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FREE DSA courses!
DSA roadmap
15 DSA patterns you must learn!

Apache Spark Question bank!!

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Mastering SQL: Key Interview Questions

Get ready to excel in your SQL interviews with this complete guide of the most common SQL interview questions for all levels. Whether you're just starting and need to understand the basics, you're an intermediate professional tackling more complicated queries, or you're an expert looking to show off your advanced skills, this guide has everything you need. It covers everything from simple SQL commands to complex techniques for optimizing queries and designing databases. Mastering these questions will boost your chances of success and make you stand out in any interview.

6 Month Technical placement Roadmap

https://topmate.io/codingdidi/1365458

Here are more resources!!
Data Analysis Basics

Resource: "Data Science for Business" by Foster Provost and Tom Fawcett
SQL and Database Management

Resource: Codecademy’s SQL Course
Data Visualization

Resource: Tableau Public & Power BI tutorials
Statistical Analysis

Resource: Khan Academy Statistics Course
Excel for Data Analysis

Resource: Excel Jet – Excel Tips & Tutorials
Python for Data Analysis

Resource: "Python for Data Analysis" by Wes McKinney
Data Cleaning & Preprocessing

Resource: Kaggle’s Data Cleaning Courses
Machine Learning Basics

Resource: Coursera's "Introduction to Machine Learning" by Andrew Ng
Big Data Tools

Resource: Hadoop and Spark tutorials on edX
Business Intelligence

Resource: "The Big Book of Dashboards" by Steve Wexler

Essential Python and SQL topics for data analysts 😄👇

Python Topics:

Python Resources - @w3schools or @geeksforgeeks

1. Data Structures
   - Lists, Tuples, and Dictionaries
   - NumPy Arrays for numerical data

2. Data Manipulation
   - Pandas DataFrames for structured data
   - Data Cleaning and Preprocessing techniques
   - Data Transformation and Reshaping

3. Data Visualization
   - Matplotlib for basic plotting
   - Seaborn for statistical visualizations
   - Plotly for interactive charts

4. Statistical Analysis
   - Descriptive Statistics
   - Hypothesis Testing
   - Regression Analysis

5. Machine Learning
   - Scikit-Learn for machine learning models
   - Model Building, Training, and Evaluation
   - Feature Engineering and Selection

6. Time Series Analysis
   - Handling Time Series Data
   - Time Series Forecasting
   - Anomaly Detection

7. Python Fundamentals
   - Control Flow (if statements, loops)
   - Functions and Modular Code
   - Exception Handling
   - File

SQL Topics:

SQL Resources - @sqltutorials

1. SQL Basics
- SQL Syntax
- SELECT Queries
- Filters

2. Data Retrieval
- Aggregation Functions (SUM, AVG, COUNT)
- GROUP BY

3. Data Filtering
- WHERE Clause
- ORDER BY

4. Data Joins
- JOIN Operations
- Subqueries

5. Advanced SQL
- Window Functions
- Indexing
- Performance Optimization

6. Database Management
- Connecting to Databases
- SQLAlchemy

7. Database Design
- Data Types
- Normalization

Remember, it's highly likely that you won't know all these concepts from the start. Data analysis is a journey where the more you learn, the more you grow. Embrace the learning process, and your skills will continually evolve and expand. Keep up the great work!

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🔉 Hiring Data Analyst (0-1 Years Experience) 🚀
Ankit is hiring for a junior data analytics role.

The candidate should be able to write complex SQL queries and should be well-versed in Excel. Having experience building Power BI dashboards will be an added advantage.

The candidate will be employed by a third party and will work for Bajaj Finance in Viman Nagar, Pune.

Please share your resume at ankit.hirani@bajajfinserv.in
if you meet the above criteria

DAX Functions

What's the most significant achievement you accomplished in 2024, and what's the target for 2025?

📊 Master DAX Functions for Power BI Excellence!

🔹What is DAX?
DAX (Data Analysis Expressions) is a powerful formula language used in Power BI, Excel, and Analysis Services to create custom calculations and insights.

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🛠Essential DAX Functions You Should Know:

1️⃣ SUM:
SUM(column_name)
- Adds up all the values in a column. Perfect for total sales or revenue calculations!

2️⃣ AVERAGE:
AVERAGE(column_name)
- Calculates the average value in a column. Great for tracking performance metrics.

3️⃣ CALCULATE:
CALCULATE(expression, filter)
- Evaluates an expression with specified filters. Ideal for custom metrics!

4️⃣IF:
IF(condition, result_if_true, result_if_false)
- Implements conditional logic in your measures or calculated columns.

5️⃣ RELATED:
RELATED(column_name)
- Pulls data from related tables. Useful for combining information in your models.

6️⃣ ALL:
ALL(column_name)
- Ignores filters to return all rows from a column. Great for creating ratios or percentages.

7️⃣ FILTER:
FILTER(table, condition)
- Returns a filtered table based on the given condition. Handy for advanced analysis.

8️⃣ RANKX:
RANKX(table, expression, [value], [order], [ties])
- Ranks items based on a specific measure or column. Use for leaderboards or comparisons.

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💡Why Learn DAX?
✅ Unlock deeper insights in Power BI.
✅ Automate complex calculations.
✅ Build interactive and dynamic dashboards.
✅ Gain a competitive edge in analytics!

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🧠 Pro Tip: Start with simple functions like SUM and IF, then explore advanced ones like CALCULATE and RANKX for greater control over your data models.

🔗 Join our Telegram channel for FREE resources and stay updated with the latest analytics tips! 🚀

Company Name: Sportskeeda

Role: Data Intern

Qualification: Any Graduate

Salary: Upto 5 LPA

Apply Link: https://sportskeeda.zohorecruit.in/jobs/Careers/59509000032505325/Data-Intern?source=CareerSite

Like for more ❤️

All the best 👍👍

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

Apply Here : https://cuvette.tech/app/other-jobs/67752de3370214371d392781?referralCode=8T994D

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!
Position: Software Development Engineer I - Data
Qualifications: Bachelor’s/ Master’s
Salary: 15 - 32 LPA LPA (Expected)
Experience: Freshers/ Experienced
Location: Bangalore, India (Hybrid)

📌Apply Now: https://jobs.lever.co/meesho/fdbc2008-63d6-4334-8d9b-0dfa94ce4256

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All the best 👍👍

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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