Data analytics is not about the the tools you master but about the people you influence.
I see many debates around the best tools such as:
- Excel vs SQL
- Python vs R
- Tableau vs PowerBI
- ChatGPT vs no ChatGPT
The truth is that business doesn't care about how you come up with your insights.
All business cares about is:
- the story line
- how well they can understand it
- your communication style
- the overall feeling after a presentation
These make the difference in being perceived as a great data analyst...
not the tools you may or may not master ๐
I see many debates around the best tools such as:
- Excel vs SQL
- Python vs R
- Tableau vs PowerBI
- ChatGPT vs no ChatGPT
The truth is that business doesn't care about how you come up with your insights.
All business cares about is:
- the story line
- how well they can understand it
- your communication style
- the overall feeling after a presentation
These make the difference in being perceived as a great data analyst...
not the tools you may or may not master ๐
๐4โค3
Python for Everything:
Python + Django = Web Development
Python + Matplotlib = Data Visualization
Python + Flask = Web Applications
Python + Pygame = Game Development
Python + PyQt = Desktop Applications
Python + TensorFlow = Machine Learning
Python + FastAPI = API Development
Python + Kivy = Mobile App Development
Python + Pandas = Data Analysis
Python + NumPy = Scientific Computing
Python + Django = Web Development
Python + Matplotlib = Data Visualization
Python + Flask = Web Applications
Python + Pygame = Game Development
Python + PyQt = Desktop Applications
Python + TensorFlow = Machine Learning
Python + FastAPI = API Development
Python + Kivy = Mobile App Development
Python + Pandas = Data Analysis
Python + NumPy = Scientific Computing
โค4๐1
Useful WhatsApp channels to learn AI Tools ๐ค
ChatGPT: https://whatsapp.com/channel/0029VapThS265yDAfwe97c23
OpenAI: https://whatsapp.com/channel/0029VbAbfqcLtOj7Zen5tt3o
Deepseek: https://whatsapp.com/channel/0029Vb9js9sGpLHJGIvX5g1w
Perplexity AI: https://whatsapp.com/channel/0029VbAa05yISTkGgBqyC00U
Copilot: https://whatsapp.com/channel/0029VbAW0QBDOQIgYcbwBd1l
Generative AI: https://whatsapp.com/channel/0029VazaRBY2UPBNj1aCrN0U
Prompt Engineering: https://whatsapp.com/channel/0029Vb6ISO1Fsn0kEemhE03b
Artificial Intelligence: https://whatsapp.com/channel/0029VaoePz73bbV94yTh6V2E
Grok AI: https://whatsapp.com/channel/0029VbAU3pWChq6T5bZxUk1r
Deeplearning AI: https://whatsapp.com/channel/0029VbAKiI1FSAt81kV3lA0t
AI Studio: https://whatsapp.com/channel/0029VbAWNue1iUxjLo2DFx2U
React โค๏ธ for more
ChatGPT: https://whatsapp.com/channel/0029VapThS265yDAfwe97c23
OpenAI: https://whatsapp.com/channel/0029VbAbfqcLtOj7Zen5tt3o
Deepseek: https://whatsapp.com/channel/0029Vb9js9sGpLHJGIvX5g1w
Perplexity AI: https://whatsapp.com/channel/0029VbAa05yISTkGgBqyC00U
Copilot: https://whatsapp.com/channel/0029VbAW0QBDOQIgYcbwBd1l
Generative AI: https://whatsapp.com/channel/0029VazaRBY2UPBNj1aCrN0U
Prompt Engineering: https://whatsapp.com/channel/0029Vb6ISO1Fsn0kEemhE03b
Artificial Intelligence: https://whatsapp.com/channel/0029VaoePz73bbV94yTh6V2E
Grok AI: https://whatsapp.com/channel/0029VbAU3pWChq6T5bZxUk1r
Deeplearning AI: https://whatsapp.com/channel/0029VbAKiI1FSAt81kV3lA0t
AI Studio: https://whatsapp.com/channel/0029VbAWNue1iUxjLo2DFx2U
React โค๏ธ for more
โค2๐1
SQL INTERVIEW Questions
Explain the concept of window functions in SQL. Provide examples to illustrate their usage.
Answer:
Window Functions:
Window functions perform calculations across a set of table rows related to the current row. Unlike aggregate functions, window functions do not group rows into a single output row; instead, they return a value for each row in the query result.
Types of Window Functions:
1. Aggregate Window Functions: Compute aggregate values like SUM, AVG, COUNT, etc.
2. Ranking Window Functions: Assign a rank to each row, such as RANK(), DENSE_RANK(), and ROW_NUMBER().
3. Analytic Window Functions: Perform calculations like LEAD(), LAG(), FIRST_VALUE(), and LAST_VALUE().
Syntax:
Examples:
1. Using ROW_NUMBER():
Assign a unique number to each row within a partition of the result set.
This query ranks employees within each department based on their salary in descending order.
2. Using AVG() with OVER():
Calculate the average salary within each department without collapsing the result set.
This query returns the average salary for each department along with each employee's salary.
3. Using LEAD():
Access the value of a subsequent row in the result set.
This query retrieves the salary of the next employee within the same department based on the current sorting order.
4. Using RANK():
Assign a rank to each row within the partition, with gaps in the ranking values if there are ties.
This query ranks employees within each department by their salary in descending order, leaving gaps for ties.
Tip: Window functions are powerful for performing calculations across a set of rows while retaining the individual rows. They are useful for running totals, moving averages, ranking, and accessing data from other rows within the same result set.
Go though SQL Learning Series to refresh your basics
Share with credits: https://t.me/sqlspecialist
Like this post if you want me to continue SQL Interview Preparation Series ๐โค๏ธ
Hope it helps :)
Explain the concept of window functions in SQL. Provide examples to illustrate their usage.
Answer:
Window Functions:
Window functions perform calculations across a set of table rows related to the current row. Unlike aggregate functions, window functions do not group rows into a single output row; instead, they return a value for each row in the query result.
Types of Window Functions:
1. Aggregate Window Functions: Compute aggregate values like SUM, AVG, COUNT, etc.
2. Ranking Window Functions: Assign a rank to each row, such as RANK(), DENSE_RANK(), and ROW_NUMBER().
3. Analytic Window Functions: Perform calculations like LEAD(), LAG(), FIRST_VALUE(), and LAST_VALUE().
Syntax:
SELECT column_name,
window_function() OVER (PARTITION BY column_name ORDER BY column_name)
FROM table_name;
Examples:
1. Using ROW_NUMBER():
Assign a unique number to each row within a partition of the result set.
SELECT employee_name, department_id, salary,
ROW_NUMBER() OVER (PARTITION BY department_id ORDER BY salary DESC) AS rank
FROM employees;
This query ranks employees within each department based on their salary in descending order.
2. Using AVG() with OVER():
Calculate the average salary within each department without collapsing the result set.
SELECT employee_name, department_id, salary,
AVG(salary) OVER (PARTITION BY department_id) AS avg_salary
FROM employees;
This query returns the average salary for each department along with each employee's salary.
3. Using LEAD():
Access the value of a subsequent row in the result set.
SELECT employee_name, department_id, salary,
LEAD(salary, 1) OVER (PARTITION BY department_id ORDER BY salary) AS next_salary
FROM employees;
This query retrieves the salary of the next employee within the same department based on the current sorting order.
4. Using RANK():
Assign a rank to each row within the partition, with gaps in the ranking values if there are ties.
SELECT employee_name, department_id, salary,
RANK() OVER (PARTITION BY department_id ORDER BY salary DESC) AS rank
FROM employees;
This query ranks employees within each department by their salary in descending order, leaving gaps for ties.
Tip: Window functions are powerful for performing calculations across a set of rows while retaining the individual rows. They are useful for running totals, moving averages, ranking, and accessing data from other rows within the same result set.
Go though SQL Learning Series to refresh your basics
Share with credits: https://t.me/sqlspecialist
Like this post if you want me to continue SQL Interview Preparation Series ๐โค๏ธ
Hope it helps :)
๐7โค2
๐ฐ Python Roadmap for Beginners
โโโ ๐ Introduction to Python
โโโ ๐งพ Installing Python & Setting Up VS Code / Jupyter
โโโ โ๏ธ Python Syntax & Indentation Basics
โโโ ๐ค Variables, Data Types (int, float, str, bool)
โโโ โ Operators (Arithmetic, Comparison, Logical)
โโโ ๐ Conditional Statements (if, elif, else)
โโโ ๐ Loops (for, while, break, continue)
โโโ ๐งฐ Functions (def, return, args, kwargs)
โโโ ๐ฆ Built-in Data Structures (List, Tuple, Set, Dictionary)
โโโ ๐ง List Comprehension & Dictionary Comprehension
โโโ ๐ File Handling (read, write, with open)
โโโ ๐ Error Handling (try, except, finally)
โโโ ๐งฑ Modules & Packages (import, pip install)
โโโ ๐ Working with Libraries (NumPy, Pandas, Matplotlib)
โโโ ๐งน Data Cleaning with Pandas
โโโ ๐งช Exploratory Data Analysis (EDA)
โโโ ๐ค Intro to OOP in Python (Class, Objects, Inheritance)
โโโ ๐ง Real-World Python Projects & Challenges
SQL Roadmap: https://t.me/sqlspecialist/1340
Power BI Roadmap: https://t.me/sqlspecialist/1397
Python Resources: https://t.me/pythonproz
Hope it helps :)
โโโ ๐ Introduction to Python
โโโ ๐งพ Installing Python & Setting Up VS Code / Jupyter
โโโ โ๏ธ Python Syntax & Indentation Basics
โโโ ๐ค Variables, Data Types (int, float, str, bool)
โโโ โ Operators (Arithmetic, Comparison, Logical)
โโโ ๐ Conditional Statements (if, elif, else)
โโโ ๐ Loops (for, while, break, continue)
โโโ ๐งฐ Functions (def, return, args, kwargs)
โโโ ๐ฆ Built-in Data Structures (List, Tuple, Set, Dictionary)
โโโ ๐ง List Comprehension & Dictionary Comprehension
โโโ ๐ File Handling (read, write, with open)
โโโ ๐ Error Handling (try, except, finally)
โโโ ๐งฑ Modules & Packages (import, pip install)
โโโ ๐ Working with Libraries (NumPy, Pandas, Matplotlib)
โโโ ๐งน Data Cleaning with Pandas
โโโ ๐งช Exploratory Data Analysis (EDA)
โโโ ๐ค Intro to OOP in Python (Class, Objects, Inheritance)
โโโ ๐ง Real-World Python Projects & Challenges
SQL Roadmap: https://t.me/sqlspecialist/1340
Power BI Roadmap: https://t.me/sqlspecialist/1397
Python Resources: https://t.me/pythonproz
Hope it helps :)
โค4๐4
5 Essential Skills Every Data Analyst Must Master in 2025
Data analytics continues to evolve rapidly, and as a data analyst, it's crucial to stay ahead of the curve. In 2025, the skills that were once optional are now essential to stand out in this competitive field. Here are five must-have skills for every data analyst this year.
1. Data Wrangling & Cleaning:
The ability to clean, organize, and prepare data for analysis is critical. No matter how sophisticated your tools are, they can't work with messy, inconsistent data. Mastering data wranglingโremoving duplicates, handling missing values, and standardizing formatsโwill help you deliver accurate and actionable insights.
Tools to master: Python (Pandas), R, SQL
2. Advanced Excel Skills:
Excel remains one of the most widely used tools in the data analysis world. Beyond the basics, you should master advanced formulas, pivot tables, and Power Query. Excel continues to be indispensable for quick analyses and prototype dashboards.
Key skills to learn: VLOOKUP, INDEX/MATCH, Power Pivot, advanced charting
3. Data Visualization:
The ability to convey your findings through compelling data visuals is what sets top analysts apart. Learn how to use tools like Tableau, Power BI, or even D3.js for web-based visualization. Your visuals should tell a story thatโs easy for stakeholders to understand at a glance.
Focus areas: Interactive dashboards, storytelling with data, advanced chart types (heat maps, scatter plots)
4. Statistical Analysis & Hypothesis Testing:
Understanding statistics is fundamental for any data analyst. Master concepts like regression analysis, probability theory, and hypothesis testing. This skill will help you not only describe trends but also make data-driven predictions and assess the significance of your findings.
Skills to focus on: T-tests, ANOVA, correlation, regression models
5. Machine Learning Basics:
While you donโt need to be a data scientist, having a basic understanding of machine learning algorithms is increasingly important. Knowledge of supervised vs unsupervised learning, decision trees, and clustering techniques will allow you to push your analysis to the next level.
Begin with: Linear regression, K-means clustering, decision trees (using Python libraries like Scikit-learn)
In 2025, data analysts must embrace a multi-faceted skill set that combines technical expertise, statistical knowledge, and the ability to communicate findings effectively.
Keep learning and adapting to these emerging trends to ensure you're ready for the challenges of tomorrow.
I have curated best 80+ top-notch Data Analytics Resources ๐๐
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Like this post for more content like this ๐โฅ๏ธ
Share with credits: https://t.me/sqlspecialist
Hope it helps :)
Data analytics continues to evolve rapidly, and as a data analyst, it's crucial to stay ahead of the curve. In 2025, the skills that were once optional are now essential to stand out in this competitive field. Here are five must-have skills for every data analyst this year.
1. Data Wrangling & Cleaning:
The ability to clean, organize, and prepare data for analysis is critical. No matter how sophisticated your tools are, they can't work with messy, inconsistent data. Mastering data wranglingโremoving duplicates, handling missing values, and standardizing formatsโwill help you deliver accurate and actionable insights.
Tools to master: Python (Pandas), R, SQL
2. Advanced Excel Skills:
Excel remains one of the most widely used tools in the data analysis world. Beyond the basics, you should master advanced formulas, pivot tables, and Power Query. Excel continues to be indispensable for quick analyses and prototype dashboards.
Key skills to learn: VLOOKUP, INDEX/MATCH, Power Pivot, advanced charting
3. Data Visualization:
The ability to convey your findings through compelling data visuals is what sets top analysts apart. Learn how to use tools like Tableau, Power BI, or even D3.js for web-based visualization. Your visuals should tell a story thatโs easy for stakeholders to understand at a glance.
Focus areas: Interactive dashboards, storytelling with data, advanced chart types (heat maps, scatter plots)
4. Statistical Analysis & Hypothesis Testing:
Understanding statistics is fundamental for any data analyst. Master concepts like regression analysis, probability theory, and hypothesis testing. This skill will help you not only describe trends but also make data-driven predictions and assess the significance of your findings.
Skills to focus on: T-tests, ANOVA, correlation, regression models
5. Machine Learning Basics:
While you donโt need to be a data scientist, having a basic understanding of machine learning algorithms is increasingly important. Knowledge of supervised vs unsupervised learning, decision trees, and clustering techniques will allow you to push your analysis to the next level.
Begin with: Linear regression, K-means clustering, decision trees (using Python libraries like Scikit-learn)
In 2025, data analysts must embrace a multi-faceted skill set that combines technical expertise, statistical knowledge, and the ability to communicate findings effectively.
Keep learning and adapting to these emerging trends to ensure you're ready for the challenges of tomorrow.
I have curated best 80+ top-notch Data Analytics Resources ๐๐
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Like this post for more content like this ๐โฅ๏ธ
Share with credits: https://t.me/sqlspecialist
Hope it helps :)
โค5
Python Learning Plan in 2025
|-- Week 1: Introduction to Python
| |-- Python Basics
| | |-- What is Python?
| | |-- Installing Python
| | |-- Introduction to IDEs (Jupyter, VS Code)
| |-- Setting up Python Environment
| | |-- Anaconda Setup
| | |-- Virtual Environments
| | |-- Basic Syntax and Data Types
| |-- First Python Program
| | |-- Writing and Running Python Scripts
| | |-- Basic Input/Output
| | |-- Simple Calculations
|
|-- Week 2: Core Python Concepts
| |-- Control Structures
| | |-- Conditional Statements (if, elif, else)
| | |-- Loops (for, while)
| | |-- Comprehensions
| |-- Functions
| | |-- Defining Functions
| | |-- Function Arguments and Return Values
| | |-- Lambda Functions
| |-- Modules and Packages
| | |-- Importing Modules
| | |-- Standard Library Overview
| | |-- Creating and Using Packages
|
|-- Week 3: Advanced Python Concepts
| |-- Data Structures
| | |-- Lists, Tuples, and Sets
| | |-- Dictionaries
| | |-- Collections Module
| |-- File Handling
| | |-- Reading and Writing Files
| | |-- Working with CSV and JSON
| | |-- Context Managers
| |-- Error Handling
| | |-- Exceptions
| | |-- Try, Except, Finally
| | |-- Custom Exceptions
|
|-- Week 4: Object-Oriented Programming
| |-- OOP Basics
| | |-- Classes and Objects
| | |-- Attributes and Methods
| | |-- Inheritance
| |-- Advanced OOP
| | |-- Polymorphism
| | |-- Encapsulation
| | |-- Magic Methods and Operator Overloading
| |-- Design Patterns
| | |-- Singleton
| | |-- Factory
| | |-- Observer
|
|-- Week 5: Python for Data Analysis
| |-- NumPy
| | |-- Arrays and Vectorization
| | |-- Indexing and Slicing
| | |-- Mathematical Operations
| |-- Pandas
| | |-- DataFrames and Series
| | |-- Data Cleaning and Manipulation
| | |-- Merging and Joining Data
| |-- Matplotlib and Seaborn
| | |-- Basic Plotting
| | |-- Advanced Visualizations
| | |-- Customizing Plots
|
|-- Week 6-8: Specialized Python Libraries
| |-- Web Development
| | |-- Flask Basics
| | |-- Django Basics
| |-- Data Science and Machine Learning
| | |-- Scikit-Learn
| | |-- TensorFlow and Keras
| |-- Automation and Scripting
| | |-- Automating Tasks with Python
| | |-- Web Scraping with BeautifulSoup and Scrapy
| |-- APIs and RESTful Services
| | |-- Working with REST APIs
| | |-- Building APIs with Flask/Django
|
|-- Week 9-11: Real-world Applications and Projects
| |-- Capstone Project
| | |-- Project Planning
| | |-- Data Collection and Preparation
| | |-- Building and Optimizing Models
| | |-- Creating and Publishing Reports
| |-- Case Studies
| | |-- Business Use Cases
| | |-- Industry-specific Solutions
| |-- Integration with Other Tools
| | |-- Python and SQL
| | |-- Python and Excel
| | |-- Python and Power BI
|
|-- Week 12: Post-Project Learning
| |-- Python for Automation
| | |-- Automating Daily Tasks
| | |-- Scripting with Python
| |-- Advanced Python Topics
| | |-- Asyncio and Concurrency
| | |-- Advanced Data Structures
| |-- Continuing Education
| | |-- Advanced Python Techniques
| | |-- Community and Forums
| | |-- Keeping Up with Updates
|
|-- Resources and Community
| |-- Online Courses (Coursera, edX, Udemy)
| |-- Books (Automate the Boring Stuff, Python Crash Course)
| |-- Python Blogs and Podcasts
| |-- GitHub Repositories
| |-- Python Communities (Reddit, Stack Overflow)
Here you can find essential Python Interview Resources๐
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Like this post for more resources like this ๐โฅ๏ธ
Share with credits: https://t.me/sqlspecialist
Hope it helps :)
|-- Week 1: Introduction to Python
| |-- Python Basics
| | |-- What is Python?
| | |-- Installing Python
| | |-- Introduction to IDEs (Jupyter, VS Code)
| |-- Setting up Python Environment
| | |-- Anaconda Setup
| | |-- Virtual Environments
| | |-- Basic Syntax and Data Types
| |-- First Python Program
| | |-- Writing and Running Python Scripts
| | |-- Basic Input/Output
| | |-- Simple Calculations
|
|-- Week 2: Core Python Concepts
| |-- Control Structures
| | |-- Conditional Statements (if, elif, else)
| | |-- Loops (for, while)
| | |-- Comprehensions
| |-- Functions
| | |-- Defining Functions
| | |-- Function Arguments and Return Values
| | |-- Lambda Functions
| |-- Modules and Packages
| | |-- Importing Modules
| | |-- Standard Library Overview
| | |-- Creating and Using Packages
|
|-- Week 3: Advanced Python Concepts
| |-- Data Structures
| | |-- Lists, Tuples, and Sets
| | |-- Dictionaries
| | |-- Collections Module
| |-- File Handling
| | |-- Reading and Writing Files
| | |-- Working with CSV and JSON
| | |-- Context Managers
| |-- Error Handling
| | |-- Exceptions
| | |-- Try, Except, Finally
| | |-- Custom Exceptions
|
|-- Week 4: Object-Oriented Programming
| |-- OOP Basics
| | |-- Classes and Objects
| | |-- Attributes and Methods
| | |-- Inheritance
| |-- Advanced OOP
| | |-- Polymorphism
| | |-- Encapsulation
| | |-- Magic Methods and Operator Overloading
| |-- Design Patterns
| | |-- Singleton
| | |-- Factory
| | |-- Observer
|
|-- Week 5: Python for Data Analysis
| |-- NumPy
| | |-- Arrays and Vectorization
| | |-- Indexing and Slicing
| | |-- Mathematical Operations
| |-- Pandas
| | |-- DataFrames and Series
| | |-- Data Cleaning and Manipulation
| | |-- Merging and Joining Data
| |-- Matplotlib and Seaborn
| | |-- Basic Plotting
| | |-- Advanced Visualizations
| | |-- Customizing Plots
|
|-- Week 6-8: Specialized Python Libraries
| |-- Web Development
| | |-- Flask Basics
| | |-- Django Basics
| |-- Data Science and Machine Learning
| | |-- Scikit-Learn
| | |-- TensorFlow and Keras
| |-- Automation and Scripting
| | |-- Automating Tasks with Python
| | |-- Web Scraping with BeautifulSoup and Scrapy
| |-- APIs and RESTful Services
| | |-- Working with REST APIs
| | |-- Building APIs with Flask/Django
|
|-- Week 9-11: Real-world Applications and Projects
| |-- Capstone Project
| | |-- Project Planning
| | |-- Data Collection and Preparation
| | |-- Building and Optimizing Models
| | |-- Creating and Publishing Reports
| |-- Case Studies
| | |-- Business Use Cases
| | |-- Industry-specific Solutions
| |-- Integration with Other Tools
| | |-- Python and SQL
| | |-- Python and Excel
| | |-- Python and Power BI
|
|-- Week 12: Post-Project Learning
| |-- Python for Automation
| | |-- Automating Daily Tasks
| | |-- Scripting with Python
| |-- Advanced Python Topics
| | |-- Asyncio and Concurrency
| | |-- Advanced Data Structures
| |-- Continuing Education
| | |-- Advanced Python Techniques
| | |-- Community and Forums
| | |-- Keeping Up with Updates
|
|-- Resources and Community
| |-- Online Courses (Coursera, edX, Udemy)
| |-- Books (Automate the Boring Stuff, Python Crash Course)
| |-- Python Blogs and Podcasts
| |-- GitHub Repositories
| |-- Python Communities (Reddit, Stack Overflow)
Here you can find essential Python Interview Resources๐
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Like this post for more resources like this ๐โฅ๏ธ
Share with credits: https://t.me/sqlspecialist
Hope it helps :)
โค6
6 essential Python functions for file handling:
๐น open(): Opens a file and returns a file object. Essential for reading and writing files
๐น read(): Reads the contents of a file
๐น write(): Writes data to a file. Great for saving output
๐น close(): Closes the file
๐น with open(): Context manager for file operations. Ensures proper file handling
๐น pd.read_excel(): Reads Excel files into a pandas DataFrame. Crucial for working with Excel data
๐น open(): Opens a file and returns a file object. Essential for reading and writing files
๐น read(): Reads the contents of a file
๐น write(): Writes data to a file. Great for saving output
๐น close(): Closes the file
๐น with open(): Context manager for file operations. Ensures proper file handling
๐น pd.read_excel(): Reads Excel files into a pandas DataFrame. Crucial for working with Excel data
โค6
๐๐ฅ๐๐ ๐ ๐ถ๐ฐ๐ฟ๐ผ๐๐ผ๐ณ๐ ๐ง๐ฒ๐ฐ๐ต ๐๐ฒ๐ฟ๐๐ถ๐ณ๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป ๐๐ผ๐๐ฟ๐๐ฒ๐๐
๐ Learn In-Demand Tech Skills for Free โ Certified by Microsoft!
These free Microsoft-certified online courses are perfect for beginners, students, and professionals looking to upskill
๐๐ข๐ง๐ค๐:-
https://pdlink.in/3Hio2Vg
Enroll For FREE & Get Certified๐๏ธ
๐ Learn In-Demand Tech Skills for Free โ Certified by Microsoft!
These free Microsoft-certified online courses are perfect for beginners, students, and professionals looking to upskill
๐๐ข๐ง๐ค๐:-
https://pdlink.in/3Hio2Vg
Enroll For FREE & Get Certified๐๏ธ
โค5
Guys, Big Announcement!
Weโve officially hit 5 Lakh followers on WhatsApp and itโs time to level up together! โค๏ธ
I've launched a Python Learning Series โ designed for beginners to those preparing for technical interviews or building real-world projects.
This will be a step-by-step journey โ from basics to advanced โ with real examples and short quizzes after each topic to help you lock in the concepts.
Hereโs what weโll cover in the coming days:
Week 1: Python Fundamentals
- Variables & Data Types
- Operators & Expressions
- Conditional Statements (if, elif, else)
- Loops (for, while)
- Functions & Parameters
- Input/Output & Basic Formatting
Week 2: Core Python Skills
- Lists, Tuples, Sets, Dictionaries
- String Manipulation
- List Comprehensions
- File Handling
- Exception Handling
Week 3: Intermediate Python
- Lambda Functions
- Map, Filter, Reduce
- Modules & Packages
- Scope & Global Variables
- Working with Dates & Time
Week 4: OOP & Pythonic Concepts
- Classes & Objects
- Inheritance & Polymorphism
- Decorators (Intro level)
- Generators & Iterators
- Writing Clean & Readable Code
Week 5: Real-World & Interview Prep
- Web Scraping (BeautifulSoup)
- Working with APIs (Requests)
- Automating Tasks
- Data Analysis Basics (Pandas)
- Interview Coding Patterns
You can join our WhatsApp channel to access it for free: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L/1527
Weโve officially hit 5 Lakh followers on WhatsApp and itโs time to level up together! โค๏ธ
I've launched a Python Learning Series โ designed for beginners to those preparing for technical interviews or building real-world projects.
This will be a step-by-step journey โ from basics to advanced โ with real examples and short quizzes after each topic to help you lock in the concepts.
Hereโs what weโll cover in the coming days:
Week 1: Python Fundamentals
- Variables & Data Types
- Operators & Expressions
- Conditional Statements (if, elif, else)
- Loops (for, while)
- Functions & Parameters
- Input/Output & Basic Formatting
Week 2: Core Python Skills
- Lists, Tuples, Sets, Dictionaries
- String Manipulation
- List Comprehensions
- File Handling
- Exception Handling
Week 3: Intermediate Python
- Lambda Functions
- Map, Filter, Reduce
- Modules & Packages
- Scope & Global Variables
- Working with Dates & Time
Week 4: OOP & Pythonic Concepts
- Classes & Objects
- Inheritance & Polymorphism
- Decorators (Intro level)
- Generators & Iterators
- Writing Clean & Readable Code
Week 5: Real-World & Interview Prep
- Web Scraping (BeautifulSoup)
- Working with APIs (Requests)
- Automating Tasks
- Data Analysis Basics (Pandas)
- Interview Coding Patterns
You can join our WhatsApp channel to access it for free: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L/1527
โค3
Forwarded from Power BI & Tableau Resources
๐ฎ๐ณ ๐ฅ๐ฒ๐ฎ๐น ๐ฃ๐ผ๐๐ฒ๐ฟ ๐๐ ๐๐ป๐๐ฒ๐ฟ๐๐ถ๐ฒ๐ ๐ค๐๐ฒ๐๐๐ถ๐ผ๐ป๐ ๐ณ๐ฟ๐ผ๐บ ๐ง๐ผ๐ฝ ๐๐ผ๐บ๐ฝ๐ฎ๐ป๐ถ๐ฒ๐ ๐๐ถ๐ธ๐ฒ ๐๐๐ , ๐๐ฎ๐ฝ๐ด๐ฒ๐บ๐ถ๐ป๐ถ & ๐๐ฒ๐น๐ผ๐ถ๐๐๐ฒ๐
This blog brings you 27 real Power BI interview questions asked by top companies like IBM, Capgemini, Deloitte, and more๐ฃ๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/4dFem3o
Most importantโinterview questionsโ ๏ธ
This blog brings you 27 real Power BI interview questions asked by top companies like IBM, Capgemini, Deloitte, and more๐ฃ๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/4dFem3o
Most importantโinterview questionsโ ๏ธ
โค1
5 Essential Skills Every Data Analyst Must Master in 2025
Data analytics continues to evolve rapidly, and as a data analyst, it's crucial to stay ahead of the curve. In 2025, the skills that were once optional are now essential to stand out in this competitive field. Here are five must-have skills for every data analyst this year.
1. Data Wrangling & Cleaning:
The ability to clean, organize, and prepare data for analysis is critical. No matter how sophisticated your tools are, they can't work with messy, inconsistent data. Mastering data wranglingโremoving duplicates, handling missing values, and standardizing formatsโwill help you deliver accurate and actionable insights.
Tools to master: Python (Pandas), R, SQL
2. Advanced Excel Skills:
Excel remains one of the most widely used tools in the data analysis world. Beyond the basics, you should master advanced formulas, pivot tables, and Power Query. Excel continues to be indispensable for quick analyses and prototype dashboards.
Key skills to learn: VLOOKUP, INDEX/MATCH, Power Pivot, advanced charting
3. Data Visualization:
The ability to convey your findings through compelling data visuals is what sets top analysts apart. Learn how to use tools like Tableau, Power BI, or even D3.js for web-based visualization. Your visuals should tell a story thatโs easy for stakeholders to understand at a glance.
Focus areas: Interactive dashboards, storytelling with data, advanced chart types (heat maps, scatter plots)
4. Statistical Analysis & Hypothesis Testing:
Understanding statistics is fundamental for any data analyst. Master concepts like regression analysis, probability theory, and hypothesis testing. This skill will help you not only describe trends but also make data-driven predictions and assess the significance of your findings.
Skills to focus on: T-tests, ANOVA, correlation, regression models
5. Machine Learning Basics:
While you donโt need to be a data scientist, having a basic understanding of machine learning algorithms is increasingly important. Knowledge of supervised vs unsupervised learning, decision trees, and clustering techniques will allow you to push your analysis to the next level.
Begin with: Linear regression, K-means clustering, decision trees (using Python libraries like Scikit-learn)
In 2025, data analysts must embrace a multi-faceted skill set that combines technical expertise, statistical knowledge, and the ability to communicate findings effectively.
Keep learning and adapting to these emerging trends to ensure you're ready for the challenges of tomorrow.
I have curated best 80+ top-notch Data Analytics Resources ๐๐
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Like this post for more content like this ๐โฅ๏ธ
Share with credits: https://t.me/sqlspecialist
Hope it helps :)
Data analytics continues to evolve rapidly, and as a data analyst, it's crucial to stay ahead of the curve. In 2025, the skills that were once optional are now essential to stand out in this competitive field. Here are five must-have skills for every data analyst this year.
1. Data Wrangling & Cleaning:
The ability to clean, organize, and prepare data for analysis is critical. No matter how sophisticated your tools are, they can't work with messy, inconsistent data. Mastering data wranglingโremoving duplicates, handling missing values, and standardizing formatsโwill help you deliver accurate and actionable insights.
Tools to master: Python (Pandas), R, SQL
2. Advanced Excel Skills:
Excel remains one of the most widely used tools in the data analysis world. Beyond the basics, you should master advanced formulas, pivot tables, and Power Query. Excel continues to be indispensable for quick analyses and prototype dashboards.
Key skills to learn: VLOOKUP, INDEX/MATCH, Power Pivot, advanced charting
3. Data Visualization:
The ability to convey your findings through compelling data visuals is what sets top analysts apart. Learn how to use tools like Tableau, Power BI, or even D3.js for web-based visualization. Your visuals should tell a story thatโs easy for stakeholders to understand at a glance.
Focus areas: Interactive dashboards, storytelling with data, advanced chart types (heat maps, scatter plots)
4. Statistical Analysis & Hypothesis Testing:
Understanding statistics is fundamental for any data analyst. Master concepts like regression analysis, probability theory, and hypothesis testing. This skill will help you not only describe trends but also make data-driven predictions and assess the significance of your findings.
Skills to focus on: T-tests, ANOVA, correlation, regression models
5. Machine Learning Basics:
While you donโt need to be a data scientist, having a basic understanding of machine learning algorithms is increasingly important. Knowledge of supervised vs unsupervised learning, decision trees, and clustering techniques will allow you to push your analysis to the next level.
Begin with: Linear regression, K-means clustering, decision trees (using Python libraries like Scikit-learn)
In 2025, data analysts must embrace a multi-faceted skill set that combines technical expertise, statistical knowledge, and the ability to communicate findings effectively.
Keep learning and adapting to these emerging trends to ensure you're ready for the challenges of tomorrow.
I have curated best 80+ top-notch Data Analytics Resources ๐๐
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Like this post for more content like this ๐โฅ๏ธ
Share with credits: https://t.me/sqlspecialist
Hope it helps :)
โค4
๐ฐ ๐ฃ๐ผ๐๐ฒ๐ฟ๐ณ๐๐น ๐๐ฟ๐ฒ๐ฒ ๐ฅ๐ผ๐ฎ๐ฑ๐บ๐ฎ๐ฝ๐ ๐๐ผ ๐ ๐ฎ๐๐๐ฒ๐ฟ ๐๐ฎ๐๐ฎ๐ฆ๐ฐ๐ฟ๐ถ๐ฝ๐, ๐๐ฎ๐๐ฎ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐ฐ๐ฒ, ๐๐/๐ ๐ & ๐๐ฟ๐ผ๐ป๐๐ฒ๐ป๐ฑ ๐๐ฒ๐๐ฒ๐น๐ผ๐ฝ๐บ๐ฒ๐ป๐ ๐
Learn Tech the Smart Way: Step-by-Step Roadmaps for Beginners๐
Learning tech doesnโt have to be overwhelmingโespecially when you have a roadmap to guide you!๐๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/45wfx2V
Enjoy Learning โ ๏ธ
Learn Tech the Smart Way: Step-by-Step Roadmaps for Beginners๐
Learning tech doesnโt have to be overwhelmingโespecially when you have a roadmap to guide you!๐๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/45wfx2V
Enjoy Learning โ ๏ธ
โค3
Top 4 Python Projects for Beginners
1. To-Do List App: Create a simple to-do list application where users can add, edit, and delete tasks. This project will help you learn about basic data handling and user interface design.
2. Weather App: Build a weather application that allows users to enter a location and see the current weather conditions. This project will introduce you to working with APIs and handling JSON data.
3. Web Scraper: Develop a web scraper that extracts information from a website and saves it to a file or database. This project will teach you about web scraping techniques and data manipulation.
4. Quiz Game: Create a quiz game where users can answer multiple-choice questions and receive a score at the end. This project will help you practice working with functions, loops, and conditional statements in Python.
1. To-Do List App: Create a simple to-do list application where users can add, edit, and delete tasks. This project will help you learn about basic data handling and user interface design.
2. Weather App: Build a weather application that allows users to enter a location and see the current weather conditions. This project will introduce you to working with APIs and handling JSON data.
3. Web Scraper: Develop a web scraper that extracts information from a website and saves it to a file or database. This project will teach you about web scraping techniques and data manipulation.
4. Quiz Game: Create a quiz game where users can answer multiple-choice questions and receive a score at the end. This project will help you practice working with functions, loops, and conditional statements in Python.
โค6
๐ฑ ๐๐ฅ๐๐ ๐ฉ๐ถ๐ฟ๐๐๐ฎ๐น ๐๐
๐ฝ๐ฒ๐ฟ๐ถ๐ฒ๐ป๐ฐ๐ฒ ๐ฃ๐ฟ๐ผ๐ด๐ฟ๐ฎ๐บ๐ ๐๐ผ ๐๐ผ๐ผ๐๐ ๐ฌ๐ผ๐๐ฟ ๐ง๐ฒ๐ฐ๐ต ๐๐ฎ๐ฟ๐ฒ๐ฒ๐ฟ ๐ถ๐ป ๐ฎ๐ฌ๐ฎ๐ฑ๐
Want to build job-ready tech skills from top companies โ all for free?๐จโ๐
These 5 virtual experience programs offer hands-on learning, beginner-friendly modules, and certificates that strengthen your resume and LinkedIn profile ๐๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/4jnOv16
All The Best ๐
Want to build job-ready tech skills from top companies โ all for free?๐จโ๐
These 5 virtual experience programs offer hands-on learning, beginner-friendly modules, and certificates that strengthen your resume and LinkedIn profile ๐๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/4jnOv16
All The Best ๐
โค2
๐ง๐ผ๐ฝ ๐๐ผ๐บ๐ฝ๐ฎ๐ป๐ถ๐ฒ๐ ๐๐ถ๐ฟ๐ถ๐ป๐ด ๐๐ฎ๐๐ฎ ๐๐ป๐ฎ๐น๐๐๐๐๐
๐๐ฝ๐ฝ๐น๐ ๐๐ถ๐ป๐ธ๐:-๐
S&P Global :- https://pdlink.in/3ZddwVz
IBM :- https://pdlink.in/4kDmMKE
TVS Credit :- https://pdlink.in/4mI0JVc
Sutherland :- https://pdlink.in/4mGYBgg
Other Jobs :- https://pdlink.in/44qEIDu
Apply before the link expires ๐ซ
๐๐ฝ๐ฝ๐น๐ ๐๐ถ๐ป๐ธ๐:-๐
S&P Global :- https://pdlink.in/3ZddwVz
IBM :- https://pdlink.in/4kDmMKE
TVS Credit :- https://pdlink.in/4mI0JVc
Sutherland :- https://pdlink.in/4mGYBgg
Other Jobs :- https://pdlink.in/44qEIDu
Apply before the link expires ๐ซ