How to master Python from scratch🚀
1. Setup and Basics 🏁
- Install Python 🖥️: Download Python and set it up.
- Hello, World! 🌍: Write your first Hello World program.
2. Basic Syntax 📜
- Variables and Data Types 📊: Learn about strings, integers, floats, and booleans.
- Control Structures 🔄: Understand if-else statements, for loops, and while loops.
- Functions 🛠️: Write reusable blocks of code.
3. Data Structures 📂
- Lists 📋: Manage collections of items.
- Dictionaries 📖: Store key-value pairs.
- Tuples 📦: Work with immutable sequences.
- Sets 🔢: Handle collections of unique items.
4. Modules and Packages 📦
- Standard Library 📚: Explore built-in modules.
- Third-Party Packages 🌐: Install and use packages with pip.
5. File Handling 📁
- Read and Write Files 📝
- CSV and JSON 📑
6. Object-Oriented Programming 🧩
- Classes and Objects 🏛️
- Inheritance and Polymorphism 👨👩👧
7. Web Development 🌐
- Flask 🍼: Start with a micro web framework.
- Django 🦄: Dive into a full-fledged web framework.
8. Data Science and Machine Learning 🧠
- NumPy 📊: Numerical operations.
- Pandas 🐼: Data manipulation and analysis.
- Matplotlib 📈 and Seaborn 📊: Data visualization.
- Scikit-learn 🤖: Machine learning.
9. Automation and Scripting 🤖
- Automate Tasks 🛠️: Use Python to automate repetitive tasks.
- APIs 🌐: Interact with web services.
10. Testing and Debugging 🐞
- Unit Testing 🧪: Write tests for your code.
- Debugging 🔍: Learn to debug efficiently.
11. Advanced Topics 🚀
- Concurrency and Parallelism 🕒
- Decorators 🌀 and Generators ⚙️
- Web Scraping 🕸️: Extract data from websites using BeautifulSoup and Scrapy.
12. Practice Projects 💡
- Calculator 🧮
- To-Do List App 📋
- Weather App ☀️
- Personal Blog 📝
13. Community and Collaboration 🤝
- Contribute to Open Source 🌍
- Join Coding Communities 💬
- Participate in Hackathons 🏆
14. Keep Learning and Improving 📈
- Read Books 📖: Like "Automate the Boring Stuff with Python".
- Watch Tutorials 🎥: Follow video courses and tutorials.
- Solve Challenges 🧩: On platforms like LeetCode, HackerRank, and CodeWars.
15. Teach and Share Knowledge 📢
- Write Blogs ✍️
- Create Video Tutorials 📹
- Mentor Others 👨🏫
I have curated the best interview resources to crack Python Interviews 👇👇
https://topmate.io/coding/898340
Hope you'll like it
Like this post if you need more resources like this 👍❤️
1. Setup and Basics 🏁
- Install Python 🖥️: Download Python and set it up.
- Hello, World! 🌍: Write your first Hello World program.
2. Basic Syntax 📜
- Variables and Data Types 📊: Learn about strings, integers, floats, and booleans.
- Control Structures 🔄: Understand if-else statements, for loops, and while loops.
- Functions 🛠️: Write reusable blocks of code.
3. Data Structures 📂
- Lists 📋: Manage collections of items.
- Dictionaries 📖: Store key-value pairs.
- Tuples 📦: Work with immutable sequences.
- Sets 🔢: Handle collections of unique items.
4. Modules and Packages 📦
- Standard Library 📚: Explore built-in modules.
- Third-Party Packages 🌐: Install and use packages with pip.
5. File Handling 📁
- Read and Write Files 📝
- CSV and JSON 📑
6. Object-Oriented Programming 🧩
- Classes and Objects 🏛️
- Inheritance and Polymorphism 👨👩👧
7. Web Development 🌐
- Flask 🍼: Start with a micro web framework.
- Django 🦄: Dive into a full-fledged web framework.
8. Data Science and Machine Learning 🧠
- NumPy 📊: Numerical operations.
- Pandas 🐼: Data manipulation and analysis.
- Matplotlib 📈 and Seaborn 📊: Data visualization.
- Scikit-learn 🤖: Machine learning.
9. Automation and Scripting 🤖
- Automate Tasks 🛠️: Use Python to automate repetitive tasks.
- APIs 🌐: Interact with web services.
10. Testing and Debugging 🐞
- Unit Testing 🧪: Write tests for your code.
- Debugging 🔍: Learn to debug efficiently.
11. Advanced Topics 🚀
- Concurrency and Parallelism 🕒
- Decorators 🌀 and Generators ⚙️
- Web Scraping 🕸️: Extract data from websites using BeautifulSoup and Scrapy.
12. Practice Projects 💡
- Calculator 🧮
- To-Do List App 📋
- Weather App ☀️
- Personal Blog 📝
13. Community and Collaboration 🤝
- Contribute to Open Source 🌍
- Join Coding Communities 💬
- Participate in Hackathons 🏆
14. Keep Learning and Improving 📈
- Read Books 📖: Like "Automate the Boring Stuff with Python".
- Watch Tutorials 🎥: Follow video courses and tutorials.
- Solve Challenges 🧩: On platforms like LeetCode, HackerRank, and CodeWars.
15. Teach and Share Knowledge 📢
- Write Blogs ✍️
- Create Video Tutorials 📹
- Mentor Others 👨🏫
I have curated the best interview resources to crack Python Interviews 👇👇
https://topmate.io/coding/898340
Hope you'll like it
Like this post if you need more resources like this 👍❤️
❤3
Roadmap to Become a Data Analyst:
📊 Learn Excel & Google Sheets (Formulas, Pivot Tables)
∟📊 Master SQL (SELECT, JOINs, CTEs, Window Functions)
∟📊 Learn Data Visualization (Power BI / Tableau)
∟📊 Understand Statistics & Probability
∟📊 Learn Python (Pandas, NumPy, Matplotlib, Seaborn)
∟📊 Work with Real Datasets (Kaggle / Public APIs)
∟📊 Learn Data Cleaning & Preprocessing Techniques
∟📊 Build Case Studies & Projects
∟📊 Create Portfolio & Resume
∟✅ Apply for Internships / Jobs
React ❤️ for More 💼
📊 Learn Excel & Google Sheets (Formulas, Pivot Tables)
∟📊 Master SQL (SELECT, JOINs, CTEs, Window Functions)
∟📊 Learn Data Visualization (Power BI / Tableau)
∟📊 Understand Statistics & Probability
∟📊 Learn Python (Pandas, NumPy, Matplotlib, Seaborn)
∟📊 Work with Real Datasets (Kaggle / Public APIs)
∟📊 Learn Data Cleaning & Preprocessing Techniques
∟📊 Build Case Studies & Projects
∟📊 Create Portfolio & Resume
∟✅ Apply for Internships / Jobs
React ❤️ for More 💼
❤6
🔟 Project Ideas for a data analyst
Customer Segmentation: Analyze customer data to segment them based on their behaviors, preferences, or demographics, helping businesses tailor their marketing strategies.
Churn Prediction: Build a model to predict customer churn, identifying factors that contribute to churn and proposing strategies to retain customers.
Sales Forecasting: Use historical sales data to create a predictive model that forecasts future sales, aiding inventory management and resource planning.
Market Basket Analysis: Analyze
transaction data to identify associations between products often purchased together, assisting retailers in optimizing product placement and cross-selling.
Sentiment Analysis: Analyze social media or customer reviews to gauge public sentiment about a product or service, providing valuable insights for brand reputation management.
Healthcare Analytics: Examine medical records to identify trends, patterns, or correlations in patient data, aiding in disease prediction, treatment optimization, and resource allocation.
Financial Fraud Detection: Develop algorithms to detect anomalous transactions and patterns in financial data, helping prevent fraud and secure transactions.
A/B Testing Analysis: Evaluate the results of A/B tests to determine the effectiveness of different strategies or changes on websites, apps, or marketing campaigns.
Energy Consumption Analysis: Analyze energy usage data to identify patterns and inefficiencies, suggesting strategies for optimizing energy consumption in buildings or industries.
Real Estate Market Analysis: Study housing market data to identify trends in property prices, rental rates, and demand, assisting buyers, sellers, and investors in making informed decisions.
Remember to choose a project that aligns with your interests and the domain you're passionate about.
Data Analyst Roadmap
https://t.me/sqlspecialist/379
ENJOY LEARNING 👍👍
Customer Segmentation: Analyze customer data to segment them based on their behaviors, preferences, or demographics, helping businesses tailor their marketing strategies.
Churn Prediction: Build a model to predict customer churn, identifying factors that contribute to churn and proposing strategies to retain customers.
Sales Forecasting: Use historical sales data to create a predictive model that forecasts future sales, aiding inventory management and resource planning.
Market Basket Analysis: Analyze
transaction data to identify associations between products often purchased together, assisting retailers in optimizing product placement and cross-selling.
Sentiment Analysis: Analyze social media or customer reviews to gauge public sentiment about a product or service, providing valuable insights for brand reputation management.
Healthcare Analytics: Examine medical records to identify trends, patterns, or correlations in patient data, aiding in disease prediction, treatment optimization, and resource allocation.
Financial Fraud Detection: Develop algorithms to detect anomalous transactions and patterns in financial data, helping prevent fraud and secure transactions.
A/B Testing Analysis: Evaluate the results of A/B tests to determine the effectiveness of different strategies or changes on websites, apps, or marketing campaigns.
Energy Consumption Analysis: Analyze energy usage data to identify patterns and inefficiencies, suggesting strategies for optimizing energy consumption in buildings or industries.
Real Estate Market Analysis: Study housing market data to identify trends in property prices, rental rates, and demand, assisting buyers, sellers, and investors in making informed decisions.
Remember to choose a project that aligns with your interests and the domain you're passionate about.
Data Analyst Roadmap
https://t.me/sqlspecialist/379
ENJOY LEARNING 👍👍
❤1
Scenario based Interview Questions & Answers for Data Analyst
1. Scenario: You are working on a SQL database that stores customer information. The database has a table called "Orders" that contains order details. Your task is to write a SQL query to retrieve the total number of orders placed by each customer.
Question:
- Write a SQL query to find the total number of orders placed by each customer.
Expected Answer:
SELECT CustomerID, COUNT(*) AS TotalOrders
FROM Orders
GROUP BY CustomerID;
2. Scenario: You are working on a SQL database that stores employee information. The database has a table called "Employees" that contains employee details. Your task is to write a SQL query to retrieve the names of all employees who have been with the company for more than 5 years.
Question:
- Write a SQL query to find the names of employees who have been with the company for more than 5 years.
Expected Answer:
SELECT Name
FROM Employees
WHERE DATEDIFF(year, HireDate, GETDATE()) > 5;
Power BI Scenario-Based Questions
1. Scenario: You have been given a dataset in Power BI that contains sales data for a company. Your task is to create a report that shows the total sales by product category and region.
Expected Answer:
- Load the dataset into Power BI.
- Create relationships if necessary.
- Use the "Fields" pane to select the necessary fields (Product Category, Region, Sales).
- Drag these fields into the "Values" area of a new visualization (e.g., a table or bar chart).
- Use the "Filters" pane to filter data as needed.
- Format the visualization to enhance clarity and readability.
2. Scenario: You have been asked to create a Power BI dashboard that displays real-time stock prices for a set of companies. The stock prices are available through an API.
Expected Answer:
- Use Power BI Desktop to connect to the API.
- Go to "Get Data" > "Web" and enter the API URL.
- Configure the data refresh settings to ensure real-time updates (e.g., setting up a scheduled refresh or using DirectQuery if supported).
- Create visualizations using the imported data.
- Publish the report to the Power BI service and set up a data gateway if needed for continuous refresh.
3. Scenario: You have been given a Power BI report that contains multiple visualizations. The report is taking a long time to load and is impacting the performance of the application.
Expected Answer:
- Analyze the current performance using Performance Analyzer.
- Optimize data model by reducing the number of columns and rows, and removing unnecessary calculations.
- Use aggregated tables to pre-compute results.
- Simplify DAX calculations.
- Optimize visualizations by reducing the number of visuals per page and avoiding complex custom visuals.
- Ensure proper indexing on the data source.
Free SQL Resources: https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v
Like if you need more similar content
Hope it helps :)
1. Scenario: You are working on a SQL database that stores customer information. The database has a table called "Orders" that contains order details. Your task is to write a SQL query to retrieve the total number of orders placed by each customer.
Question:
- Write a SQL query to find the total number of orders placed by each customer.
Expected Answer:
SELECT CustomerID, COUNT(*) AS TotalOrders
FROM Orders
GROUP BY CustomerID;
2. Scenario: You are working on a SQL database that stores employee information. The database has a table called "Employees" that contains employee details. Your task is to write a SQL query to retrieve the names of all employees who have been with the company for more than 5 years.
Question:
- Write a SQL query to find the names of employees who have been with the company for more than 5 years.
Expected Answer:
SELECT Name
FROM Employees
WHERE DATEDIFF(year, HireDate, GETDATE()) > 5;
Power BI Scenario-Based Questions
1. Scenario: You have been given a dataset in Power BI that contains sales data for a company. Your task is to create a report that shows the total sales by product category and region.
Expected Answer:
- Load the dataset into Power BI.
- Create relationships if necessary.
- Use the "Fields" pane to select the necessary fields (Product Category, Region, Sales).
- Drag these fields into the "Values" area of a new visualization (e.g., a table or bar chart).
- Use the "Filters" pane to filter data as needed.
- Format the visualization to enhance clarity and readability.
2. Scenario: You have been asked to create a Power BI dashboard that displays real-time stock prices for a set of companies. The stock prices are available through an API.
Expected Answer:
- Use Power BI Desktop to connect to the API.
- Go to "Get Data" > "Web" and enter the API URL.
- Configure the data refresh settings to ensure real-time updates (e.g., setting up a scheduled refresh or using DirectQuery if supported).
- Create visualizations using the imported data.
- Publish the report to the Power BI service and set up a data gateway if needed for continuous refresh.
3. Scenario: You have been given a Power BI report that contains multiple visualizations. The report is taking a long time to load and is impacting the performance of the application.
Expected Answer:
- Analyze the current performance using Performance Analyzer.
- Optimize data model by reducing the number of columns and rows, and removing unnecessary calculations.
- Use aggregated tables to pre-compute results.
- Simplify DAX calculations.
- Optimize visualizations by reducing the number of visuals per page and avoiding complex custom visuals.
- Ensure proper indexing on the data source.
Free SQL Resources: https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v
Like if you need more similar content
Hope it helps :)
❤2
Python Top 40 Important Interview Questions and Answers ✅
❤2
9 tips to get started with Data Analysis:
Learn Excel, SQL, and a programming language (Python or R)
Understand basic statistics and probability
Practice with real-world datasets (Kaggle, Data.gov)
Clean and preprocess data effectively
Visualize data using charts and graphs
Ask the right questions before diving into data
Use libraries like Pandas, NumPy, and Matplotlib
Focus on storytelling with data insights
Build small projects to apply what you learn
Data Science & Machine Learning Resources: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
ENJOY LEARNING 👍👍
Learn Excel, SQL, and a programming language (Python or R)
Understand basic statistics and probability
Practice with real-world datasets (Kaggle, Data.gov)
Clean and preprocess data effectively
Visualize data using charts and graphs
Ask the right questions before diving into data
Use libraries like Pandas, NumPy, and Matplotlib
Focus on storytelling with data insights
Build small projects to apply what you learn
Data Science & Machine Learning Resources: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
ENJOY LEARNING 👍👍
❤4
Data Analysis Books | Python | SQL | Excel | Artificial Intelligence | Power BI | Tableau | AI Resources
Complete DAX 100.pdf
DAX 100 Most Asked Interview Questions 🚀🚀🔥
❤2👍1
Essential Excel Functions for Data Analysts 🚀
1️⃣ Basic Functions
SUM() – Adds a range of numbers. =SUM(A1:A10)
AVERAGE() – Calculates the average. =AVERAGE(A1:A10)
MIN() / MAX() – Finds the smallest/largest value. =MIN(A1:A10)
2️⃣ Logical Functions
IF() – Conditional logic. =IF(A1>50, "Pass", "Fail")
IFS() – Multiple conditions. =IFS(A1>90, "A", A1>80, "B", TRUE, "C")
AND() / OR() – Checks multiple conditions. =AND(A1>50, B1<100)
3️⃣ Text Functions
LEFT() / RIGHT() / MID() – Extract text from a string.
=LEFT(A1, 3) (First 3 characters)
=MID(A1, 3, 2) (2 characters from the 3rd position)
LEN() – Counts characters. =LEN(A1)
TRIM() – Removes extra spaces. =TRIM(A1)
UPPER() / LOWER() / PROPER() – Changes text case.
4️⃣ Lookup Functions
VLOOKUP() – Searches for a value in a column.
=VLOOKUP(1001, A2:B10, 2, FALSE)
HLOOKUP() – Searches in a row.
XLOOKUP() – Advanced lookup replacing VLOOKUP.
=XLOOKUP(1001, A2:A10, B2:B10, "Not Found")
5️⃣ Date & Time Functions
TODAY() – Returns the current date.
NOW() – Returns the current date and time.
YEAR(), MONTH(), DAY() – Extracts parts of a date.
DATEDIF() – Calculates the difference between two dates.
6️⃣ Data Cleaning Functions
REMOVE DUPLICATES – Found in the "Data" tab.
CLEAN() – Removes non-printable characters.
SUBSTITUTE() – Replaces text within a string.
=SUBSTITUTE(A1, "old", "new")
7️⃣ Advanced Functions
INDEX() & MATCH() – More flexible alternative to VLOOKUP.
TEXTJOIN() – Joins text with a delimiter.
UNIQUE() – Returns unique values from a range.
FILTER() – Filters data dynamically.
=FILTER(A2:B10, B2:B10>50)
8️⃣ Pivot Tables & Power Query
PIVOT TABLES – Summarizes data dynamically.
GETPIVOTDATA() – Extracts data from a Pivot Table.
POWER QUERY – Automates data cleaning & transformation.
You can find Free Excel Resources here: https://t.me/excel_data
Hope it helps :)
#dataanalytics
1️⃣ Basic Functions
SUM() – Adds a range of numbers. =SUM(A1:A10)
AVERAGE() – Calculates the average. =AVERAGE(A1:A10)
MIN() / MAX() – Finds the smallest/largest value. =MIN(A1:A10)
2️⃣ Logical Functions
IF() – Conditional logic. =IF(A1>50, "Pass", "Fail")
IFS() – Multiple conditions. =IFS(A1>90, "A", A1>80, "B", TRUE, "C")
AND() / OR() – Checks multiple conditions. =AND(A1>50, B1<100)
3️⃣ Text Functions
LEFT() / RIGHT() / MID() – Extract text from a string.
=LEFT(A1, 3) (First 3 characters)
=MID(A1, 3, 2) (2 characters from the 3rd position)
LEN() – Counts characters. =LEN(A1)
TRIM() – Removes extra spaces. =TRIM(A1)
UPPER() / LOWER() / PROPER() – Changes text case.
4️⃣ Lookup Functions
VLOOKUP() – Searches for a value in a column.
=VLOOKUP(1001, A2:B10, 2, FALSE)
HLOOKUP() – Searches in a row.
XLOOKUP() – Advanced lookup replacing VLOOKUP.
=XLOOKUP(1001, A2:A10, B2:B10, "Not Found")
5️⃣ Date & Time Functions
TODAY() – Returns the current date.
NOW() – Returns the current date and time.
YEAR(), MONTH(), DAY() – Extracts parts of a date.
DATEDIF() – Calculates the difference between two dates.
6️⃣ Data Cleaning Functions
REMOVE DUPLICATES – Found in the "Data" tab.
CLEAN() – Removes non-printable characters.
SUBSTITUTE() – Replaces text within a string.
=SUBSTITUTE(A1, "old", "new")
7️⃣ Advanced Functions
INDEX() & MATCH() – More flexible alternative to VLOOKUP.
TEXTJOIN() – Joins text with a delimiter.
UNIQUE() – Returns unique values from a range.
FILTER() – Filters data dynamically.
=FILTER(A2:B10, B2:B10>50)
8️⃣ Pivot Tables & Power Query
PIVOT TABLES – Summarizes data dynamically.
GETPIVOTDATA() – Extracts data from a Pivot Table.
POWER QUERY – Automates data cleaning & transformation.
You can find Free Excel Resources here: https://t.me/excel_data
Hope it helps :)
#dataanalytics
❤3
Data Analysis Books | Python | SQL | Excel | Artificial Intelligence | Power BI | Tableau | AI Resources
SQL Interview Questions -2.pdf
Most Commonly Asked SQL Interview Questions 🚀🔥
👍2❤1
Career Path for a Data Analyst
Education: Start by earning a bachelor's degree in fields like math, stats, economics, or computer science.
Skills Growth: Learn programming (Python/R), data tools (SQL/Excel), and visualization. Master data analysis basics.
Entry-Level Role: Begin as a Junior Data Analyst. Learn data cleaning, organization, and basic analysis.
Specialization: Deepen your expertise in a specific industry. Explore advanced analytics and visualization tools.
Advanced Analytics: Move up to Senior Data Analyst. Tackle complex projects and predictive modeling.
Machine Learning: Explore machine learning and data modeling techniques. Familiarize yourself with algorithms, and learn how to implement predictive and classification models.
Domain Expertise: Develop expertise in a particular industry, such as healthcare, finance, e-commerce, etc. This knowledge will enable you to provide more valuable insights from data.
Leadership Roles: As you gain experience, you can move into roles like Data Analytics Manager or Data Science Manager, where you'll oversee teams and projects.
Continuous Learning: Stay updated with the latest tools, techniques, and industry trends. Attend workshops, conferences, and online courses to keep your skills relevant.
Networking: Build a strong professional network within the data analytics community. This can open up opportunities and help you stay informed about industry developments.
Remember, your career path can be personalized based on your interests and strengths. Continuous learning and adaptability are key in the ever-evolving field of data analysis :)
Education: Start by earning a bachelor's degree in fields like math, stats, economics, or computer science.
Skills Growth: Learn programming (Python/R), data tools (SQL/Excel), and visualization. Master data analysis basics.
Entry-Level Role: Begin as a Junior Data Analyst. Learn data cleaning, organization, and basic analysis.
Specialization: Deepen your expertise in a specific industry. Explore advanced analytics and visualization tools.
Advanced Analytics: Move up to Senior Data Analyst. Tackle complex projects and predictive modeling.
Machine Learning: Explore machine learning and data modeling techniques. Familiarize yourself with algorithms, and learn how to implement predictive and classification models.
Domain Expertise: Develop expertise in a particular industry, such as healthcare, finance, e-commerce, etc. This knowledge will enable you to provide more valuable insights from data.
Leadership Roles: As you gain experience, you can move into roles like Data Analytics Manager or Data Science Manager, where you'll oversee teams and projects.
Continuous Learning: Stay updated with the latest tools, techniques, and industry trends. Attend workshops, conferences, and online courses to keep your skills relevant.
Networking: Build a strong professional network within the data analytics community. This can open up opportunities and help you stay informed about industry developments.
Remember, your career path can be personalized based on your interests and strengths. Continuous learning and adaptability are key in the ever-evolving field of data analysis :)
❤6
Many people pay too much to learn Power BI, but my mission is to break down barriers. I have shared complete learning series to learn Power BI from scratch.
Here are the links to the Power BI series
Complete Power BI Topics for Data Analyst: https://t.me/sqlspecialist/588
Part-1: https://t.me/sqlspecialist/589
Part-2: https://t.me/sqlspecialist/590
Part-3: https://t.me/sqlspecialist/592
Part-4: https://t.me/sqlspecialist/595
Part-5: https://t.me/sqlspecialist/597
Part-6: https://t.me/sqlspecialist/600
Part-7: https://t.me/sqlspecialist/603
Part-8: https://t.me/sqlspecialist/604
Part-9: https://t.me/sqlspecialist/605
Part-10: https://t.me/sqlspecialist/606
Part-11: https://t.me/sqlspecialist/609
Part-12:
https://t.me/sqlspecialist/610
Part-13: https://t.me/sqlspecialist/613
Part-14: https://t.me/sqlspecialist/614
More Power BI Resources: https://t.me/PowerBI_analyst
I'll continue with learning series on Excel & Tableau. I am also planning to start with Interview Preparation Series as have already covered essential concepts of Python, SQL & Power BI in detail.
Thanks to all who support our channel and share the content with proper credits. You guys are really amazing.
Hope it helps :)
Here are the links to the Power BI series
Complete Power BI Topics for Data Analyst: https://t.me/sqlspecialist/588
Part-1: https://t.me/sqlspecialist/589
Part-2: https://t.me/sqlspecialist/590
Part-3: https://t.me/sqlspecialist/592
Part-4: https://t.me/sqlspecialist/595
Part-5: https://t.me/sqlspecialist/597
Part-6: https://t.me/sqlspecialist/600
Part-7: https://t.me/sqlspecialist/603
Part-8: https://t.me/sqlspecialist/604
Part-9: https://t.me/sqlspecialist/605
Part-10: https://t.me/sqlspecialist/606
Part-11: https://t.me/sqlspecialist/609
Part-12:
https://t.me/sqlspecialist/610
Part-13: https://t.me/sqlspecialist/613
Part-14: https://t.me/sqlspecialist/614
More Power BI Resources: https://t.me/PowerBI_analyst
I'll continue with learning series on Excel & Tableau. I am also planning to start with Interview Preparation Series as have already covered essential concepts of Python, SQL & Power BI in detail.
Thanks to all who support our channel and share the content with proper credits. You guys are really amazing.
Hope it helps :)
❤1🔥1
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.me/excel_data
Hope it helps :)
#dataanalyst
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.me/excel_data
Hope it helps :)
#dataanalyst
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