Data science carrier patha?

 🎯Data Engineering is the foundation of the data science process. Without proper data engineering, data scientists would not have the data they need to perform their analysis and generate insights.

 

 🎯Data engineers work with raw data to make it accessible and useful for data scientists and other stakeholders. They need good understanding about large distributed systems.

 

 📌Data modeling : An essential part of Data Engineering, DO NOT SKIP THIS!!!

   ✅ Building Data Models

 📌Programming: Learn programming fundamentals, data structures, and algorithms.

 ✅Python

 ✅R Programming

 ✅SQL

 

 📌Databases: Databases are a critical component of data engineering. Learn how to work with SQL and NoSQL databases. Understand how to model data and design databases.

 ✅DBMS

 

 📌Learn big data technologies: Learn how to work with these tools to process and store large amounts of data.

 ✅Hadoop

 ✅Spark

 ✅Kafta

 

 📌Data warehousing: Data warehousing is the process of collecting, storing, and managing data from different sources to support business intelligence and decision-making. Learn about data warehousing concepts and technologies .

 ✅Snowflake

 ✅Redshift

 ✅BigQuery

 

 📌Cloud computing: Many data engineering projects are hosted in the cloud. Learn how to work with cloud services.

 ✅AWS

 ✅GCP

 ✅Azure

 

 📌Data integration: Data integration involves combining data from different sources to create a unified view.

 ✅Data pipelines

 ✅ETL(extract, transform, load) processes

 

 📌Data quality and governance: Data quality and governance are important considerations in data engineering.

 ✅Data quality assurance

 ✅Data Lineage

 ✅Data privacy

 

 📌Data visualization: involves the creation of visual representations of data to communicate information and insights.

 ✅PowerBi

 ✅Tableau

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          Happy Learning 📚

 

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