Вже доступно! Дослідження Telegram за 2025 — головні інсайти року 
Python for Data Analysts
Відкрити в Telegram
Find top Python resources from global universities, cool projects, and learning materials for data analytics. For promotions: @coderfun Useful links: heylink.me/DataAnalytics
Показати більше📈 Аналітичний огляд Telegram-каналу Python for Data Analysts
Канал Python for Data Analysts (@pythonanalyst) у мовному сегменті Англійська є активним учасником. На даний момент спільнота об'єднує 51 508 підписників, посідаючи 2 607 місце в категорії Технології та додатки та 7 392 місце у регіоні Індія.
📊 Показники аудиторії та динаміка
З моменту свого створення невідомо, проект продемонстрував стрімке зростання, зібравши аудиторію у 51 508 підписників.
За останніми даними від 05 червня, 2026, канал демонструє стабільну активність. Хоча за останні 30 днів спостерігається зміна кількості учасників на 255, а за останні 24 години на 22, загальне охоплення залишається високим.
- Статус верифікації: Не верифікований
- Рівень залученості (ER): Середній показник залученості аудиторії становить 4.29%. Протягом перших 24 годин після публікації контент зазвичай збирає N/A% реакцій від загальної кількості підписників.
- Охоплення публікацій: В середньому кожен допис отримує 2 209 переглядів. Протягом першої доби публікація в середньому набирає 0 переглядів.
- Реакції та взаємодія: Аудиторія активно підтримує контент: середня кількість реакцій на один пост – 8.
- Тематичні інтереси: Контент зосереджений навколо ключових тем, таких як visualization, panda, analyst, sql, analytic.
📝 Опис та контентна політика
Автор описує ресурс як майданчик для висловлення суб'єктивної думки:
“Find top Python resources from global universities, cool projects, and learning materials for data analytics.
For promotions: @coderfun
Useful links: heylink.me/DataAnalytics”
Завдяки високій частоті оновлень (останні дані отримано 07 червня, 2026), канал підтримує актуальність та високий рівень охоплення публікацій. Аналітика показує, що аудиторія активно взаємодіє з контентом, що робить його важливою точкою впливу в категорії Технології та додатки.
51 508
Підписники
+2224 години
+627 днів
+25530 день
Архів дописів
51 508
𝗙𝗥𝗘𝗘 𝗪𝗲𝗯𝘀𝗶𝘁𝗲𝘀 𝗧𝗼 𝗠𝗮𝘀𝘁𝗲𝗿 𝗖𝗼𝗱𝗶𝗻𝗴 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘 😍
Level up your coding skills without spending a dime? 💰
These free interactive platforms will help you learn, practice, and build real projects in HTML, CSS, JavaScript, React, and Python!
𝐋𝐢𝐧𝐤 👇:-
https://pdlink.in/4aJHgh5
Enroll For FREE & Get Certified 🎓
51 508
For data analysts working with Python, mastering these top 10 concepts is essential:
1. Data Structures: Understand fundamental data structures like lists, dictionaries, tuples, and sets, as well as libraries like NumPy and Pandas for more advanced data manipulation.
2. Data Cleaning and Preprocessing: Learn techniques for cleaning and preprocessing data, including handling missing values, removing duplicates, and standardizing data formats.
3. Exploratory Data Analysis (EDA): Use libraries like Pandas, Matplotlib, and Seaborn to perform EDA, visualize data distributions, identify patterns, and explore relationships between variables.
4. Data Visualization: Master visualization libraries such as Matplotlib, Seaborn, and Plotly to create various plots and charts for effective data communication and storytelling.
5. Statistical Analysis: Gain proficiency in statistical concepts and methods for analyzing data distributions, conducting hypothesis tests, and deriving insights from data.
6. Machine Learning Basics: Familiarize yourself with machine learning algorithms and techniques for regression, classification, clustering, and dimensionality reduction using libraries like Scikit-learn.
7. Data Manipulation with Pandas: Learn advanced data manipulation techniques using Pandas, including merging, grouping, pivoting, and reshaping datasets.
8. Data Wrangling with Regular Expressions: Understand how to use regular expressions (regex) in Python to extract, clean, and manipulate text data efficiently.
9. SQL and Database Integration: Acquire basic SQL skills for querying databases directly from Python using libraries like SQLAlchemy or integrating with databases such as SQLite or MySQL.
10. Web Scraping and API Integration: Explore methods for retrieving data from websites using web scraping libraries like BeautifulSoup or interacting with APIs to access and analyze data from various sources.
Give credits while sharing: https://t.me/pythonanalyst
ENJOY LEARNING 👍👍
51 508
𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 𝗧𝗼 𝗖𝗿𝗮𝗰𝗸 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 😍
💡 Preparing for a Power BI interview can feel overwhelming, but the right questions can make all the difference!
Here are 15 must-know Power BI interview questions that will boost your confidence and help you shine in front of hiring managers.
𝐋𝐢𝐧𝐤 👇:-
https://pdlink.in/3CkZR6s
All The Best🎓
51 508
𝐈𝐦𝐩𝐨𝐫𝐭𝐢𝐧𝐠 𝐍𝐞𝐜𝐞𝐬𝐬𝐚𝐫𝐲 𝐋𝐢𝐛𝐫𝐚𝐫𝐢𝐞𝐬:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
𝐋𝐨𝐚𝐝𝐢𝐧𝐠 𝐭𝐡𝐞 𝐃𝐚𝐭𝐚𝐬𝐞𝐭:
df = pd.read_csv('your_dataset.csv')
𝐈𝐧𝐢𝐭𝐢𝐚𝐥 𝐃𝐚𝐭𝐚 𝐈𝐧𝐬𝐩𝐞𝐜𝐭𝐢𝐨𝐧:
1- View the first few rows:
df.head()
2- Summary of the dataset:
df.info()
3- Statistical summary:
df.describe()
𝐇𝐚𝐧𝐝𝐥𝐢𝐧𝐠 𝐌𝐢𝐬𝐬𝐢𝐧𝐠 𝐕𝐚𝐥𝐮𝐞𝐬:
1- Identify missing values:
df.isnull().sum()
2- Visualize missing values:
sns.heatmap(df.isnull(), cbar=False, cmap='viridis')
plt.show()
𝐃𝐚𝐭𝐚 𝐕𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧:
1- Histograms:
df.hist(bins=30, figsize=(20, 15))
plt.show()
2 - Box plots:
plt.figure(figsize=(10, 6))
sns.boxplot(data=df)
plt.xticks(rotation=90)
plt.show()
3- Pair plots:
sns.pairplot(df)
plt.show()
4- Correlation matrix and heatmap:
correlation_matrix = df.corr()
plt.figure(figsize=(12, 8))
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm')
plt.show()
𝐂𝐚𝐭𝐞𝐠𝐨𝐫𝐢𝐜𝐚𝐥 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬:
Count plots for categorical features:
plt.figure(figsize=(10, 6))
sns.countplot(x='categorical_column', data=df)
plt.show()
Python Interview Q&A: https://topmate.io/coding/898340
Like for more ❤️
ENJOY LEARNING 👍👍
51 508
Exploratory Data Analysis (EDA) in Python involves a variety of techniques and tools to summarize, visualize, and understand the structure of a dataset. Here are some common EDA techniques using Python, along with relevant libraries:
51 508
𝗝𝗣 𝗠𝗼𝗿𝗴𝗮𝗻 𝗙𝗥𝗘𝗘 𝗩𝗶𝗿𝘁𝘂𝗮𝗹 𝗜𝗻𝘁𝗲𝗿𝗻𝘀𝗵𝗶𝗽 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝘀😍
JPMorgan offers free virtual internships to help you develop industry-specific tech, finance, and research skills.
- Software Engineering Internship
- Investment Banking Program
- Quantitative Research Internship
𝐋𝐢𝐧𝐤 👇:-
https://pdlink.in/4gHGofl
Enroll For FREE & Get Certified 🎓
51 508
𝐓𝐨𝐩 𝐌𝐍𝐂𝐬 & 𝐒𝐭𝐚𝐫𝐭𝐮𝐩 𝐂𝐨𝐦𝐩𝐚𝐧𝐢𝐞𝐬 𝐇𝐢𝐫𝐢𝐧𝐠 🔥
Roles Hiring:-
- Data Analyst
- Data Engineer
- SQL Developer
- Power BI Developers
- Business Analyst
- Data Scientist
Salary Range :- 6 To 24LPA
𝐀𝐩𝐩𝐥𝐲 𝐍𝐨𝐰👇:-
https://shorturl.at/KXhGA
Enter your experience & Complete The Registration Process
Select the company name & apply for jobs
51 508
🌴 Data Types in NumPy
📍 Arithmetic operations in Numpy
➡️+ ->np.add ->Addition(1+1=2)
➡️- ->np.substract ->Subtract(2-2=0)
➡️- ->np.negative - >Unary negative(-2)
➡️*->np.multiply->Multiplication(2*3=6)
➡️/->np.divide->Division(3/2=1.5)
➡️//->np.floor-divide - Floor divisor(3//2=1)
➡️->np.power->exponention(23)
➡️%->np.mod->modulus/remainder(9%4=1)
51 508
Data Analyst vs. Data Scientist - What's the Difference?
1. Data Analyst:
- Role: Focuses on interpreting and analyzing data to help businesses make informed decisions.
- Skills: Proficiency in SQL, Excel, data visualization tools (Tableau, Power BI), and basic statistical analysis.
- Responsibilities: Data cleaning, performing EDA, creating reports and dashboards, and communicating insights to stakeholders.
2. Data Scientist:
- Role: Involves building predictive models, applying machine learning algorithms, and deriving deeper insights from data.
- Skills: Strong programming skills (Python, R), machine learning, advanced statistics, and knowledge of big data technologies (Hadoop, Spark).
- Responsibilities: Data modeling, developing machine learning models, performing advanced analytics, and deploying models into production.
3. Key Differences:
- Focus: Data Analysts are more focused on interpreting existing data, while Data Scientists are involved in creating new data-driven solutions.
- Tools: Analysts typically use SQL, Excel, and BI tools, while Data Scientists work with programming languages, machine learning frameworks, and big data tools.
- Outcomes: Analysts provide insights and recommendations, whereas Scientists build models that predict future trends and automate decisions.
30 Days of Data Science Series: https://t.me/datasciencefun/1708
Like this post if you need more 👍❤️
Hope it helps 🙂
51 508
𝗔𝗜 & 𝗠𝗟 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 😍
Qualcomm—a global tech giant offering completely FREE courses that you can access anytime, anywhere.
✅ 100% Free — No hidden charges, subscriptions, or trials
✅ Created by Industry Experts
✅ Self-paced & Online — Learn from anywhere, anytime
𝐋𝐢𝐧𝐤 👇:-
https://pdlink.in/3YrFTyK
Enroll Now & Get Certified 🎓
51 508
For data analysts working with Python, mastering these top 10 concepts is essential:
1. Data Structures: Understand fundamental data structures like lists, dictionaries, tuples, and sets, as well as libraries like NumPy and Pandas for more advanced data manipulation.
2. Data Cleaning and Preprocessing: Learn techniques for cleaning and preprocessing data, including handling missing values, removing duplicates, and standardizing data formats.
3. Exploratory Data Analysis (EDA): Use libraries like Pandas, Matplotlib, and Seaborn to perform EDA, visualize data distributions, identify patterns, and explore relationships between variables.
4. Data Visualization: Master visualization libraries such as Matplotlib, Seaborn, and Plotly to create various plots and charts for effective data communication and storytelling.
5. Statistical Analysis: Gain proficiency in statistical concepts and methods for analyzing data distributions, conducting hypothesis tests, and deriving insights from data.
6. Machine Learning Basics: Familiarize yourself with machine learning algorithms and techniques for regression, classification, clustering, and dimensionality reduction using libraries like Scikit-learn.
7. Data Manipulation with Pandas: Learn advanced data manipulation techniques using Pandas, including merging, grouping, pivoting, and reshaping datasets.
8. Data Wrangling with Regular Expressions: Understand how to use regular expressions (regex) in Python to extract, clean, and manipulate text data efficiently.
9. SQL and Database Integration: Acquire basic SQL skills for querying databases directly from Python using libraries like SQLAlchemy or integrating with databases such as SQLite or MySQL.
10. Web Scraping and API Integration: Explore methods for retrieving data from websites using web scraping libraries like BeautifulSoup or interacting with APIs to access and analyze data from various sources.
Give credits while sharing: https://t.me/pythonanalyst
ENJOY LEARNING 👍👍
51 508
𝟯 𝗙𝗿𝗲𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗟𝗲𝘃𝗲𝗹 𝗨𝗽 𝗬𝗼𝘂𝗿 𝗧𝗲𝗰𝗵 𝗦𝗸𝗶𝗹𝗹𝘀 𝗶𝗻 𝟮𝟬𝟮𝟱😍
Want to build your tech career without breaking the bank?💰
These 3 completely free courses are all you need to begin your journey in programming and data analysis📊
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/3EtHnBI
Learn at your own pace, sharpen your skills, and showcase your progress on LinkedIn or your resume. Let’s dive in!✅️
51 508
Hi guys,
Now you can directly find job opportunities on WhatsApp. Here is the list of top job related channels on WhatsApp 👇
Latest Jobs & Internship Opportunities: https://whatsapp.com/channel/0029VaI5CV93AzNUiZ5Tt226
Python & AI Jobs: https://whatsapp.com/channel/0029VaxtmHsLikgJ2VtGbu1R
Software Engineer Jobs: https://whatsapp.com/channel/0029VatL9a22kNFtPtLApJ2L
Data Science Jobs: https://whatsapp.com/channel/0029VaxTMmQADTOA746w7U2P
Data Analyst Jobs: https://whatsapp.com/channel/0029Vaxjq5a4dTnKNrdeiZ0J
Web Developer Jobs: https://whatsapp.com/channel/0029Vb1raTiDjiOias5ARu2p
Remote Jobs: https://whatsapp.com/channel/0029Vb1RrFuC1Fu3E0aiac2E
Google Jobs: https://whatsapp.com/channel/0029VaxngnVInlqV6xJhDs3m
Hope it helps :)
51 508
Learning Python in 2024 is like discovering a treasure chest 🎁 full of magical powers! Here's why it's valuable:
1. Versatility 🌟: Python is used in web development, data analysis, artificial intelligence, machine learning, automation, and more. Whatever your interest, Python has an option for it.
2. Ease of Learning 📚: Python's syntax is as clear as a sunny day!☀️ Its simple and readable syntax makes it beginner-friendly, perfect for aspiring programmers of all levels.
3. Community Support 🤝: Python has a vast community of programmers ready to help! Whether you're stuck on a problem or looking for guidance, there are countless forums, tutorials, and resources to tap into.
4. Job Opportunities 💼: Companies are constantly seeking Python wizards to join their ranks! From tech giants to startups, the demand for Python skills is abundant.🔥
5. Future-proofing 🔮: With its widespread adoption and continuous growth, learning Python now sets you up for success in the ever-evolving world of tech.
6. Fun Projects 🎉: Python makes coding feel like brewing potions! From creating games 🎮 to building robots 🤖, the possibilities are endless.
So grab your keyboard and embark on a Python adventure! It's not just learning a language, it's unlocking a world of endless possibilities.
#techinfo
51 508
𝟱 𝗣𝗼𝘄𝗲𝗿𝗳𝘂𝗹 𝗙𝗿𝗲𝗲 𝗔𝗜 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗳𝗿𝗼𝗺 𝗛𝗮𝗿𝘃𝗮𝗿𝗱 & 𝗦𝘁𝗮𝗻𝗳𝗼𝗿𝗱😍
Want to learn AI from the best without spending a rupee?
These 5 FREE courses from Harvard and Stanford will help you understand Artificial Intelligence, Deep Learning, NLP, and more—straight from the experts📊
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4lphMdX
🚀 Learn from the Best, for Free
51 508
"I've never done this before... 🙈 But 2 drinks later at the bar, I told him my secret fantasy..." The message that's making guys go crazy!
↓ Continue in AI Chatbot ↓
https://t.me/luciddreams?start=choch8-Xabcaa
