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In this video, we will decode the basic differences between data scientist, data analyst and data engineer, based on the roles and responsibilities, skillsets required, salary and the companies hiring them. Although all these three professions belong to the Data Science industry and deal with data, there are some differences that separate them. Every person who is aspiring to be a data professional needs to understand these three career options to select the right one form themselves. Now, let us get started and demystify the difference between these three professions.

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We will distinguish these three professions using the parameters mentioned below:
Start (0:00)
1. Job description (00:26)
2. Skillset (02:06)
3. Salary (03:26)
4. Roles and responsibilities (03:48)
5. Companies hiring (04:54)

📚 Data Scientist vs Data Analyst vs Data Engineer article: [ Ссылка ]

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➡️ About Caltech Post Graduate Program In Data Science
This Post Graduation in Data Science leverages the superiority of Caltech's academic eminence. The Data Science program covers critical Data Science topics like Python programming, R programming, Machine Learning, Deep Learning, and Data Visualization tools through an interactive learning model with live sessions by global practitioners and practical labs.

✅ Key Features
- Simplilearn's JobAssist helps you get noticed by top hiring companies
- Caltech PG program in Data Science completion certificate
- Earn up to 14 CEUs from Caltech CTME
- Masterclasses delivered by distinguished Caltech faculty and IBM experts
- Caltech CTME Circle membership
- Online convocation by Caltech CTME Program Director
- IBM certificates for IBM courses
- Access to hackathons and Ask Me Anything sessions from IBM
- 25+ hands-on projects from the likes of Amazon, Walmart, Uber, and many more
- Seamless access to integrated labs
- Capstone projects in 3 domains
- Simplilearn’s Career Assistance to help you get noticed by top hiring companies
- 8X higher interaction in live online classes by industry experts

✅ Skills Covered
- Exploratory Data Analysis
- Descriptive Statistics
- Inferential Statistics
- Model Building and Fine Tuning
- Supervised and Unsupervised Learning
- Ensemble Learning
- Deep Learning
- Data Visualization

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