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Role: Data Intern

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Salary: Upto 5 LPA

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Difference between Data science vs Data Analyst

EXCEL is must for Data analysis

Razorpay hiring for Analyst role

1-2 year experience
6-10 LPA CTC

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Learn Deep Learning!!

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📊 Ace Your Data Analyst Interview with Confidence! 💼✨
Are you gearing up for your data analyst interview? 🤔 Don’t worry! We've compiled a list of common and top interview questions along with expert answers to help you shine during the process. 🌟

In this PDF, you'll find:

🛠 Key Topics Covered:
1️⃣ Technical Skills:

Master questions on SQL, Excel, Python, and data visualization tools like Tableau and Power BI.
Understand common concepts like joins, GROUP BY, and data cleaning techniques.
2️⃣ Analytical Thinking:

Learn how to approach real-world business problems with data-driven solutions.
Get sample scenarios and tips to showcase your critical thinking skills.
3️⃣ Behavioral Questions:

Prepare to answer “Tell me about yourself” and “Describe a challenging project” like a pro!
Use STAR (Situation, Task, Action, Result) to structure your answers effectively.
ETC.

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https://www.linkedin.com/posts/akansha-yadav24_github-thealgorithmspython-all-algorithms-activity-7280526994176405505-NNVm?utm_source=share&utm_medium=member_android


GitHub repos for learning data analysis and data science. ✅

Meesho is hiring!
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Hyperparameter tuning in machine learning is the process of finding the best values for the hyperparameters of a model. Hyperparameters are settings that control the training process, such as learning rate, batch size, and the number of layers in a neural network.

Unlike regular parameters, which are learned by the model during training (like weights), hyperparameters need to be set before training starts. The goal of hyperparameter tuning is to improve the model’s performance, making it more accurate and efficient.

To find the best hyperparameters, techniques like grid search, random search, or more advanced methods like Bayesian optimization are used. This process can take time, but it’s crucial for getting the best possible model for a specific task.

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📊 Linear Regression in Simple Terms
- Purpose: Predict a target value based on input features.
- Model: Fits a straight line (linear) to data points.
- Formula: y = mx + b
- y = predicted value
- m = slope (how steep the line is)
- x = input feature
- b = y-intercept (where the line crosses the y-axis)
- Assumptions:
- Relationship between input and output is linear.
- Data points are scattered around the line.
- Used for:
- Predicting continuous values (e.g., price, temperature).
- Types:
- Simple: One feature, one output.
- Multiple: Multiple features, one

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Internship Calendar 2025

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Pandas Cheetsheet!!

Hope this helps !!

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♥️😇Hiring : W3Global
Package : 10-16 Lakh

Apply Here
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🖥 Website To Learn Programming & Data Analytics

1. Learn HTML :- html.com
2. Learn CSS :- css-tricks.com
3. Learn Tailwind CSS :- tailwindcss.com
4. Learn JavaScript :- imp.i115008.net/mgGagX
5. Learn Bootstrap :- getbootstrap.com
6. Learn DSA :- t.me/dsabooks
7. Learn Git :- git-scm.com
8. Learn React :- react-tutorial.app
9. Learn API :- rapidapi.com/learn
10. Learn Python :- t.me/pythondevelopersindia
11. Learn SQL :- t.me/sqlspecialist
12. Learn Web3 :- learnweb3.io
13. Learn JQuery :- learn.jquery.com
14. Learn ExpressJS :- expressjs.com
15. Learn NodeJS :- nodejs.dev/learn
16. Learn MongoDB :- learn.mongodb.com
17. Learn PHP :- phptherightway.com/
18. Learn Golang :- learn-golang.org/
19. Learn Power BI :- t.me/powerbi_analyst
20. Learn Data Analytics:- datasimplifier.com

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Data Science Question and Answer!!

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🐍 PySpark vs Pandas
1. Data Handling
✅Pandas:

Best for small to medium-sized datasets.
Works on a single machine (in-memory processing).
Suitable for datasets that fit in memory.
✅PySpark:

Designed for large-scale data processing.
Can handle big datasets that don’t fit in memory (distributed processing).
Works across multiple machines (clusters).
2. Performance
✅Pandas:

Faster for small datasets (single-machine operations).
May slow down with very large datasets.
✅PySpark:

Faster for large datasets (distributed computing).
Optimized for parallel processing.
3. Ease of Use
✅Pandas:

Simple and easy to use for data manipulation and analysis.
Rich set of functions and operations.
✅PySpark:

More complex and requires setup (cluster, Spark context).
Similar operations to Pandas, but for distributed data.

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What is Apache Spark and Where to learn them?


Apache Spark is a powerful distributed data processing framework used for big data and machine learning tasks. Here are some excellent resources to learn Apache Spark, catering to various levels of expertise:

1. Follow - Apache Spark Official Documentation

- Great starting point with detailed tutorials and guides.
- Covers installation, core concepts, and APIs for Scala, Python (PySpark), Java, and R.

2. YouTube Tutorials

- Free video tutorials by channels like Simplilearn or Data Engineering Simplified.

3. Coursera and edX Courses

- Coursera: Big Data Analysis with Scala and Spark (offered by École Polytechnique Fédérale de Lausanne).
- edX: Introduction to Big Data with Apache Spark (offered by UC Berkeley).

Capgemini is hiring!
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Here are some SQL project ideas tailored for data analysis:

🔟 SQL Project Ideas for Data Analysts

1. Sales Database Analysis: Create a database to track sales transactions. Write SQL queries to analyze sales performance by product, region, and time period.

2. Customer Churn Analysis: Build a database with customer data and track churn rates. Use SQL to identify factors contributing to churn and segment customers.

3. E-commerce Order Tracking: Design a database for an e-commerce platform. Write queries to analyze order trends, average order value, and customer purchase history.

4. Employee Performance Metrics: Create a database for employee records and performance reviews. Analyze employee performance trends and identify high performers using SQL.

5. Inventory Management System: Set up a database to track inventory levels. Write SQL queries to monitor stock levels, identify slow-moving items, and generate restock reports.

6. Healthcare Patient Analysis: Build a database to manage patient records and treatments. Use SQL to analyze treatment outcomes, readmission rates, and patient demographics.

7. Social Media Engagement Analysis: Create a database to track user interactions on a social media platform. Write queries to analyze engagement metrics like likes, shares, and comments.

8. Financial Transaction Analysis: Set up a database for financial transactions. Use SQL to identify spending patterns, categorize expenses, and generate monthly financial reports.

9. Website Traffic Analysis: Build a database to track website visitors. Write queries to analyze traffic sources, user behavior, and page performance.

10. Survey Results Analysis: Create a database to store survey responses. Use SQL to analyze responses, identify trends, and visualize findings based on demographic data.

Here you can find essential SQL Interview Resources👇
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60 Most Asked Interview Question

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