Essential Skills Excel for Data Analysts ๐
1๏ธโฃ Data Cleaning & Transformation
Remove Duplicates โ Ensure unique records.
Find & Replace โ Quick data modifications.
Text Functions โ TRIM, LEN, LEFT, RIGHT, MID, PROPER.
Data Validation โ Restrict input values.
2๏ธโฃ Data Analysis & Manipulation
Sorting & Filtering โ Organize and extract key insights.
Conditional Formatting โ Highlight trends, outliers.
Pivot Tables โ Summarize large datasets efficiently.
Power Query โ Automate data transformation.
3๏ธโฃ Essential Formulas & Functions
Lookup Functions โ VLOOKUP, HLOOKUP, XLOOKUP, INDEX-MATCH.
Logical Functions โ IF, AND, OR, IFERROR, IFS.
Aggregation Functions โ SUM, AVERAGE, MIN, MAX, COUNT, COUNTA.
Text Functions โ CONCATENATE, TEXTJOIN, SUBSTITUTE.
4๏ธโฃ Data Visualization
Charts & Graphs โ Bar, Line, Pie, Scatter, Histogram.
Sparklines โ Miniature charts inside cells.
Conditional Formatting โ Color scales, data bars.
Dashboard Creation โ Interactive and dynamic reports.
5๏ธโฃ Advanced Excel Techniques
Array Formulas โ Dynamic calculations with multiple values.
Power Pivot & DAX โ Advanced data modeling.
What-If Analysis โ Goal Seek, Scenario Manager.
Macros & VBA โ Automate repetitive tasks.
6๏ธโฃ Data Import & Export
CSV & TXT Files โ Import and clean raw data.
Power Query โ Connect to databases, web sources.
Exporting Reports โ PDF, CSV, Excel formats.
Here you can find some free Excel books & useful resources: https://t.iss.one/excel_data
Hope it helps :)
#dataanalyst
1๏ธโฃ Data Cleaning & Transformation
Remove Duplicates โ Ensure unique records.
Find & Replace โ Quick data modifications.
Text Functions โ TRIM, LEN, LEFT, RIGHT, MID, PROPER.
Data Validation โ Restrict input values.
2๏ธโฃ Data Analysis & Manipulation
Sorting & Filtering โ Organize and extract key insights.
Conditional Formatting โ Highlight trends, outliers.
Pivot Tables โ Summarize large datasets efficiently.
Power Query โ Automate data transformation.
3๏ธโฃ Essential Formulas & Functions
Lookup Functions โ VLOOKUP, HLOOKUP, XLOOKUP, INDEX-MATCH.
Logical Functions โ IF, AND, OR, IFERROR, IFS.
Aggregation Functions โ SUM, AVERAGE, MIN, MAX, COUNT, COUNTA.
Text Functions โ CONCATENATE, TEXTJOIN, SUBSTITUTE.
4๏ธโฃ Data Visualization
Charts & Graphs โ Bar, Line, Pie, Scatter, Histogram.
Sparklines โ Miniature charts inside cells.
Conditional Formatting โ Color scales, data bars.
Dashboard Creation โ Interactive and dynamic reports.
5๏ธโฃ Advanced Excel Techniques
Array Formulas โ Dynamic calculations with multiple values.
Power Pivot & DAX โ Advanced data modeling.
What-If Analysis โ Goal Seek, Scenario Manager.
Macros & VBA โ Automate repetitive tasks.
6๏ธโฃ Data Import & Export
CSV & TXT Files โ Import and clean raw data.
Power Query โ Connect to databases, web sources.
Exporting Reports โ PDF, CSV, Excel formats.
Here you can find some free Excel books & useful resources: https://t.iss.one/excel_data
Hope it helps :)
#dataanalyst
โค8
SQL Cheat Sheet For Data Analysts ๐ โ
1๏ธโฃ Basic Aggregates
โฆ SUM() โ Adds up values:
SELECT SUM(sales) FROM orders;
โฆ AVG() โ Calculates average:
SELECT AVG(score) FROM tests;
โฆ MIN() / MAX() โ Smallest/largest value:
SELECT MIN(age), MAX(age) FROM users;
โฆ COUNT() โ Counts rows:
SELECT COUNT(*) FROM customers;
2๏ธโฃ Conditional Logic
โฆ CASE WHEN โ If/else logic:
โฆ COALESCE() โ Returns first non-null:
SELECT COALESCE(phone, 'N/A') FROM contacts;
3๏ธโฃ String Functions
โฆ LEFT(), RIGHT(), SUBSTRING() โ Extract text:
SELECT LEFT(name, 3) FROM employees;
โฆ LENGTH() โ Counts characters:
SELECT LENGTH(address) FROM users;
โฆ TRIM(), UPPER(), LOWER() โ Clean/change case:
SELECT TRIM(email), UPPER(city) FROM users;
โฆ CONCAT() โ Combine text:
SELECT CONCAT(first_name, ' ', last_name) FROM users;
4๏ธโฃ Lookup/Join
โฆ JOIN โ Combine tables:
โฆ IN / EXISTS โ Check for values:
SELECT * FROM products WHERE category_id IN (1,2,3);
5๏ธโฃ Date & Time
โฆ CURRENT_DATE, CURRENT_TIMESTAMP โ Today/now:
SELECT CURRENT_DATE;
โฆ EXTRACT() โ Get year/month/day:
SELECT EXTRACT(YEAR FROM order_date) FROM orders;
โฆ DATEDIFF() โ Days between dates:
SELECT DATEDIFF('2025-07-08', '2025-01-01');
6๏ธโฃ Data Cleaning
โฆ DISTINCT โ Unique values:
SELECT DISTINCT city FROM customers;
โฆ REPLACE() โ Replace text:
SELECT REPLACE(email, '.com', '.org') FROM users;
โฆ NULLIF() โ Set value to NULL if condition met:
SELECT NULLIF(status, 'unknown') FROM orders;
7๏ธโฃ Advanced Functions
โฆ GROUP BY โ Aggregate by group:
SELECT department, COUNT(*) FROM employees GROUP BY department;
โฆ HAVING โ Filter after aggregation:
SELECT department, COUNT(*) FROM employees GROUP BY department HAVING COUNT(*) > 5;
โฆ WINDOW FUNCTIONS โ Running totals, ranks:
SELECT name, salary, RANK() OVER (ORDER BY salary DESC) FROM staff;
8๏ธโฃ Views & CTEs
โฆ VIEW โ Save a query:
CREATE VIEW top_customers AS SELECT * FROM customers WHERE spend > 1000;
โฆ CTE โ Temporary result set:
Free Resources to learn SQL: https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v
ENJOY LEARNING๐ ๐
1๏ธโฃ Basic Aggregates
โฆ SUM() โ Adds up values:
SELECT SUM(sales) FROM orders;
โฆ AVG() โ Calculates average:
SELECT AVG(score) FROM tests;
โฆ MIN() / MAX() โ Smallest/largest value:
SELECT MIN(age), MAX(age) FROM users;
โฆ COUNT() โ Counts rows:
SELECT COUNT(*) FROM customers;
2๏ธโฃ Conditional Logic
โฆ CASE WHEN โ If/else logic:
SELECT name,
CASE WHEN score > 50 THEN 'Pass' ELSE 'Fail' END AS result
FROM students;
โฆ COALESCE() โ Returns first non-null:
SELECT COALESCE(phone, 'N/A') FROM contacts;
3๏ธโฃ String Functions
โฆ LEFT(), RIGHT(), SUBSTRING() โ Extract text:
SELECT LEFT(name, 3) FROM employees;
โฆ LENGTH() โ Counts characters:
SELECT LENGTH(address) FROM users;
โฆ TRIM(), UPPER(), LOWER() โ Clean/change case:
SELECT TRIM(email), UPPER(city) FROM users;
โฆ CONCAT() โ Combine text:
SELECT CONCAT(first_name, ' ', last_name) FROM users;
4๏ธโฃ Lookup/Join
โฆ JOIN โ Combine tables:
SELECT o.order_id, c.name
FROM orders o
JOIN customers c ON o.customer_id = c.id;
โฆ IN / EXISTS โ Check for values:
SELECT * FROM products WHERE category_id IN (1,2,3);
5๏ธโฃ Date & Time
โฆ CURRENT_DATE, CURRENT_TIMESTAMP โ Today/now:
SELECT CURRENT_DATE;
โฆ EXTRACT() โ Get year/month/day:
SELECT EXTRACT(YEAR FROM order_date) FROM orders;
โฆ DATEDIFF() โ Days between dates:
SELECT DATEDIFF('2025-07-08', '2025-01-01');
6๏ธโฃ Data Cleaning
โฆ DISTINCT โ Unique values:
SELECT DISTINCT city FROM customers;
โฆ REPLACE() โ Replace text:
SELECT REPLACE(email, '.com', '.org') FROM users;
โฆ NULLIF() โ Set value to NULL if condition met:
SELECT NULLIF(status, 'unknown') FROM orders;
7๏ธโฃ Advanced Functions
โฆ GROUP BY โ Aggregate by group:
SELECT department, COUNT(*) FROM employees GROUP BY department;
โฆ HAVING โ Filter after aggregation:
SELECT department, COUNT(*) FROM employees GROUP BY department HAVING COUNT(*) > 5;
โฆ WINDOW FUNCTIONS โ Running totals, ranks:
SELECT name, salary, RANK() OVER (ORDER BY salary DESC) FROM staff;
8๏ธโฃ Views & CTEs
โฆ VIEW โ Save a query:
CREATE VIEW top_customers AS SELECT * FROM customers WHERE spend > 1000;
โฆ CTE โ 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
Please open Telegram to view this post
VIEW IN TELEGRAM
โค11๐1
๐๐ฎ๐๐ฎ ๐๐ป๐ฎ๐น๐๐๐ถ๐ฐ๐ ๐ฅ๐ผ๐ฎ๐ฑ๐บ๐ฎ๐ฝ
๐ญ. ๐ฃ๐ฟ๐ผ๐ด๐ฟ๐ฎ๐บ๐บ๐ถ๐ป๐ด ๐๐ฎ๐ป๐ด๐๐ฎ๐ด๐ฒ๐: Master Python, SQL, and R for data manipulation and analysis.
๐ฎ. ๐๐ฎ๐๐ฎ ๐ ๐ฎ๐ป๐ถ๐ฝ๐๐น๐ฎ๐๐ถ๐ผ๐ป ๐ฎ๐ป๐ฑ ๐ฃ๐ฟ๐ผ๐ฐ๐ฒ๐๐๐ถ๐ป๐ด: Use Excel, Pandas, and ETL tools like Alteryx and Talend for data processing.
๐ฏ. ๐๐ฎ๐๐ฎ ๐ฉ๐ถ๐๐๐ฎ๐น๐ถ๐๐ฎ๐๐ถ๐ผ๐ป: Learn Tableau, Power BI, and Matplotlib/Seaborn for creating insightful visualizations.
๐ฐ. ๐ฆ๐๐ฎ๐๐ถ๐๐๐ถ๐ฐ๐ ๐ฎ๐ป๐ฑ ๐ ๐ฎ๐๐ต๐ฒ๐บ๐ฎ๐๐ถ๐ฐ๐: Understand Descriptive and Inferential Statistics, Probability, Regression, and Time Series Analysis.
๐ฑ. ๐ ๐ฎ๐ฐ๐ต๐ถ๐ป๐ฒ ๐๐ฒ๐ฎ๐ฟ๐ป๐ถ๐ป๐ด: Get proficient in Supervised and Unsupervised Learning, along with Time Series Forecasting.
๐ฒ. ๐๐ถ๐ด ๐๐ฎ๐๐ฎ ๐ง๐ผ๐ผ๐น๐: Utilize Google BigQuery, AWS Redshift, and NoSQL databases like MongoDB for large-scale data management.
๐ณ. ๐ ๐ผ๐ป๐ถ๐๐ผ๐ฟ๐ถ๐ป๐ด ๐ฎ๐ป๐ฑ ๐ฅ๐ฒ๐ฝ๐ผ๐ฟ๐๐ถ๐ป๐ด: Implement Data Quality Monitoring (Great Expectations) and Performance Tracking (Prometheus, Grafana).
๐ด. ๐๐ป๐ฎ๐น๐๐๐ถ๐ฐ๐ ๐ง๐ผ๐ผ๐น๐: Work with Data Orchestration tools (Airflow, Prefect) and visualization tools like D3.js and Plotly.
๐ต. ๐ฅ๐ฒ๐๐ผ๐๐ฟ๐ฐ๐ฒ ๐ ๐ฎ๐ป๐ฎ๐ด๐ฒ๐ฟ: Manage resources using Jupyter Notebooks and Power BI.
๐ญ๐ฌ. ๐๐ฎ๐๐ฎ ๐๐ผ๐๐ฒ๐ฟ๐ป๐ฎ๐ป๐ฐ๐ฒ ๐ฎ๐ป๐ฑ ๐๐๐ต๐ถ๐ฐ๐: Ensure compliance with GDPR, Data Privacy, and Data Quality standards.
๐ญ๐ญ. ๐๐น๐ผ๐๐ฑ ๐๐ผ๐บ๐ฝ๐๐๐ถ๐ป๐ด: Leverage AWS, Google Cloud, and Azure for scalable data solutions.
๐ญ๐ฎ. ๐๐ฎ๐๐ฎ ๐ช๐ฟ๐ฎ๐ป๐ด๐น๐ถ๐ป๐ด ๐ฎ๐ป๐ฑ ๐๐น๐ฒ๐ฎ๐ป๐ถ๐ป๐ด: Master data cleaning (OpenRefine, Trifacta) and transformation techniques.
Data Analytics Resources
๐๐
https://t.iss.one/sqlspecialist
Hope this helps you ๐
๐ญ. ๐ฃ๐ฟ๐ผ๐ด๐ฟ๐ฎ๐บ๐บ๐ถ๐ป๐ด ๐๐ฎ๐ป๐ด๐๐ฎ๐ด๐ฒ๐: Master Python, SQL, and R for data manipulation and analysis.
๐ฎ. ๐๐ฎ๐๐ฎ ๐ ๐ฎ๐ป๐ถ๐ฝ๐๐น๐ฎ๐๐ถ๐ผ๐ป ๐ฎ๐ป๐ฑ ๐ฃ๐ฟ๐ผ๐ฐ๐ฒ๐๐๐ถ๐ป๐ด: Use Excel, Pandas, and ETL tools like Alteryx and Talend for data processing.
๐ฏ. ๐๐ฎ๐๐ฎ ๐ฉ๐ถ๐๐๐ฎ๐น๐ถ๐๐ฎ๐๐ถ๐ผ๐ป: Learn Tableau, Power BI, and Matplotlib/Seaborn for creating insightful visualizations.
๐ฐ. ๐ฆ๐๐ฎ๐๐ถ๐๐๐ถ๐ฐ๐ ๐ฎ๐ป๐ฑ ๐ ๐ฎ๐๐ต๐ฒ๐บ๐ฎ๐๐ถ๐ฐ๐: Understand Descriptive and Inferential Statistics, Probability, Regression, and Time Series Analysis.
๐ฑ. ๐ ๐ฎ๐ฐ๐ต๐ถ๐ป๐ฒ ๐๐ฒ๐ฎ๐ฟ๐ป๐ถ๐ป๐ด: Get proficient in Supervised and Unsupervised Learning, along with Time Series Forecasting.
๐ฒ. ๐๐ถ๐ด ๐๐ฎ๐๐ฎ ๐ง๐ผ๐ผ๐น๐: Utilize Google BigQuery, AWS Redshift, and NoSQL databases like MongoDB for large-scale data management.
๐ณ. ๐ ๐ผ๐ป๐ถ๐๐ผ๐ฟ๐ถ๐ป๐ด ๐ฎ๐ป๐ฑ ๐ฅ๐ฒ๐ฝ๐ผ๐ฟ๐๐ถ๐ป๐ด: Implement Data Quality Monitoring (Great Expectations) and Performance Tracking (Prometheus, Grafana).
๐ด. ๐๐ป๐ฎ๐น๐๐๐ถ๐ฐ๐ ๐ง๐ผ๐ผ๐น๐: Work with Data Orchestration tools (Airflow, Prefect) and visualization tools like D3.js and Plotly.
๐ต. ๐ฅ๐ฒ๐๐ผ๐๐ฟ๐ฐ๐ฒ ๐ ๐ฎ๐ป๐ฎ๐ด๐ฒ๐ฟ: Manage resources using Jupyter Notebooks and Power BI.
๐ญ๐ฌ. ๐๐ฎ๐๐ฎ ๐๐ผ๐๐ฒ๐ฟ๐ป๐ฎ๐ป๐ฐ๐ฒ ๐ฎ๐ป๐ฑ ๐๐๐ต๐ถ๐ฐ๐: Ensure compliance with GDPR, Data Privacy, and Data Quality standards.
๐ญ๐ญ. ๐๐น๐ผ๐๐ฑ ๐๐ผ๐บ๐ฝ๐๐๐ถ๐ป๐ด: Leverage AWS, Google Cloud, and Azure for scalable data solutions.
๐ญ๐ฎ. ๐๐ฎ๐๐ฎ ๐ช๐ฟ๐ฎ๐ป๐ด๐น๐ถ๐ป๐ด ๐ฎ๐ป๐ฑ ๐๐น๐ฒ๐ฎ๐ป๐ถ๐ป๐ด: Master data cleaning (OpenRefine, Trifacta) and transformation techniques.
Data Analytics Resources
๐๐
https://t.iss.one/sqlspecialist
Hope this helps you ๐
โค4
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 script 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 ๐
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 script 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 ๐
โค21๐1
SQL ๐ข๐ฟ๐ฑ๐ฒ๐ฟ ๐ข๐ณ ๐๐
๐ฒ๐ฐ๐๐๐ถ๐ผ๐ป โ
1 โ FROM (Tables selected).
2 โ WHERE (Filters applied).
3 โ GROUP BY (Rows grouped).
4 โ HAVING (Filter on grouped data).
5 โ SELECT (Columns selected).
6 โ ORDER BY (Sort the data).
7 โ LIMIT (Restrict number of rows).
๐๐ผ๐บ๐บ๐ผ๐ป ๐ค๐๐ฒ๐ฟ๐ถ๐ฒ๐ ๐ง๐ผ ๐ฃ๐ฟ๐ฎ๐ฐ๐๐ถ๐ฐ๐ฒ โ
โฌ Find the second-highest salary:
SELECT MAX(Salary) FROM Employees WHERE Salary < (SELECT MAX(Salary) FROM Employees);
โฌ Find duplicate records:
SELECT Name, COUNT(*)
FROM Emp
GROUP BY Name
HAVING COUNT(*) > 1;
1 โ FROM (Tables selected).
2 โ WHERE (Filters applied).
3 โ GROUP BY (Rows grouped).
4 โ HAVING (Filter on grouped data).
5 โ SELECT (Columns selected).
6 โ ORDER BY (Sort the data).
7 โ LIMIT (Restrict number of rows).
๐๐ผ๐บ๐บ๐ผ๐ป ๐ค๐๐ฒ๐ฟ๐ถ๐ฒ๐ ๐ง๐ผ ๐ฃ๐ฟ๐ฎ๐ฐ๐๐ถ๐ฐ๐ฒ โ
โฌ Find the second-highest salary:
SELECT MAX(Salary) FROM Employees WHERE Salary < (SELECT MAX(Salary) FROM Employees);
โฌ Find duplicate records:
SELECT Name, COUNT(*)
FROM Emp
GROUP BY Name
HAVING COUNT(*) > 1;
โค9
๐ Complete Roadmap to Become a Data Scientist in 5 Months
๐ Week 1-2: Fundamentals
โ Day 1-3: Introduction to Data Science, its applications, and roles.
โ Day 4-7: Brush up on Python programming ๐.
โ Day 8-10: Learn basic statistics ๐ and probability ๐ฒ.
๐ Week 3-4: Data Manipulation & Visualization
๐ Day 11-15: Master Pandas for data manipulation.
๐ Day 16-20: Learn Matplotlib & Seaborn for data visualization.
๐ค Week 5-6: Machine Learning Foundations
๐ฌ Day 21-25: Introduction to scikit-learn.
๐ Day 26-30: Learn Linear & Logistic Regression.
๐ Week 7-8: Advanced Machine Learning
๐ณ Day 31-35: Explore Decision Trees & Random Forests.
๐ Day 36-40: Learn Clustering (K-Means, DBSCAN) & Dimensionality Reduction.
๐ง Week 9-10: Deep Learning
๐ค Day 41-45: Basics of Neural Networks with TensorFlow/Keras.
๐ธ Day 46-50: Learn CNNs & RNNs for image & text data.
๐ Week 11-12: Data Engineering
๐ Day 51-55: Learn SQL & Databases.
๐งน Day 56-60: Data Preprocessing & Cleaning.
๐ Week 13-14: Model Evaluation & Optimization
๐ Day 61-65: Learn Cross-validation & Hyperparameter Tuning.
๐ Day 66-70: Understand Evaluation Metrics (Accuracy, Precision, Recall, F1-score).
๐ Week 15-16: Big Data & Tools
๐ Day 71-75: Introduction to Big Data Technologies (Hadoop, Spark).
โ๏ธ Day 76-80: Learn Cloud Computing (AWS, GCP, Azure).
๐ Week 17-18: Deployment & Production
๐ Day 81-85: Deploy models using Flask or FastAPI.
๐ฆ Day 86-90: Learn Docker & Cloud Deployment (AWS, Heroku).
๐ฏ Week 19-20: Specialization
๐ Day 91-95: Choose NLP or Computer Vision, based on your interest.
๐ Week 21-22: Projects & Portfolio
๐ Day 96-100: Work on Personal Data Science Projects.
๐ฌ Week 23-24: Soft Skills & Networking
๐ค Day 101-105: Improve Communication & Presentation Skills.
๐ Day 106-110: Attend Online Meetups & Forums.
๐ฏ Week 25-26: Interview Preparation
๐ป Day 111-115: Practice Coding Interviews (LeetCode, HackerRank).
๐ Day 116-120: Review your projects & prepare for discussions.
๐จโ๐ป Week 27-28: Apply for Jobs
๐ฉ Day 121-125: Start applying for Entry-Level Data Scientist positions.
๐ค Week 29-30: Interviews
๐ Day 126-130: Attend Interviews & Practice Whiteboard Problems.
๐ Week 31-32: Continuous Learning
๐ฐ Day 131-135: Stay updated with the Latest Data Science Trends.
๐ Week 33-34: Accepting Offers
๐ Day 136-140: Evaluate job offers & Negotiate Your Salary.
๐ข Week 35-36: Settling In
๐ฏ Day 141-150: Start your New Data Science Job, adapt & keep learning!
๐ Enjoy Learning & Build Your Dream Career in Data Science! ๐๐ฅ
๐ Week 1-2: Fundamentals
โ Day 1-3: Introduction to Data Science, its applications, and roles.
โ Day 4-7: Brush up on Python programming ๐.
โ Day 8-10: Learn basic statistics ๐ and probability ๐ฒ.
๐ Week 3-4: Data Manipulation & Visualization
๐ Day 11-15: Master Pandas for data manipulation.
๐ Day 16-20: Learn Matplotlib & Seaborn for data visualization.
๐ค Week 5-6: Machine Learning Foundations
๐ฌ Day 21-25: Introduction to scikit-learn.
๐ Day 26-30: Learn Linear & Logistic Regression.
๐ Week 7-8: Advanced Machine Learning
๐ณ Day 31-35: Explore Decision Trees & Random Forests.
๐ Day 36-40: Learn Clustering (K-Means, DBSCAN) & Dimensionality Reduction.
๐ง Week 9-10: Deep Learning
๐ค Day 41-45: Basics of Neural Networks with TensorFlow/Keras.
๐ธ Day 46-50: Learn CNNs & RNNs for image & text data.
๐ Week 11-12: Data Engineering
๐ Day 51-55: Learn SQL & Databases.
๐งน Day 56-60: Data Preprocessing & Cleaning.
๐ Week 13-14: Model Evaluation & Optimization
๐ Day 61-65: Learn Cross-validation & Hyperparameter Tuning.
๐ Day 66-70: Understand Evaluation Metrics (Accuracy, Precision, Recall, F1-score).
๐ Week 15-16: Big Data & Tools
๐ Day 71-75: Introduction to Big Data Technologies (Hadoop, Spark).
โ๏ธ Day 76-80: Learn Cloud Computing (AWS, GCP, Azure).
๐ Week 17-18: Deployment & Production
๐ Day 81-85: Deploy models using Flask or FastAPI.
๐ฆ Day 86-90: Learn Docker & Cloud Deployment (AWS, Heroku).
๐ฏ Week 19-20: Specialization
๐ Day 91-95: Choose NLP or Computer Vision, based on your interest.
๐ Week 21-22: Projects & Portfolio
๐ Day 96-100: Work on Personal Data Science Projects.
๐ฌ Week 23-24: Soft Skills & Networking
๐ค Day 101-105: Improve Communication & Presentation Skills.
๐ Day 106-110: Attend Online Meetups & Forums.
๐ฏ Week 25-26: Interview Preparation
๐ป Day 111-115: Practice Coding Interviews (LeetCode, HackerRank).
๐ Day 116-120: Review your projects & prepare for discussions.
๐จโ๐ป Week 27-28: Apply for Jobs
๐ฉ Day 121-125: Start applying for Entry-Level Data Scientist positions.
๐ค Week 29-30: Interviews
๐ Day 126-130: Attend Interviews & Practice Whiteboard Problems.
๐ Week 31-32: Continuous Learning
๐ฐ Day 131-135: Stay updated with the Latest Data Science Trends.
๐ Week 33-34: Accepting Offers
๐ Day 136-140: Evaluate job offers & Negotiate Your Salary.
๐ข Week 35-36: Settling In
๐ฏ Day 141-150: Start your New Data Science Job, adapt & keep learning!
๐ Enjoy Learning & Build Your Dream Career in Data Science! ๐๐ฅ
โค10๐2
Step-by-step guide to become a Data Analyst in 2025โ๐
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 bachelorโs 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 projectsโuse 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 detailโthese are just as important as technical skills.
7. Apply for Entry-Level Jobs:
Look for roles like โJunior Data Analystโ or โBusiness Analyst.โ 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 โค๏ธ 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 bachelorโs 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 projectsโuse 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 detailโthese are just as important as technical skills.
7. Apply for Entry-Level Jobs:
Look for roles like โJunior Data Analystโ or โBusiness Analyst.โ 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 โค๏ธ for more
โค12๐ฅฐ1
โ
Data Analytics Roadmap for Freshers in 2025 ๐๐
1๏ธโฃ Understand What a Data Analyst Does
๐ Analyze data, find insights, create dashboards, support business decisions.
2๏ธโฃ Start with Excel
๐ Learn:
โ Basic formulas
โ Charts & Pivot Tables
โ Data cleaning
๐ก Excel is still the #1 tool in many companies.
3๏ธโฃ Learn SQL
๐งฉ SQL helps you pull and analyze data from databases.
Start with:
โ SELECT, WHERE, JOIN, GROUP BY
๐ ๏ธ Practice on platforms like W3Schools or Mode Analytics.
4๏ธโฃ Pick a Programming Language
๐ Start with Python (easier) or R
โ Learn pandas, matplotlib, numpy
โ Do small projects (e.g. analyze sales data)
5๏ธโฃ Data Visualization Tools
๐ Learn:
โ Power BI or Tableau
โ Build simple dashboards
๐ก Start with free versions or YouTube tutorials.
6๏ธโฃ Practice with Real Data
๐ Use sites like Kaggle or Data.gov
โ Clean, analyze, visualize
โ Try small case studies (sales report, customer trends)
7๏ธโฃ Create a Portfolio
๐ป Share projects on:
โ GitHub
โ Notion or a simple website
๐ Add visuals + brief explanations of your insights.
8๏ธโฃ Improve Soft Skills
๐ฃ๏ธ Focus on:
โ Presenting data in simple words
โ Asking good questions
โ Thinking critically about patterns
9๏ธโฃ Certifications to Stand Out
๐ Try:
โ Google Data Analytics (Coursera)
โ IBM Data Analyst
โ LinkedIn Learning basics
๐ Apply for Internships & Entry Jobs
๐ฏ Titles to look for:
โ Data Analyst (Intern)
โ Junior Analyst
โ Business Analyst
๐ฌ React โค๏ธ for more!
1๏ธโฃ Understand What a Data Analyst Does
๐ Analyze data, find insights, create dashboards, support business decisions.
2๏ธโฃ Start with Excel
๐ Learn:
โ Basic formulas
โ Charts & Pivot Tables
โ Data cleaning
๐ก Excel is still the #1 tool in many companies.
3๏ธโฃ Learn SQL
๐งฉ SQL helps you pull and analyze data from databases.
Start with:
โ SELECT, WHERE, JOIN, GROUP BY
๐ ๏ธ Practice on platforms like W3Schools or Mode Analytics.
4๏ธโฃ Pick a Programming Language
๐ Start with Python (easier) or R
โ Learn pandas, matplotlib, numpy
โ Do small projects (e.g. analyze sales data)
5๏ธโฃ Data Visualization Tools
๐ Learn:
โ Power BI or Tableau
โ Build simple dashboards
๐ก Start with free versions or YouTube tutorials.
6๏ธโฃ Practice with Real Data
๐ Use sites like Kaggle or Data.gov
โ Clean, analyze, visualize
โ Try small case studies (sales report, customer trends)
7๏ธโฃ Create a Portfolio
๐ป Share projects on:
โ GitHub
โ Notion or a simple website
๐ Add visuals + brief explanations of your insights.
8๏ธโฃ Improve Soft Skills
๐ฃ๏ธ Focus on:
โ Presenting data in simple words
โ Asking good questions
โ Thinking critically about patterns
9๏ธโฃ Certifications to Stand Out
๐ Try:
โ Google Data Analytics (Coursera)
โ IBM Data Analyst
โ LinkedIn Learning basics
๐ Apply for Internships & Entry Jobs
๐ฏ Titles to look for:
โ Data Analyst (Intern)
โ Junior Analyst
โ Business Analyst
๐ฌ React โค๏ธ for more!
โค14๐2
๐ Excel vs SQL vs Python (Pandas):
1๏ธโฃ Filtering Data
โณ Excel: =FILTER(A2:D100, B2:B100>50) (Excel 365 users)
โณ SQL: SELECT * FROM table WHERE column > 50;
โณ Python: df_filtered = df[df['column'] > 50]
2๏ธโฃ Sorting Data
โณ Excel: Data โ Sort (or =SORT(A2:A100, 1, TRUE))
โณ SQL: SELECT * FROM table ORDER BY column ASC;
โณ Python: df_sorted = df.sort_values(by="column")
3๏ธโฃ Counting Rows
โณ Excel: =COUNTA(A:A)
โณ SQL: SELECT COUNT(*) FROM table;
โณ Python: row_count = len(df)
4๏ธโฃ Removing Duplicates
โณ Excel: Data โ Remove Duplicates
โณ SQL: SELECT DISTINCT * FROM table;
โณ Python: df_unique = df.drop_duplicates()
5๏ธโฃ Joining Tables
โณ Excel: Power Query โ Merge Queries (or VLOOKUP/XLOOKUP)
โณ SQL: SELECT * FROM table1 JOIN table2 ON table1.id = table2.id;
โณ Python: df_merged = pd.merge(df1, df2, on="id")
6๏ธโฃ Ranking Data
โณ Excel: =RANK.EQ(A2, $A$2:$A$100)
โณ SQL: SELECT column, RANK() OVER (ORDER BY column DESC) AS rank FROM table;
โณ Python: df["rank"] = df["column"].rank(method="min", ascending=False)
7๏ธโฃ Moving Average Calculation
โณ Excel: =AVERAGE(B2:B4) (manually for rolling window)
โณ SQL: SELECT date, AVG(value) OVER (ORDER BY date ROWS BETWEEN 2 PRECEDING AND CURRENT ROW) AS moving_avg FROM table;
โณ Python: df["moving_avg"] = df["value"].rolling(window=3).mean()
8๏ธโฃ Running Total
โณ Excel: =SUM($B$2:B2) (drag down)
โณ SQL: SELECT date, SUM(value) OVER (ORDER BY date) AS running_total FROM table;
โณ Python: df["running_total"] = df["value"].cumsum()
1๏ธโฃ Filtering Data
โณ Excel: =FILTER(A2:D100, B2:B100>50) (Excel 365 users)
โณ SQL: SELECT * FROM table WHERE column > 50;
โณ Python: df_filtered = df[df['column'] > 50]
2๏ธโฃ Sorting Data
โณ Excel: Data โ Sort (or =SORT(A2:A100, 1, TRUE))
โณ SQL: SELECT * FROM table ORDER BY column ASC;
โณ Python: df_sorted = df.sort_values(by="column")
3๏ธโฃ Counting Rows
โณ Excel: =COUNTA(A:A)
โณ SQL: SELECT COUNT(*) FROM table;
โณ Python: row_count = len(df)
4๏ธโฃ Removing Duplicates
โณ Excel: Data โ Remove Duplicates
โณ SQL: SELECT DISTINCT * FROM table;
โณ Python: df_unique = df.drop_duplicates()
5๏ธโฃ Joining Tables
โณ Excel: Power Query โ Merge Queries (or VLOOKUP/XLOOKUP)
โณ SQL: SELECT * FROM table1 JOIN table2 ON table1.id = table2.id;
โณ Python: df_merged = pd.merge(df1, df2, on="id")
6๏ธโฃ Ranking Data
โณ Excel: =RANK.EQ(A2, $A$2:$A$100)
โณ SQL: SELECT column, RANK() OVER (ORDER BY column DESC) AS rank FROM table;
โณ Python: df["rank"] = df["column"].rank(method="min", ascending=False)
7๏ธโฃ Moving Average Calculation
โณ Excel: =AVERAGE(B2:B4) (manually for rolling window)
โณ SQL: SELECT date, AVG(value) OVER (ORDER BY date ROWS BETWEEN 2 PRECEDING AND CURRENT ROW) AS moving_avg FROM table;
โณ Python: df["moving_avg"] = df["value"].rolling(window=3).mean()
8๏ธโฃ Running Total
โณ Excel: =SUM($B$2:B2) (drag down)
โณ SQL: SELECT date, SUM(value) OVER (ORDER BY date) AS running_total FROM table;
โณ Python: df["running_total"] = df["value"].cumsum()
โค23๐1๐ฅ1
How do analysts use SQL in a company?
SQL is every data analystโs 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 *โyour future self (and the database) will thank you!)
Clean & Transform
Use SQL functions to clean raw data.
Think TRIM(), COALESCE(), CAST()โlike giving data a fresh haircut.
Summarize & Analyze
Group and aggregate to spot trends and patterns.
GROUP BY, SUM(), AVG() โ 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 โWhatโs working?โโyouโve got the answers.
And remember: write smart queries, not lazy ones. Say no to SELECT * unless you really mean it!
Hit โฅ๏ธ if you want me to share more real-world examples to make data analytics easier to understand!
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
SQL is every data analystโs 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 *โyour future self (and the database) will thank you!)
Clean & Transform
Use SQL functions to clean raw data.
Think TRIM(), COALESCE(), CAST()โlike giving data a fresh haircut.
Summarize & Analyze
Group and aggregate to spot trends and patterns.
GROUP BY, SUM(), AVG() โ 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 โWhatโs working?โโyouโve got the answers.
And remember: write smart queries, not lazy ones. Say no to SELECT * unless you really mean it!
Hit โฅ๏ธ if you want me to share more real-world examples to make data analytics easier to understand!
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
โค8
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)
- Descriptive 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
๐จโ๐ป 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://t.iss.one/PythonInterviews
5. https://www.w3schools.com/python/python_exercises.asp
6. https://t.iss.one/pythonfreebootcamp/134
7. https://t.iss.one/pythonanalyst
8. https://pythonbasics.org/exercises/
9. https://t.iss.one/pythondevelopersindia/300
10. https://www.geeksforgeeks.org/python-programming-language/learn-python-tutorial
11. https://t.iss.one/pythonspecialist/33
Join @free4unow_backup for more free resources
ENJOY LEARNING ๐๐
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)
- Descriptive 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
๐จโ๐ป 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://t.iss.one/PythonInterviews
5. https://www.w3schools.com/python/python_exercises.asp
6. https://t.iss.one/pythonfreebootcamp/134
7. https://t.iss.one/pythonanalyst
8. https://pythonbasics.org/exercises/
9. https://t.iss.one/pythondevelopersindia/300
10. https://www.geeksforgeeks.org/python-programming-language/learn-python-tutorial
11. https://t.iss.one/pythonspecialist/33
Join @free4unow_backup for more free resources
ENJOY LEARNING ๐๐
โค10
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:
โ Accessing and managing data efficiently
โ Writing queries to extract insights
โ 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. ๐
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 skillโitโs the foundation of your data career. ๐
Here you can find essential SQL Interview Resources๐
https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v
Like this post if you need more ๐โค๏ธ
Hope it helps :)
โ Accessing and managing data efficiently
โ Writing queries to extract insights
โ 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. ๐
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 skillโitโs the foundation of your data career. ๐
Here you can find essential SQL Interview Resources๐
https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v
Like this post if you need more ๐โค๏ธ
Hope it helps :)
โค7
Want to become a Data Scientist?
Hereโs 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 ๐๐
#datascience
Hereโs 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 ๐๐
#datascience
โค10
Essential Python and SQL topics for data analysts ๐๐
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
- Descriptive Statistics
- Hypothesis Testing
- Regression Analysis
5. Machine Learning
- Scikit-Learn for machine learning models
- Model Building, Training, and Evaluation
- Feature Engineering and Selection
6. Time Series Analysis
- Handling Time Series Data
- Time Series Forecasting
- Anomaly Detection
7. Python Fundamentals
- Control Flow (if statements, loops)
- Functions and Modular Code
- Exception Handling
- File
SQL Topics:
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!
Python Resources - https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L
SQL Resources - https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v
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
- Descriptive Statistics
- Hypothesis Testing
- Regression Analysis
5. Machine Learning
- Scikit-Learn for machine learning models
- Model Building, Training, and Evaluation
- Feature Engineering and Selection
6. Time Series Analysis
- Handling Time Series Data
- Time Series Forecasting
- Anomaly Detection
7. Python Fundamentals
- Control Flow (if statements, loops)
- Functions and Modular Code
- Exception Handling
- File
SQL Topics:
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!
Python Resources - https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L
SQL Resources - https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v
Hope it helps :)
โค4
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:
Descriptive 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://t.iss.one/excel_data
Hope this helps you ๐
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:
Descriptive 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://t.iss.one/excel_data
Hope this helps you ๐
โค5๐ฅฐ1๐1
Hey guys,
I have curated some best WhatsApp Channels for free education ๐๐
Free Udemy Courses with Certificate: https://whatsapp.com/channel/0029VbB8ROL4inogeP9o8E1l
SQL Programming: https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v
Python for Data Science: https://whatsapp.com/channel/0029VauCKUI6WaKrgTHrRD0i
Power BI: https://whatsapp.com/channel/0029Vai1xKf1dAvuk6s1v22c
Python Programming: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L
Tableau: https://whatsapp.com/channel/0029VasYW1V5kg6z4EHOHG1t
Excel: https://whatsapp.com/channel/0029VaifY548qIzv0u1AHz3i
Remote Jobs: https://whatsapp.com/channel/0029Vb1RrFuC1Fu3E0aiac2E
Frontend Development: https://whatsapp.com/channel/0029VaxfCpv2v1IqQjv6Ke0r
Software Engineer Jobs: https://whatsapp.com/channel/0029VatL9a22kNFtPtLApJ2L
Machine Learning: https://whatsapp.com/channel/0029VawtYcJ1iUxcMQoEuP0O
English Speaking & Communication Skills: https://whatsapp.com/channel/0029VaiaucV4NVik7Fx6HN2n
GitHub: https://whatsapp.com/channel/0029Vawixh9IXnlk7VfY6w43
Artificial Intelligence: https://whatsapp.com/channel/0029VaoePz73bbV94yTh6V2E
Python Projects: https://whatsapp.com/channel/0029Vau5fZECsU9HJFLacm2a
Data Science Projects: https://whatsapp.com/channel/0029VaxbzNFCxoAmYgiGTL3Z
Coding Projects: https://whatsapp.com/channel/0029VazkxJ62UPB7OQhBE502
Data Engineers: https://whatsapp.com/channel/0029Vaovs0ZKbYMKXvKRYi3C
AI Tools: https://whatsapp.com/channel/0029VaojSv9LCoX0gBZUxX3B
Javascript: https://whatsapp.com/channel/0029VavR9OxLtOjJTXrZNi32
Cybersecurity: https://whatsapp.com/channel/0029VancSnGG8l5KQYOOyL1T
Health & Fitness: https://whatsapp.com/channel/0029VazUhie6RGJIYNbHCt3B
Business & Startup Ideas: https://whatsapp.com/channel/0029Vb2N3YA2phHJfsMrHZ0b
Personality Development & Motivation: https://whatsapp.com/channel/0029VavaBiTDeON0O54Bca0q
Web Development Jobs: https://whatsapp.com/channel/0029Vb1raTiDjiOias5ARu2p
Python & AI Jobs: https://whatsapp.com/channel/0029VaxtmHsLikgJ2VtGbu1R
Generative AI: https://whatsapp.com/channel/0029VazaRBY2UPBNj1aCrN0U
Data Science Jobs: https://whatsapp.com/channel/0029VaxTMmQADTOA746w7U2P
ChatGPT: https://whatsapp.com/channel/0029VapThS265yDAfwe97c23
Do react with โฅ๏ธ if you need more free resources
ENJOY LEARNING ๐๐
I have curated some best WhatsApp Channels for free education ๐๐
Free Udemy Courses with Certificate: https://whatsapp.com/channel/0029VbB8ROL4inogeP9o8E1l
SQL Programming: https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v
Python for Data Science: https://whatsapp.com/channel/0029VauCKUI6WaKrgTHrRD0i
Power BI: https://whatsapp.com/channel/0029Vai1xKf1dAvuk6s1v22c
Python Programming: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L
Tableau: https://whatsapp.com/channel/0029VasYW1V5kg6z4EHOHG1t
Excel: https://whatsapp.com/channel/0029VaifY548qIzv0u1AHz3i
Remote Jobs: https://whatsapp.com/channel/0029Vb1RrFuC1Fu3E0aiac2E
Frontend Development: https://whatsapp.com/channel/0029VaxfCpv2v1IqQjv6Ke0r
Software Engineer Jobs: https://whatsapp.com/channel/0029VatL9a22kNFtPtLApJ2L
Machine Learning: https://whatsapp.com/channel/0029VawtYcJ1iUxcMQoEuP0O
English Speaking & Communication Skills: https://whatsapp.com/channel/0029VaiaucV4NVik7Fx6HN2n
GitHub: https://whatsapp.com/channel/0029Vawixh9IXnlk7VfY6w43
Artificial Intelligence: https://whatsapp.com/channel/0029VaoePz73bbV94yTh6V2E
Python Projects: https://whatsapp.com/channel/0029Vau5fZECsU9HJFLacm2a
Data Science Projects: https://whatsapp.com/channel/0029VaxbzNFCxoAmYgiGTL3Z
Coding Projects: https://whatsapp.com/channel/0029VazkxJ62UPB7OQhBE502
Data Engineers: https://whatsapp.com/channel/0029Vaovs0ZKbYMKXvKRYi3C
AI Tools: https://whatsapp.com/channel/0029VaojSv9LCoX0gBZUxX3B
Javascript: https://whatsapp.com/channel/0029VavR9OxLtOjJTXrZNi32
Cybersecurity: https://whatsapp.com/channel/0029VancSnGG8l5KQYOOyL1T
Health & Fitness: https://whatsapp.com/channel/0029VazUhie6RGJIYNbHCt3B
Business & Startup Ideas: https://whatsapp.com/channel/0029Vb2N3YA2phHJfsMrHZ0b
Personality Development & Motivation: https://whatsapp.com/channel/0029VavaBiTDeON0O54Bca0q
Web Development Jobs: https://whatsapp.com/channel/0029Vb1raTiDjiOias5ARu2p
Python & AI Jobs: https://whatsapp.com/channel/0029VaxtmHsLikgJ2VtGbu1R
Generative AI: https://whatsapp.com/channel/0029VazaRBY2UPBNj1aCrN0U
Data Science Jobs: https://whatsapp.com/channel/0029VaxTMmQADTOA746w7U2P
ChatGPT: https://whatsapp.com/channel/0029VapThS265yDAfwe97c23
Do react with โฅ๏ธ if you need more free resources
ENJOY LEARNING ๐๐
โค9๐1
SQL can be simpleโif you learn it the smart way..
If youโre aiming to become a data analyst, mastering SQL is non-negotiable.
Hereโs 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! ๐ช
Here you can find essential SQL Interview Resources๐
https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v
Like this post if you need more ๐โค๏ธ
Hope it helps :)
If youโre aiming to become a data analyst, mastering SQL is non-negotiable.
Hereโs 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! ๐ช
Here you can find essential SQL Interview Resources๐
https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v
Like this post if you need more ๐โค๏ธ
Hope it helps :)
โค10
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, โฆ): 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, โฆ): Returns the first non-null value.
Like for more free Cheatsheets โค๏ธ
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
#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, โฆ): 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, โฆ): Returns the first non-null value.
Like for more free Cheatsheets โค๏ธ
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
#dataanalytics
โค14
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 โStevenโ;
With this command, we will be able to extract all the records where the first name is like โStevenโ.
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 โค๏ธ for more
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 โStevenโ;
With this command, we will be able to extract all the records where the first name is like โStevenโ.
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 โค๏ธ for more
โค8๐2
๐ Excel vs SQL vs Python (Pandas):
1๏ธโฃ Filtering Data
โณ Excel: =FILTER(A2:D100, B2:B100>50) (Excel 365 users)
โณ SQL: SELECT * FROM table WHERE column > 50;
โณ Python: df_filtered = df[df['column'] > 50]
2๏ธโฃ Sorting Data
โณ Excel: Data โ Sort (or =SORT(A2:A100, 1, TRUE))
โณ SQL: SELECT * FROM table ORDER BY column ASC;
โณ Python: df_sorted = df.sort_values(by="column")
3๏ธโฃ Counting Rows
โณ Excel: =COUNTA(A:A)
โณ SQL: SELECT COUNT(*) FROM table;
โณ Python: row_count = len(df)
4๏ธโฃ Removing Duplicates
โณ Excel: Data โ Remove Duplicates
โณ SQL: SELECT DISTINCT * FROM table;
โณ Python: df_unique = df.drop_duplicates()
5๏ธโฃ Joining Tables
โณ Excel: Power Query โ Merge Queries (or VLOOKUP/XLOOKUP)
โณ SQL: SELECT * FROM table1 JOIN table2 ON table1.id = table2.id;
โณ Python: df_merged = pd.merge(df1, df2, on="id")
6๏ธโฃ Ranking Data
โณ Excel: =RANK.EQ(A2, $A$2:$A$100)
โณ SQL: SELECT column, RANK() OVER (ORDER BY column DESC) AS rank FROM table;
โณ Python: df["rank"] = df["column"].rank(method="min", ascending=False)
7๏ธโฃ Moving Average Calculation
โณ Excel: =AVERAGE(B2:B4) (manually for rolling window)
โณ SQL: SELECT date, AVG(value) OVER (ORDER BY date ROWS BETWEEN 2 PRECEDING AND CURRENT ROW) AS moving_avg FROM table;
โณ Python: df["moving_avg"] = df["value"].rolling(window=3).mean()
8๏ธโฃ Running Total
โณ Excel: =SUM($B$2:B2) (drag down)
โณ SQL: SELECT date, SUM(value) OVER (ORDER BY date) AS running_total FROM table;
โณ Python: df["running_total"] = df["value"].cumsum()
1๏ธโฃ Filtering Data
โณ Excel: =FILTER(A2:D100, B2:B100>50) (Excel 365 users)
โณ SQL: SELECT * FROM table WHERE column > 50;
โณ Python: df_filtered = df[df['column'] > 50]
2๏ธโฃ Sorting Data
โณ Excel: Data โ Sort (or =SORT(A2:A100, 1, TRUE))
โณ SQL: SELECT * FROM table ORDER BY column ASC;
โณ Python: df_sorted = df.sort_values(by="column")
3๏ธโฃ Counting Rows
โณ Excel: =COUNTA(A:A)
โณ SQL: SELECT COUNT(*) FROM table;
โณ Python: row_count = len(df)
4๏ธโฃ Removing Duplicates
โณ Excel: Data โ Remove Duplicates
โณ SQL: SELECT DISTINCT * FROM table;
โณ Python: df_unique = df.drop_duplicates()
5๏ธโฃ Joining Tables
โณ Excel: Power Query โ Merge Queries (or VLOOKUP/XLOOKUP)
โณ SQL: SELECT * FROM table1 JOIN table2 ON table1.id = table2.id;
โณ Python: df_merged = pd.merge(df1, df2, on="id")
6๏ธโฃ Ranking Data
โณ Excel: =RANK.EQ(A2, $A$2:$A$100)
โณ SQL: SELECT column, RANK() OVER (ORDER BY column DESC) AS rank FROM table;
โณ Python: df["rank"] = df["column"].rank(method="min", ascending=False)
7๏ธโฃ Moving Average Calculation
โณ Excel: =AVERAGE(B2:B4) (manually for rolling window)
โณ SQL: SELECT date, AVG(value) OVER (ORDER BY date ROWS BETWEEN 2 PRECEDING AND CURRENT ROW) AS moving_avg FROM table;
โณ Python: df["moving_avg"] = df["value"].rolling(window=3).mean()
8๏ธโฃ Running Total
โณ Excel: =SUM($B$2:B2) (drag down)
โณ SQL: SELECT date, SUM(value) OVER (ORDER BY date) AS running_total FROM table;
โณ Python: df["running_total"] = df["value"].cumsum()
โค8๐1