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14 Top Data Pipeline Key Terms Explained

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 Here are some key terms commonly used in data pipelines 1. Data Sources Definition: Points where data originates (e.g., databases, APIs, files, IoT devices). Examples: Relational databases (PostgreSQL, MySQL), APIs, cloud storage (S3), streaming data (Kafka), and on-premise systems. 2. Data Ingestion Definition: The process of importing or collecting raw data from various sources into a system for processing or storage. Methods: Batch ingestion, real-time/streaming ingestion. 3. Data Transformation Definition: Modifying, cleaning, or enriching data to make it usable for analysis or storage. Examples: Data cleaning (removing duplicates, fixing missing values). Data enrichment (joining with other data sources). ETL (Extract, Transform, Load). ELT (Extract, Load, Transform). 4. Data Storage Definition: Locations where data is stored after ingestion and transformation. Types: Data Lakes: Store raw, unstructured, or semi-structured data (e.g., S3, Azure Data Lake). Data Warehous...
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How to Show Data Science Project in Resume Correctly

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The data scientist resume should have mentioned the project correctly. Here are my ideas on how to show the project in the resume.

Resume: How to Show Data Science Project


How to Show Data Science Project?

1. Preparation of Resume. 

The first step for an interview for any project is you need Resume. You need to tell clearly about your resume. 


2. Answering about Project. 

In interviews, you will be asked questions about your project. So the second step is you need to be in a position to explain the project.


3. Answering your Project Role. 

The third point is you need to explain the roles you performed in your data science project. If you mention the roles correctly, then, you will have a 100% chance to shortlist your resume.

Based on your experience your resume can be 1 page or 2 pages. 

4. Technologies in the Resume.

In interviews, again they will be asked how you used different tools to complete your data science project. So, you need to be in a position to explain how you used different options present in the tools. 

Sometimes, in interviews, they may ask about the specific role in Tools specifically you used. You should be in a position to answer these questions too.

How to Prepare Resume. 

  1. Write a clear description.
  2. Write a specific role.
  3. Explain Tools. 
  4. Write about data flow.

1. Write a Clear Description.  In any data science project, you will find a few things like Client name, the expectation of the client, and what you are going to deliver. These things you need to present clearly in your Resume. 


2. Write Specific Roles. Roles. To convince your interviewer, you need to tell about your team roles and your specific role. In general, you can find the following roles:
  • Architect.
  • Data scientist.
  • Business Analyst.
  • Development team.
  • Testing Team.
  • Integration testing team.
  • Production release team.

3. Explain Tools. You need to present all the Tools your project is using and your specific tools. Then, in the face-to-face interview, you need to tell what options you used to achieve what.

For example, I used some integration tool, to receive data to the development region, and to send out after unit testing. If you explain, these key points, I can say, 100% sure, you will be selected.

4. Write about data flow. You need to explain how data is coming, is it in the sequential data set or document data. Something you need to tell clearly.

You also need to tell, after unit testing, which forms you will send the data to the next region. If you know this flow correctly, then you can convince easily your interviewer.

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