Pooja Pawar, PhD

Data Analyst | BI & Data Visualization
SQL | Python | Power BI | Excel
Data insights, career thoughts and practical learning
180K+ community across platforms

[Pooja Pawar, PhD @SheExploresData]

Excel vs SQL vs Python

https://t.co/GmucTj3QF8

[Pooja Pawar, PhD @SheExploresData]

SQL or Python for Data Analysis? You don’t have to choose just one. SQL excels at querying structured data, while Python shines in cleaning, exploration, automation, and advanced analysis. Learn both.

#SQL #Python #DataAnalytics #DataScience #DataAnalyst #Pandas #LearnSQL

[Pooja Pawar, PhD @SheExploresData]

Power BI is much more than building dashboards. It’s an end-to-end analytics workflow: connect, clean, model, calculate, visualize, share, and turn data into decisions that people can actually use.

#PowerBI #DataAnalytics #BusinessIntelligence #DAX #DataVisualization #MicrosoftFabric #DataAnalysis

[Pooja Pawar, PhD @SheExploresData]

SQL and Python often solve the same data problems in different ways. Learn how filtering, grouping, sorting, joins, updates, and more translate from SQL to Pandas.

#SQL #Python #Pandas #DataAnalytics #DataScience #DataAnalyst #LearnPython

[Pooja Pawar, PhD @SheExploresData]

Python visualization is not just about making charts—it’s about finding patterns, relationships, trends, and anomalies during EDA. Keep these essential commands handy for your next analysis.

#Python #DataVisualization #EDA #DataAnalytics #DataScience #Pandas #Seaborn

[Pooja Pawar, PhD @SheExploresData]

Data wrangling gets easier when you understand the logic behind each tool. Compare 10 essential operations across SQL, Pandas, and Power BI—from filtering and joins to ranking and running totals.

#SQL #Pandas #PowerBI #DataWrangling #DataAnalytics #Python #DataAnalyst

[Pooja Pawar, PhD @SheExploresData]

List, Tuple, Set or Dictionary—which Python data structure should you use? Understanding how they differ in order, mutability, duplicates and access makes choosing the right one much easier.

#Python #LearnPython #PythonProgramming #DataScience #DataAnalytics #Coding #Programming

[Pooja Pawar, PhD @SheExploresData]

Excel vs SQL vs Python

https://t.co/GmucTj3QF8

[Pooja Pawar, PhD @SheExploresData]

Excel, SQL, or Pandas—which tool should you use for data analysis? Compare how each handles filtering, sorting, aggregation, joins, missing data, and visualization.

#Excel #SQL #Pandas #Python #DataAnalytics #DataAnalyst #DataScience

[Pooja Pawar, PhD @SheExploresData]

SQL gets easier when the core functions become second nature. From aggregations and window functions to strings, dates, conditional logic, and filtering, these are the functions analysts use constantly.

#SQL #SQLTips #DataAnalytics #DataAnalyst #Database #DataScience #Analytics

[Pooja Pawar, PhD @SheExploresData]

SQL becomes practical when you can turn business questions into queries. Filtering, grouping, joins, ranking, CTEs, subqueries, and conditional logic cover a huge share of everyday analyst work.

#SQL #DataAnalytics #DataAnalyst #SQLTips #Database #DataScience #Analytics

[Pooja Pawar, PhD @SheExploresData]

Every data scientist should know the Git basics. From initializing repositories and branching to merging, stashing, and collaborating, these commands make experiments reproducible, projects organized, and teamwork seamless. #Git #GitHub #DataScience #VersionControl #Programming

[Pooja Pawar, PhD @SheExploresData]

DAX is where Power BI calculations become powerful. These 20 functions cover filter context, aggregation, relationships, time intelligence, ranking, conditional logic, and more.

#PowerBI #DAX #DataAnalytics #BusinessIntelligence #DataAnalyst #MicrosoftFabric #DataVisualization

[Pooja Pawar, PhD @SheExploresData]

Claude can become more useful in Power BI workflows when it connects with the tools where data and analytics work already happens—from Fabric and SQL to DAX, Power Query, Python, and automation.

#ClaudeAI #PowerBI #MicrosoftFabric #DataAnalytics #BusinessIntelligence #DAX #AI

[Pooja Pawar, PhD @SheExploresData]

Pandas becomes much easier when you know the commands you’ll use repeatedly. From loading and inspecting data to filtering, grouping, merging, sorting, and handling missing values—these are worth mastering.

#Python #Pandas #DataAnalytics #DataScience #DataAnalyst #PythonProgramming #Data

[Pooja Pawar, PhD @SheExploresData]

AI is changing what Power BI developers can build—and how quickly they can build it. From DAX and modeling to automation, optimization, research, and storytelling, these are the skills worth adding to your BI stack.

#PowerBI #AI #DataAnalytics #BusinessIntelligence #DAX #MicrosoftFabric #DataVisualization

[Pooja Pawar, PhD @SheExploresData]

SQL for data analytics is about turning tables into answers: query with SELECT, filter with WHERE, summarize with functions, connect data with joins, simplify logic with CTEs, and clean messy records.

#SQL #DataAnalytics #LearnSQL #Database #DataScience #BI #Tech

[Pooja Pawar, PhD @SheExploresData]

SQL commands make more sense when grouped by purpose: DDL builds structure, DML changes data, DCL controls access, TCL manages transactions, and DQL retrieves results.

#SQL #LearnSQL #Database #DataAnalytics #DataScience #BigData #Tech

[Pooja Pawar, PhD @SheExploresData]

Moving from Excel to Python? The concepts are more familiar than you think. See how everyday Excel tasks translate into Pandas—from filtering and sorting to grouping, joins, and missing data.

#Excel #Python #Pandas #DataAnalytics #DataScience #DataAnalyst #LearnPython

[Pooja Pawar, PhD @SheExploresData]

A strong DSA foundation is built on concepts, not memorization. Master complexity analysis, core data structures, algorithms, common patterns, and consistent practice to solve problems efficiently and perform confidently in coding interviews. #DSA #Algorithms #CodingInterview #Programming #LeetCode