30-day Roadmap plan for SQL covers beginner, intermediate, and advanced topics 👇

Week 1: Beginner Level

Day 1-3: Introduction and Setup
1. Day 1: Introduction to SQL, its importance, and various database systems.
2. Day 2: Installing a SQL database (e.g., MySQL, PostgreSQL).
3. Day 3: Setting up a sample database and practicing basic commands.

Day 4-7: Basic SQL Queries
4. Day 4: SELECT statement, retrieving data from a single table.
5. Day 5: WHERE clause and filtering data.
6. Day 6: Sorting data with ORDER BY.
7. Day 7: Aggregating data with GROUP BY and using aggregate functions (COUNT, SUM, AVG).

Week 2-3: Intermediate Level

Day 8-14: Working with Multiple Tables
8. Day 8: Introduction to JOIN operations.
9. Day 9: INNER JOIN and LEFT JOIN.
10. Day 10: RIGHT JOIN and FULL JOIN.
11. Day 11: Subqueries and correlated subqueries.
12. Day 12: Creating and modifying tables with CREATE, ALTER, and DROP.
13. Day 13: INSERT, UPDATE, and DELETE statements.
14. Day 14: Understanding indexes and optimizing queries.

Day 15-21: Data Manipulation
15. Day 15: CASE statements for conditional logic.
16. Day 16: Using UNION and UNION ALL.
17. Day 17: Data type conversions (CAST and CONVERT).
18. Day 18: Working with date and time functions.
19. Day 19: String manipulation functions.
20. Day 20: Error handling with TRY...CATCH.
21. Day 21: Practice complex queries and data manipulation tasks.

Week 4: Advanced Level

Day 22-28: Advanced Topics
22. Day 22: Working with Views.
23. Day 23: Stored Procedures and Functions.
24. Day 24: Triggers and transactions.
25. Day 25: Windows Function

Day 26-30: Real-World Projects
26. Day 26: SQL Project-1
27. Day 27: SQL Project-2
28. Day 28: SQL Project-3
29. Day 29: Practice questions set
30. Day 30: Final review and practice, explore advanced topics in depth, or work on a personal project.

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If you are trying to transition into the data analytics domain and getting started with SQL, focus on the most useful concept that will help you solve the majority of the problems, and then try to learn the rest of the topics:

👉🏻 Basic Aggregation function:
1️⃣ AVG
2️⃣ COUNT
3️⃣ SUM
4️⃣ MIN
5️⃣ MAX

👉🏻 JOINS
1️⃣ Left
2️⃣ Inner
3️⃣ Self (Important, Practice questions on self join)

👉🏻 Windows Function (Important)
1️⃣ Learn how partitioning works
2️⃣ Learn the different use cases where Ranking/Numbering Functions are used? ( ROW_NUMBER,RANK, DENSE_RANK, NTILE)
3️⃣ Use Cases of LEAD & LAG functions
4️⃣ Use cases of Aggregate window functions

👉🏻 GROUP BY
👉🏻 WHERE vs HAVING
👉🏻 CASE STATEMENT
👉🏻 UNION vs Union ALL
👉🏻 LOGICAL OPERATORS

Other Commonly used functions:
👉🏻 IFNULL
👉🏻 COALESCE
👉🏻 ROUND
👉🏻 Working with Date Functions
1️⃣ EXTRACTING YEAR/MONTH/WEEK/DAY
2️⃣ Calculating date differences

👉🏻CTE
👉🏻Views & Triggers (optional)

Here is an amazing resources to learn & practice SQL: https://bit.ly/3FxxKPz

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Python for Data Analysis: Must-Know Libraries 👇👇

Python is one of the most powerful tools for Data Analysts, and these libraries will supercharge your data analysis workflow by helping you clean, manipulate, and visualize data efficiently.

🔥 Essential Python Libraries for Data Analysis:

✅ Pandas – The go-to library for data manipulation. It helps in filtering, grouping, merging datasets, handling missing values, and transforming data into a structured format.

📌 Example: Loading a CSV file and displaying the first 5 rows:

import pandas as pd df = pd.read_csv('data.csv') print(df.head()) 

✅ NumPy – Used for handling numerical data and performing complex calculations. It provides support for multi-dimensional arrays and efficient mathematical operations.

📌 Example: Creating an array and performing basic operations:

import numpy as np arr = np.array([10, 20, 30]) print(arr.mean()) # Calculates the average 

✅ Matplotlib & Seaborn – These are used for creating visualizations like line graphs, bar charts, and scatter plots to understand trends and patterns in data.

📌 Example: Creating a basic bar chart:

import matplotlib.pyplot as plt plt.bar(['A', 'B', 'C'], [5, 7, 3]) plt.show() 

✅ Scikit-Learn – A must-learn library if you want to apply machine learning techniques like regression, classification, and clustering on your dataset.

✅ OpenPyXL – Helps in automating Excel reports using Python by reading, writing, and modifying Excel files.

💡 Challenge for You!
Try writing a Python script that:
1️⃣ Reads a CSV file
2️⃣ Cleans missing data
3️⃣ Creates a simple visualization

React with ♥️ if you want me to post the script for above challenge! ⬇️

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𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁 𝘃𝘀 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁 𝘃𝘀 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗔𝗻𝗮𝗹𝘆𝘀𝘁 — 𝗪𝗵𝗶𝗰𝗵 𝗣𝗮𝘁𝗵 𝗶𝘀 𝗥𝗶𝗴𝗵𝘁 𝗳𝗼𝗿 𝗬𝗼𝘂? 🤔

In today’s data-driven world, career clarity can make all the difference. Whether you’re starting out in analytics, pivoting into data science, or aligning business with data as an analyst — understanding the core responsibilities, skills, and tools of each role is crucial.

🔍 Here’s a quick breakdown from a visual I often refer to when mentoring professionals:

🔹 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁

󠁯•󠁏 Focus: Analyzing historical data to inform decisions.

󠁯•󠁏 Skills: SQL, basic stats, data visualization, reporting.

󠁯•󠁏 Tools: Excel, Tableau, Power BI, SQL.

🔹 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁

󠁯•󠁏 Focus: Predictive modeling, ML, complex data analysis.

󠁯•󠁏 Skills: Programming, ML, deep learning, stats.

󠁯•󠁏 Tools: Python, R, TensorFlow, Scikit-Learn, Spark.

🔹 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗔𝗻𝗮𝗹𝘆𝘀𝘁

󠁯•󠁏 Focus: Bridging business needs with data insights.

󠁯•󠁏 Skills: Communication, stakeholder management, process modeling.

󠁯•󠁏 Tools: Microsoft Office, BI tools, business process frameworks.

👉 𝗠𝘆 𝗔𝗱𝘃𝗶𝗰𝗲:

Start with what interests you the most and aligns with your current strengths. Are you business-savvy? Start as a Business Analyst. Love solving puzzles with data?

Explore Data Analyst. Want to build models and uncover deep insights? Head into Data Science.

🔗 𝗧𝗮𝗸𝗲 𝘁𝗶𝗺𝗲 𝘁𝗼 𝘀𝗲𝗹𝗳-𝗮𝘀𝘀𝗲𝘀𝘀 𝗮𝗻𝗱 𝗰𝗵𝗼𝗼𝘀𝗲 𝗮 𝗽𝗮𝘁𝗵 𝘁𝗵𝗮𝘁 𝗲𝗻𝗲𝗿𝗴𝗶𝘇𝗲𝘀 𝘆𝗼𝘂, not just one that’s trending.

SQL Basics for Data Analysts

SQL (Structured Query Language) is used to retrieve, manipulate, and analyze data stored in databases.

1️⃣ Understanding Databases & Tables

Databases store structured data in tables.

Tables contain rows (records) and columns (fields).

Each column has a specific data type (INTEGER, VARCHAR, DATE, etc.).

2️⃣ Basic SQL Commands

Let's start with some fundamental queries:

🔹 SELECT – Retrieve Data

SELECT * FROM employees; -- Fetch all columns from 'employees' table SELECT name, salary FROM employees; -- Fetch specific columns 

🔹 WHERE – Filter Data

SELECT * FROM employees WHERE department = 'Sales'; -- Filter by department SELECT * FROM employees WHERE salary > 50000; -- Filter by salary 

🔹 ORDER BY – Sort Data

SELECT * FROM employees ORDER BY salary DESC; -- Sort by salary (highest first) SELECT name, hire_date FROM employees ORDER BY hire_date ASC; -- Sort by hire date (oldest first) 

🔹 LIMIT – Restrict Number of Results

SELECT * FROM employees LIMIT 5; -- Fetch only 5 rows SELECT * FROM employees WHERE department = 'HR' LIMIT 10; -- Fetch first 10 HR employees 

🔹 DISTINCT – Remove Duplicates

SELECT DISTINCT department FROM employees; -- Show unique departments 

Mini Task for You: Try to write an SQL query to fetch the top 3 highest-paid employees from an "employees" table.

You can find free SQL Resources here
👇👇
https://t.iss.one/mysqldata

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Essential Skills Excel for Data Analysts 🚀

1️⃣ Data Cleaning & Transformation

Remove Duplicates – Ensure unique records.
Find & Replace – Quick data modifications.
Text Functions – TRIM, LEN, LEFT, RIGHT, MID, PROPER.
Data Validation – Restrict input values.

2️⃣ Data Analysis & Manipulation

Sorting & Filtering – Organize and extract key insights.
Conditional Formatting – Highlight trends, outliers.
Pivot Tables – Summarize large datasets efficiently.
Power Query – Automate data transformation.

3️⃣ Essential Formulas & Functions

Lookup Functions – VLOOKUP, HLOOKUP, XLOOKUP, INDEX-MATCH.
Logical Functions – IF, AND, OR, IFERROR, IFS.
Aggregation Functions – SUM, AVERAGE, MIN, MAX, COUNT, COUNTA.
Text Functions – CONCATENATE, TEXTJOIN, SUBSTITUTE.

4️⃣ Data Visualization
Charts & Graphs – Bar, Line, Pie, Scatter, Histogram.

Sparklines – Miniature charts inside cells.
Conditional Formatting – Color scales, data bars.
Dashboard Creation – Interactive and dynamic reports.

5️⃣ Advanced Excel Techniques
Array Formulas – Dynamic calculations with multiple values.
Power Pivot & DAX – Advanced data modeling.
What-If Analysis – Goal Seek, Scenario Manager.
Macros & VBA – Automate repetitive tasks.

6️⃣ Data Import & Export
CSV & TXT Files – Import and clean raw data.
Power Query – Connect to databases, web sources.
Exporting Reports – PDF, CSV, Excel formats.

Here you can find some free Excel books & useful resources: https://t.iss.one/excel_data

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#dataanalyst

Here are some essential SQL tips for beginners 👇👇

◆ Primary Key = Unique Key + Not Null constraint
◆ To perform case insensitive search use UPPER() function ex. UPPER(customer_name) LIKE ‘A%A’
◆ LIKE operator is for string data type
◆ COUNT(*), COUNT(1), COUNT(0) all are same
◆ All aggregate functions ignore the NULL values
◆ Aggregate functions MIN, MAX, SUM, AVG, COUNT are for int data type whereas STRING_AGG is for string data type
◆ For row level filtration use WHERE and aggregate level filtration use HAVING
◆ UNION ALL will include duplicates where as UNION excludes duplicates 
◆ If the results will not have any duplicates, use UNION ALL instead of UNION
◆ We have to alias the subquery if we are using the columns in the outer select query
◆ Subqueries can be used as output with NOT IN condition.
◆ CTEs look better than subqueries. Performance wise both are same.
◆ When joining two tables , if one table has only one value then we can use 1=1 as a condition to join the tables. This will be considered as CROSS JOIN.
◆ Window functions work at ROW level.
◆ The difference between RANK() and DENSE_RANK() is that RANK() skips the rank if the values are the same.
◆ EXISTS works on true/false conditions. If the query returns at least one value, the condition is TRUE. All the records corresponding to the conditions are returned.

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Guys, Big Announcement!

We’ve officially hit 2 MILLION followers — and it’s time to take our Python journey to the next level!

I’m super excited to launch the 30-Day Python Coding Challenge — perfect for absolute beginners, interview prep, or anyone wanting to build real projects from scratch.

This challenge is your daily dose of Python — bite-sized lessons with hands-on projects so you actually code every day and level up fast.

Here’s what you’ll learn over the next 30 days:

Week 1: Python Fundamentals

- Variables & Data Types (Build your own bio/profile script)

- Operators (Mini calculator to sharpen math skills)

- Strings & String Methods (Word counter & palindrome checker)

- Lists & Tuples (Manage a grocery list like a pro)

- Dictionaries & Sets (Create your own contact book)

- Conditionals (Make a guess-the-number game)

- Loops (Multiplication tables & pattern printing)

Week 2: Functions & Logic — Make Your Code Smarter

- Functions (Prime number checker)

- Function Arguments (Tip calculator with custom tips)

- Recursion Basics (Factorials & Fibonacci series)

- Lambda, map & filter (Process lists efficiently)

- List Comprehensions (Filter odd/even numbers easily)

- Error Handling (Build a safe input reader)

- Review + Mini Project (Command-line to-do list)


Week 3: Files, Modules & OOP

- Reading & Writing Files (Save and load notes)

- Custom Modules (Create your own utility math module)

- Classes & Objects (Student grade tracker)

- Inheritance & OOP (RPG character system)

- Dunder Methods (Build a custom string class)

- OOP Mini Project (Simple bank account system)

- Review & Practice (Quiz app using OOP concepts)


Week 4: Real-World Python & APIs — Build Cool Apps

- JSON & APIs (Fetch weather data)

- Web Scraping (Extract titles from HTML)

- Regular Expressions (Find emails & phone numbers)

- Tkinter GUI (Create a simple counter app)

- CLI Tools (Command-line calculator with argparse)

- Automation (File organizer script)

- Final Project (Choose, build, and polish your app!)

React with ❤️ if you're ready for this new journey

You can join our WhatsApp channel to access it for free: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L/1661

The best doesn't come from working more.

It comes from working smarter.

The most common mistakes people make,
With practical tips to avoid each:

1) Working late every night.

• Prioritize quality time with loved ones.

Understand that long hours won't be remembered as fondly as time spent with family and friends.

2) Believing more hours mean more productivity.

• Focus on efficiency.

Complete tasks in less time to free up hours for personal activities and rest.

3) Ignoring the need for breaks.

• Take regular breaks to rejuvenate your mind.

Creativity and productivity suffer without proper rest.

4) Sacrificing personal well-being.

• Maintain a healthy work-life balance.

Ensure you don't compromise your health or relationships for work.

5) Feeling pressured to constantly produce.

• Quality over quantity.

6) Neglecting hobbies and interests.

• Engage in activities you love outside of work.

This helps to keep your mind fresh and inspired.

7) Failing to set boundaries.

• Set clear work hours and stick to them.

This helps to prevent overworking and ensures you have time for yourself.

8) Not delegating tasks.

• Delegate when possible.

Sharing the workload can enhance productivity and give you more free time.

9) Overlooking the importance of sleep.

• Prioritize sleep for better performance.

A well-rested mind is more creative and effective.

10) Underestimating the impact of overworking.

• Recognize the long-term effects.

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How do analysts use SQL in a company?

SQL is every data analyst’s superpower! Here's how they use it in the real world:

Extract Data

Pull data from multiple tables to answer business questions.

Example:

SELECT name, revenue FROM sales WHERE region = 'North America';

(P.S. Avoid SELECT *—your future self (and the database) will thank you!)


Clean & Transform

Use SQL functions to clean raw data.

Think TRIM(), COALESCE(), CAST()—like giving data a fresh haircut.


Summarize & Analyze

Group and aggregate to spot trends and patterns.

GROUP BY, SUM(), AVG() – your best friends for quick insights.


Build Dashboards

Feed SQL queries into Power BI, Tableau, or Excel to create visual stories that make data talk.

Run A/B Tests

Evaluate product changes and campaigns by comparing user groups.

SQL makes sure your decisions are backed by data, not just gut feeling.


Use Views & CTEs

Simplify complex queries with Views and Common Table Expressions.

Clean, reusable, and boss-approved.


Drive Decisions

SQL powers decisions across Marketing, Product, Sales, and Finance.

When someone asks “What’s working?”—you’ve got the answers.


And remember: write smart queries, not lazy ones. Say no to SELECT * unless you really mean it!

Hit ♥️ if you want me to share more real-world examples to make data analytics easier to understand!

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📊 Data Science Essentials: What Every Data Enthusiast Should Know!

1️⃣ Understand Your Data
Always start with data exploration. Check for missing values, outliers, and overall distribution to avoid misleading insights.

2️⃣ Data Cleaning Matters
Noisy data leads to inaccurate predictions. Standardize formats, remove duplicates, and handle missing data effectively.

3️⃣ Use Descriptive & Inferential Statistics
Mean, median, mode, variance, standard deviation, correlation, hypothesis testing—these form the backbone of data interpretation.

4️⃣ Master Data Visualization
Bar charts, histograms, scatter plots, and heatmaps make insights more accessible and actionable.

5️⃣ Learn SQL for Efficient Data Extraction
Write optimized queries (SELECT, JOIN, GROUP BY, WHERE) to retrieve relevant data from databases.

6️⃣ Build Strong Programming Skills
Python (Pandas, NumPy, Scikit-learn) and R are essential for data manipulation and analysis.

7️⃣ Understand Machine Learning Basics
Know key algorithms—linear regression, decision trees, random forests, and clustering—to develop predictive models.

8️⃣ Learn Dashboarding & Storytelling
Power BI and Tableau help convert raw data into actionable insights for stakeholders.

🔥 Pro Tip: Always cross-check your results with different techniques to ensure accuracy!

Data Science Learning Series: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D

DOUBLE TAP ❤️ IF YOU FOUND THIS HELPFUL!

Most popular Python libraries for data visualization:

Matplotlib – The most fundamental library for static charts. Best for basic visualizations like line, bar, and scatter plots. Highly customizable but requires more coding.

Seaborn – Built on Matplotlib, it simplifies statistical data visualization with beautiful defaults. Ideal for correlation heatmaps, categorical plots, and distribution analysis.

Plotly – Best for interactive visualizations with zooming, hovering, and real-time updates. Great for dashboards, web applications, and 3D plotting.

Bokeh – Designed for interactive and web-based visualizations. Excellent for handling large datasets, streaming data, and integrating with Flask/Django.

Altair – A declarative library that makes complex statistical plots easy with minimal code. Best for quick and clean data exploration.

For static charts, start with Matplotlib or Seaborn. If you need interactivity, use Plotly or Bokeh. For quick EDA, Altair is a great choice.

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#python

Advanced SQL Optimization Tips for Data Analysts

1. Use Proper Indexing
Create indexes on frequently queried columns to speed up data retrieval.

2. Avoid `SELECT *`
Specify only the columns you need to reduce the amount of data processed.

3. Use `WHERE` Instead of `HAVING`
Filter your data as early as possible in the query to optimize performance.

4. Limit Joins
Try to keep joins to a minimum to reduce query complexity and processing time.

5. Apply `LIMIT` or `TOP`
Retrieve only the required rows to save on resources.

6. Optimize Joins
Use INNER JOIN instead of OUTER JOIN whenever possible.

7. Use Temporary Tables
Break large, complex queries into smaller parts using temporary tables.

8. Avoid Functions on Indexed Columns
Using functions on indexed columns often prevents the index from being used.

9. Use CTEs for Readability
Common Table Expressions help simplify nested queries and improve clarity.

10. Analyze Execution Plans
Leverage execution plans to identify bottlenecks and make targeted optimizations.

Happy querying!

🔍 Best Data Analytics Roles Based on Your Graduation Background!

🚀 For Mathematics/Statistics Graduates:
🔹 Data Analyst
🔹 Statistical Analyst
🔹 Quantitative Analyst
🔹 Risk Analyst

🚀 For Computer Science/IT Graduates:
🔹 Data Scientist
🔹 Business Intelligence Developer
🔹 Data Engineer
🔹 Data Architect

🚀 For Economics/Finance Graduates:
🔹 Financial Analyst
🔹 Market Research Analyst
🔹 Economic Consultant
🔹 Data Journalist

🚀 For Business/Management Graduates:
🔹 Business Analyst
🔹 Operations Research Analyst
🔹 Marketing Analytics Manager
🔹 Supply Chain Analyst

🚀 For Engineering Graduates:
🔹 Data Scientist
🔹 Industrial Engineer
🔹 Operations Research Analyst
🔹 Quality Engineer

🚀 For Social Science Graduates:
🔹 Data Analyst
🔹 Research Assistant
🔹 Social Media Analyst
🔹 Public Health Analyst

🚀 For Biology/Healthcare Graduates:
🔹 Clinical Data Analyst
🔹 Biostatistician
🔹 Research Coordinator
🔹 Healthcare Consultant

Some of these roles may require additional certifications or upskilling in SQL, Python, Power BI, Tableau, or Machine Learning to stand out in the job market.

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