre-processing and transforming the data so machine learning models that the business has established can be run on clean datasets.
  • Conduct A/B testing: Creating two independent tests and testing them on frameworks and model quality.
  • Create algorithms that help businesses: Develop processes and tools that drive optimization and improvement of product development, marketing techniques, and business strategies.
  • Use pre-existing data to solve a problem: Instead of discovering opportunities they use pre-existing data to answer questions that support the day-to-day decision making.
  • Performing various types of analytics: This includes descriptive analytics, diagnostic analytics, predictive analytics
  • Create reports and dashboards: Help translate data into visualizations, metrics, and goals that are outlined by the company.
  • Data querying using SQL: Write SQL queries to extract data from the data warehouse.

Qualifications

What do you need to become a Data Scientist vs. a Data Analyst?

  • Master’s or Ph.D. in computer science, statistics, or mathematics: A Master’s or Ph.D. is not entirely necessary but is highly preferred.
  • Understanding of machine learning concepts: Such as clustering, random forests, and deep learning networks.
  • Knowledge of statistical concepts and practices: This includes probability distributions, statistical tests, regression/classification, and generalized linear models.
  • Proficient with statistical computer languages: Such as R, Python, SAS, MATLAB, SQL, etc.
  • Proficient with big data and their associated computing tools: This includes Hadoop, Spark, Hive, MySQL, etc.
  • Degree in mathematics, statistics, or business: Roles usually only require that you have a bachelor’s in a related field.
  • Knowledge of the fundamentals in various data-related languages: Such as SQL, R, Python, etc.
  • Knowledge of the fundamentals in various data visualization tools: Such as Tableau, Power BI, Matplotlib, etc.
  • Advanced ability with Excel: Cleaning, filtering, and transforming data in spreadsheets.
  • Strong communication skills: Be able to communicate the insights from the data to company leads.

Skills

Common job skills of Data Scientists vs. Data Analysts.

  • Math, Statistics, Computer Science
  • Machine Learning
  • SAS, SQL, R/Python
  • Tableau/PowerBI
  • Apache Spark
  • Natural Language Processing
  • Data Mining
  • Math, Statistics
  • R/Python
  • Excel, Office
  • Tableau/PowerBI
  • Data Mining
  • SQL

Salary

How much does a Data Scientist vs. Data Analyst make? The salaries below are according to Glassdoor.

  • Entry-level: $85,000 - $95,000
  • Mid-level: $100,000 - $120,000
  • Senior-level: $120,000 - $150,000
  • Entry-level: $45,000 - $60,000
  • Mid-level: $65,000 - $85,000
  • Senior-level: $85,000 - $110,000

Final Thoughts

Data analysts and data scientists are two of the hottest jobs in tech and are both a great career move.

But if your goal is to be a data scientist and your skills and qualifications mainly match those of a data analyst, as many of yours will, don’t be discouraged.

Many data scientists start out as a data analyst and after years of experience and improving their skills, they then make their way to that data scientist role.

Don’t give up, keep learning and you will soon identify will the data scientist’s qualifications, skills, responsibilities, and salary above.

Join my group Clean Code Z with 1k+ people for tips, job advice, and coding ideas for up and coming Data Scientists.

[1] Kunis, L. Difference Between Data Analyst vs. Data Scientist. https://www.springboard.com/blog/data-analyst-vs-data-scientist/

[2] Burnham, K. Data Analytics vs. Data Science: A Breakdown. https://www.northeastern.edu/graduate/blog/data-analytics-vs-data-science/

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Christopher Zita

Written by

Data Scientist | I Do Dumb and Cool Things with Code| Proud Canadian | pages.christopherzita.com/clean-code-z

Analytics Vidhya

Analytics Vidhya is a community of Analytics and Data Science professionals. We are building the next-gen data science ecosystem https://www.analyticsvidhya.com

Christopher Zita

Written by

Data Scientist | I Do Dumb and Cool Things with Code| Proud Canadian | pages.christopherzita.com/clean-code-z

Analytics Vidhya

Analytics Vidhya is a community of Analytics and Data Science professionals. We are building the next-gen data science ecosystem https://www.analyticsvidhya.com

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