๐๐ฟ๐ฒ๐ฒ ๐๐ผ๐๐ฟ๐๐ฒ ๐ผ๐ป ๐๐ต๐ฎ๐๐๐ฃ๐ง ๐ฃ๐ฟ๐ผ๐บ๐ฝ๐ ๐๐ป๐ด๐ถ๐ป๐ฒ๐ฒ๐ฟ๐ถ๐ป๐ด ๐ฏ๐ ๐๐ฒ๐ฒ๐ฝ๐๐ฒ๐ฎ๐ฟ๐ป๐ถ๐ป๐ด.๐๐ & ๐ข๐ฝ๐ฒ๐ป๐๐๐
๐ก Think ChatGPT is Just for Fun? Think Again๐
In todayโs AI-driven world, knowing how to communicate effectively with large language models (LLMs) is more than just a bonus โ itโs a competitive edge๐๐ฏ
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Use ChatGPT like a developer โ not just a casual userโ ๏ธ
๐ก Think ChatGPT is Just for Fun? Think Again๐
In todayโs AI-driven world, knowing how to communicate effectively with large language models (LLMs) is more than just a bonus โ itโs a competitive edge๐๐ฏ
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โค4
Lists ๐ Tuples ๐ Dictionaries
What's the difference?
Lists are mutable.
Tuples are immutable.
Dictionaries are associative.
When should you use each?
Lists:
โถ When you want to add or remove elements
โถ When you want to sort elements
โถ When you want to slice elements
Tuples:
โถ When you want a constant object
โถ When you want to send multiple in a function
โถ When you want to return multiple from a function
Dictionaries:
โถ When you want to map keys to values
โถ When you want to loop over the keys
โถ When you want to validate if key exists
Now, pick your weapon of mass data analysis and become a Python pro!
Python Interview Q&A: https://topmate.io/coding/898340
Like for more โค๏ธ
ENJOY LEARNING ๐๐
What's the difference?
Lists are mutable.
Tuples are immutable.
Dictionaries are associative.
When should you use each?
Lists:
โถ When you want to add or remove elements
โถ When you want to sort elements
โถ When you want to slice elements
Tuples:
โถ When you want a constant object
โถ When you want to send multiple in a function
โถ When you want to return multiple from a function
Dictionaries:
โถ When you want to map keys to values
โถ When you want to loop over the keys
โถ When you want to validate if key exists
Now, pick your weapon of mass data analysis and become a Python pro!
Python Interview Q&A: https://topmate.io/coding/898340
Like for more โค๏ธ
ENJOY LEARNING ๐๐
โค5
๐ฑ ๐ ๐๐๐-๐๐ผ๐น๐น๐ผ๐ ๐ฌ๐ผ๐๐ง๐๐ฏ๐ฒ ๐๐ต๐ฎ๐ป๐ป๐ฒ๐น๐ ๐ณ๐ผ๐ฟ ๐๐๐ฝ๐ถ๐ฟ๐ถ๐ป๐ด ๐๐ฎ๐๐ฎ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐๐ถ๐๐๐ ๐ถ๐ป ๐ฎ๐ฌ๐ฎ๐ฑ๐
Want to Become a Data Scientist in 2025? Start Here!๐ฏ
If youโre serious about becoming a Data Scientist in 2025, the learning doesnโt have to be expensive โ or boring!๐
๐๐ข๐ง๐ค๐:-
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Perfect for beginners and aspiring prosโ ๏ธ
Want to Become a Data Scientist in 2025? Start Here!๐ฏ
If youโre serious about becoming a Data Scientist in 2025, the learning doesnโt have to be expensive โ or boring!๐
๐๐ข๐ง๐ค๐:-
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Perfect for beginners and aspiring prosโ ๏ธ
โค2
7 Must-Have Tools for Data Analysts in 2025:
โ SQL โ Still the #1 skill for querying and managing structured data
โ Excel / Google Sheets โ Quick analysis, pivot tables, and essential calculations
โ Python (Pandas, NumPy) โ For deep data manipulation and automation
โ Power BI โ Transform data into interactive dashboards
โ Tableau โ Visualize data patterns and trends with ease
โ Jupyter Notebook โ Document, code, and visualize all in one place
โ Looker Studio โ A free and sleek way to create shareable reports with live data.
Perfect blend of code, visuals, and storytelling.
React with โค๏ธ for free tutorials on each tool
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
โ SQL โ Still the #1 skill for querying and managing structured data
โ Excel / Google Sheets โ Quick analysis, pivot tables, and essential calculations
โ Python (Pandas, NumPy) โ For deep data manipulation and automation
โ Power BI โ Transform data into interactive dashboards
โ Tableau โ Visualize data patterns and trends with ease
โ Jupyter Notebook โ Document, code, and visualize all in one place
โ Looker Studio โ A free and sleek way to create shareable reports with live data.
Perfect blend of code, visuals, and storytelling.
React with โค๏ธ for free tutorials on each tool
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
โค2
๐ ๐๐ฒ๐ฎ๐ฟ๐ป ๐๐ผ๐บ๐ฝ๐๐๐ฒ๐ฟ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐ฐ๐ฒ ๐ณ๐ผ๐ฟ ๐๐ฟ๐ฒ๐ฒ ๐ณ๐ฟ๐ผ๐บ ๐๐ฎ๐ฟ๐๐ฎ๐ฟ๐ฑ, ๐ฆ๐๐ฎ๐ป๐ณ๐ผ๐ฟ๐ฑ, ๐ ๐๐ง & ๐๐ผ๐ผ๐ด๐น๐ฒ๐
Why pay thousands when you can access world-class Computer Science courses for free? ๐
Top institutions like Harvard, Stanford, MIT, and Google offer high-quality learning resources to help you master in-demand tech skills๐จโ๐๐
๐๐ข๐ง๐ค๐:-
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Perfect for students, self-learners, and career switchersโ ๏ธ
Why pay thousands when you can access world-class Computer Science courses for free? ๐
Top institutions like Harvard, Stanford, MIT, and Google offer high-quality learning resources to help you master in-demand tech skills๐จโ๐๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/3ZyQpFd
Perfect for students, self-learners, and career switchersโ ๏ธ
Free Resources for Python
Codebasics python tutorials (first 16) โ
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 โ
https://www.youtube.com/playlist?list=PLPbgcxheSpE1DJKfdko58_AIZRIT0TjpO
Useful Python resources for beginners
https://t.iss.one/programming_guide/8
Python 3 Book for beginners
https://t.iss.one/pythondevelopersindia/272?single
Codebasics python tutorials (first 16) โ
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 โ
https://www.youtube.com/playlist?list=PLPbgcxheSpE1DJKfdko58_AIZRIT0TjpO
Useful Python resources for beginners
https://t.iss.one/programming_guide/8
Python 3 Book for beginners
https://t.iss.one/pythondevelopersindia/272?single
โค1
๐ 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
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
โค4
๐๐ฑ๐ฑ ๐๐ฒ๐น๐ผ๐ถ๐๐๐ฒ ๐๐ผ ๐ฌ๐ผ๐๐ฟ ๐ฅ๐ฒ๐๐๐บ๐ฒ โ ๐ก๐ผ ๐๐ป๐๐ฒ๐ฟ๐๐ถ๐ฒ๐ ๐ก๐ฒ๐ฒ๐ฑ๐ฒ๐ฑ!๐
๐ฏ Want to Add Deloitte to Your Resume Without an Interview?๐ฃ
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๐ฏ Want to Add Deloitte to Your Resume Without an Interview?๐ฃ
Now you can โ thanks to this free Deloitte virtual internship, open to everyone!๐จโ๐ป๐
๐๐ข๐ง๐ค๐:-
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All 100% online, self-paced, and with a certificate of completion you can proudly share on LinkedIn and your resume๐โ ๏ธ
โค2
Python for Data Analytics - Quick Cheatsheet with Cod e Example ๐
1๏ธโฃ Data Manipulation with Pandas
2๏ธโฃ Numerical Operations with NumPy
3๏ธโฃ Data Visualization with Matplotlib & Seaborn
4๏ธโฃ Exploratory Data Analysis (EDA)
5๏ธโฃ Working with Databases (SQL + Python)
React with โค๏ธ for more
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
1๏ธโฃ 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)
2๏ธโฃ 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)
3๏ธโฃ 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()
4๏ธโฃ Exploratory Data Analysis (EDA)
df.isnull().sum()
df.corr()
sns.histplot(df["sales"], bins=30)
sns.boxplot(y=df["price"])
5๏ธโฃ 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)
React with โค๏ธ for more
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
โค12
๐ฆ๐ค๐ ๐ญ๐ฌ๐ฌ% ๐๐ฅ๐๐ ๐๐ฒ๐ฟ๐๐ถ๐ณ๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป ๐๐ผ๐๐ฟ๐๐ฒ๐ ๐
Looking to master SQL for Data Analytics or prep for your dream tech job? ๐ผ
These 3 Free SQL resources will help you go from beginner to job-readyโwithout spending a single rupee! ๐โจ
๐๐ข๐ง๐ค๐:-
https://pdlink.in/3TcvfsA
๐ฅ Start learning today and build the skills top companies want!โ ๏ธ
Looking to master SQL for Data Analytics or prep for your dream tech job? ๐ผ
These 3 Free SQL resources will help you go from beginner to job-readyโwithout spending a single rupee! ๐โจ
๐๐ข๐ง๐ค๐:-
https://pdlink.in/3TcvfsA
๐ฅ Start learning today and build the skills top companies want!โ ๏ธ
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โค3