Darshil Parmar

One of the most important skills for a Data Analyst is to understand business, tools come and go, but the overall goal is to help businesses make data-driven decisions. Once you start with this mindset then you can learn high-demand tools, here are: 📍 Excel: It's a goto tool, you can do everything in Excel only, from basic analytics to building interactive dashboards. 📍 PowerBI: Businesses don't want to see numbers, they like visuals, so you can easily build highly advanced dashboards. 📍Communicating your insights: Your insights are useless if you can't convey this to another person, having good communication is a key skill. Overall understand your role and try to find how you can help businesses using data!

Simple Strategy: Copy, Paste & Add your creativity! It’s difficult to gain experience without working in a job so you create your own experience by building projects. I have published 8 End-To-End projects for Data Engineering Just go and do them! Now these will not make you master, you followed exact steps from the video. Modify projects as per you learning goal and create good story around it. Showcasing experience comes with telling a good story - How did you handle this challenge? - Why did you choose this tool? - What was the outcome of this? Telling a story around your projects showcases that you have done the work required to gain some experience. It also shows you are a problem solver. Gain experience by solving problem.

📍Start with a problem: Do you like Football, Cricket, E-commerce, or something else? Pick something that you understand 📍Find a good dataset: - AWS Open Data - Google Data Search - Microsoft Open Data - Kaggle There are many amazing website to find good dataset 📍Create simple architecture for project and pick tools Dataset > Data Modelling (RDBMS) > S3(Storage) > Glue/EMR (Transformation) > S3 > Athena/Redshift You can replace tools as per your requirements 👉Redshift <> Snowflake 👉EMR <> Databricks As per your learning goal you can modify your project! Also check my YouTube to get basic understanding. 📍Publish on GitHub Create good repo and share everything you did - Dataset - Code - Data model - Architecture

You need to focus on two things: 1. Fundamental understanding of concepts 2. High-demand market tools to apply them Start with basics - Basic CS fundamentals (makes you better engineer) - Python & SQL (Must have) 📕Start reading Fundamentals of Data Engineering (you don’t need anything else than this) Learn core data engineering skills - Data Warehouse Concept <> Tool (Snowflake/BigQuery) - Apache Spark <> Tool (Databricks) - Apache Kafka (Kinesis/DataFlow) - Modern Data Stack (dbt, mage, airbytr, etc…) Cloud platform - pick one AWS/GCP/Azure and learn core data services You can also prepare for certification to validate your knowledge Build simple pipeline by integrating these tools