Abhishek Mishra

1. Technical Proficiency and Tools
The technical stack is the foundation of any data role. Proficiency starts with SQL Structured Query Language, which is essential for pulling data from relational databases. You must master joins, subqueries, and window functions. Python has become the primary language for analysis due to libraries like Pandas for data manipulation, NumPy for numerical calculations, and Matplotlib or Seaborn for basic plotting. While many focus on code, advanced Excel remains a staple for quick analysis, requiring knowledge of Pivot Tables, Power Query, and complex formulas. Additionally, familiarity with Cloud Data Warehouses like Snowflake, BigQuery, or AWS Redshift is increasingly vital as companies move away from local storage.
​2. Statistical and Mathematical Grounding
Beyond the tools, an analyst must understand the why behind the numbers. This includes descriptive statistics mean, median, variance to summarize data and inferential statistics