7 Must-Have Tools for Data Analysts in 2025:

✅ SQL – Still the #1 skill for querying and managing structured data
✅ Excel / Google Sheets – Quick analysis, pivot tables, and essential calculations
✅ Python (Pandas, NumPy) – For deep data manipulation and automation
✅ Power BI – Transform data into interactive dashboards
✅ Tableau – Visualize data patterns and trends with ease
✅ Jupyter Notebook – Document, code, and visualize all in one place
✅ Looker Studio – A free and sleek way to create shareable reports with live data.

Perfect blend of code, visuals, and storytelling.

React with ❤️ for free tutorials on each tool

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🎓 𝗟𝗲𝗮𝗿𝗻 𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗳𝗼𝗿 𝗙𝗿𝗲𝗲 𝗳𝗿𝗼𝗺 𝗛𝗮𝗿𝘃𝗮𝗿𝗱, 𝗦𝘁𝗮𝗻𝗳𝗼𝗿𝗱, 𝗠𝗜𝗧 & 𝗚𝗼𝗼𝗴𝗹𝗲😍

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Top institutions like Harvard, Stanford, MIT, and Google offer high-quality learning resources to help you master in-demand tech skills👨‍🎓📌

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Perfect for students, self-learners, and career switchers✅️

Free Resources for Python

Codebasics python tutorials (first 16) — 
https://www.youtube.com/playlist?list=PLeo1K3hjS3uv5U-Lmlnucd7gqF-3ehIh0

Practice Python course
https://dabeaz-course.github.io/practical-python/Notes/Contents.html

Codebasics python HINDI tutorials —
 https://www.youtube.com/playlist?list=PLPbgcxheSpE1DJKfdko58_AIZRIT0TjpO

Useful Python resources for beginners
https://t.me/programming_guide/8

Python 3 Book for beginners
https://t.me/pythondevelopersindia/272?single

🚀 Essential Python snippets to explore data:
 
1.   .head() - Review top rows
2.   .tail() - Review bottom rows
3.   .info() - Summary of DataFrame
4.   .shape - Shape of DataFrame
5.   .describe() - Descriptive stats
6.   .isnull().sum() - Check missing values
7.   .dtypes - Data types of columns
8.   .unique() - Unique values in a column
9.   .nunique() - Count unique values
10.   .value_counts() - Value counts in a column
11.   .corr() - Correlation matrix

𝗔𝗱𝗱 𝗗𝗲𝗹𝗼𝗶𝘁𝘁𝗲 𝘁𝗼 𝗬𝗼𝘂𝗿 𝗥𝗲𝘀𝘂𝗺𝗲 — 𝗡𝗼 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗡𝗲𝗲𝗱𝗲𝗱!😍

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Python for Data Analytics - Quick Cheatsheet with Cod e Example 🚀

1️⃣ Data Manipulation with Pandas

import pandas as pd  
df = pd.read_csv("data.csv")  
df.to_excel("output.xlsx")  
df.head()  
df.info()  
df.describe()  
df[df["sales"] > 1000]  
df[["name", "price"]]  
df.fillna(0, inplace=True)  
df.dropna(inplace=True)  

2️⃣ Numerical Operations with NumPy

import numpy as np  
arr = np.array([1, 2, 3, 4])  
print(arr.shape)  
np.mean(arr)  
np.median(arr)  
np.std(arr)  

3️⃣ Data Visualization with Matplotlib & Seaborn

import matplotlib.pyplot as plt  
plt.plot([1, 2, 3, 4], [10, 20, 30, 40])  
plt.bar(["A", "B", "C"], [5, 15, 25])  
plt.show()  
import seaborn as sns  
sns.heatmap(df.corr(), annot=True)  
sns.boxplot(x="category", y="sales", data=df)  
plt.show()  

4️⃣ Exploratory Data Analysis (EDA)

df.isnull().sum()  
df.corr()  
sns.histplot(df["sales"], bins=30)  
sns.boxplot(y=df["price"])  

5️⃣ Working with Databases (SQL + Python)

import sqlite3  
conn = sqlite3.connect("database.db")  
df = pd.read_sql("SELECT * FROM sales", conn)  
conn.close()  
cursor = conn.cursor()  
cursor.execute("SELECT AVG(price) FROM products")  
result = cursor.fetchone()  
print(result)

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𝗦𝗤𝗟 𝟭𝟬𝟬% 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 😍

Looking to master SQL for Data Analytics or prep for your dream tech job? 💼

These 3 Free SQL resources will help you go from beginner to job-ready—without spending a single rupee! 📊✨

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WhatsApp is no longer a platform just for chat.

It's an educational goldmine.

If you do, you’re sleeping on a goldmine of knowledge and community. WhatsApp channels are a great way to practice data science, make your own community, and find accountability partners.

I have curated the list of best WhatsApp channels to learn coding & data science for FREE

Free Courses with Certificate
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SQL For Data Analysis
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Excel for Data Analyst
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ENJOY LEARNING 👍👍

Career Path for a Data Analyst

Education: Start by earning a bachelor's degree in fields like math, stats, economics, or computer science.

Skills Growth: Learn programming (Python/R), data tools (SQL/Excel), and visualization. Master data analysis basics.

Entry-Level Role: Begin as a Junior Data Analyst. Learn data cleaning, organization, and basic analysis.

Specialization: Deepen your expertise in a specific industry. Explore advanced analytics and visualization tools.

Advanced Analytics: Move up to Senior Data Analyst. Tackle complex projects and predictive modeling.

Machine Learning: Explore machine learning and data modeling techniques. Familiarize yourself with algorithms, and learn how to implement predictive and classification models.

Domain Expertise: Develop expertise in a particular industry, such as healthcare, finance, e-commerce, etc. This knowledge will enable you to provide more valuable insights from data.

Leadership Roles: As you gain experience, you can move into roles like Data Analytics Manager or Data Science Manager, where you'll oversee teams and projects.

Continuous Learning: Stay updated with the latest tools, techniques, and industry trends. Attend workshops, conferences, and online courses to keep your skills relevant.

Networking: Build a strong professional network within the data analytics community. This can open up opportunities and help you stay informed about industry developments.

Remember, your career path can be personalized based on your interests and strengths. Continuous learning and adaptability are key in the ever-evolving field of data analysis :)

𝟭𝟬𝟬% 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀😍

𝗦𝗤𝗟:- https://pdlink.in/3TcvfsA

𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲:- https://pdlink.in/3Hfpwjc

𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝗦𝗰𝗶𝗲𝗻𝗰𝗲:- https://pdlink.in/3ZyQpFd

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𝗗𝗲𝘃𝗢𝗽𝘀 :- https://pdlink.in/4jyxBwS

𝗪𝗲𝗯 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 :- https://pdlink.in/4jCAtJ5

Enroll for FREE & Get Certified 🎓

SQL Interview Questions

1. How would you find duplicate records in SQL?
2.What are various types of SQL joins?
3.What is a trigger in SQL?
4.What are different DDL,DML commands in SQL?
5.What is difference between Delete, Drop and Truncate?
6.What is difference between Union and Union all?
7.Which command give Unique values?
8. What is the difference between Where and Having Clause?
9.Give the execution of keywords in SQL?
10. What is difference between IN and BETWEEN Operator?
11. What is primary and Foreign key?
12. What is an aggregate Functions?
13. What is the difference between Rank and Dense Rank?
14. List the ACID Properties and explain what they are?
15. What is the difference between % and _ in like operator?
16. What does CTE stands for?
17. What is database?what is DBMS?What is RDMS?
18.What is Alias in SQL?
19. What is Normalisation?Describe various form?
20. How do you sort the results of a query?
21. Explain the types of Window functions?
22. What is limit and offset?
23. What is candidate key?
24. Describe various types of Alter command?
25. What is Cartesian product?

Like this post if you need more content like this ❤️

Almost everyone knows that these are the tools a Data Analyst works with:

➡️ SQL
➡️ Excel
➡️ Power BI/Tableau
➡️ Python

But people getting started with analytics are confused about the preferences of picking these tools.

There are various kinds of data analytics roles available in the market :

➡️ BI + SQL: Will primarily be involved in BI development.

➡️ SQL + Excel: Will primarily work on Excel reporting.

➡️ SQL + Python: Will primarily do data analysis using python.

Now, If you are getting started with learning analytics, choose any one role that interests you the most and focus on completing the primary tools that the role requires. Learn them VERY WELL.

Learn any of the above combinations that interests you first and then start looking out for opportunities which ask for these primary tools and simultaneously start learning the basics of the 3rd tool.

You don't have to focus on being good with each and every tool but being good with any of the above combinations always works.

Join this channel to learn everything about Data Analytics 👇
https://t.me/sqlspecialist

Hope this helps you 😊

10 SQL Concepts Every Data Analyst Should Master 👇

✅ SELECT, WHERE, ORDER BY – Core of querying your data
✅ JOINs (INNER, LEFT, RIGHT, FULL) – Combine data from multiple tables
✅ GROUP BY & HAVING – Aggregate and filter grouped data
✅ Subqueries – Nest queries inside queries for complex logic
✅ CTEs (Common Table Expressions) – Write cleaner, reusable SQL logic
✅ Window Functions – Perform advanced analytics like rankings & running totals
✅ Indexes – Boost your query performance
✅ Normalization – Structure your database efficiently
✅ UNION vs UNION ALL – Combine result sets with or without duplicates
✅ Stored Procedures & Functions – Reusable logic inside your DB

React with ❤️ if you want me to cover each topic in detail

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Hope it helps :)

𝗠𝗮𝘀𝘁𝗲𝗿 𝗣𝗿𝗼𝗺𝗽𝘁 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗳𝗼𝗿 𝗙𝗿𝗲𝗲 𝗶𝗻 𝟮𝟬𝟮𝟱!😍

Want to communicate with AI like a pro? 🤖

Whether you’re a data analyst, AI developer, content creator, or student, this is the must-have skill of 2025✨️

𝐋𝐢𝐧𝐤👇:-

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