Python is a popular programming language in the field of data analysis due to its versatility, ease of use, and extensive libraries for data manipulation, visualization, and analysis. Here are some key Python skills that are important for data analysts:
1. Basic Python Programming: Understanding basic Python syntax, data types, control structures, functions, and object-oriented programming concepts is essential for data analysis in Python.
2. NumPy: NumPy is a fundamental package for scientific computing in Python. It provides support for large multidimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays.
3. Pandas: Pandas is a powerful library for data manipulation and analysis in Python. It provides data structures like DataFrames and Series that make it easy to work with structured data and perform tasks such as filtering, grouping, joining, and reshaping data.
4. Matplotlib and Seaborn: Matplotlib is a versatile library for creating static, interactive, and animated visualizations in Python. Seaborn is built on top of Matplotlib and provides a higher-level interface for creating attractive statistical graphics.
5. Scikit-learn: Scikit-learn is a popular machine learning library in Python that provides tools for building predictive models, performing clustering and classification tasks, and evaluating model performance.
6. Jupyter Notebooks: Jupyter Notebooks are an interactive computing environment that allows you to create and share documents containing live code, equations, visualizations, and narrative text. They are commonly used by data analysts for exploratory data analysis and sharing insights.
7. SQLAlchemy: SQLAlchemy is a Python SQL toolkit and Object-Relational Mapping (ORM) library that provides a high-level interface for interacting with relational databases using Python.
8. Regular Expressions: Regular expressions (regex) are powerful tools for pattern matching and text processing in Python. They are useful for extracting specific information from text data or performing data cleaning tasks.
9. Data Visualization Libraries: In addition to Matplotlib and Seaborn, data analysts may also use other visualization libraries like Plotly, Bokeh, or Altair to create interactive visualizations in Python.
10. Web Scraping: Knowledge of web scraping techniques using libraries like BeautifulSoup or Scrapy can be useful for collecting data from websites for analysis.
By mastering these Python skills and applying them to real-world data analysis projects, you can enhance your proficiency as a data analyst and unlock new opportunities in the field.
1. Basic Python Programming: Understanding basic Python syntax, data types, control structures, functions, and object-oriented programming concepts is essential for data analysis in Python.
2. NumPy: NumPy is a fundamental package for scientific computing in Python. It provides support for large multidimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays.
3. Pandas: Pandas is a powerful library for data manipulation and analysis in Python. It provides data structures like DataFrames and Series that make it easy to work with structured data and perform tasks such as filtering, grouping, joining, and reshaping data.
4. Matplotlib and Seaborn: Matplotlib is a versatile library for creating static, interactive, and animated visualizations in Python. Seaborn is built on top of Matplotlib and provides a higher-level interface for creating attractive statistical graphics.
5. Scikit-learn: Scikit-learn is a popular machine learning library in Python that provides tools for building predictive models, performing clustering and classification tasks, and evaluating model performance.
6. Jupyter Notebooks: Jupyter Notebooks are an interactive computing environment that allows you to create and share documents containing live code, equations, visualizations, and narrative text. They are commonly used by data analysts for exploratory data analysis and sharing insights.
7. SQLAlchemy: SQLAlchemy is a Python SQL toolkit and Object-Relational Mapping (ORM) library that provides a high-level interface for interacting with relational databases using Python.
8. Regular Expressions: Regular expressions (regex) are powerful tools for pattern matching and text processing in Python. They are useful for extracting specific information from text data or performing data cleaning tasks.
9. Data Visualization Libraries: In addition to Matplotlib and Seaborn, data analysts may also use other visualization libraries like Plotly, Bokeh, or Altair to create interactive visualizations in Python.
10. Web Scraping: Knowledge of web scraping techniques using libraries like BeautifulSoup or Scrapy can be useful for collecting data from websites for analysis.
By mastering these Python skills and applying them to real-world data analysis projects, you can enhance your proficiency as a data analyst and unlock new opportunities in the field.
❤3
🚀 AI Journey Contest 2025: Test your AI skills!
Join our international online AI competition. Register now for the contest! Award fund — RUB 6.5 mln!
Choose your track:
· 🤖 Agent-as-Judge — build a universal “judge” to evaluate AI-generated texts.
· 🧠 Human-centered AI Assistant — develop a personalized assistant based on GigaChat that mimics human behavior and anticipates preferences. Participants will receive API tokens and a chance to get an additional 1M tokens.
· 💾 GigaMemory — design a long-term memory mechanism for LLMs so the assistant can remember and use important facts in dialogue.
Why Join
Level up your skills, add a strong line to your resume, tackle pro-level tasks, compete for an award, and get an opportunity to showcase your work at AI Journey, a leading international AI conference.
How to Join
1. Register here.
2. Choose your track.
3. Create your solution and submit it by 30 October 2025.
🚀 Ready for a challenge? Join a global developer community and show your AI skills!
Join our international online AI competition. Register now for the contest! Award fund — RUB 6.5 mln!
Choose your track:
· 🤖 Agent-as-Judge — build a universal “judge” to evaluate AI-generated texts.
· 🧠 Human-centered AI Assistant — develop a personalized assistant based on GigaChat that mimics human behavior and anticipates preferences. Participants will receive API tokens and a chance to get an additional 1M tokens.
· 💾 GigaMemory — design a long-term memory mechanism for LLMs so the assistant can remember and use important facts in dialogue.
Why Join
Level up your skills, add a strong line to your resume, tackle pro-level tasks, compete for an award, and get an opportunity to showcase your work at AI Journey, a leading international AI conference.
How to Join
1. Register here.
2. Choose your track.
3. Create your solution and submit it by 30 October 2025.
🚀 Ready for a challenge? Join a global developer community and show your AI skills!
❤5
✅Python Checklist for Data Analysts 🧠
1. Python Basics
▪ Variables, data types (int, float, str, bool)
▪ Control flow: if-else, loops (for, while)
▪ Functions and lambda expressions
▪ List, dict, tuple, set basics
2. Data Handling & Manipulation
▪ NumPy: arrays, vectorized operations, broadcasting
▪ Pandas: Series & DataFrame, reading/writing CSV, Excel
▪ Data inspection:
▪ Filtering, sorting, grouping (
▪ Handling missing data (
3. Data Visualization
▪ Matplotlib basics: plots, histograms, scatter plots
▪ Seaborn: statistical visualizations (heatmaps, boxplots)
▪ Plotly (optional): interactive charts
4. Statistics & Probability
▪ Descriptive stats (mean, median, std)
▪ Probability distributions, hypothesis testing (SciPy, statsmodels)
▪ Correlation, covariance
5. Working with APIs & Data Sources
▪ Fetching data via APIs (
▪ Reading JSON, XML
▪ Web scraping basics (
6. Automation & Scripting
▪ Automate repetitive data tasks using loops, functions
▪ Excel automation (
▪ File handling and regular expressions
7. Machine Learning Basics (Optional starting point)
▪ Scikit-learn for basic models (regression, classification)
▪ Train-test split, evaluation metrics
8. Version Control & Collaboration
▪ Git basics: init, commit, push, pull
▪ Sharing notebooks or scripts via GitHub
9. Environment & Tools
▪ Jupyter Notebook / JupyterLab for interactive analysis
▪ Python IDEs (VSCode, PyCharm)
▪ Virtual environments (
10. Projects & Portfolio
▪ Analyze real datasets (Kaggle, UCI)
▪ Document insights in notebooks or blogs
▪ Showcase code & analysis on GitHub
💡 Tips:
⦁ Practice coding daily with mini-projects and challenges
⦁ Use interactive platforms like Kaggle, DataCamp, or LeetCode (Python)
⦁ Combine SQL + Python skills for powerful data querying & analysis
Python Programming Resources: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L
Double Tap ♥️ For More
1. Python Basics
▪ Variables, data types (int, float, str, bool)
▪ Control flow: if-else, loops (for, while)
▪ Functions and lambda expressions
▪ List, dict, tuple, set basics
2. Data Handling & Manipulation
▪ NumPy: arrays, vectorized operations, broadcasting
▪ Pandas: Series & DataFrame, reading/writing CSV, Excel
▪ Data inspection:
head(), info(), describe() ▪ Filtering, sorting, grouping (
groupby), merging/joining datasets ▪ Handling missing data (
isnull(), fillna(), dropna())3. Data Visualization
▪ Matplotlib basics: plots, histograms, scatter plots
▪ Seaborn: statistical visualizations (heatmaps, boxplots)
▪ Plotly (optional): interactive charts
4. Statistics & Probability
▪ Descriptive stats (mean, median, std)
▪ Probability distributions, hypothesis testing (SciPy, statsmodels)
▪ Correlation, covariance
5. Working with APIs & Data Sources
▪ Fetching data via APIs (
requests library) ▪ Reading JSON, XML
▪ Web scraping basics (
BeautifulSoup, Scrapy)6. Automation & Scripting
▪ Automate repetitive data tasks using loops, functions
▪ Excel automation (
openpyxl, xlrd) ▪ File handling and regular expressions
7. Machine Learning Basics (Optional starting point)
▪ Scikit-learn for basic models (regression, classification)
▪ Train-test split, evaluation metrics
8. Version Control & Collaboration
▪ Git basics: init, commit, push, pull
▪ Sharing notebooks or scripts via GitHub
9. Environment & Tools
▪ Jupyter Notebook / JupyterLab for interactive analysis
▪ Python IDEs (VSCode, PyCharm)
▪ Virtual environments (
venv, conda)10. Projects & Portfolio
▪ Analyze real datasets (Kaggle, UCI)
▪ Document insights in notebooks or blogs
▪ Showcase code & analysis on GitHub
💡 Tips:
⦁ Practice coding daily with mini-projects and challenges
⦁ Use interactive platforms like Kaggle, DataCamp, or LeetCode (Python)
⦁ Combine SQL + Python skills for powerful data querying & analysis
Python Programming Resources: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L
Double Tap ♥️ For More
❤6
💻 Python Programming Roadmap
🔹 Stage 1: Python Basics (Syntax, Variables, Data Types)
🔹 Stage 2: Control Flow (if/else, loops)
🔹 Stage 3: Functions & Modules
🔹 Stage 4: Data Structures (Lists, Tuples, Sets, Dicts)
🔹 Stage 5: File Handling (Read/Write, CSV, JSON)
🔹 Stage 6: Error Handling (try/except, custom exceptions)
🔹 Stage 7: Object-Oriented Programming (Classes, Inheritance)
🔹 Stage 8: Standard Libraries (os, datetime, math)
🔹 Stage 9: Virtual Environments & pip package management
🔹 Stage 10: Working with APIs (Requests, JSON data)
🔹 Stage 11: Web Development Basics (Flask/Django)
🔹 Stage 12: Databases (SQLite, PostgreSQL, SQLAlchemy ORM)
🔹 Stage 13: Testing (unittest, pytest frameworks)
🔹 Stage 14: Version Control with Git & GitHub
🔹 Stage 15: Package Development (setup.py, publishing on PyPI)
🔹 Stage 16: Data Analysis (Pandas, NumPy libraries)
🔹 Stage 17: Data Visualization (Matplotlib, Seaborn)
🔹 Stage 18: Web Scraping (BeautifulSoup, Selenium)
🔹 Stage 19: Automation & Scripting projects
🔹 Stage 20: Advanced Topics (AsyncIO, Type Hints, Design Patterns)
💡 Tip: Master one stage before moving to the next. Build mini-projects to solidify your learning.
You can find detailed explanation here: 👇 https://whatsapp.com/channel/0029VbBDoisBvvscrno41d1l
Double Tap ♥️ For More ✅
🔹 Stage 1: Python Basics (Syntax, Variables, Data Types)
🔹 Stage 2: Control Flow (if/else, loops)
🔹 Stage 3: Functions & Modules
🔹 Stage 4: Data Structures (Lists, Tuples, Sets, Dicts)
🔹 Stage 5: File Handling (Read/Write, CSV, JSON)
🔹 Stage 6: Error Handling (try/except, custom exceptions)
🔹 Stage 7: Object-Oriented Programming (Classes, Inheritance)
🔹 Stage 8: Standard Libraries (os, datetime, math)
🔹 Stage 9: Virtual Environments & pip package management
🔹 Stage 10: Working with APIs (Requests, JSON data)
🔹 Stage 11: Web Development Basics (Flask/Django)
🔹 Stage 12: Databases (SQLite, PostgreSQL, SQLAlchemy ORM)
🔹 Stage 13: Testing (unittest, pytest frameworks)
🔹 Stage 14: Version Control with Git & GitHub
🔹 Stage 15: Package Development (setup.py, publishing on PyPI)
🔹 Stage 16: Data Analysis (Pandas, NumPy libraries)
🔹 Stage 17: Data Visualization (Matplotlib, Seaborn)
🔹 Stage 18: Web Scraping (BeautifulSoup, Selenium)
🔹 Stage 19: Automation & Scripting projects
🔹 Stage 20: Advanced Topics (AsyncIO, Type Hints, Design Patterns)
💡 Tip: Master one stage before moving to the next. Build mini-projects to solidify your learning.
You can find detailed explanation here: 👇 https://whatsapp.com/channel/0029VbBDoisBvvscrno41d1l
Double Tap ♥️ For More ✅
❤14
✅ How Much Python is Enough to Crack a Data Analyst Interview? 🐍📊
Python is a must-have for data analyst roles in 2025—interviewers expect you to handle data cleaning, analysis, and basic viz with it. You don't need to be an expert in ML or advanced scripting; focus on practical skills to process and interpret data efficiently. Based on current trends, here's what gets you interview-ready:
📌 Basic Syntax & Data Types
⦁ Variables, strings, integers, floats
⦁ Lists, tuples, dictionaries, sets
🔁 Conditions & Loops
⦁ if, elif, else
⦁ for and while loops
🧰 Functions & Scope
⦁ def, parameters, return values
⦁ Lambda functions, *args, **kwargs
📦 Pandas Foundation
⦁ DataFrame, Series
⦁ read_csv(), head(), info(), describe()
⦁ Filtering, sorting, indexing
🧮 Data Analysis
⦁ groupby(), agg(), pivot_table()
⦁ Handling missing values: isnull(), fillna()
⦁ Duplicates & outliers
📊 Visualization
⦁ matplotlib.pyplot & seaborn
⦁ Line, bar, scatter, histogram
⦁ Styling and labeling charts
🗃️ Working with Files
⦁ Reading/writing CSV, Excel
⦁ JSON basics
⦁ Using with open() for text files
📅 Date & Time
⦁ datetime, pd.to_datetime()
⦁ Extracting day, month, year
⦁ Time-based filtering
✅ Must-Have Strengths:
⦁ Writing clean, readable Python code
⦁ Analyzing DataFrames confidently
⦁ Explaining logic behind analysis
⦁ Connecting analysis to business goals
Aim for 2-3 months of consistent practice (20-30 hours/week) on platforms like DataCamp or LeetCode. Pair it with SQL and Excel for a strong edge—many jobs test Python via coding challenges on datasets.
Python Resources: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L
💬 Tap ❤️ for more!
Python is a must-have for data analyst roles in 2025—interviewers expect you to handle data cleaning, analysis, and basic viz with it. You don't need to be an expert in ML or advanced scripting; focus on practical skills to process and interpret data efficiently. Based on current trends, here's what gets you interview-ready:
📌 Basic Syntax & Data Types
⦁ Variables, strings, integers, floats
⦁ Lists, tuples, dictionaries, sets
🔁 Conditions & Loops
⦁ if, elif, else
⦁ for and while loops
🧰 Functions & Scope
⦁ def, parameters, return values
⦁ Lambda functions, *args, **kwargs
📦 Pandas Foundation
⦁ DataFrame, Series
⦁ read_csv(), head(), info(), describe()
⦁ Filtering, sorting, indexing
🧮 Data Analysis
⦁ groupby(), agg(), pivot_table()
⦁ Handling missing values: isnull(), fillna()
⦁ Duplicates & outliers
📊 Visualization
⦁ matplotlib.pyplot & seaborn
⦁ Line, bar, scatter, histogram
⦁ Styling and labeling charts
🗃️ Working with Files
⦁ Reading/writing CSV, Excel
⦁ JSON basics
⦁ Using with open() for text files
📅 Date & Time
⦁ datetime, pd.to_datetime()
⦁ Extracting day, month, year
⦁ Time-based filtering
✅ Must-Have Strengths:
⦁ Writing clean, readable Python code
⦁ Analyzing DataFrames confidently
⦁ Explaining logic behind analysis
⦁ Connecting analysis to business goals
Aim for 2-3 months of consistent practice (20-30 hours/week) on platforms like DataCamp or LeetCode. Pair it with SQL and Excel for a strong edge—many jobs test Python via coding challenges on datasets.
Python Resources: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L
💬 Tap ❤️ for more!
❤14👏2👍1🥰1
Python for Data Analysts
Pandas Cheatsheet .pdf
🚀 Pandas Cheatsheet – Master Data Analysis Like a Pro! 📊
Free Data Analytics Courses With Certificate
👇👇
https://www.linkedin.com/posts/sql-analysts_dataanalyst-datascience-datacamp-activity-7392164126371958784-cFIc
Double Tap ♥️ For More Free Resources
👇👇
https://www.linkedin.com/posts/sql-analysts_dataanalyst-datascience-datacamp-activity-7392164126371958784-cFIc
Double Tap ♥️ For More Free Resources
❤7👏1
🚀 The Ultimate Data Science Roadmap — 2025 Edition
Ready to start or upgrade your Data Science journey? Here’s your quick guide from basics to Gen AI 👇
🧮 1️⃣ Math & Stats – Master algebra, probability & calculus — the core of ML & AI.
💻 2️⃣ Python & SQL – Learn Python (NumPy, APIs, OOPs) & SQL for data wrangling.
📊 3️⃣ Excel – Still key for quick analysis, pivot tables & data cleaning.
📈 4️⃣ Data Analysis – Do EDA, build dashboards (Power BI/Tableau), and visualize with Pandas.
🤖 5️⃣ Machine Learning – Start with regression, classification & model tuning.
🧠 6️⃣ Deep Learning – Learn CNNs, RNNs & model deployment for CV & NLP.
⚙️ 7️⃣ Generative AI & LLMs – Explore RAG, AutoGPT & reasoning frameworks.
🤯 8️⃣ Agentic AI – Dive into LangChain, OpenAI APIs & intelligent agents.
🎯 Pro Tip:
Don’t rush. Be consistent. Build projects, join Kaggle, and solve real problems — that’s where real learning happens.
Ready to start or upgrade your Data Science journey? Here’s your quick guide from basics to Gen AI 👇
🧮 1️⃣ Math & Stats – Master algebra, probability & calculus — the core of ML & AI.
💻 2️⃣ Python & SQL – Learn Python (NumPy, APIs, OOPs) & SQL for data wrangling.
📊 3️⃣ Excel – Still key for quick analysis, pivot tables & data cleaning.
📈 4️⃣ Data Analysis – Do EDA, build dashboards (Power BI/Tableau), and visualize with Pandas.
🤖 5️⃣ Machine Learning – Start with regression, classification & model tuning.
🧠 6️⃣ Deep Learning – Learn CNNs, RNNs & model deployment for CV & NLP.
⚙️ 7️⃣ Generative AI & LLMs – Explore RAG, AutoGPT & reasoning frameworks.
🤯 8️⃣ Agentic AI – Dive into LangChain, OpenAI APIs & intelligent agents.
🎯 Pro Tip:
Don’t rush. Be consistent. Build projects, join Kaggle, and solve real problems — that’s where real learning happens.
❤12
The program for the 10th AI Journey 2025 international conference has been unveiled: scientists, visionaries, and global AI practitioners will come together on one stage. Here, you will hear the voices of those who don't just believe in the future—they are creating it!
Speakers include visionaries Kai-Fu Lee and Chen Qufan, as well as dozens of global AI gurus from around the world!
On the first day of the conference, November 19, we will talk about how AI is already being used in various areas of life, helping to unlock human potential for the future and changing creative industries, and what impact it has on humans and on a sustainable future.
On November 20, we will focus on the role of AI in business and economic development and present technologies that will help businesses and developers be more effective by unlocking human potential.
On November 21, we will talk about how engineers and scientists are making scientific and technological breakthroughs and creating the future today!
Ride the wave with AI into the future!
Tune in to the AI Journey webcast on November 19-21.
Speakers include visionaries Kai-Fu Lee and Chen Qufan, as well as dozens of global AI gurus from around the world!
On the first day of the conference, November 19, we will talk about how AI is already being used in various areas of life, helping to unlock human potential for the future and changing creative industries, and what impact it has on humans and on a sustainable future.
On November 20, we will focus on the role of AI in business and economic development and present technologies that will help businesses and developers be more effective by unlocking human potential.
On November 21, we will talk about how engineers and scientists are making scientific and technological breakthroughs and creating the future today!
Ride the wave with AI into the future!
Tune in to the AI Journey webcast on November 19-21.
❤4👏1
Tune in to the 10th AI Journey 2025 international conference: scientists, visionaries, and global AI practitioners will come together on one stage. Here, you will hear the voices of those who don't just believe in the future—they are creating it!
Speakers include visionaries Kai-Fu Lee and Chen Qufan, as well as dozens of global AI gurus! Do you agree with their predictions about AI?
On November 20, we will focus on the role of AI in business and economic development and present technologies that will help businesses and developers be more effective by unlocking human potential.
On November 21, we will talk about how engineers and scientists are making scientific and technological breakthroughs and creating the future today! The day's program includes presentations by scientists from around the world:
- Ajit Abraham (Sai University, India) will present on “Generative AI in Healthcare”
- Nebojša Bačanin Džakula (Singidunum University, Serbia) will talk about the latest advances in bio-inspired metaheuristics
- AIexandre Ferreira Ramos (University of São Paulo, Brazil) will present his work on using thermodynamic models to study the regulatory logic of transcriptional control at the DNA level
- Anderson Rocha (University of Campinas, Brazil) will give a presentation entitled “AI in the New Era: From Basics to Trends, Opportunities, and Global Cooperation”.
And in the special AIJ Junior track, we will talk about how AI helps us learn, create and ride the wave with AI.
The day will conclude with an award ceremony for the winners of the AI Challenge for aspiring data scientists and the AIJ Contest for experienced AI specialists. The results of an open selection of AIJ Science research papers will be announced.
Ride the wave with AI into the future!
Tune in to the AI Journey webcast on November 19-21.
Speakers include visionaries Kai-Fu Lee and Chen Qufan, as well as dozens of global AI gurus! Do you agree with their predictions about AI?
On November 20, we will focus on the role of AI in business and economic development and present technologies that will help businesses and developers be more effective by unlocking human potential.
On November 21, we will talk about how engineers and scientists are making scientific and technological breakthroughs and creating the future today! The day's program includes presentations by scientists from around the world:
- Ajit Abraham (Sai University, India) will present on “Generative AI in Healthcare”
- Nebojša Bačanin Džakula (Singidunum University, Serbia) will talk about the latest advances in bio-inspired metaheuristics
- AIexandre Ferreira Ramos (University of São Paulo, Brazil) will present his work on using thermodynamic models to study the regulatory logic of transcriptional control at the DNA level
- Anderson Rocha (University of Campinas, Brazil) will give a presentation entitled “AI in the New Era: From Basics to Trends, Opportunities, and Global Cooperation”.
And in the special AIJ Junior track, we will talk about how AI helps us learn, create and ride the wave with AI.
The day will conclude with an award ceremony for the winners of the AI Challenge for aspiring data scientists and the AIJ Contest for experienced AI specialists. The results of an open selection of AIJ Science research papers will be announced.
Ride the wave with AI into the future!
Tune in to the AI Journey webcast on November 19-21.
❤4👍2
Greetings from PVR Cloud Tech!! 🌈
🚀 Along with our highly successful Azure Data Engineering program, we are now launching a brand-new Data Engineering with Snowflake, DBT, and Airflow training track!
Course: Snowflake + DBT + Airflow
📌 Start Date: 24th Nov 2025
⏰ Time: 8 PM – 9 PM IST | Monday
🔹 Course Content:
https://drive.google.com/file/d/1luKHrhYZ6zKuXZpVPGzMydrU_6R2yQnL/view
📱 Join WhatsApp Group:
https://chat.whatsapp.com/EZghn5PVmryDgJZ1TjIMRk?mode=wwt
📥 Register Now:
https://forms.gle/Vaofd52rkJcUpKPV7
📺 WhatsApp Channel:
https://www.whatsapp.com/channel/0029Vb60rGU8V0thkpbFFW2n
Team
PVR Cloud Tech:)
+91-9346060794
🚀 Along with our highly successful Azure Data Engineering program, we are now launching a brand-new Data Engineering with Snowflake, DBT, and Airflow training track!
Course: Snowflake + DBT + Airflow
📌 Start Date: 24th Nov 2025
⏰ Time: 8 PM – 9 PM IST | Monday
🔹 Course Content:
https://drive.google.com/file/d/1luKHrhYZ6zKuXZpVPGzMydrU_6R2yQnL/view
📱 Join WhatsApp Group:
https://chat.whatsapp.com/EZghn5PVmryDgJZ1TjIMRk?mode=wwt
📥 Register Now:
https://forms.gle/Vaofd52rkJcUpKPV7
📺 WhatsApp Channel:
https://www.whatsapp.com/channel/0029Vb60rGU8V0thkpbFFW2n
Team
PVR Cloud Tech:)
+91-9346060794
❤2