How you can learn Data Analytics in 28 days:
Week 1: Excel
โข Learn functions (VLOOKUP, Pivot Tables)
โข Clean and format data
โข Analyze trends
Week 2: SQL
โข Learn SELECT, WHERE, JOIN
โข Query real datasets
โข Aggregate and filter data
Week 3: Power BI/Tableau
โข Build dashboards
โข Create data visualizations
โข Tell stories with data
Week 4: Real-World Project
โข Analyze a data
โข Share insights
โข Build a portfolio
One skill at a time โ Real progress in a month! Start today
Week 1: Excel
โข Learn functions (VLOOKUP, Pivot Tables)
โข Clean and format data
โข Analyze trends
Week 2: SQL
โข Learn SELECT, WHERE, JOIN
โข Query real datasets
โข Aggregate and filter data
Week 3: Power BI/Tableau
โข Build dashboards
โข Create data visualizations
โข Tell stories with data
Week 4: Real-World Project
โข Analyze a data
โข Share insights
โข Build a portfolio
One skill at a time โ Real progress in a month! Start today
โค18๐2
Hey guys,
Today, letโs talk about SQL conceptual questions that are often asked in data analyst interviews. These questions test not only your technical skills but also your conceptual understanding of SQL and its real-world applications.
1. What is the difference between SQL and NoSQL?
- SQL (Structured Query Language) is a relational database management system, meaning it uses tables (rows and columns) to store data.
- NoSQL databases, on the other hand, handle unstructured data and donโt rely on a schema, making them more flexible in terms of data storage and retrieval.
- Interview Tip: Don't just memorize definitions. Be prepared to explain scenarios where youโd use SQL over NoSQL, and vice versa.
2. What is the difference between INNER JOIN and OUTER JOIN?
- An INNER JOIN returns records that have matching values in both tables.
- An OUTER JOIN returns all records from one table and the matched records from the second table. If there's no match, NULL values are returned.
3. How do you optimize a SQL query for better performance?
- Indexing: Create indexes on columns used frequently in WHERE, JOIN, or GROUP BY clauses.
- Query optimization: Use appropriate WHERE clauses to reduce the data set and avoid unnecessary calculations.
- Avoid SELECT *: Always specify the columns you need to reduce the amount of data retrieved.
- Limit results: If you only need a subset of the data, use the LIMIT clause.
4. What are the different types of SQL constraints?
Constraints are used to enforce rules on data in a table. They ensure the accuracy and reliability of the data. The most common types are:
- PRIMARY KEY: Ensures each record is unique and not null.
- FOREIGN KEY: Enforces a relationship between two tables.
- UNIQUE: Ensures all values in a column are unique.
- NOT NULL: Prevents NULL values from being entered into a column.
- CHECK: Ensures a column's values meet a specific condition.
5. What is normalization? What are the different normal forms?
Normalization is the process of organizing data to reduce redundancy and improve data integrity. Hereโs a quick overview of normal forms:
- 1NF (First Normal Form): Ensures that all values in a table are atomic (indivisible).
- 2NF (Second Normal Form): Ensures that the table is in 1NF and that all non-key columns are fully dependent on the primary key.
- 3NF (Third Normal Form): Ensures that the table is in 2NF and all columns are independent of each other except for the primary key.
6. What is a subquery?
A subquery is a query within another query. It's used to perform operations that need intermediate results before generating the final query.
Example:
In this case, the subquery calculates the average salary, and the outer query selects employees whose salary is greater than the average.
7. What is the difference between a UNION and a UNION ALL?
- UNION combines the result sets of two SELECT statements and removes duplicates.
- UNION ALL combines the result sets and includes duplicates.
8. What is the difference between WHERE and HAVING clause?
- WHERE filters rows before any groupings are made. Itโs used with SELECT, INSERT, UPDATE, or DELETE statements.
- HAVING filters groups after the GROUP BY clause.
9. How would you handle NULL values in SQL?
NULL values can represent missing or unknown data. Hereโs how to manage them:
- Use IS NULL or IS NOT NULL in WHERE clauses to filter null values.
- Use COALESCE() or IFNULL() to replace NULL values with default ones.
Example:
10. What is the purpose of the GROUP BY clause?
The GROUP BY clause groups rows with the same values into summary rows. Itโs often used with aggregate functions like COUNT, SUM, AVG, etc.
Example:
Here you can find SQL Interview Resources๐
https://t.iss.one/DataSimplifier
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
Today, letโs talk about SQL conceptual questions that are often asked in data analyst interviews. These questions test not only your technical skills but also your conceptual understanding of SQL and its real-world applications.
1. What is the difference between SQL and NoSQL?
- SQL (Structured Query Language) is a relational database management system, meaning it uses tables (rows and columns) to store data.
- NoSQL databases, on the other hand, handle unstructured data and donโt rely on a schema, making them more flexible in terms of data storage and retrieval.
- Interview Tip: Don't just memorize definitions. Be prepared to explain scenarios where youโd use SQL over NoSQL, and vice versa.
2. What is the difference between INNER JOIN and OUTER JOIN?
- An INNER JOIN returns records that have matching values in both tables.
- An OUTER JOIN returns all records from one table and the matched records from the second table. If there's no match, NULL values are returned.
3. How do you optimize a SQL query for better performance?
- Indexing: Create indexes on columns used frequently in WHERE, JOIN, or GROUP BY clauses.
- Query optimization: Use appropriate WHERE clauses to reduce the data set and avoid unnecessary calculations.
- Avoid SELECT *: Always specify the columns you need to reduce the amount of data retrieved.
- Limit results: If you only need a subset of the data, use the LIMIT clause.
4. What are the different types of SQL constraints?
Constraints are used to enforce rules on data in a table. They ensure the accuracy and reliability of the data. The most common types are:
- PRIMARY KEY: Ensures each record is unique and not null.
- FOREIGN KEY: Enforces a relationship between two tables.
- UNIQUE: Ensures all values in a column are unique.
- NOT NULL: Prevents NULL values from being entered into a column.
- CHECK: Ensures a column's values meet a specific condition.
5. What is normalization? What are the different normal forms?
Normalization is the process of organizing data to reduce redundancy and improve data integrity. Hereโs a quick overview of normal forms:
- 1NF (First Normal Form): Ensures that all values in a table are atomic (indivisible).
- 2NF (Second Normal Form): Ensures that the table is in 1NF and that all non-key columns are fully dependent on the primary key.
- 3NF (Third Normal Form): Ensures that the table is in 2NF and all columns are independent of each other except for the primary key.
6. What is a subquery?
A subquery is a query within another query. It's used to perform operations that need intermediate results before generating the final query.
Example:
SELECT employee_id, name
FROM employees
WHERE salary > (SELECT AVG(salary) FROM employees);
In this case, the subquery calculates the average salary, and the outer query selects employees whose salary is greater than the average.
7. What is the difference between a UNION and a UNION ALL?
- UNION combines the result sets of two SELECT statements and removes duplicates.
- UNION ALL combines the result sets and includes duplicates.
8. What is the difference between WHERE and HAVING clause?
- WHERE filters rows before any groupings are made. Itโs used with SELECT, INSERT, UPDATE, or DELETE statements.
- HAVING filters groups after the GROUP BY clause.
9. How would you handle NULL values in SQL?
NULL values can represent missing or unknown data. Hereโs how to manage them:
- Use IS NULL or IS NOT NULL in WHERE clauses to filter null values.
- Use COALESCE() or IFNULL() to replace NULL values with default ones.
Example:
SELECT name, COALESCE(age, 0) AS age
FROM employees;
10. What is the purpose of the GROUP BY clause?
The GROUP BY clause groups rows with the same values into summary rows. Itโs often used with aggregate functions like COUNT, SUM, AVG, etc.
Example:
SELECT department, COUNT(*)
FROM employees
GROUP BY department;
Here you can find SQL Interview Resources๐
https://t.iss.one/DataSimplifier
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
โค11
Everyone thinks being a great data analyst is about advanced algorithms and complex dashboards.
But real data excellence comes from methodical habits that build trust and deliver real insights.
Here are 20 signs of a truly effective analyst ๐
โ They document every step of their analysis
โ Clear notes make their work reproducible and trustworthy.
โ They check data quality before the analysis begins
โ Garbage in = garbage out. Always validate first.
โ They use version control religiously
โ Every code change is tracked. Nothing gets lost.
โ They explore data thoroughly before diving in
โ Understanding context prevents costly misinterpretations.
โ They create automated scripts for repetitive tasks
โ Efficiency isnโt a luxuryโitโs a necessity.
โ They maintain a reusable code library
โ Smart analysts never solve the same problem twice.
โ They test assumptions with multiple validation methods
โ One test isnโt enough; they triangulate confidence.
โ They organize project files logically
โ Their work is navigable by anyone, not just themselves.
โ They seek peer reviews on critical work
โ Fresh eyes catch blind spots.
โ They continuously absorb industry knowledge
โ Learning never stops. Trends change too quickly.
โ They prioritize business-impacting projects
โ Every analysis must drive real decisions.
โ They explain complex findings simply
โ Technical brilliance is useless without clarity.
โ They write readable, well-commented code
โ Their work is accessible to others, long after they're gone.
โ They maintain robust backup systems
โ Data loss is never an option.
โ They learn from analytical mistakes
โ Errors become stepping stones, not roadblocks.
โ They build strong stakeholder relationships
โ Data is only valuable when people use it.
โ They break complex projects into manageable chunks
โ Progress happens through disciplined, incremental work.
โ They handle sensitive data with proper security
โ Compliance isnโt optionalโitโs foundational.
โ They create visualizations that tell clear stories
โ A chart without a narrative is just decoration.
โ They actively seek evidence against their conclusions
โ Confirmation bias is their biggest enemy.
The best analysts arenโt the ones with the most toolsโtheyโre the ones with the most rigorous practices.
But real data excellence comes from methodical habits that build trust and deliver real insights.
Here are 20 signs of a truly effective analyst ๐
โ They document every step of their analysis
โ Clear notes make their work reproducible and trustworthy.
โ They check data quality before the analysis begins
โ Garbage in = garbage out. Always validate first.
โ They use version control religiously
โ Every code change is tracked. Nothing gets lost.
โ They explore data thoroughly before diving in
โ Understanding context prevents costly misinterpretations.
โ They create automated scripts for repetitive tasks
โ Efficiency isnโt a luxuryโitโs a necessity.
โ They maintain a reusable code library
โ Smart analysts never solve the same problem twice.
โ They test assumptions with multiple validation methods
โ One test isnโt enough; they triangulate confidence.
โ They organize project files logically
โ Their work is navigable by anyone, not just themselves.
โ They seek peer reviews on critical work
โ Fresh eyes catch blind spots.
โ They continuously absorb industry knowledge
โ Learning never stops. Trends change too quickly.
โ They prioritize business-impacting projects
โ Every analysis must drive real decisions.
โ They explain complex findings simply
โ Technical brilliance is useless without clarity.
โ They write readable, well-commented code
โ Their work is accessible to others, long after they're gone.
โ They maintain robust backup systems
โ Data loss is never an option.
โ They learn from analytical mistakes
โ Errors become stepping stones, not roadblocks.
โ They build strong stakeholder relationships
โ Data is only valuable when people use it.
โ They break complex projects into manageable chunks
โ Progress happens through disciplined, incremental work.
โ They handle sensitive data with proper security
โ Compliance isnโt optionalโitโs foundational.
โ They create visualizations that tell clear stories
โ A chart without a narrative is just decoration.
โ They actively seek evidence against their conclusions
โ Confirmation bias is their biggest enemy.
The best analysts arenโt the ones with the most toolsโtheyโre the ones with the most rigorous practices.
โค8
Hey guys,
Today, letโs talk about SQL conceptual questions that are often asked in data analyst interviews. These questions test not only your technical skills but also your conceptual understanding of SQL and its real-world applications.
1. What is the difference between SQL and NoSQL?
- SQL (Structured Query Language) is a relational database management system, meaning it uses tables (rows and columns) to store data.
- NoSQL databases, on the other hand, handle unstructured data and donโt rely on a schema, making them more flexible in terms of data storage and retrieval.
- Interview Tip: Don't just memorize definitions. Be prepared to explain scenarios where youโd use SQL over NoSQL, and vice versa.
2. What is the difference between INNER JOIN and OUTER JOIN?
- An INNER JOIN returns records that have matching values in both tables.
- An OUTER JOIN returns all records from one table and the matched records from the second table. If there's no match, NULL values are returned.
3. How do you optimize a SQL query for better performance?
- Indexing: Create indexes on columns used frequently in WHERE, JOIN, or GROUP BY clauses.
- Query optimization: Use appropriate WHERE clauses to reduce the data set and avoid unnecessary calculations.
- Avoid SELECT *: Always specify the columns you need to reduce the amount of data retrieved.
- Limit results: If you only need a subset of the data, use the LIMIT clause.
4. What are the different types of SQL constraints?
Constraints are used to enforce rules on data in a table. They ensure the accuracy and reliability of the data. The most common types are:
- PRIMARY KEY: Ensures each record is unique and not null.
- FOREIGN KEY: Enforces a relationship between two tables.
- UNIQUE: Ensures all values in a column are unique.
- NOT NULL: Prevents NULL values from being entered into a column.
- CHECK: Ensures a column's values meet a specific condition.
5. What is normalization? What are the different normal forms?
Normalization is the process of organizing data to reduce redundancy and improve data integrity. Hereโs a quick overview of normal forms:
- 1NF (First Normal Form): Ensures that all values in a table are atomic (indivisible).
- 2NF (Second Normal Form): Ensures that the table is in 1NF and that all non-key columns are fully dependent on the primary key.
- 3NF (Third Normal Form): Ensures that the table is in 2NF and all columns are independent of each other except for the primary key.
6. What is a subquery?
A subquery is a query within another query. It's used to perform operations that need intermediate results before generating the final query.
Example:
In this case, the subquery calculates the average salary, and the outer query selects employees whose salary is greater than the average.
7. What is the difference between a UNION and a UNION ALL?
- UNION combines the result sets of two SELECT statements and removes duplicates.
- UNION ALL combines the result sets and includes duplicates.
8. What is the difference between WHERE and HAVING clause?
- WHERE filters rows before any groupings are made. Itโs used with SELECT, INSERT, UPDATE, or DELETE statements.
- HAVING filters groups after the GROUP BY clause.
9. How would you handle NULL values in SQL?
NULL values can represent missing or unknown data. Hereโs how to manage them:
- Use IS NULL or IS NOT NULL in WHERE clauses to filter null values.
- Use COALESCE() or IFNULL() to replace NULL values with default ones.
Example:
10. What is the purpose of the GROUP BY clause?
The GROUP BY clause groups rows with the same values into summary rows. Itโs often used with aggregate functions like COUNT, SUM, AVG, etc.
Example:
Here you can find SQL Interview Resources๐
https://t.iss.one/DataSimplifier
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
Today, letโs talk about SQL conceptual questions that are often asked in data analyst interviews. These questions test not only your technical skills but also your conceptual understanding of SQL and its real-world applications.
1. What is the difference between SQL and NoSQL?
- SQL (Structured Query Language) is a relational database management system, meaning it uses tables (rows and columns) to store data.
- NoSQL databases, on the other hand, handle unstructured data and donโt rely on a schema, making them more flexible in terms of data storage and retrieval.
- Interview Tip: Don't just memorize definitions. Be prepared to explain scenarios where youโd use SQL over NoSQL, and vice versa.
2. What is the difference between INNER JOIN and OUTER JOIN?
- An INNER JOIN returns records that have matching values in both tables.
- An OUTER JOIN returns all records from one table and the matched records from the second table. If there's no match, NULL values are returned.
3. How do you optimize a SQL query for better performance?
- Indexing: Create indexes on columns used frequently in WHERE, JOIN, or GROUP BY clauses.
- Query optimization: Use appropriate WHERE clauses to reduce the data set and avoid unnecessary calculations.
- Avoid SELECT *: Always specify the columns you need to reduce the amount of data retrieved.
- Limit results: If you only need a subset of the data, use the LIMIT clause.
4. What are the different types of SQL constraints?
Constraints are used to enforce rules on data in a table. They ensure the accuracy and reliability of the data. The most common types are:
- PRIMARY KEY: Ensures each record is unique and not null.
- FOREIGN KEY: Enforces a relationship between two tables.
- UNIQUE: Ensures all values in a column are unique.
- NOT NULL: Prevents NULL values from being entered into a column.
- CHECK: Ensures a column's values meet a specific condition.
5. What is normalization? What are the different normal forms?
Normalization is the process of organizing data to reduce redundancy and improve data integrity. Hereโs a quick overview of normal forms:
- 1NF (First Normal Form): Ensures that all values in a table are atomic (indivisible).
- 2NF (Second Normal Form): Ensures that the table is in 1NF and that all non-key columns are fully dependent on the primary key.
- 3NF (Third Normal Form): Ensures that the table is in 2NF and all columns are independent of each other except for the primary key.
6. What is a subquery?
A subquery is a query within another query. It's used to perform operations that need intermediate results before generating the final query.
Example:
SELECT employee_id, name
FROM employees
WHERE salary > (SELECT AVG(salary) FROM employees);
In this case, the subquery calculates the average salary, and the outer query selects employees whose salary is greater than the average.
7. What is the difference between a UNION and a UNION ALL?
- UNION combines the result sets of two SELECT statements and removes duplicates.
- UNION ALL combines the result sets and includes duplicates.
8. What is the difference between WHERE and HAVING clause?
- WHERE filters rows before any groupings are made. Itโs used with SELECT, INSERT, UPDATE, or DELETE statements.
- HAVING filters groups after the GROUP BY clause.
9. How would you handle NULL values in SQL?
NULL values can represent missing or unknown data. Hereโs how to manage them:
- Use IS NULL or IS NOT NULL in WHERE clauses to filter null values.
- Use COALESCE() or IFNULL() to replace NULL values with default ones.
Example:
SELECT name, COALESCE(age, 0) AS age
FROM employees;
10. What is the purpose of the GROUP BY clause?
The GROUP BY clause groups rows with the same values into summary rows. Itโs often used with aggregate functions like COUNT, SUM, AVG, etc.
Example:
SELECT department, COUNT(*)
FROM employees
GROUP BY department;
Here you can find SQL Interview Resources๐
https://t.iss.one/DataSimplifier
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
โค5
30 days roadmap to learn Python for Data Analysis๐
Days 1-5: Introduction to Python
1. Day 1: Install Python and a code editor (e.g., Anaconda, Jupyter Notebook).
2. Day 2-5: Learn Python basics (variables, data types, and basic operations).
Days 6-10: Control Flow and Functions
6. Day 6-8: Study control flow (if statements, loops).
9. Day 9-10: Learn about functions and modules in Python.
Days 11-15: Data Structures
11. Day 11-12: Explore lists, tuples, and dictionaries.
13. Day 13-15: Study sets and string manipulation.
Days 16-20: Libraries for Data Analysis
16. Day 16-17: Get familiar with NumPy for numerical operations.
18. Day 18-19: Dive into Pandas for data manipulation.
20. Day 20: Basic data visualization with Matplotlib.
Days 21-25: Data Cleaning and Analysis
21. Day 21-22: Data cleaning and preprocessing using Pandas.
23. Day 23-25: Exploratory data analysis (EDA) techniques.
Days 26-30: Advanced Topics
26. Day 26-27: Introduction to data visualization with Seaborn.
27. Day 28-29: Introduction to machine learning with Scikit-Learn.
30. Day 30: Create a small data analysis project.
Use platforms like Kaggle to find datasets for projects & GeekforGeeks to practice coding problems.
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
Days 1-5: Introduction to Python
1. Day 1: Install Python and a code editor (e.g., Anaconda, Jupyter Notebook).
2. Day 2-5: Learn Python basics (variables, data types, and basic operations).
Days 6-10: Control Flow and Functions
6. Day 6-8: Study control flow (if statements, loops).
9. Day 9-10: Learn about functions and modules in Python.
Days 11-15: Data Structures
11. Day 11-12: Explore lists, tuples, and dictionaries.
13. Day 13-15: Study sets and string manipulation.
Days 16-20: Libraries for Data Analysis
16. Day 16-17: Get familiar with NumPy for numerical operations.
18. Day 18-19: Dive into Pandas for data manipulation.
20. Day 20: Basic data visualization with Matplotlib.
Days 21-25: Data Cleaning and Analysis
21. Day 21-22: Data cleaning and preprocessing using Pandas.
23. Day 23-25: Exploratory data analysis (EDA) techniques.
Days 26-30: Advanced Topics
26. Day 26-27: Introduction to data visualization with Seaborn.
27. Day 28-29: Introduction to machine learning with Scikit-Learn.
30. Day 30: Create a small data analysis project.
Use platforms like Kaggle to find datasets for projects & GeekforGeeks to practice coding problems.
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
โค5๐ฅ4๐1
Preparing for a SQL interview?
Focus on mastering these essential topics:
1. Joins: Get comfortable with inner, left, right, and outer joins.
Knowing when to use what kind of join is important!
2. Window Functions: Understand when to use
ROW_NUMBER, RANK(), DENSE_RANK(), LAG, and LEAD for complex analytical queries.
3. Query Execution Order: Know the sequence from FROM to
ORDER BY. This is crucial for writing efficient, error-free queries.
4. Common Table Expressions (CTEs): Use CTEs to simplify and structure complex queries for better readability.
5. Aggregations & Window Functions: Combine aggregate functions with window functions for in-depth data analysis.
6. Subqueries: Learn how to use subqueries effectively within main SQL statements for complex data manipulations.
7. Handling NULLs: Be adept at managing NULL values to ensure accurate data processing and avoid potential pitfalls.
8. Indexing: Understand how proper indexing can significantly boost query performance.
9. GROUP BY & HAVING: Master grouping data and filtering groups with HAVING to refine your query results.
10. String Manipulation Functions: Get familiar with string functions like CONCAT, SUBSTRING, and REPLACE to handle text data efficiently.
11. Set Operations: Know how to use UNION, INTERSECT, and EXCEPT to combine or compare result sets.
12. Optimizing Queries: Learn techniques to optimize your queries for performance, especially with large datasets.
If we master/ Practice in these topics we can track any SQL interviews..
Like this post if you need more ๐โค๏ธ
Hope it helps :)
Focus on mastering these essential topics:
1. Joins: Get comfortable with inner, left, right, and outer joins.
Knowing when to use what kind of join is important!
2. Window Functions: Understand when to use
ROW_NUMBER, RANK(), DENSE_RANK(), LAG, and LEAD for complex analytical queries.
3. Query Execution Order: Know the sequence from FROM to
ORDER BY. This is crucial for writing efficient, error-free queries.
4. Common Table Expressions (CTEs): Use CTEs to simplify and structure complex queries for better readability.
5. Aggregations & Window Functions: Combine aggregate functions with window functions for in-depth data analysis.
6. Subqueries: Learn how to use subqueries effectively within main SQL statements for complex data manipulations.
7. Handling NULLs: Be adept at managing NULL values to ensure accurate data processing and avoid potential pitfalls.
8. Indexing: Understand how proper indexing can significantly boost query performance.
9. GROUP BY & HAVING: Master grouping data and filtering groups with HAVING to refine your query results.
10. String Manipulation Functions: Get familiar with string functions like CONCAT, SUBSTRING, and REPLACE to handle text data efficiently.
11. Set Operations: Know how to use UNION, INTERSECT, and EXCEPT to combine or compare result sets.
12. Optimizing Queries: Learn techniques to optimize your queries for performance, especially with large datasets.
If we master/ Practice in these topics we can track any SQL interviews..
Like this post if you need more ๐โค๏ธ
Hope it helps :)
โค11๐2๐ฅ1
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
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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
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