Roadmap to become a data analyst

1. Foundation Skills:

•Strengthen Mathematics: Focus on statistics relevant to data analysis.

•Excel Basics: Master fundamental Excel functions and formulas.

2. SQL Proficiency:

•Learn SQL Basics: Understand SELECT statements, JOINs, and filtering.

•Practice Database Queries: Work with databases to retrieve and manipulate data.

3. Excel Advanced Techniques:

•Data Cleaning in Excel: Learn to handle missing data and outliers.

•PivotTables and PivotCharts: Master these powerful tools for data summarization.

4. Data Visualization with Excel:

•Create Visualizations: Learn to build charts and graphs in Excel.

•Dashboard Creation: Understand how to design effective dashboards.

5. Power BI Introduction:

•Install and Explore Power BI: Familiarize yourself with the interface.

•Import Data: Learn to import and transform data using Power BI.

6. Power BI Data Modeling:

•Relationships: Understand and establish relationships between tables.

•DAX (Data Analysis Expressions): Learn the basics of DAX for calculations.

7. Advanced Power BI Features:

•Advanced Visualizations: Explore complex visualizations in Power BI.

•Custom Measures and Columns: Utilize DAX for customized data calculations.

8. Integration of Excel, SQL, and Power BI:

•Importing Data from SQL to Power BI: Practice connecting and importing data.

•Excel and Power BI Integration: Learn how to use Excel data in Power BI.

9. Business Intelligence Best Practices:

•Data Storytelling: Develop skills in presenting insights effectively.

•Performance Optimization: Optimize reports and dashboards for efficiency.

10. Build a Portfolio:

•Showcase Excel Projects: Highlight your data analysis skills using Excel.

•Power BI Projects: Feature Power BI dashboards and reports in your portfolio.

11. Continuous Learning and Certification:

•Stay Updated: Keep track of new features in Excel, SQL, and Power BI.

•Consider Certifications: Obtain relevant certifications to validate your skills.

Let’s dive into a comprehensive guide on SQL for Data Science! 

I have broken it down into three key sections to help you:

𝟭. 𝗦𝗤𝗟 𝗖𝗼𝗻𝗰𝗲𝗽𝘁𝘀:

Get a handle on the essentials -> SELECT statements, filtering, aggregations, joins, window functions, and more.

𝟮. 𝗦𝗤𝗟 𝗶𝗻 𝗗𝗮𝘆-𝘁𝗼-𝗗𝗮𝘆 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲:

See how SQL fits into the daily data science workflow. From quick data queries and deep-dive analysis to building pipelines and dashboards, SQL is really useful for data scientists, especially for product data scientists.

𝟯. 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗦𝗤𝗟 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝘀:

Learn what interviewers look for in terms of technical skills, design and engineering expertise, communication abilities, and the importance of speed and accuracy.