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

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𝟯 𝗕𝗲𝗴𝗶𝗻𝗻𝗲𝗿-𝗙𝗿𝗶𝗲𝗻𝗱𝗹𝘆 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝘁𝗼 𝗕𝘂𝗶𝗹𝗱 𝗬𝗼𝘂𝗿 𝗣𝗼𝗿𝘁𝗳𝗼𝗹𝗶𝗼 𝗶𝗻 𝟮𝟬𝟮𝟱😍

👩‍💻 Want to Break into Data Science but Don’t Know Where to Start?🚀

The best way to begin your data science journey is with hands-on projects using real-world datasets.👨‍💻📌

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𝗚𝗼𝗼𝗴𝗹𝗲 𝗧𝗼𝗽 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀😍

If you’re job hunting, switching careers, or just want to upgrade your skill set — Google Skillshop is your go-to platform in 2025!

Google offers completely free certifications that are globally recognized and valued by employers in tech, digital marketing, business, and analytics📊

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𝟳 𝗕𝗲𝘀𝘁 𝗪𝗲𝗯𝘀𝗶𝘁𝗲𝘀 𝘁𝗼 𝗟𝗲𝗮𝗿𝗻 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘 𝗶𝗻 𝟮𝟬𝟮𝟱 (𝗡𝗼 𝗖𝗼𝘀𝘁, 𝗡𝗼 𝗖𝗮𝘁𝗰𝗵!)😍

Want to become a Data Scientist in 2025 without spending a single rupee? You’re in the right place📌

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SNOWFLAKES AND DATABRICKS

Snowflake and Databricks are leading cloud data platforms, but how do you choose the right one for your needs?

🌐  𝐒𝐧𝐨𝐰𝐟𝐥𝐚𝐤𝐞

❄️ 𝐍𝐚𝐭𝐮𝐫𝐞: Snowflake operates as a cloud-native data warehouse-as-a-service, streamlining data storage and management without the need for complex infrastructure setup.

❄️ 𝐒𝐭𝐫𝐞𝐧𝐠𝐭𝐡𝐬: It provides robust ELT (Extract, Load, Transform) capabilities primarily through its COPY command, enabling efficient data loading.
❄️  Snowflake offers dedicated schema and file object definitions, enhancing data organization and accessibility.

❄️  𝐅𝐥𝐞𝐱𝐢𝐛𝐢𝐥𝐢𝐭𝐲: One of its standout features is the ability to create multiple independent compute clusters that can operate on a single data copy. This flexibility allows for enhanced resource allocation based on varying workloads.

❄️ 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠: While Snowflake primarily adopts an ELT approach, it seamlessly integrates with popular third-party ETL tools such as Fivetran, Talend, and supports DBT installation. This integration makes it a versatile choice for organizations looking to leverage existing tools.

🌐 𝐃𝐚𝐭𝐚𝐛𝐫𝐢𝐜𝐤𝐬

❄️  𝐂𝐨𝐫𝐞: Databricks is fundamentally built around processing power, with native support for Apache Spark, making it an exceptional platform for ETL tasks. This integration allows users to perform complex data transformations efficiently.

❄️ 𝐒𝐭𝐨𝐫𝐚𝐠𝐞: It utilizes a 'data lakehouse' architecture, which combines the features of a data lake with the ability to run SQL queries. This model is gaining traction as organizations seek to leverage both structured and unstructured data in a unified framework.

🌐 𝐊𝐞𝐲 𝐓𝐚𝐤𝐞𝐚𝐰𝐚𝐲𝐬

❄️ 𝐃𝐢𝐬𝐭𝐢𝐧𝐜𝐭 𝐍𝐞𝐞𝐝𝐬: Both Snowflake and Databricks excel in their respective areas, addressing different data management requirements.

❄️ 𝐒𝐧𝐨𝐰𝐟𝐥𝐚𝐤𝐞’𝐬 𝐈𝐝𝐞𝐚𝐥 𝐔𝐬𝐞 𝐂𝐚𝐬𝐞: If you are equipped with established ETL tools like Fivetran, Talend, or Tibco, Snowflake could be the perfect choice. It efficiently manages the complexities of database infrastructure, including partitioning, scalability, and indexing.

❄️ 𝐃𝐚𝐭𝐚𝐛𝐫𝐢𝐜𝐤𝐬 𝐟𝐨𝐫 𝐂𝐨𝐦𝐩𝐥𝐞𝐱 𝐋𝐚𝐧𝐝𝐬𝐜𝐚𝐩𝐞𝐬: Conversely, if your organization deals with a complex data landscape characterized by unpredictable sources and schemas, Databricks—with its schema-on-read technique—may be more advantageous.

🌐 𝐂𝐨𝐧𝐜𝐥𝐮𝐬𝐢𝐨𝐧:

Ultimately, the decision between Snowflake and Databricks should align with your specific data needs and organizational goals. Both platforms have established their niches, and understanding their strengths will guide you in selecting the right tool for your data strategy.

𝗕𝗿𝗲𝗮𝗸 𝗜𝗻𝘁𝗼 𝗗𝗲𝗲𝗽 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗶𝗻 𝟮𝟬𝟮𝟱 𝘄𝗶𝘁𝗵 𝗧𝗵𝗶𝘀 𝗙𝗥𝗘𝗘 𝗠𝗜𝗧 𝗖𝗼𝘂𝗿𝘀𝗲😍

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Offered by MIT’s top researchers and engineers, this online course is open to everyone, no matter where you live or work🎯

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Here are some commonly asked SQL interview questions along with brief answers:

1. What is SQL?
- SQL stands for Structured Query Language, used for managing and manipulating relational databases.

2. What are the types of SQL commands?
- SQL commands can be broadly categorized into four types: Data Definition Language (DDL), Data Manipulation Language (DML), Data Control Language (DCL), and Transaction Control Language (TCL).

3. What is the difference between CHAR and VARCHAR data types?
- CHAR is a fixed-length character data type, while VARCHAR is a variable-length character data type. CHAR will always occupy the same amount of storage space, while VARCHAR will only use the necessary space to store the actual data.

4. What is a primary key?
- A primary key is a column or a set of columns that uniquely identifies each row in a table. It ensures data integrity by enforcing uniqueness and can be used to establish relationships between tables.

5. What is a foreign key?
- A foreign key is a column or a set of columns in one table that refers to the primary key in another table. It establishes a relationship between two tables and ensures referential integrity.

6. What is a JOIN in SQL?
- JOIN is used to combine rows from two or more tables based on a related column between them. There are different types of JOINs, including INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL JOIN.

7. What is the difference between INNER JOIN and OUTER JOIN?
- INNER JOIN returns only the rows that have matching values in both tables, while OUTER JOIN (LEFT, RIGHT, FULL) returns all rows from one or both tables, with NULL values in columns where there is no match.

8. What is the difference between GROUP BY and ORDER BY?
- GROUP BY is used to group rows that have the same values into summary rows, typically used with aggregate functions like SUM, COUNT, AVG, etc., while ORDER BY is used to sort the result set based on one or more columns.

9. What is a subquery?
- A subquery is a query nested within another query, used to return data that will be used in the main query. Subqueries can be used in SELECT, INSERT, UPDATE, and DELETE statements.

10. What is normalization in SQL?
- Normalization is the process of organizing data in a database to reduce redundancy and dependency. It involves dividing large tables into smaller tables and defining relationships between them to improve data integrity and efficiency.

Around 90% questions will be asked from sql in data analytics interview, so please make sure to practice SQL skills using websites like stratascratch. ☺️💪

𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗧𝗼 𝗘𝗻𝗿𝗼𝗹𝗹 𝗜𝗻 𝟮𝟬𝟮𝟱 😍

Data Analytics :- https://pdlink.in/3Fq7E4p

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SQL :- https://pdlink.in/3EyjUPt

Python :- https://pdlink.in/4c7hGDL

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Netflix Analytics Engineer Interview Experience:


SQL Questions:

1️⃣ SQL Question 1: Identify VIP Users for Netflix

Question: To better cater to its most dedicated users, Netflix would like to identify its “VIP users” - those who are most active in terms of the number of hours of content they watch. Write a SQL query that will retrieve the top 10 users with the most watched hours in the last month.

Tables:
• users table: user_id (integer), sign_up_date (date), subnoscription_type (text)
• watching_activity table: activity_id (integer), user_id (integer), date_time (timestamp), show_id (integer), hours_watched (float)

2️⃣ SQL Question 2: Analyzing Ratings For Netflix Shows

Question: Given a table of user ratings for Netflix shows, calculate the average rating for each show within a given month. Assume that there is a column for user_id, show_id, rating (out of 5 stars), and date of review. Order the results by month and then by average rating (descending order).

Tables:
• show_reviews table: review_id (integer), user_id (integer), review_date (timestamp), show_id (integer), stars (integer)

3️⃣ SQL Question 3: What does EXCEPT / MINUS SQL commands do?

Question: Explain the purpose and usage of the EXCEPT (or MINUS in some SQL dialects) SQL commands.

4️⃣ SQL Question 4: Filter Netflix Users Based on Viewing History and Subnoscription Status

Question: You are given a database of Netflix’s user viewing history and their current subnoscription status. Write a SQL query to find all active customers who watched more than 10 episodes of a show called “Stranger Things” in the last 30 days.

Tables:
• users table: user_id (integer), active (boolean)
• viewing_history table: user_id (integer), show_id (integer), episode_id (integer), watch_date (date)
• shows table: show_id (integer), show_name (text)

5️⃣ SQL Question 5: What does it mean to denormalize a database?

Question: Explain the concept and implications of denormalizing a database.

6️⃣ SQL Question 6: Filter and Match Customer’s Viewing Records

Question: As a data analyst at Netflix, you are asked to analyze the customer’s viewing records. You confirmed that Netflix is especially interested in customers who have been continuously watching a particular genre - ‘Documentary’ over the last month. The task is to find the name and email of those customers who have viewed more than five ‘Documentary’ movies within the last month. ‘Documentary’ could be a part of a broader genre category in the genre field (for example, ‘Documentary, History’). Therefore, the matching pattern could occur anywhere within the string.

Tables:
• movies table: movie_id (integer), noscript (text), genre (text), release_year (integer)
• customer table: user_id (integer), name (text), email (text), last_movie_watched (integer), date_watched (date)

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Looking to kickstart your coding journey with Python? 🐍

Whether you’re an aspiring data analyst, a student, or preparing for tech roles, these free Python courses are perfect for beginners!📊📌

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These platforms offer high-quality learning — no fees, no catch✅️

𝗧𝗼𝗽 𝗠𝗡𝗖𝘀 𝗢𝗳𝗳𝗲𝗿𝗶𝗻𝗴 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 😍

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𝗙𝗥𝗘𝗘 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗧𝗲𝗰𝗵 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀😍

🚀 Learn In-Demand Tech Skills for Free — Certified by Microsoft!

These free Microsoft-certified online courses are perfect for beginners, students, and professionals looking to upskill

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𝗙𝗥𝗘𝗘 𝗧𝗔𝗧𝗔 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗩𝗶𝗿𝘁𝘂𝗮𝗹 𝗜𝗻𝘁𝗲𝗿𝗻𝘀𝗵𝗶𝗽😍

Gain Real-World Data Analytics Experience with TATA – 100% Free!

This free TATA Data Analytics Virtual Internship on Forage lets you step into the shoes of a data analyst — no experience required!

𝐋𝐢𝐧𝐤👇:-

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𝟰 𝗣𝗼𝘄𝗲𝗿𝗳𝘂𝗹 𝗙𝗿𝗲𝗲 𝗥𝗼𝗮𝗱𝗺𝗮𝗽𝘀 𝘁𝗼 𝗠𝗮𝘀𝘁𝗲𝗿 𝗝𝗮𝘃𝗮𝗦𝗰𝗿𝗶𝗽𝘁, 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲, 𝗔𝗜/𝗠𝗟 & 𝗙𝗿𝗼𝗻𝘁𝗲𝗻𝗱 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 😍

Learn Tech the Smart Way: Step-by-Step Roadmaps for Beginners🚀

Learning tech doesn’t have to be overwhelming—especially when you have a roadmap to guide you!📊📌

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Enjoy Learning ✅️

Data Analyst vs Data Engineer vs Data Scientist ✅

Skills required to become a Data Analyst 👇

- Advanced Excel: Proficiency in Excel is crucial for data manipulation, analysis, and creating dashboards.
- SQL/Oracle: SQL is essential for querying databases to extract, manipulate, and analyze data.
- Python/R: Basic noscripting knowledge in Python or R for data cleaning, analysis, and simple automations.
- Data Visualization: Tools like Power BI or Tableau for creating interactive reports and dashboards.
- Statistical Analysis: Understanding of basic statistical concepts to analyze data trends and patterns.


Skills required to become a Data Engineer: 👇

- Programming Languages: Strong skills in Python or Java for building data pipelines and processing data.
- SQL and NoSQL: Knowledge of relational databases (SQL) and non-relational databases (NoSQL) like Cassandra or MongoDB.
- Big Data Technologies: Proficiency in Hadoop, Hive, Pig, or Spark for processing and managing large data sets.
- Data Warehousing: Experience with tools like Amazon Redshift, Google BigQuery, or Snowflake for storing and querying large datasets.
- ETL Processes: Expertise in Extract, Transform, Load (ETL) tools and processes for data integration.


Skills required to become a Data Scientist: 👇

- Advanced Tools: Deep knowledge of R, Python, or SAS for statistical analysis and data modeling.
- Machine Learning Algorithms: Understanding and implementation of algorithms using libraries like scikit-learn, TensorFlow, and Keras.
- SQL and NoSQL: Ability to work with both structured and unstructured data using SQL and NoSQL databases.
- Data Wrangling & Preprocessing: Skills in cleaning, transforming, and preparing data for analysis.
- Statistical and Mathematical Modeling: Strong grasp of statistics, probability, and mathematical techniques for building predictive models.
- Cloud Computing: Familiarity with AWS, Azure, or Google Cloud for deploying machine learning models.

Bonus Skills Across All Roles:

- Data Visualization: Mastery in tools like Power BI and Tableau to visualize and communicate insights effectively.
- Advanced Statistics: Strong statistical foundation to interpret and validate data findings.
- Domain Knowledge: Industry-specific knowledge (e.g., finance, healthcare) to apply data insights in context.
- Communication Skills: Ability to explain complex technical concepts to non-technical stakeholders.

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𝟴 𝗕𝗲𝘀𝘁 𝗙𝗿𝗲𝗲 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗳𝗿𝗼𝗺 𝗛𝗮𝗿𝘃𝗮𝗿𝗱, 𝗠𝗜𝗧 & 𝗦𝘁𝗮𝗻𝗳𝗼𝗿𝗱😍

🎓 Learn Data Science for Free from the World’s Best Universities🚀

Top institutions like Harvard, MIT, and Stanford are offering world-class data science courses online — and they’re 100% free. 🎯📍

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All The Best 👍

🔹 🔥 Pro Tips for Aspiring Data Engineers

1. Learn SQL deeply – it's still the foundation of everything
2. Understand data formats: JSON, Parquet, Avro, ORC
3. Master Apache Spark — it's everywhere
4. Learn to use Airflow for orchestrating workflows
5. Practice writing ETL pipelines — build your own mini data warehouse
6. Get comfortable with cloud platforms (start with AWS/GCP free tiers)
7. Version-control your work using Git + DVC for data versioning
8. Learn Docker & Kubernetes basics — modern data infra depends on it
9. Explore real-time processing: Kafka, Flink, and Spark Streaming
10. Follow best practices for data modeling — star/snowflake schemas, SCDs, etc

𝗟𝗲𝗮𝗿𝗻 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗶𝗻 𝗝𝘂𝘀𝘁 𝟯 𝗠𝗼𝗻𝘁𝗵𝘀 𝘄𝗶𝘁𝗵 𝗧𝗵𝗶𝘀 𝗙𝗿𝗲𝗲 𝗚𝗶𝘁𝗛𝘂𝗯 𝗥𝗼𝗮𝗱𝗺𝗮𝗽😍

🎯 Want to Master Data Science in Just 3 Months?📊

Feeling overwhelmed by the sheer volume of resources and don’t know where to start? You’re not alone🚀

𝐋𝐢𝐧𝐤👇:-

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This FREE GitHub roadmap is a game-changer for anyone✅️