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://news.1rj.ru/str/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://news.1rj.ru/str/excel_data
Hope it helps :)
#dataanalyst
❤8
SQL Cheat Sheet For Data Analysts 📚 ✅
1️⃣ Basic Aggregates
⦁ SUM() – Adds up values:
SELECT SUM(sales) FROM orders;
⦁ AVG() – Calculates average:
SELECT AVG(score) FROM tests;
⦁ MIN() / MAX() – Smallest/largest value:
SELECT MIN(age), MAX(age) FROM users;
⦁ COUNT() – Counts rows:
SELECT COUNT(*) FROM customers;
2️⃣ Conditional Logic
⦁ CASE WHEN – If/else logic:
⦁ COALESCE() – Returns first non-null:
SELECT COALESCE(phone, 'N/A') FROM contacts;
3️⃣ String Functions
⦁ LEFT(), RIGHT(), SUBSTRING() – Extract text:
SELECT LEFT(name, 3) FROM employees;
⦁ LENGTH() – Counts characters:
SELECT LENGTH(address) FROM users;
⦁ TRIM(), UPPER(), LOWER() – Clean/change case:
SELECT TRIM(email), UPPER(city) FROM users;
⦁ CONCAT() – Combine text:
SELECT CONCAT(first_name, ' ', last_name) FROM users;
4️⃣ Lookup/Join
⦁ JOIN – Combine tables:
⦁ IN / EXISTS – Check for values:
SELECT * FROM products WHERE category_id IN (1,2,3);
5️⃣ Date & Time
⦁ CURRENT_DATE, CURRENT_TIMESTAMP – Today/now:
SELECT CURRENT_DATE;
⦁ EXTRACT() – Get year/month/day:
SELECT EXTRACT(YEAR FROM order_date) FROM orders;
⦁ DATEDIFF() – Days between dates:
SELECT DATEDIFF('2025-07-08', '2025-01-01');
6️⃣ Data Cleaning
⦁ DISTINCT – Unique values:
SELECT DISTINCT city FROM customers;
⦁ REPLACE() – Replace text:
SELECT REPLACE(email, '.com', '.org') FROM users;
⦁ NULLIF() – Set value to NULL if condition met:
SELECT NULLIF(status, 'unknown') FROM orders;
7️⃣ Advanced Functions
⦁ GROUP BY – Aggregate by group:
SELECT department, COUNT(*) FROM employees GROUP BY department;
⦁ HAVING – Filter after aggregation:
SELECT department, COUNT(*) FROM employees GROUP BY department HAVING COUNT(*) > 5;
⦁ WINDOW FUNCTIONS – Running totals, ranks:
SELECT name, salary, RANK() OVER (ORDER BY salary DESC) FROM staff;
8️⃣ Views & CTEs
⦁ VIEW – Save a query:
CREATE VIEW top_customers AS SELECT * FROM customers WHERE spend > 1000;
⦁ CTE – Temporary result set:
Free Resources to learn SQL: https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v
ENJOY LEARNING👍 👍
1️⃣ Basic Aggregates
⦁ SUM() – Adds up values:
SELECT SUM(sales) FROM orders;
⦁ AVG() – Calculates average:
SELECT AVG(score) FROM tests;
⦁ MIN() / MAX() – Smallest/largest value:
SELECT MIN(age), MAX(age) FROM users;
⦁ COUNT() – Counts rows:
SELECT COUNT(*) FROM customers;
2️⃣ Conditional Logic
⦁ CASE WHEN – If/else logic:
SELECT name,
CASE WHEN score > 50 THEN 'Pass' ELSE 'Fail' END AS result
FROM students;
⦁ COALESCE() – Returns first non-null:
SELECT COALESCE(phone, 'N/A') FROM contacts;
3️⃣ String Functions
⦁ LEFT(), RIGHT(), SUBSTRING() – Extract text:
SELECT LEFT(name, 3) FROM employees;
⦁ LENGTH() – Counts characters:
SELECT LENGTH(address) FROM users;
⦁ TRIM(), UPPER(), LOWER() – Clean/change case:
SELECT TRIM(email), UPPER(city) FROM users;
⦁ CONCAT() – Combine text:
SELECT CONCAT(first_name, ' ', last_name) FROM users;
4️⃣ Lookup/Join
⦁ JOIN – Combine tables:
SELECT o.order_id, c.name
FROM orders o
JOIN customers c ON o.customer_id = c.id;
⦁ IN / EXISTS – Check for values:
SELECT * FROM products WHERE category_id IN (1,2,3);
5️⃣ Date & Time
⦁ CURRENT_DATE, CURRENT_TIMESTAMP – Today/now:
SELECT CURRENT_DATE;
⦁ EXTRACT() – Get year/month/day:
SELECT EXTRACT(YEAR FROM order_date) FROM orders;
⦁ DATEDIFF() – Days between dates:
SELECT DATEDIFF('2025-07-08', '2025-01-01');
6️⃣ Data Cleaning
⦁ DISTINCT – Unique values:
SELECT DISTINCT city FROM customers;
⦁ REPLACE() – Replace text:
SELECT REPLACE(email, '.com', '.org') FROM users;
⦁ NULLIF() – Set value to NULL if condition met:
SELECT NULLIF(status, 'unknown') FROM orders;
7️⃣ Advanced Functions
⦁ GROUP BY – Aggregate by group:
SELECT department, COUNT(*) FROM employees GROUP BY department;
⦁ HAVING – Filter after aggregation:
SELECT department, COUNT(*) FROM employees GROUP BY department HAVING COUNT(*) > 5;
⦁ WINDOW FUNCTIONS – Running totals, ranks:
SELECT name, salary, RANK() OVER (ORDER BY salary DESC) FROM staff;
8️⃣ Views & CTEs
⦁ VIEW – Save a query:
CREATE VIEW top_customers AS SELECT * FROM customers WHERE spend > 1000;
⦁ CTE – Temporary result set:
WITH high_sales AS (
SELECT * FROM sales WHERE amount > 1000
)
SELECT * FROM high_sales;
Free Resources to learn SQL: https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v
ENJOY LEARNING
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❤11👏1
𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗥𝗼𝗮𝗱𝗺𝗮𝗽
𝟭. 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲𝘀: Master Python, SQL, and R for data manipulation and analysis.
𝟮. 𝗗𝗮𝘁𝗮 𝗠𝗮𝗻𝗶𝗽𝘂𝗹𝗮𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴: Use Excel, Pandas, and ETL tools like Alteryx and Talend for data processing.
𝟯. 𝗗𝗮𝘁𝗮 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻: Learn Tableau, Power BI, and Matplotlib/Seaborn for creating insightful visualizations.
𝟰. 𝗦𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝘀 𝗮𝗻𝗱 𝗠𝗮𝘁𝗵𝗲𝗺𝗮𝘁𝗶𝗰𝘀: Understand Denoscriptive and Inferential Statistics, Probability, Regression, and Time Series Analysis.
𝟱. 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴: Get proficient in Supervised and Unsupervised Learning, along with Time Series Forecasting.
𝟲. 𝗕𝗶𝗴 𝗗𝗮𝘁𝗮 𝗧𝗼𝗼𝗹𝘀: Utilize Google BigQuery, AWS Redshift, and NoSQL databases like MongoDB for large-scale data management.
𝟳. 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 𝗮𝗻𝗱 𝗥𝗲𝗽𝗼𝗿𝘁𝗶𝗻𝗴: Implement Data Quality Monitoring (Great Expectations) and Performance Tracking (Prometheus, Grafana).
𝟴. 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗧𝗼𝗼𝗹𝘀: Work with Data Orchestration tools (Airflow, Prefect) and visualization tools like D3.js and Plotly.
𝟵. 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲 𝗠𝗮𝗻𝗮𝗴𝗲𝗿: Manage resources using Jupyter Notebooks and Power BI.
𝟭𝟬. 𝗗𝗮𝘁𝗮 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗮𝗻𝗱 𝗘𝘁𝗵𝗶𝗰𝘀: Ensure compliance with GDPR, Data Privacy, and Data Quality standards.
𝟭𝟭. 𝗖𝗹𝗼𝘂𝗱 𝗖𝗼𝗺𝗽𝘂𝘁𝗶𝗻𝗴: Leverage AWS, Google Cloud, and Azure for scalable data solutions.
𝟭𝟮. 𝗗𝗮𝘁𝗮 𝗪𝗿𝗮𝗻𝗴𝗹𝗶𝗻𝗴 𝗮𝗻𝗱 𝗖𝗹𝗲𝗮𝗻𝗶𝗻𝗴: Master data cleaning (OpenRefine, Trifacta) and transformation techniques.
Data Analytics Resources
👇👇
https://news.1rj.ru/str/sqlspecialist
Hope this helps you 😊
𝟭. 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲𝘀: Master Python, SQL, and R for data manipulation and analysis.
𝟮. 𝗗𝗮𝘁𝗮 𝗠𝗮𝗻𝗶𝗽𝘂𝗹𝗮𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴: Use Excel, Pandas, and ETL tools like Alteryx and Talend for data processing.
𝟯. 𝗗𝗮𝘁𝗮 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻: Learn Tableau, Power BI, and Matplotlib/Seaborn for creating insightful visualizations.
𝟰. 𝗦𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝘀 𝗮𝗻𝗱 𝗠𝗮𝘁𝗵𝗲𝗺𝗮𝘁𝗶𝗰𝘀: Understand Denoscriptive and Inferential Statistics, Probability, Regression, and Time Series Analysis.
𝟱. 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴: Get proficient in Supervised and Unsupervised Learning, along with Time Series Forecasting.
𝟲. 𝗕𝗶𝗴 𝗗𝗮𝘁𝗮 𝗧𝗼𝗼𝗹𝘀: Utilize Google BigQuery, AWS Redshift, and NoSQL databases like MongoDB for large-scale data management.
𝟳. 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 𝗮𝗻𝗱 𝗥𝗲𝗽𝗼𝗿𝘁𝗶𝗻𝗴: Implement Data Quality Monitoring (Great Expectations) and Performance Tracking (Prometheus, Grafana).
𝟴. 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗧𝗼𝗼𝗹𝘀: Work with Data Orchestration tools (Airflow, Prefect) and visualization tools like D3.js and Plotly.
𝟵. 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲 𝗠𝗮𝗻𝗮𝗴𝗲𝗿: Manage resources using Jupyter Notebooks and Power BI.
𝟭𝟬. 𝗗𝗮𝘁𝗮 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗮𝗻𝗱 𝗘𝘁𝗵𝗶𝗰𝘀: Ensure compliance with GDPR, Data Privacy, and Data Quality standards.
𝟭𝟭. 𝗖𝗹𝗼𝘂𝗱 𝗖𝗼𝗺𝗽𝘂𝘁𝗶𝗻𝗴: Leverage AWS, Google Cloud, and Azure for scalable data solutions.
𝟭𝟮. 𝗗𝗮𝘁𝗮 𝗪𝗿𝗮𝗻𝗴𝗹𝗶𝗻𝗴 𝗮𝗻𝗱 𝗖𝗹𝗲𝗮𝗻𝗶𝗻𝗴: Master data cleaning (OpenRefine, Trifacta) and transformation techniques.
Data Analytics Resources
👇👇
https://news.1rj.ru/str/sqlspecialist
Hope this helps you 😊
❤4
Junior-level Data Analyst interview questions:
Introduction and Background
1. Can you tell me about your background and how you became interested in data analysis?
2. What do you know about our company/organization?
3. Why do you want to work as a data analyst?
Data Analysis and Interpretation
1. What is your experience with data analysis tools like Excel, SQL, or Tableau?
2. How would you approach analyzing a large dataset to identify trends and patterns?
3. Can you explain the concept of correlation versus causation?
4. How do you handle missing or incomplete data?
5. Can you walk me through a time when you had to interpret complex data results?
Technical Skills
1. Write a SQL query to extract data from a database.
2. How do you create a pivot table in Excel?
3. Can you explain the difference between a histogram and a box plot?
4. How do you perform data visualization using Tableau or Power BI?
5. Can you write a simple Python or R noscript to manipulate data?
Statistics and Math
1. What is the difference between mean, median, and mode?
2. Can you explain the concept of standard deviation and variance?
3. How do you calculate probability and confidence intervals?
4. Can you describe a time when you applied statistical concepts to a real-world problem?
5. How do you approach hypothesis testing?
Communication and Storytelling
1. Can you explain a complex data concept to a non-technical person?
2. How do you present data insights to stakeholders?
3. Can you walk me through a time when you had to communicate data results to a team?
4. How do you create effective data visualizations?
5. Can you tell a story using data?
Case Studies and Scenarios
1. You are given a dataset with customer purchase history. How would you analyze it to identify trends?
2. A company wants to increase sales. How would you use data to inform marketing strategies?
3. You notice a discrepancy in sales data. How would you investigate and resolve the issue?
4. Can you describe a time when you had to work with a stakeholder to understand their data needs?
5. How would you prioritize data projects with limited resources?
Behavioral Questions
1. Can you describe a time when you overcame a difficult data analysis challenge?
2. How do you handle tight deadlines and multiple projects?
3. Can you tell me about a project you worked on and your role in it?
4. How do you stay up-to-date with new data tools and technologies?
5. Can you describe a time when you received feedback on your data analysis work?
Final Questions
1. Do you have any questions about the company or role?
2. What do you think sets you apart from other candidates?
3. Can you summarize your experience and qualifications?
4. What are your long-term career goals?
Hope this helps you 😊
Introduction and Background
1. Can you tell me about your background and how you became interested in data analysis?
2. What do you know about our company/organization?
3. Why do you want to work as a data analyst?
Data Analysis and Interpretation
1. What is your experience with data analysis tools like Excel, SQL, or Tableau?
2. How would you approach analyzing a large dataset to identify trends and patterns?
3. Can you explain the concept of correlation versus causation?
4. How do you handle missing or incomplete data?
5. Can you walk me through a time when you had to interpret complex data results?
Technical Skills
1. Write a SQL query to extract data from a database.
2. How do you create a pivot table in Excel?
3. Can you explain the difference between a histogram and a box plot?
4. How do you perform data visualization using Tableau or Power BI?
5. Can you write a simple Python or R noscript to manipulate data?
Statistics and Math
1. What is the difference between mean, median, and mode?
2. Can you explain the concept of standard deviation and variance?
3. How do you calculate probability and confidence intervals?
4. Can you describe a time when you applied statistical concepts to a real-world problem?
5. How do you approach hypothesis testing?
Communication and Storytelling
1. Can you explain a complex data concept to a non-technical person?
2. How do you present data insights to stakeholders?
3. Can you walk me through a time when you had to communicate data results to a team?
4. How do you create effective data visualizations?
5. Can you tell a story using data?
Case Studies and Scenarios
1. You are given a dataset with customer purchase history. How would you analyze it to identify trends?
2. A company wants to increase sales. How would you use data to inform marketing strategies?
3. You notice a discrepancy in sales data. How would you investigate and resolve the issue?
4. Can you describe a time when you had to work with a stakeholder to understand their data needs?
5. How would you prioritize data projects with limited resources?
Behavioral Questions
1. Can you describe a time when you overcame a difficult data analysis challenge?
2. How do you handle tight deadlines and multiple projects?
3. Can you tell me about a project you worked on and your role in it?
4. How do you stay up-to-date with new data tools and technologies?
5. Can you describe a time when you received feedback on your data analysis work?
Final Questions
1. Do you have any questions about the company or role?
2. What do you think sets you apart from other candidates?
3. Can you summarize your experience and qualifications?
4. What are your long-term career goals?
Hope this helps you 😊
❤21👍1
SQL 𝗢𝗿𝗱𝗲𝗿 𝗢𝗳 𝗘𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻 ↓
1 → FROM (Tables selected).
2 → WHERE (Filters applied).
3 → GROUP BY (Rows grouped).
4 → HAVING (Filter on grouped data).
5 → SELECT (Columns selected).
6 → ORDER BY (Sort the data).
7 → LIMIT (Restrict number of rows).
𝗖𝗼𝗺𝗺𝗼𝗻 𝗤𝘂𝗲𝗿𝗶𝗲𝘀 𝗧𝗼 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲 ↓
↬ Find the second-highest salary:
SELECT MAX(Salary) FROM Employees WHERE Salary < (SELECT MAX(Salary) FROM Employees);
↬ Find duplicate records:
SELECT Name, COUNT(*)
FROM Emp
GROUP BY Name
HAVING COUNT(*) > 1;
1 → FROM (Tables selected).
2 → WHERE (Filters applied).
3 → GROUP BY (Rows grouped).
4 → HAVING (Filter on grouped data).
5 → SELECT (Columns selected).
6 → ORDER BY (Sort the data).
7 → LIMIT (Restrict number of rows).
𝗖𝗼𝗺𝗺𝗼𝗻 𝗤𝘂𝗲𝗿𝗶𝗲𝘀 𝗧𝗼 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲 ↓
↬ Find the second-highest salary:
SELECT MAX(Salary) FROM Employees WHERE Salary < (SELECT MAX(Salary) FROM Employees);
↬ Find duplicate records:
SELECT Name, COUNT(*)
FROM Emp
GROUP BY Name
HAVING COUNT(*) > 1;
❤9
🚀 Complete Roadmap to Become a Data Scientist in 5 Months
📅 Week 1-2: Fundamentals
✅ Day 1-3: Introduction to Data Science, its applications, and roles.
✅ Day 4-7: Brush up on Python programming 🐍.
✅ Day 8-10: Learn basic statistics 📊 and probability 🎲.
🔍 Week 3-4: Data Manipulation & Visualization
📝 Day 11-15: Master Pandas for data manipulation.
📈 Day 16-20: Learn Matplotlib & Seaborn for data visualization.
🤖 Week 5-6: Machine Learning Foundations
🔬 Day 21-25: Introduction to scikit-learn.
📊 Day 26-30: Learn Linear & Logistic Regression.
🏗 Week 7-8: Advanced Machine Learning
🌳 Day 31-35: Explore Decision Trees & Random Forests.
📌 Day 36-40: Learn Clustering (K-Means, DBSCAN) & Dimensionality Reduction.
🧠 Week 9-10: Deep Learning
🤖 Day 41-45: Basics of Neural Networks with TensorFlow/Keras.
📸 Day 46-50: Learn CNNs & RNNs for image & text data.
🏛 Week 11-12: Data Engineering
🗄 Day 51-55: Learn SQL & Databases.
🧹 Day 56-60: Data Preprocessing & Cleaning.
📊 Week 13-14: Model Evaluation & Optimization
📏 Day 61-65: Learn Cross-validation & Hyperparameter Tuning.
📉 Day 66-70: Understand Evaluation Metrics (Accuracy, Precision, Recall, F1-score).
🏗 Week 15-16: Big Data & Tools
🐘 Day 71-75: Introduction to Big Data Technologies (Hadoop, Spark).
☁️ Day 76-80: Learn Cloud Computing (AWS, GCP, Azure).
🚀 Week 17-18: Deployment & Production
🛠 Day 81-85: Deploy models using Flask or FastAPI.
📦 Day 86-90: Learn Docker & Cloud Deployment (AWS, Heroku).
🎯 Week 19-20: Specialization
📝 Day 91-95: Choose NLP or Computer Vision, based on your interest.
🏆 Week 21-22: Projects & Portfolio
📂 Day 96-100: Work on Personal Data Science Projects.
💬 Week 23-24: Soft Skills & Networking
🎤 Day 101-105: Improve Communication & Presentation Skills.
🌐 Day 106-110: Attend Online Meetups & Forums.
🎯 Week 25-26: Interview Preparation
💻 Day 111-115: Practice Coding Interviews (LeetCode, HackerRank).
📂 Day 116-120: Review your projects & prepare for discussions.
👨💻 Week 27-28: Apply for Jobs
📩 Day 121-125: Start applying for Entry-Level Data Scientist positions.
🎤 Week 29-30: Interviews
📝 Day 126-130: Attend Interviews & Practice Whiteboard Problems.
🔄 Week 31-32: Continuous Learning
📰 Day 131-135: Stay updated with the Latest Data Science Trends.
🏆 Week 33-34: Accepting Offers
📝 Day 136-140: Evaluate job offers & Negotiate Your Salary.
🏢 Week 35-36: Settling In
🎯 Day 141-150: Start your New Data Science Job, adapt & keep learning!
🎉 Enjoy Learning & Build Your Dream Career in Data Science! 🚀🔥
📅 Week 1-2: Fundamentals
✅ Day 1-3: Introduction to Data Science, its applications, and roles.
✅ Day 4-7: Brush up on Python programming 🐍.
✅ Day 8-10: Learn basic statistics 📊 and probability 🎲.
🔍 Week 3-4: Data Manipulation & Visualization
📝 Day 11-15: Master Pandas for data manipulation.
📈 Day 16-20: Learn Matplotlib & Seaborn for data visualization.
🤖 Week 5-6: Machine Learning Foundations
🔬 Day 21-25: Introduction to scikit-learn.
📊 Day 26-30: Learn Linear & Logistic Regression.
🏗 Week 7-8: Advanced Machine Learning
🌳 Day 31-35: Explore Decision Trees & Random Forests.
📌 Day 36-40: Learn Clustering (K-Means, DBSCAN) & Dimensionality Reduction.
🧠 Week 9-10: Deep Learning
🤖 Day 41-45: Basics of Neural Networks with TensorFlow/Keras.
📸 Day 46-50: Learn CNNs & RNNs for image & text data.
🏛 Week 11-12: Data Engineering
🗄 Day 51-55: Learn SQL & Databases.
🧹 Day 56-60: Data Preprocessing & Cleaning.
📊 Week 13-14: Model Evaluation & Optimization
📏 Day 61-65: Learn Cross-validation & Hyperparameter Tuning.
📉 Day 66-70: Understand Evaluation Metrics (Accuracy, Precision, Recall, F1-score).
🏗 Week 15-16: Big Data & Tools
🐘 Day 71-75: Introduction to Big Data Technologies (Hadoop, Spark).
☁️ Day 76-80: Learn Cloud Computing (AWS, GCP, Azure).
🚀 Week 17-18: Deployment & Production
🛠 Day 81-85: Deploy models using Flask or FastAPI.
📦 Day 86-90: Learn Docker & Cloud Deployment (AWS, Heroku).
🎯 Week 19-20: Specialization
📝 Day 91-95: Choose NLP or Computer Vision, based on your interest.
🏆 Week 21-22: Projects & Portfolio
📂 Day 96-100: Work on Personal Data Science Projects.
💬 Week 23-24: Soft Skills & Networking
🎤 Day 101-105: Improve Communication & Presentation Skills.
🌐 Day 106-110: Attend Online Meetups & Forums.
🎯 Week 25-26: Interview Preparation
💻 Day 111-115: Practice Coding Interviews (LeetCode, HackerRank).
📂 Day 116-120: Review your projects & prepare for discussions.
👨💻 Week 27-28: Apply for Jobs
📩 Day 121-125: Start applying for Entry-Level Data Scientist positions.
🎤 Week 29-30: Interviews
📝 Day 126-130: Attend Interviews & Practice Whiteboard Problems.
🔄 Week 31-32: Continuous Learning
📰 Day 131-135: Stay updated with the Latest Data Science Trends.
🏆 Week 33-34: Accepting Offers
📝 Day 136-140: Evaluate job offers & Negotiate Your Salary.
🏢 Week 35-36: Settling In
🎯 Day 141-150: Start your New Data Science Job, adapt & keep learning!
🎉 Enjoy Learning & Build Your Dream Career in Data Science! 🚀🔥
❤10👍2
Step-by-step guide to become a Data Analyst in 2025—📊
1. Learn the Fundamentals:
Start with Excel, basic statistics, and data visualization concepts.
2. Pick Up Key Tools & Languages:
Master SQL, Python (or R), and data visualization tools like Tableau or Power BI.
3. Get Formal Education or Certification:
A bachelor’s degree in a relevant field (like Computer Science, Math, or Economics) helps, but you can also do online courses or certifications in data analytics.
4. Build Hands-on Experience:
Work on real-world projects—use Kaggle datasets, internships, or freelance gigs to practice data cleaning, analysis, and visualization.
5. Create a Portfolio:
Showcase your projects on GitHub or a personal website. Include dashboards, reports, and code samples.
6. Develop Soft Skills:
Focus on communication, problem-solving, teamwork, and attention to detail—these are just as important as technical skills.
7. Apply for Entry-Level Jobs:
Look for roles like “Junior Data Analyst” or “Business Analyst.” Tailor your resume to highlight your skills and portfolio.
8. Keep Learning:
Stay updated with new tools (like AI-driven analytics), trends, and advanced topics such as machine learning or domain-specific analytics.
React ❤️ for more
1. Learn the Fundamentals:
Start with Excel, basic statistics, and data visualization concepts.
2. Pick Up Key Tools & Languages:
Master SQL, Python (or R), and data visualization tools like Tableau or Power BI.
3. Get Formal Education or Certification:
A bachelor’s degree in a relevant field (like Computer Science, Math, or Economics) helps, but you can also do online courses or certifications in data analytics.
4. Build Hands-on Experience:
Work on real-world projects—use Kaggle datasets, internships, or freelance gigs to practice data cleaning, analysis, and visualization.
5. Create a Portfolio:
Showcase your projects on GitHub or a personal website. Include dashboards, reports, and code samples.
6. Develop Soft Skills:
Focus on communication, problem-solving, teamwork, and attention to detail—these are just as important as technical skills.
7. Apply for Entry-Level Jobs:
Look for roles like “Junior Data Analyst” or “Business Analyst.” Tailor your resume to highlight your skills and portfolio.
8. Keep Learning:
Stay updated with new tools (like AI-driven analytics), trends, and advanced topics such as machine learning or domain-specific analytics.
React ❤️ for more
❤11🥰1
✅ Data Analytics Roadmap for Freshers in 2025 🚀📊
1️⃣ Understand What a Data Analyst Does
🔍 Analyze data, find insights, create dashboards, support business decisions.
2️⃣ Start with Excel
📈 Learn:
– Basic formulas
– Charts & Pivot Tables
– Data cleaning
💡 Excel is still the #1 tool in many companies.
3️⃣ Learn SQL
🧩 SQL helps you pull and analyze data from databases.
Start with:
– SELECT, WHERE, JOIN, GROUP BY
🛠️ Practice on platforms like W3Schools or Mode Analytics.
4️⃣ Pick a Programming Language
🐍 Start with Python (easier) or R
– Learn pandas, matplotlib, numpy
– Do small projects (e.g. analyze sales data)
5️⃣ Data Visualization Tools
📊 Learn:
– Power BI or Tableau
– Build simple dashboards
💡 Start with free versions or YouTube tutorials.
6️⃣ Practice with Real Data
🔍 Use sites like Kaggle or Data.gov
– Clean, analyze, visualize
– Try small case studies (sales report, customer trends)
7️⃣ Create a Portfolio
💻 Share projects on:
– GitHub
– Notion or a simple website
📌 Add visuals + brief explanations of your insights.
8️⃣ Improve Soft Skills
🗣️ Focus on:
– Presenting data in simple words
– Asking good questions
– Thinking critically about patterns
9️⃣ Certifications to Stand Out
🎓 Try:
– Google Data Analytics (Coursera)
– IBM Data Analyst
– LinkedIn Learning basics
🔟 Apply for Internships & Entry Jobs
🎯 Titles to look for:
– Data Analyst (Intern)
– Junior Analyst
– Business Analyst
💬 React ❤️ for more!
1️⃣ Understand What a Data Analyst Does
🔍 Analyze data, find insights, create dashboards, support business decisions.
2️⃣ Start with Excel
📈 Learn:
– Basic formulas
– Charts & Pivot Tables
– Data cleaning
💡 Excel is still the #1 tool in many companies.
3️⃣ Learn SQL
🧩 SQL helps you pull and analyze data from databases.
Start with:
– SELECT, WHERE, JOIN, GROUP BY
🛠️ Practice on platforms like W3Schools or Mode Analytics.
4️⃣ Pick a Programming Language
🐍 Start with Python (easier) or R
– Learn pandas, matplotlib, numpy
– Do small projects (e.g. analyze sales data)
5️⃣ Data Visualization Tools
📊 Learn:
– Power BI or Tableau
– Build simple dashboards
💡 Start with free versions or YouTube tutorials.
6️⃣ Practice with Real Data
🔍 Use sites like Kaggle or Data.gov
– Clean, analyze, visualize
– Try small case studies (sales report, customer trends)
7️⃣ Create a Portfolio
💻 Share projects on:
– GitHub
– Notion or a simple website
📌 Add visuals + brief explanations of your insights.
8️⃣ Improve Soft Skills
🗣️ Focus on:
– Presenting data in simple words
– Asking good questions
– Thinking critically about patterns
9️⃣ Certifications to Stand Out
🎓 Try:
– Google Data Analytics (Coursera)
– IBM Data Analyst
– LinkedIn Learning basics
🔟 Apply for Internships & Entry Jobs
🎯 Titles to look for:
– Data Analyst (Intern)
– Junior Analyst
– Business Analyst
💬 React ❤️ for more!
❤14👍2
🚀 Excel vs SQL vs Python (Pandas):
1️⃣ Filtering Data
↳ Excel: =FILTER(A2:D100, B2:B100>50) (Excel 365 users)
↳ SQL: SELECT * FROM table WHERE column > 50;
↳ Python: df_filtered = df[df['column'] > 50]
2️⃣ Sorting Data
↳ Excel: Data → Sort (or =SORT(A2:A100, 1, TRUE))
↳ SQL: SELECT * FROM table ORDER BY column ASC;
↳ Python: df_sorted = df.sort_values(by="column")
3️⃣ Counting Rows
↳ Excel: =COUNTA(A:A)
↳ SQL: SELECT COUNT(*) FROM table;
↳ Python: row_count = len(df)
4️⃣ Removing Duplicates
↳ Excel: Data → Remove Duplicates
↳ SQL: SELECT DISTINCT * FROM table;
↳ Python: df_unique = df.drop_duplicates()
5️⃣ Joining Tables
↳ Excel: Power Query → Merge Queries (or VLOOKUP/XLOOKUP)
↳ SQL: SELECT * FROM table1 JOIN table2 ON table1.id = table2.id;
↳ Python: df_merged = pd.merge(df1, df2, on="id")
6️⃣ Ranking Data
↳ Excel: =RANK.EQ(A2, $A$2:$A$100)
↳ SQL: SELECT column, RANK() OVER (ORDER BY column DESC) AS rank FROM table;
↳ Python: df["rank"] = df["column"].rank(method="min", ascending=False)
7️⃣ Moving Average Calculation
↳ Excel: =AVERAGE(B2:B4) (manually for rolling window)
↳ SQL: SELECT date, AVG(value) OVER (ORDER BY date ROWS BETWEEN 2 PRECEDING AND CURRENT ROW) AS moving_avg FROM table;
↳ Python: df["moving_avg"] = df["value"].rolling(window=3).mean()
8️⃣ Running Total
↳ Excel: =SUM($B$2:B2) (drag down)
↳ SQL: SELECT date, SUM(value) OVER (ORDER BY date) AS running_total FROM table;
↳ Python: df["running_total"] = df["value"].cumsum()
1️⃣ Filtering Data
↳ Excel: =FILTER(A2:D100, B2:B100>50) (Excel 365 users)
↳ SQL: SELECT * FROM table WHERE column > 50;
↳ Python: df_filtered = df[df['column'] > 50]
2️⃣ Sorting Data
↳ Excel: Data → Sort (or =SORT(A2:A100, 1, TRUE))
↳ SQL: SELECT * FROM table ORDER BY column ASC;
↳ Python: df_sorted = df.sort_values(by="column")
3️⃣ Counting Rows
↳ Excel: =COUNTA(A:A)
↳ SQL: SELECT COUNT(*) FROM table;
↳ Python: row_count = len(df)
4️⃣ Removing Duplicates
↳ Excel: Data → Remove Duplicates
↳ SQL: SELECT DISTINCT * FROM table;
↳ Python: df_unique = df.drop_duplicates()
5️⃣ Joining Tables
↳ Excel: Power Query → Merge Queries (or VLOOKUP/XLOOKUP)
↳ SQL: SELECT * FROM table1 JOIN table2 ON table1.id = table2.id;
↳ Python: df_merged = pd.merge(df1, df2, on="id")
6️⃣ Ranking Data
↳ Excel: =RANK.EQ(A2, $A$2:$A$100)
↳ SQL: SELECT column, RANK() OVER (ORDER BY column DESC) AS rank FROM table;
↳ Python: df["rank"] = df["column"].rank(method="min", ascending=False)
7️⃣ Moving Average Calculation
↳ Excel: =AVERAGE(B2:B4) (manually for rolling window)
↳ SQL: SELECT date, AVG(value) OVER (ORDER BY date ROWS BETWEEN 2 PRECEDING AND CURRENT ROW) AS moving_avg FROM table;
↳ Python: df["moving_avg"] = df["value"].rolling(window=3).mean()
8️⃣ Running Total
↳ Excel: =SUM($B$2:B2) (drag down)
↳ SQL: SELECT date, SUM(value) OVER (ORDER BY date) AS running_total FROM table;
↳ Python: df["running_total"] = df["value"].cumsum()
❤22👍1🔥1
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:
(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!
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
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!
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
❤8
Complete roadmap to learn Python for data analysis
Step 1: Fundamentals of Python
1. Basics of Python Programming
- Introduction to Python
- Data types (integers, floats, strings, booleans)
- Variables and constants
- Basic operators (arithmetic, comparison, logical)
2. Control Structures
- Conditional statements (if, elif, else)
- Loops (for, while)
- List comprehensions
3. Functions and Modules
- Defining functions
- Function arguments and return values
- Importing modules
- Built-in functions vs. user-defined functions
4. Data Structures
- Lists, tuples, sets, dictionaries
- Manipulating data structures (add, remove, update elements)
Step 2: Advanced Python
1. File Handling
- Reading from and writing to files
- Working with different file formats (txt, csv, json)
2. Error Handling
- Try, except blocks
- Handling exceptions and errors gracefully
3. Object-Oriented Programming (OOP)
- Classes and objects
- Inheritance and polymorphism
- Encapsulation
Step 3: Libraries for Data Analysis
1. NumPy
- Understanding arrays and array operations
- Indexing, slicing, and iterating
- Mathematical functions and statistical operations
2. Pandas
- Series and DataFrames
- Reading and writing data (csv, excel, sql, json)
- Data cleaning and preparation
- Merging, joining, and concatenating data
- Grouping and aggregating data
3. Matplotlib and Seaborn
- Data visualization with Matplotlib
- Plotting different types of graphs (line, bar, scatter, histogram)
- Customizing plots
- Advanced visualizations with Seaborn
Step 4: Data Manipulation and Analysis
1. Data Wrangling
- Handling missing values
- Data transformation
- Feature engineering
2. Exploratory Data Analysis (EDA)
- Denoscriptive statistics
- Data visualization techniques
- Identifying patterns and outliers
3. Statistical Analysis
- Hypothesis testing
- Correlation and regression analysis
- Probability distributions
Step 5: Advanced Topics
1. Time Series Analysis
- Working with datetime objects
- Time series decomposition
- Forecasting models
2. Machine Learning Basics
- Introduction to machine learning
- Supervised vs. unsupervised learning
- Using Scikit-Learn for machine learning
- Building and evaluating models
3. Big Data and Cloud Computing
- Introduction to big data frameworks (e.g., Hadoop, Spark)
- Using cloud services for data analysis (e.g., AWS, Google Cloud)
Step 6: Practical Projects
1. Hands-on Projects
- Analyzing datasets from Kaggle
- Building interactive dashboards with Plotly or Dash
- Developing end-to-end data analysis projects
2. Collaborative Projects
- Participating in data science competitions
- Contributing to open-source projects
👨💻 FREE Resources to Learn & Practice Python
1. https://www.freecodecamp.org/learn/data-analysis-with-python/#data-analysis-with-python-course
2. https://www.hackerrank.com/domains/python
3. https://www.hackerearth.com/practice/python/getting-started/numbers/practice-problems/
4. https://news.1rj.ru/str/PythonInterviews
5. https://www.w3schools.com/python/python_exercises.asp
6. https://news.1rj.ru/str/pythonfreebootcamp/134
7. https://news.1rj.ru/str/pythonanalyst
8. https://pythonbasics.org/exercises/
9. https://news.1rj.ru/str/pythondevelopersindia/300
10. https://www.geeksforgeeks.org/python-programming-language/learn-python-tutorial
11. https://news.1rj.ru/str/pythonspecialist/33
Join @free4unow_backup for more free resources
ENJOY LEARNING 👍👍
Step 1: Fundamentals of Python
1. Basics of Python Programming
- Introduction to Python
- Data types (integers, floats, strings, booleans)
- Variables and constants
- Basic operators (arithmetic, comparison, logical)
2. Control Structures
- Conditional statements (if, elif, else)
- Loops (for, while)
- List comprehensions
3. Functions and Modules
- Defining functions
- Function arguments and return values
- Importing modules
- Built-in functions vs. user-defined functions
4. Data Structures
- Lists, tuples, sets, dictionaries
- Manipulating data structures (add, remove, update elements)
Step 2: Advanced Python
1. File Handling
- Reading from and writing to files
- Working with different file formats (txt, csv, json)
2. Error Handling
- Try, except blocks
- Handling exceptions and errors gracefully
3. Object-Oriented Programming (OOP)
- Classes and objects
- Inheritance and polymorphism
- Encapsulation
Step 3: Libraries for Data Analysis
1. NumPy
- Understanding arrays and array operations
- Indexing, slicing, and iterating
- Mathematical functions and statistical operations
2. Pandas
- Series and DataFrames
- Reading and writing data (csv, excel, sql, json)
- Data cleaning and preparation
- Merging, joining, and concatenating data
- Grouping and aggregating data
3. Matplotlib and Seaborn
- Data visualization with Matplotlib
- Plotting different types of graphs (line, bar, scatter, histogram)
- Customizing plots
- Advanced visualizations with Seaborn
Step 4: Data Manipulation and Analysis
1. Data Wrangling
- Handling missing values
- Data transformation
- Feature engineering
2. Exploratory Data Analysis (EDA)
- Denoscriptive statistics
- Data visualization techniques
- Identifying patterns and outliers
3. Statistical Analysis
- Hypothesis testing
- Correlation and regression analysis
- Probability distributions
Step 5: Advanced Topics
1. Time Series Analysis
- Working with datetime objects
- Time series decomposition
- Forecasting models
2. Machine Learning Basics
- Introduction to machine learning
- Supervised vs. unsupervised learning
- Using Scikit-Learn for machine learning
- Building and evaluating models
3. Big Data and Cloud Computing
- Introduction to big data frameworks (e.g., Hadoop, Spark)
- Using cloud services for data analysis (e.g., AWS, Google Cloud)
Step 6: Practical Projects
1. Hands-on Projects
- Analyzing datasets from Kaggle
- Building interactive dashboards with Plotly or Dash
- Developing end-to-end data analysis projects
2. Collaborative Projects
- Participating in data science competitions
- Contributing to open-source projects
👨💻 FREE Resources to Learn & Practice Python
1. https://www.freecodecamp.org/learn/data-analysis-with-python/#data-analysis-with-python-course
2. https://www.hackerrank.com/domains/python
3. https://www.hackerearth.com/practice/python/getting-started/numbers/practice-problems/
4. https://news.1rj.ru/str/PythonInterviews
5. https://www.w3schools.com/python/python_exercises.asp
6. https://news.1rj.ru/str/pythonfreebootcamp/134
7. https://news.1rj.ru/str/pythonanalyst
8. https://pythonbasics.org/exercises/
9. https://news.1rj.ru/str/pythondevelopersindia/300
10. https://www.geeksforgeeks.org/python-programming-language/learn-python-tutorial
11. https://news.1rj.ru/str/pythonspecialist/33
Join @free4unow_backup for more free resources
ENJOY LEARNING 👍👍
❤9
SQL (Structured Query Language) is the universal language of databases. Whether you're analyzing sales data, optimizing marketing campaigns, or tracking user behavior, SQL is your go-to tool for:
✅ Accessing and managing data efficiently
✅ Writing queries to extract insights
✅ Building a strong foundation for advanced tools like Python, R, or Power BI
In short, SQL is the bridge between raw data and actionable insights. 🌉
SQL Topics to Learn for Data Analyst/Business Analyst Roles
1. Basic:
* SELECT statements
* WHERE clause
* JOINs (INNER, LEFT, RIGHT, FULL)
* GROUP BY and HAVING
* ORDER BY
* Basic Aggregate Functions (COUNT, SUM, AVG, MIN, MAX)
2. Intermediate:
* Subqueries
* CASE statements
* UNION and UNION ALL
* Common Table Expressions (CTEs)
* Window Functions (ROW_NUMBER, RANK, DENSE_RANK, OVER)
* Data Manipulation (INSERT, UPDATE, DELETE)
* Indexes and Performance Tuning
3. Advanced:
* Advanced Window Functions (LEAD, LAG, NTILE)
* Complex Subqueries and Correlated Subqueries
* Advanced Performance Tuning
SQL is not just a skill—it’s the foundation of your data career. 🌟
Here you can find essential SQL Interview Resources👇
https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v
Like this post if you need more 👍❤️
Hope it helps :)
✅ Accessing and managing data efficiently
✅ Writing queries to extract insights
✅ Building a strong foundation for advanced tools like Python, R, or Power BI
In short, SQL is the bridge between raw data and actionable insights. 🌉
SQL Topics to Learn for Data Analyst/Business Analyst Roles
1. Basic:
* SELECT statements
* WHERE clause
* JOINs (INNER, LEFT, RIGHT, FULL)
* GROUP BY and HAVING
* ORDER BY
* Basic Aggregate Functions (COUNT, SUM, AVG, MIN, MAX)
2. Intermediate:
* Subqueries
* CASE statements
* UNION and UNION ALL
* Common Table Expressions (CTEs)
* Window Functions (ROW_NUMBER, RANK, DENSE_RANK, OVER)
* Data Manipulation (INSERT, UPDATE, DELETE)
* Indexes and Performance Tuning
3. Advanced:
* Advanced Window Functions (LEAD, LAG, NTILE)
* Complex Subqueries and Correlated Subqueries
* Advanced Performance Tuning
SQL is not just a skill—it’s the foundation of your data career. 🌟
Here you can find essential SQL Interview Resources👇
https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v
Like this post if you need more 👍❤️
Hope it helps :)
❤7
Want to become a Data Scientist?
Here’s a quick roadmap with essential concepts:
1. Mathematics & Statistics
Linear Algebra: Matrix operations, eigenvalues, eigenvectors, and decomposition, which are crucial for machine learning.
Probability & Statistics: Hypothesis testing, probability distributions, Bayesian inference, confidence intervals, and statistical significance.
Calculus: Derivatives, integrals, and gradients, especially partial derivatives, which are essential for understanding model optimization.
2. Programming
Python or R: Choose a primary programming language for data science.
Python: Libraries like NumPy, Pandas for data manipulation, and Scikit-Learn for machine learning.
R: Especially popular in academia and finance, with libraries like dplyr and ggplot2 for data manipulation and visualization.
SQL: Master querying and database management, essential for accessing, joining, and filtering large datasets.
3. Data Wrangling & Preprocessing
Data Cleaning: Handle missing values, outliers, duplicates, and data formatting.
Feature Engineering: Create meaningful features, handle categorical variables, and apply transformations (scaling, encoding, etc.).
Exploratory Data Analysis (EDA): Visualize data distributions, correlations, and trends to generate hypotheses and insights.
4. Data Visualization
Python Libraries: Use Matplotlib, Seaborn, and Plotly to visualize data.
Tableau or Power BI: Learn interactive visualization tools for building dashboards.
Storytelling: Develop skills to interpret and present data in a meaningful way to stakeholders.
5. Machine Learning
Supervised Learning: Understand algorithms like Linear Regression, Logistic Regression, Decision Trees, Random Forest, Gradient Boosting, and Support Vector Machines (SVM).
Unsupervised Learning: Study clustering (K-means, DBSCAN) and dimensionality reduction (PCA, t-SNE).
Evaluation Metrics: Understand accuracy, precision, recall, F1-score for classification and RMSE, MAE for regression.
6. Advanced Machine Learning & Deep Learning
Neural Networks: Understand the basics of neural networks and backpropagation.
Deep Learning: Get familiar with Convolutional Neural Networks (CNNs) for image processing and Recurrent Neural Networks (RNNs) for sequential data.
Transfer Learning: Apply pre-trained models for specific use cases.
Frameworks: Use TensorFlow Keras for building deep learning models.
7. Natural Language Processing (NLP)
Text Preprocessing: Tokenization, stemming, lemmatization, stop-word removal.
NLP Techniques: Understand bag-of-words, TF-IDF, and word embeddings (Word2Vec, GloVe).
NLP Models: Work with recurrent neural networks (RNNs), transformers (BERT, GPT) for text classification, sentiment analysis, and translation.
8. Big Data Tools (Optional)
Distributed Data Processing: Learn Hadoop and Spark for handling large datasets. Use Google BigQuery for big data storage and processing.
9. Data Science Workflows & Pipelines (Optional)
ETL & Data Pipelines: Extract, Transform, and Load data using tools like Apache Airflow for automation. Set up reproducible workflows for data transformation, modeling, and monitoring.
Model Deployment: Deploy models in production using Flask, FastAPI, or cloud services (AWS SageMaker, Google AI Platform).
10. Model Validation & Tuning
Cross-Validation: Techniques like K-fold cross-validation to avoid overfitting.
Hyperparameter Tuning: Use Grid Search, Random Search, and Bayesian Optimization to optimize model performance.
Bias-Variance Trade-off: Understand how to balance bias and variance in models for better generalization.
11. Time Series Analysis
Statistical Models: ARIMA, SARIMA, and Holt-Winters for time-series forecasting.
Time Series: Handle seasonality, trends, and lags. Use LSTMs or Prophet for more advanced time-series forecasting.
12. Experimentation & A/B Testing
Experiment Design: Learn how to set up and analyze controlled experiments.
A/B Testing: Statistical techniques for comparing groups & measuring the impact of changes.
ENJOY LEARNING 👍👍
#datascience
Here’s a quick roadmap with essential concepts:
1. Mathematics & Statistics
Linear Algebra: Matrix operations, eigenvalues, eigenvectors, and decomposition, which are crucial for machine learning.
Probability & Statistics: Hypothesis testing, probability distributions, Bayesian inference, confidence intervals, and statistical significance.
Calculus: Derivatives, integrals, and gradients, especially partial derivatives, which are essential for understanding model optimization.
2. Programming
Python or R: Choose a primary programming language for data science.
Python: Libraries like NumPy, Pandas for data manipulation, and Scikit-Learn for machine learning.
R: Especially popular in academia and finance, with libraries like dplyr and ggplot2 for data manipulation and visualization.
SQL: Master querying and database management, essential for accessing, joining, and filtering large datasets.
3. Data Wrangling & Preprocessing
Data Cleaning: Handle missing values, outliers, duplicates, and data formatting.
Feature Engineering: Create meaningful features, handle categorical variables, and apply transformations (scaling, encoding, etc.).
Exploratory Data Analysis (EDA): Visualize data distributions, correlations, and trends to generate hypotheses and insights.
4. Data Visualization
Python Libraries: Use Matplotlib, Seaborn, and Plotly to visualize data.
Tableau or Power BI: Learn interactive visualization tools for building dashboards.
Storytelling: Develop skills to interpret and present data in a meaningful way to stakeholders.
5. Machine Learning
Supervised Learning: Understand algorithms like Linear Regression, Logistic Regression, Decision Trees, Random Forest, Gradient Boosting, and Support Vector Machines (SVM).
Unsupervised Learning: Study clustering (K-means, DBSCAN) and dimensionality reduction (PCA, t-SNE).
Evaluation Metrics: Understand accuracy, precision, recall, F1-score for classification and RMSE, MAE for regression.
6. Advanced Machine Learning & Deep Learning
Neural Networks: Understand the basics of neural networks and backpropagation.
Deep Learning: Get familiar with Convolutional Neural Networks (CNNs) for image processing and Recurrent Neural Networks (RNNs) for sequential data.
Transfer Learning: Apply pre-trained models for specific use cases.
Frameworks: Use TensorFlow Keras for building deep learning models.
7. Natural Language Processing (NLP)
Text Preprocessing: Tokenization, stemming, lemmatization, stop-word removal.
NLP Techniques: Understand bag-of-words, TF-IDF, and word embeddings (Word2Vec, GloVe).
NLP Models: Work with recurrent neural networks (RNNs), transformers (BERT, GPT) for text classification, sentiment analysis, and translation.
8. Big Data Tools (Optional)
Distributed Data Processing: Learn Hadoop and Spark for handling large datasets. Use Google BigQuery for big data storage and processing.
9. Data Science Workflows & Pipelines (Optional)
ETL & Data Pipelines: Extract, Transform, and Load data using tools like Apache Airflow for automation. Set up reproducible workflows for data transformation, modeling, and monitoring.
Model Deployment: Deploy models in production using Flask, FastAPI, or cloud services (AWS SageMaker, Google AI Platform).
10. Model Validation & Tuning
Cross-Validation: Techniques like K-fold cross-validation to avoid overfitting.
Hyperparameter Tuning: Use Grid Search, Random Search, and Bayesian Optimization to optimize model performance.
Bias-Variance Trade-off: Understand how to balance bias and variance in models for better generalization.
11. Time Series Analysis
Statistical Models: ARIMA, SARIMA, and Holt-Winters for time-series forecasting.
Time Series: Handle seasonality, trends, and lags. Use LSTMs or Prophet for more advanced time-series forecasting.
12. Experimentation & A/B Testing
Experiment Design: Learn how to set up and analyze controlled experiments.
A/B Testing: Statistical techniques for comparing groups & measuring the impact of changes.
ENJOY LEARNING 👍👍
#datascience
❤9
Essential Python and SQL topics for data analysts 😄👇
Python Topics:
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
- Denoscriptive 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:
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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Hope it helps :)
Python Topics:
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
- Denoscriptive 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:
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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SQL Resources - https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v
Hope it helps :)
❤4
Complete step-by-step syllabus of #Excel for Data Analytics
Introduction to Excel for Data Analytics:
Overview of Excel's capabilities for data analysis
Introduction to Excel's interface: ribbons, worksheets, cells, etc.
Differences between Excel desktop version and Excel Online (web version)
Data Import and Preparation:
Importing data from various sources: CSV, text files, databases, web queries, etc.
Data cleaning and manipulation techniques: sorting, filtering, removing duplicates, etc.
Data types and formatting in Excel
Data validation and error handling
Data Analysis Techniques in Excel:
Basic formulas and functions: SUM, AVERAGE, COUNT, IF, VLOOKUP, etc.
Advanced functions for data analysis: INDEX-MATCH, SUMIFS, COUNTIFS, etc.
PivotTables and PivotCharts for summarizing and analyzing data
Advanced data analysis tools: Goal Seek, Solver, What-If Analysis, etc.
Data Visualization in Excel:
Creating basic charts: column, bar, line, pie, scatter, etc.
Formatting and customizing charts for better visualization
Using sparklines for visualizing trends in data
Creating interactive dashboards with slicers and timelines
Advanced Data Analysis Features:
Data modeling with Excel Tables and Relationships
Using Power Query for data transformation and cleaning
Introduction to Power Pivot for data modeling and DAX calculations
Advanced charting techniques: combination charts, waterfall charts, etc.
Statistical Analysis in Excel:
Denoscriptive statistics: mean, median, mode, standard deviation, etc.
Hypothesis testing: t-tests, chi-square tests, ANOVA, etc.
Regression analysis and correlation
Forecasting techniques: moving averages, exponential smoothing, etc.
Data Visualization Tools in Excel:
Introduction to Excel add-ins for enhanced visualization (e.g., Power Map, Power View)
Creating interactive reports with Excel add-ins
Introduction to Excel Data Model for handling large datasets
Real-world Projects and Case Studies:
Analyzing real-world datasets
Solving business problems with Excel
Portfolio development showcasing Excel skills
Free Resources: https://news.1rj.ru/str/excel_data
Hope this helps you 😊
Introduction to Excel for Data Analytics:
Overview of Excel's capabilities for data analysis
Introduction to Excel's interface: ribbons, worksheets, cells, etc.
Differences between Excel desktop version and Excel Online (web version)
Data Import and Preparation:
Importing data from various sources: CSV, text files, databases, web queries, etc.
Data cleaning and manipulation techniques: sorting, filtering, removing duplicates, etc.
Data types and formatting in Excel
Data validation and error handling
Data Analysis Techniques in Excel:
Basic formulas and functions: SUM, AVERAGE, COUNT, IF, VLOOKUP, etc.
Advanced functions for data analysis: INDEX-MATCH, SUMIFS, COUNTIFS, etc.
PivotTables and PivotCharts for summarizing and analyzing data
Advanced data analysis tools: Goal Seek, Solver, What-If Analysis, etc.
Data Visualization in Excel:
Creating basic charts: column, bar, line, pie, scatter, etc.
Formatting and customizing charts for better visualization
Using sparklines for visualizing trends in data
Creating interactive dashboards with slicers and timelines
Advanced Data Analysis Features:
Data modeling with Excel Tables and Relationships
Using Power Query for data transformation and cleaning
Introduction to Power Pivot for data modeling and DAX calculations
Advanced charting techniques: combination charts, waterfall charts, etc.
Statistical Analysis in Excel:
Denoscriptive statistics: mean, median, mode, standard deviation, etc.
Hypothesis testing: t-tests, chi-square tests, ANOVA, etc.
Regression analysis and correlation
Forecasting techniques: moving averages, exponential smoothing, etc.
Data Visualization Tools in Excel:
Introduction to Excel add-ins for enhanced visualization (e.g., Power Map, Power View)
Creating interactive reports with Excel add-ins
Introduction to Excel Data Model for handling large datasets
Real-world Projects and Case Studies:
Analyzing real-world datasets
Solving business problems with Excel
Portfolio development showcasing Excel skills
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Hope this helps you 😊
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SQL can be simple—if you learn it the smart way..
If you’re aiming to become a data analyst, mastering SQL is non-negotiable.
Here’s a smart roadmap to ace it:
1. Basics First: Understand data types, simple queries (SELECT, FROM, WHERE). Master basic filtering.
2. Joins & Relationships: Dive into INNER, LEFT, RIGHT joins. Practice combining tables to extract meaningful insights.
3. Aggregations & Functions: Get comfortable with COUNT, SUM, AVG, MAX, GROUP BY, and HAVING clauses. These are essential for summarizing data.
4. Subqueries & Nested Queries: Learn how to query within queries. This is powerful for handling complex datasets.
5. Window Functions: Explore ranking, cumulative sums, and sliding windows to work with running totals and moving averages.
6. Optimization: Study indexing and query optimization for faster, more efficient queries.
7. Real-World Scenarios: Apply your SQL knowledge to solve real-world business problems.
The journey may seem tough, but each step sharpens your skills and brings you closer to data analysis excellence. Stay consistent, practice regularly, and let SQL become your superpower! 💪
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4. Subqueries & Nested Queries: Learn how to query within queries. This is powerful for handling complex datasets.
5. Window Functions: Explore ranking, cumulative sums, and sliding windows to work with running totals and moving averages.
6. Optimization: Study indexing and query optimization for faster, more efficient queries.
7. Real-World Scenarios: Apply your SQL knowledge to solve real-world business problems.
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❤10
Quick SQL functions cheat sheet for beginners
Aggregate Functions
COUNT(*): Counts rows.
SUM(column): Total sum.
AVG(column): Average value.
MAX(column): Maximum value.
MIN(column): Minimum value.
String Functions
CONCAT(a, b, …): Concatenates strings.
SUBSTRING(s, start, length): Extracts part of a string.
UPPER(s) / LOWER(s): Converts string case.
TRIM(s): Removes leading/trailing spaces.
Date & Time Functions
CURRENT_DATE / CURRENT_TIME / CURRENT_TIMESTAMP: Current date/time.
EXTRACT(unit FROM date): Retrieves a date part (e.g., year, month).
DATE_ADD(date, INTERVAL n unit): Adds an interval to a date.
Numeric Functions
ROUND(num, decimals): Rounds to a specified decimal.
CEIL(num) / FLOOR(num): Rounds up/down.
ABS(num): Absolute value.
MOD(a, b): Returns the remainder.
Control Flow Functions
CASE: Conditional logic.
COALESCE(val1, val2, …): Returns the first non-null value.
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#dataanalytics
Aggregate Functions
COUNT(*): Counts rows.
SUM(column): Total sum.
AVG(column): Average value.
MAX(column): Maximum value.
MIN(column): Minimum value.
String Functions
CONCAT(a, b, …): Concatenates strings.
SUBSTRING(s, start, length): Extracts part of a string.
UPPER(s) / LOWER(s): Converts string case.
TRIM(s): Removes leading/trailing spaces.
Date & Time Functions
CURRENT_DATE / CURRENT_TIME / CURRENT_TIMESTAMP: Current date/time.
EXTRACT(unit FROM date): Retrieves a date part (e.g., year, month).
DATE_ADD(date, INTERVAL n unit): Adds an interval to a date.
Numeric Functions
ROUND(num, decimals): Rounds to a specified decimal.
CEIL(num) / FLOOR(num): Rounds up/down.
ABS(num): Absolute value.
MOD(a, b): Returns the remainder.
Control Flow Functions
CASE: Conditional logic.
COALESCE(val1, val2, …): Returns the first non-null value.
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#dataanalytics
❤14
SQL Interview Questions with Answers
1. How to change a table name in SQL?
This is the command to change a table name in SQL:
ALTER TABLE table_name
RENAME TO new_table_name;
We will start off by giving the keywords ALTER TABLE, then we will follow it up by giving the original name of the table, after that, we will give in the keywords RENAME TO and finally, we will give the new table name.
2. How to use LIKE in SQL?
The LIKE operator checks if an attribute value matches a given string pattern. Here is an example of LIKE operator
SELECT * FROM employees WHERE first_name like ‘Steven’;
With this command, we will be able to extract all the records where the first name is like “Steven”.
3. If we drop a table, does it also drop related objects like constraints, indexes, columns, default, views and sorted procedures?
Yes, SQL server drops all related objects, which exists inside a table like constraints, indexes, columns, defaults etc. But dropping a table will not drop views and sorted procedures as they exist outside the table.
4. Explain SQL Constraints.
SQL Constraints are used to specify the rules of data type in a table. They can be specified while creating and altering the table. The following are the constraints in SQL: NOT NULL CHECK DEFAULT UNIQUE PRIMARY KEY FOREIGN KEY
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1. How to change a table name in SQL?
This is the command to change a table name in SQL:
ALTER TABLE table_name
RENAME TO new_table_name;
We will start off by giving the keywords ALTER TABLE, then we will follow it up by giving the original name of the table, after that, we will give in the keywords RENAME TO and finally, we will give the new table name.
2. How to use LIKE in SQL?
The LIKE operator checks if an attribute value matches a given string pattern. Here is an example of LIKE operator
SELECT * FROM employees WHERE first_name like ‘Steven’;
With this command, we will be able to extract all the records where the first name is like “Steven”.
3. If we drop a table, does it also drop related objects like constraints, indexes, columns, default, views and sorted procedures?
Yes, SQL server drops all related objects, which exists inside a table like constraints, indexes, columns, defaults etc. But dropping a table will not drop views and sorted procedures as they exist outside the table.
4. Explain SQL Constraints.
SQL Constraints are used to specify the rules of data type in a table. They can be specified while creating and altering the table. The following are the constraints in SQL: NOT NULL CHECK DEFAULT UNIQUE PRIMARY KEY FOREIGN KEY
React ❤️ for more
❤7👌2
🚀 Excel vs SQL vs Python (Pandas):
1️⃣ Filtering Data
↳ Excel: =FILTER(A2:D100, B2:B100>50) (Excel 365 users)
↳ SQL: SELECT * FROM table WHERE column > 50;
↳ Python: df_filtered = df[df['column'] > 50]
2️⃣ Sorting Data
↳ Excel: Data → Sort (or =SORT(A2:A100, 1, TRUE))
↳ SQL: SELECT * FROM table ORDER BY column ASC;
↳ Python: df_sorted = df.sort_values(by="column")
3️⃣ Counting Rows
↳ Excel: =COUNTA(A:A)
↳ SQL: SELECT COUNT(*) FROM table;
↳ Python: row_count = len(df)
4️⃣ Removing Duplicates
↳ Excel: Data → Remove Duplicates
↳ SQL: SELECT DISTINCT * FROM table;
↳ Python: df_unique = df.drop_duplicates()
5️⃣ Joining Tables
↳ Excel: Power Query → Merge Queries (or VLOOKUP/XLOOKUP)
↳ SQL: SELECT * FROM table1 JOIN table2 ON table1.id = table2.id;
↳ Python: df_merged = pd.merge(df1, df2, on="id")
6️⃣ Ranking Data
↳ Excel: =RANK.EQ(A2, $A$2:$A$100)
↳ SQL: SELECT column, RANK() OVER (ORDER BY column DESC) AS rank FROM table;
↳ Python: df["rank"] = df["column"].rank(method="min", ascending=False)
7️⃣ Moving Average Calculation
↳ Excel: =AVERAGE(B2:B4) (manually for rolling window)
↳ SQL: SELECT date, AVG(value) OVER (ORDER BY date ROWS BETWEEN 2 PRECEDING AND CURRENT ROW) AS moving_avg FROM table;
↳ Python: df["moving_avg"] = df["value"].rolling(window=3).mean()
8️⃣ Running Total
↳ Excel: =SUM($B$2:B2) (drag down)
↳ SQL: SELECT date, SUM(value) OVER (ORDER BY date) AS running_total FROM table;
↳ Python: df["running_total"] = df["value"].cumsum()
1️⃣ Filtering Data
↳ Excel: =FILTER(A2:D100, B2:B100>50) (Excel 365 users)
↳ SQL: SELECT * FROM table WHERE column > 50;
↳ Python: df_filtered = df[df['column'] > 50]
2️⃣ Sorting Data
↳ Excel: Data → Sort (or =SORT(A2:A100, 1, TRUE))
↳ SQL: SELECT * FROM table ORDER BY column ASC;
↳ Python: df_sorted = df.sort_values(by="column")
3️⃣ Counting Rows
↳ Excel: =COUNTA(A:A)
↳ SQL: SELECT COUNT(*) FROM table;
↳ Python: row_count = len(df)
4️⃣ Removing Duplicates
↳ Excel: Data → Remove Duplicates
↳ SQL: SELECT DISTINCT * FROM table;
↳ Python: df_unique = df.drop_duplicates()
5️⃣ Joining Tables
↳ Excel: Power Query → Merge Queries (or VLOOKUP/XLOOKUP)
↳ SQL: SELECT * FROM table1 JOIN table2 ON table1.id = table2.id;
↳ Python: df_merged = pd.merge(df1, df2, on="id")
6️⃣ Ranking Data
↳ Excel: =RANK.EQ(A2, $A$2:$A$100)
↳ SQL: SELECT column, RANK() OVER (ORDER BY column DESC) AS rank FROM table;
↳ Python: df["rank"] = df["column"].rank(method="min", ascending=False)
7️⃣ Moving Average Calculation
↳ Excel: =AVERAGE(B2:B4) (manually for rolling window)
↳ SQL: SELECT date, AVG(value) OVER (ORDER BY date ROWS BETWEEN 2 PRECEDING AND CURRENT ROW) AS moving_avg FROM table;
↳ Python: df["moving_avg"] = df["value"].rolling(window=3).mean()
8️⃣ Running Total
↳ Excel: =SUM($B$2:B2) (drag down)
↳ SQL: SELECT date, SUM(value) OVER (ORDER BY date) AS running_total FROM table;
↳ Python: df["running_total"] = df["value"].cumsum()
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