Data Scientist vs Data Analyst vs Data Engineer: What's the difference?
TLDRThe video discusses different types of data jobs to clear misconception and confusion. It explains how companies collect, transform, store and use data, starting with engineers collecting data and engineers & data engineers processing it. Data analysts & scientists then interpret aggregated data to provide insights to guide business decisions. Data scientists can also build ML models. Understanding this hierarchy helps determine whether a data job, whose title nowadays is often blurred, matches one's interest and skillset.
Takeaways
- 😀 Many companies use different definitions for data science jobs, so read the full job description to understand the day-to-day work.
- 👩💻 Data engineers focus on collecting, storing, moving and transforming data to make it usable.
- 📊 Data analysts query and visualize data to answer questions and make product decisions.
- 🤖 AI and deep learning require clean, well-labeled data and identified features to work well.
- 🔢 SQL is an easy standard language that allows different roles to query databases.
- 📈 A/B testing frameworks help determine feature impact and guide development.
- 😎 At some companies, software engineers do end-to-end ML system building and testing.
- 🎓 Data scientists often have PhDs and specialized knowledge to work on complex ML projects.
- 🗂️ The hierarchy of data needs shows how each role contributes to using data effectively.
- 🤝 Good data analysts have technical, product and communication skills to drive strategy.
Q & A
What are the main differences between a data scientist, data engineer, and data analyst?
-Data engineers focus on building data infrastructure and pipelines. Data analysts interpret and analyze data to drive business decisions. Data scientists build advanced analytics models and algorithms using techniques like machine learning and AI.
What are some examples of tasks a data analyst would do?
-A data analyst may query data, create reports and dashboards, identify trends and insights, communicate findings to guide business strategy, and aggregate data to support decision making.
Why is having clean, quality data so important?
-Clean, quality data is critical for getting accurate insights and building effective models. Without proper data collection, storage, and preprocessing, advanced techniques like AI and machine learning will not work properly.
What tools are commonly used to query and visualize data?
-Common tools include SQL, Tableau, Power BI, Looker, MicroStrategy, and Domo as well as custom internal tools at companies like Facebook.
How can A/B testing help a business?
-A/B testing allows businesses to test different versions of a product or feature to see which one performs better. This helps guide data-driven product decisions.
What skills make a good data analyst?
-Great data analysts have technical skills to work with data, analytical ability to interpret and identify insights, product intuition to guide strategy, and communication skills to explain findings.
Why do some companies call data analysts data scientists?
-Data scientists are sometimes used to do analyst work since they have strong technical skills. However, true data science roles focus more on advanced analytics and research.
How can understanding these data roles help someone find the right job?
-Reading job descriptions closely and mapping to the data hierarchy of needs can help identify whether a role aligns with your skills and interests.
What do research scientists at companies work on?
-Research scientists focus on cutting edge techniques like deep learning and AI. They are often PhDs conducting specialized research, supported by machine learning engineers.
How were roles divided on your team at Google?
-At Google, software engineers did a wide variety of tasks including data analysis, modeling, A/B testing, and productionalizing models since teams were small.
Outlines
😄 Introducing Data Roles: Scientist, Engineer, Analyst
The paragraph introduces the key data roles - data scientist, data engineer, and data analyst. It explains that companies define these roles differently and one should read the job description to understand the day-to-day work. It then sets up an illustration to explain the differences between the roles in the context of a data hierarchy.
😎 Comparing Data Roles: Overlaps and Distinctions
The paragraph compares the data roles, noting overlaps as well as distinctions. Many companies use 'data scientist' title for analyst roles. Data scientists can work across multiple areas of the hierarchy. Roles also depend on company size. It uses a Google example to show how roles may be blended based on team size.
Mindmap
Keywords
💡data engineer
💡data analyst
💡data scientist
💡SQL
💡machine learning
💡deep learning
💡data hierarchy
💡ETL
💡product analytics
💡A/B testing
Highlights
Explains the differences between data scientist, data engineer and data analyst using an illustration
If you can't collect data properly, there's no point working on AI or deep learning
Data engineers work on transforming, cleaning, and preparing data so it's usable and queryable
SQL allows anyone in a company to easily query data
Data analysts interpret data and aggregate it to help make business decisions
Many companies call data analysts data scientists nowadays
Data engineers work on collecting, exploring, transforming, moving, and storing data
Data analysts work on aggregating and interpreting data to drive business decisions
Data scientists build machine learning models and work on AI and deep learning
Software engineers collect data and build data pipelines
Roles are often blurred, with engineers doing analysis, modeling, A/B testing
Read job descriptions to see where the role fits into the data hierarchy
Having clean, labeled data is imperative before doing deep learning or AI
A good analyst communicates insights to the company with product intuition
Simple machine learning models often provide enough value for most companies
Transcripts
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