โ
SQL for Data Science ๐๏ธ๐
๐ SQL is one of the most important skills for Data Scientists and Data Analysts.
Almost every company stores data inside databases, and SQL helps retrieve and analyze that data.
๐น 1. What is SQL?
SQL = Structured Query Language
๐ Used to:
โ Store data
โ Retrieve data
โ Filter data
โ Analyze data
๐ฅ 2. Common Database Systems
โ MySQL
โ PostgreSQL
โ SQLite
โ Microsoft SQL Server
๐น 3. Basic SQL Query
โ SELECT Statement
Used to retrieve data from a table.
SELECT * FROM employees;
๐ ** means all columns.
๐น 4. Select Specific Columns
SELECT name, salary FROM employees;
๐น 5. WHERE Clause โญ
Used for filtering data.
SELECT * FROM employees
WHERE salary > 50000;
๐น 6. ORDER BY
Sort data.
SELECT * FROM employees
ORDER BY salary DESC;
โ ASC โ Ascending
โ DESC โ Descending
๐น 7. Aggregate Functions โญ
Used for calculations.
Function: COUNT()
Purpose: Count rows
Function: SUM()
Purpose: Total
Function: AVG()
Purpose: Average
Function: MAX()
Purpose: Highest value
Function: MIN()
Purpose: Lowest value
โ Example
SELECT AVG(salary)
FROM employees;
๐น 8. GROUP BY โญ
Used to group data.
SELECT department, AVG(salary)
FROM employees
GROUP BY department;
๐น 9. Why SQL is Important?
โ Most asked interview skill
โ Used daily by analysts & data scientists
โ Essential for working with databases
๐ฏ Todayโs Goal
โ Learn SELECT queries
โ Filter using WHERE
โ Use aggregate functions
โ Understand GROUP BY
๐ SQL Resources: https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v ๐๏ธ๐ฅ
๐ฌ Tap โค๏ธ for more!
๐ SQL is one of the most important skills for Data Scientists and Data Analysts.
Almost every company stores data inside databases, and SQL helps retrieve and analyze that data.
๐น 1. What is SQL?
SQL = Structured Query Language
๐ Used to:
โ Store data
โ Retrieve data
โ Filter data
โ Analyze data
๐ฅ 2. Common Database Systems
โ MySQL
โ PostgreSQL
โ SQLite
โ Microsoft SQL Server
๐น 3. Basic SQL Query
โ SELECT Statement
Used to retrieve data from a table.
SELECT * FROM employees;
๐ ** means all columns.
๐น 4. Select Specific Columns
SELECT name, salary FROM employees;
๐น 5. WHERE Clause โญ
Used for filtering data.
SELECT * FROM employees
WHERE salary > 50000;
๐น 6. ORDER BY
Sort data.
SELECT * FROM employees
ORDER BY salary DESC;
โ ASC โ Ascending
โ DESC โ Descending
๐น 7. Aggregate Functions โญ
Used for calculations.
Function: COUNT()
Purpose: Count rows
Function: SUM()
Purpose: Total
Function: AVG()
Purpose: Average
Function: MAX()
Purpose: Highest value
Function: MIN()
Purpose: Lowest value
โ Example
SELECT AVG(salary)
FROM employees;
๐น 8. GROUP BY โญ
Used to group data.
SELECT department, AVG(salary)
FROM employees
GROUP BY department;
๐น 9. Why SQL is Important?
โ Most asked interview skill
โ Used daily by analysts & data scientists
โ Essential for working with databases
๐ฏ Todayโs Goal
โ Learn SELECT queries
โ Filter using WHERE
โ Use aggregate functions
โ Understand GROUP BY
๐ SQL Resources: https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v ๐๏ธ๐ฅ
๐ฌ Tap โค๏ธ for more!
โค12๐1
โ
SQL JOINS ๐๏ธ๐
๐ SQL JOINS are used to combine data from multiple tables.
๐น 1. Why JOINS are Needed?
In real databases, data is stored in different tables.
Example:
Employees Table
emp_id: 1
name: Rahul
Salary Table
emp_id: 1
salary: 50000
๐ To combine employee name with salary โ use JOIN.
๐ฅ 2. INNER JOIN โญ
Returns only matching rows from both tables.
โ Most commonly used JOIN.
๐น 3. LEFT JOIN
Returns:
โ All rows from left table
โ Matching rows from right table
๐ Non-matching rows return NULL.
๐น 4. RIGHT JOIN
Returns:
โ All rows from right table
โ Matching rows from left table
๐น 5. FULL JOIN
Returns all rows from both tables.
๐น 6. SELF JOIN โญ
Joining a table with itself.
Used for:
โ Employee-manager relationships
๐น 7. Visual Understanding
โข INNER JOIN โ Matching only
โข LEFT JOIN โ All left + matching right
โข RIGHT JOIN โ All right + matching left
โข FULL JOIN โ Everything
๐น 8. Why JOINS are Important?
โ Used daily in real projects
โ Most asked interview topic
โ Combines business data from multiple tables
๐ฏ Todayโs Goal
โ Understand INNER JOIN
โ Learn LEFT/RIGHT/FULL JOIN
โ Understand real-world use cases
SQL Notes: https://whatsapp.com/channel/0029VbCyzS02ZjCwoShXXc2j
๐ฌ Tap โค๏ธ for more!
๐ SQL JOINS are used to combine data from multiple tables.
๐น 1. Why JOINS are Needed?
In real databases, data is stored in different tables.
Example:
Employees Table
emp_id: 1
name: Rahul
Salary Table
emp_id: 1
salary: 50000
๐ To combine employee name with salary โ use JOIN.
๐ฅ 2. INNER JOIN โญ
Returns only matching rows from both tables.
SELECT employees.name, salary.salary
FROM employees
INNER JOIN salary
ON employees.emp_id = salary.emp_id;
โ Most commonly used JOIN.
๐น 3. LEFT JOIN
Returns:
โ All rows from left table
โ Matching rows from right table
SELECT *
FROM employees
LEFT JOIN salary
ON employees.emp_id = salary.emp_id;
๐ Non-matching rows return NULL.
๐น 4. RIGHT JOIN
Returns:
โ All rows from right table
โ Matching rows from left table
SELECT *
FROM employees
RIGHT JOIN salary
ON employees.emp_id = salary.emp_id;
๐น 5. FULL JOIN
Returns all rows from both tables.
SELECT *
FROM employees
FULL OUTER JOIN salary
ON employees.emp_id = salary.emp_id;
๐น 6. SELF JOIN โญ
Joining a table with itself.
Used for:
โ Employee-manager relationships
๐น 7. Visual Understanding
โข INNER JOIN โ Matching only
โข LEFT JOIN โ All left + matching right
โข RIGHT JOIN โ All right + matching left
โข FULL JOIN โ Everything
๐น 8. Why JOINS are Important?
โ Used daily in real projects
โ Most asked interview topic
โ Combines business data from multiple tables
๐ฏ Todayโs Goal
โ Understand INNER JOIN
โ Learn LEFT/RIGHT/FULL JOIN
โ Understand real-world use cases
SQL Notes: https://whatsapp.com/channel/0029VbCyzS02ZjCwoShXXc2j
๐ฌ Tap โค๏ธ for more!
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DATA ANALYST Interview Questions (0-3 yr) (SQL, Power BI)
๐ Power BI:
Q1: Explain step-by-step how you will create a sales dashboard from scratch.
Q2: Explain how you can optimize a slow Power BI report.
Q3: Explain Any 5 Chart Types and Their Uses in Representing Different Aspects of Data.
๐SQL:
Q1: Explain the difference between RANK(), DENSE_RANK(), and ROW_NUMBER() functions using example.
Q2 โ Q4 use Table: employee (EmpID, ManagerID, JoinDate, Dept, Salary)
Q2: Find the nth highest salary from the Employee table.
Q3: You have an employee table with employee ID and manager ID. Find all employees under a specific manager, including their subordinates at any level.
Q4: Write a query to find the cumulative salary of employees department-wise, who have joined the company in the last 30 days.
Q5: Find the top 2 customers with the highest order amount for each product category, handling ties appropriately. Table: Customer (CustomerID, ProductCategory, OrderAmount)
๐Behavioral:
Q1: Why do you want to become a data analyst and why did you apply to this company?
Q2: Describe a time when you had to manage a difficult task with tight deadlines. How did you handle it?
I have curated best top-notch Data Analytics Resources ๐๐
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Hope this helps you ๐
๐ Power BI:
Q1: Explain step-by-step how you will create a sales dashboard from scratch.
Q2: Explain how you can optimize a slow Power BI report.
Q3: Explain Any 5 Chart Types and Their Uses in Representing Different Aspects of Data.
๐SQL:
Q1: Explain the difference between RANK(), DENSE_RANK(), and ROW_NUMBER() functions using example.
Q2 โ Q4 use Table: employee (EmpID, ManagerID, JoinDate, Dept, Salary)
Q2: Find the nth highest salary from the Employee table.
Q3: You have an employee table with employee ID and manager ID. Find all employees under a specific manager, including their subordinates at any level.
Q4: Write a query to find the cumulative salary of employees department-wise, who have joined the company in the last 30 days.
Q5: Find the top 2 customers with the highest order amount for each product category, handling ties appropriately. Table: Customer (CustomerID, ProductCategory, OrderAmount)
๐Behavioral:
Q1: Why do you want to become a data analyst and why did you apply to this company?
Q2: Describe a time when you had to manage a difficult task with tight deadlines. How did you handle it?
I have curated best top-notch Data Analytics Resources ๐๐
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Hope this helps you ๐
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A-Z of essential data science concepts
A: Algorithm - A set of rules or instructions for solving a problem or completing a task.
B: Big Data - Large and complex datasets that traditional data processing applications are unable to handle efficiently.
C: Classification - A type of machine learning task that involves assigning labels to instances based on their characteristics.
D: Data Mining - The process of discovering patterns and extracting useful information from large datasets.
E: Ensemble Learning - A machine learning technique that combines multiple models to improve predictive performance.
F: Feature Engineering - The process of selecting, extracting, and transforming features from raw data to improve model performance.
G: Gradient Descent - An optimization algorithm used to minimize the error of a model by adjusting its parameters iteratively.
H: Hypothesis Testing - A statistical method used to make inferences about a population based on sample data.
I: Imputation - The process of replacing missing values in a dataset with estimated values.
J: Joint Probability - The probability of the intersection of two or more events occurring simultaneously.
K: K-Means Clustering - A popular unsupervised machine learning algorithm used for clustering data points into groups.
L: Logistic Regression - A statistical model used for binary classification tasks.
M: Machine Learning - A subset of artificial intelligence that enables systems to learn from data and improve performance over time.
N: Neural Network - A computer system inspired by the structure of the human brain, used for various machine learning tasks.
O: Outlier Detection - The process of identifying observations in a dataset that significantly deviate from the rest of the data points.
P: Precision and Recall - Evaluation metrics used to assess the performance of classification models.
Q: Quantitative Analysis - The process of using mathematical and statistical methods to analyze and interpret data.
R: Regression Analysis - A statistical technique used to model the relationship between a dependent variable and one or more independent variables.
S: Support Vector Machine - A supervised machine learning algorithm used for classification and regression tasks.
T: Time Series Analysis - The study of data collected over time to detect patterns, trends, and seasonal variations.
U: Unsupervised Learning - Machine learning techniques used to identify patterns and relationships in data without labeled outcomes.
V: Validation - The process of assessing the performance and generalization of a machine learning model using independent datasets.
W: Weka - A popular open-source software tool used for data mining and machine learning tasks.
X: XGBoost - An optimized implementation of gradient boosting that is widely used for classification and regression tasks.
Y: Yarn - A resource manager used in Apache Hadoop for managing resources across distributed clusters.
Z: Zero-Inflated Model - A statistical model used to analyze data with excess zeros, commonly found in count data.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://t.me/datasciencefun
Like if you need similar content ๐๐
Hope this helps you ๐
A: Algorithm - A set of rules or instructions for solving a problem or completing a task.
B: Big Data - Large and complex datasets that traditional data processing applications are unable to handle efficiently.
C: Classification - A type of machine learning task that involves assigning labels to instances based on their characteristics.
D: Data Mining - The process of discovering patterns and extracting useful information from large datasets.
E: Ensemble Learning - A machine learning technique that combines multiple models to improve predictive performance.
F: Feature Engineering - The process of selecting, extracting, and transforming features from raw data to improve model performance.
G: Gradient Descent - An optimization algorithm used to minimize the error of a model by adjusting its parameters iteratively.
H: Hypothesis Testing - A statistical method used to make inferences about a population based on sample data.
I: Imputation - The process of replacing missing values in a dataset with estimated values.
J: Joint Probability - The probability of the intersection of two or more events occurring simultaneously.
K: K-Means Clustering - A popular unsupervised machine learning algorithm used for clustering data points into groups.
L: Logistic Regression - A statistical model used for binary classification tasks.
M: Machine Learning - A subset of artificial intelligence that enables systems to learn from data and improve performance over time.
N: Neural Network - A computer system inspired by the structure of the human brain, used for various machine learning tasks.
O: Outlier Detection - The process of identifying observations in a dataset that significantly deviate from the rest of the data points.
P: Precision and Recall - Evaluation metrics used to assess the performance of classification models.
Q: Quantitative Analysis - The process of using mathematical and statistical methods to analyze and interpret data.
R: Regression Analysis - A statistical technique used to model the relationship between a dependent variable and one or more independent variables.
S: Support Vector Machine - A supervised machine learning algorithm used for classification and regression tasks.
T: Time Series Analysis - The study of data collected over time to detect patterns, trends, and seasonal variations.
U: Unsupervised Learning - Machine learning techniques used to identify patterns and relationships in data without labeled outcomes.
V: Validation - The process of assessing the performance and generalization of a machine learning model using independent datasets.
W: Weka - A popular open-source software tool used for data mining and machine learning tasks.
X: XGBoost - An optimized implementation of gradient boosting that is widely used for classification and regression tasks.
Y: Yarn - A resource manager used in Apache Hadoop for managing resources across distributed clusters.
Z: Zero-Inflated Model - A statistical model used to analyze data with excess zeros, commonly found in count data.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://t.me/datasciencefun
Like if you need similar content ๐๐
Hope this helps you ๐
โค7๐ฅฐ1๐1
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โ
Advanced SQL (Subqueries & CTEs) ๐๏ธ๐ฅ
๐ Now we move to advanced SQL concepts heavily used in:
โ Data Analysis
โ Reporting
โ Dashboards
โ Interviews
๐น 1. What is a Subquery?
A subquery is a query written inside another query.
๐ Also called:
โ Nested Query
๐ฅ 2. Example of Subquery
๐ Find employees earning above average salary.
SELECT name, salary
FROM employees
WHERE salary > (
SELECT AVG(salary)
FROM employees
);
How it works:
1๏ธโฃ Inner query calculates average salary
2๏ธโฃ Outer query filters employees
๐น 3. Types of Subqueries
โ Single-row subquery
โ Multiple-row subquery
โ Correlated subquery
๐น 4. Correlated Subquery โญ
๐ Inner query depends on outer query.
SELECT e1.name
FROM employees e1
WHERE salary > (
SELECT AVG(salary)
FROM employees e2
WHERE e1.department = e2.department
);
๐ฅ 5. What is a CTE?
CTE = Common Table Expression
๐ Temporary result set used inside a query.
Defined using:
WITH
๐น 6. Example of CTE โญ
WITH avg_salary AS (
SELECT AVG(salary) AS avg_sal
FROM employees
)
SELECT *
FROM employees
WHERE salary > (
SELECT avg_sal FROM avg_salary
);
๐น 7. Why Use CTEs?
โ Makes queries readable
โ Simplifies complex logic
โ Easier debugging
๐น 8. Difference Between Subquery & CTE
Subquery : Nested inside query
CTE : Defined separately
Subquery : Harder to read
CTE : More readable
Subquery : Repeated logic possible
CTE : Reusable
๐น 9. Why This is Important?
โ Frequently asked in interviews
โ Used in dashboards & analytics
โ Important for real-world SQL projects
๐ฏ Todayโs Goal
โ Understand subqueries
โ Learn correlated subqueries
โ Understand CTEs
โ Write cleaner SQL queries
๐ SQL Notes: https://whatsapp.com/channel/0029VbCyzS02ZjCwoShXXc2j
๐ฌ Tap โค๏ธ for more!
๐ Now we move to advanced SQL concepts heavily used in:
โ Data Analysis
โ Reporting
โ Dashboards
โ Interviews
๐น 1. What is a Subquery?
A subquery is a query written inside another query.
๐ Also called:
โ Nested Query
๐ฅ 2. Example of Subquery
๐ Find employees earning above average salary.
SELECT name, salary
FROM employees
WHERE salary > (
SELECT AVG(salary)
FROM employees
);
How it works:
1๏ธโฃ Inner query calculates average salary
2๏ธโฃ Outer query filters employees
๐น 3. Types of Subqueries
โ Single-row subquery
โ Multiple-row subquery
โ Correlated subquery
๐น 4. Correlated Subquery โญ
๐ Inner query depends on outer query.
SELECT e1.name
FROM employees e1
WHERE salary > (
SELECT AVG(salary)
FROM employees e2
WHERE e1.department = e2.department
);
๐ฅ 5. What is a CTE?
CTE = Common Table Expression
๐ Temporary result set used inside a query.
Defined using:
WITH
๐น 6. Example of CTE โญ
WITH avg_salary AS (
SELECT AVG(salary) AS avg_sal
FROM employees
)
SELECT *
FROM employees
WHERE salary > (
SELECT avg_sal FROM avg_salary
);
๐น 7. Why Use CTEs?
โ Makes queries readable
โ Simplifies complex logic
โ Easier debugging
๐น 8. Difference Between Subquery & CTE
Subquery : Nested inside query
CTE : Defined separately
Subquery : Harder to read
CTE : More readable
Subquery : Repeated logic possible
CTE : Reusable
๐น 9. Why This is Important?
โ Frequently asked in interviews
โ Used in dashboards & analytics
โ Important for real-world SQL projects
๐ฏ Todayโs Goal
โ Understand subqueries
โ Learn correlated subqueries
โ Understand CTEs
โ Write cleaner SQL queries
๐ SQL Notes: https://whatsapp.com/channel/0029VbCyzS02ZjCwoShXXc2j
๐ฌ Tap โค๏ธ for more!
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๐ Pandas Cheatsheet Every Data Analyst Should Save
Pandas is one of the most important tools for data analysis. Master these core operations to work faster and more efficiently:
๐น Read & Inspect Data
head(), shape, dtypes, describe()
๐น Select & Filter Data
Extract relevant rows and columns with ease.
๐น Row Selection
Use loc[] (labels) and iloc[] (positions).
๐น Handle Missing Values
isnull(), dropna(), fillna()
๐น Group & Aggregate
Summarize data using groupby() and aggregation functions.
๐น Merge & Join Data
Combine datasets with merge() using different join types.
๐ก Key Insight :
Strong Pandas skills help transform raw data into actionable insights faster and more effectively.
๐ Whether you're a beginner or an experienced analyst, mastering these fundamentals is essential for data analytics success.
Pandas is one of the most important tools for data analysis. Master these core operations to work faster and more efficiently:
๐น Read & Inspect Data
head(), shape, dtypes, describe()
๐น Select & Filter Data
Extract relevant rows and columns with ease.
๐น Row Selection
Use loc[] (labels) and iloc[] (positions).
๐น Handle Missing Values
isnull(), dropna(), fillna()
๐น Group & Aggregate
Summarize data using groupby() and aggregation functions.
๐น Merge & Join Data
Combine datasets with merge() using different join types.
๐ก Key Insight :
Strong Pandas skills help transform raw data into actionable insights faster and more effectively.
๐ Whether you're a beginner or an experienced analyst, mastering these fundamentals is essential for data analytics success.
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These FREE courses can help you develop industry-relevant skills and create a strong foundation in ML & AI. ๐
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โ Beginner-Friendly Content
โ Hands-On Projects
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๐ Save this post and start your AI journey today!
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โ
Power BI Basics ๐๐
๐ Power BI is one of the most popular Business Intelligence BI tools used for:
โ Data visualization
โ Dashboard creation
โ Business reporting
It is widely used by:
โ Data Analysts
โ Business Analysts
โ Data Scientists
๐น 1. What is Power BI?
Power BI is a Microsoft tool used to transform raw data into:
๐ Interactive dashboards
๐ Reports
๐ Visual insights
๐ฅ 2. Components of Power BI
โ Power BI Desktop
๐ Used to create reports & dashboards.
โ Power BI Service
๐ Cloud platform for sharing reports online.
โ Power BI Mobile
๐ Access dashboards on mobile devices.
๐น 3. Power BI Workflow โญ
Data โ Cleaning โ Modeling โ Visualization โ Dashboard โ Sharing
๐น 4. Connecting Data Sources
Power BI can connect with:
โ Excel
โ SQL Database
โ CSV Files
โ APIs
โ Cloud services
๐น 5. Power Query Data Cleaning
Used for:
โ Removing duplicates
โ Changing data types
โ Filtering rows
โ Merging data
๐ Similar to data cleaning in Pandas.
๐น 6. Data Modeling
๐ Relationships between tables.
Examples:
โ One-to-Many
โ Many-to-One
๐ฅ 7. Visualizations in Power BI
Popular visuals:
โ Bar Chart
โ Line Chart
โ Pie Chart
โ Table
โ KPI Cards
โ Maps
๐น 8. DAX Data Analysis Expressions
DAX is the formula language of Power BI.
Example:
Total Sales = SUM(Sales[Amount])
๐น 9. Why Power BI is Important?
โ Highly demanded skill
โ Used in real companies
โ Important for dashboards & reporting
โ Great for storytelling with data
๐ฏ Todayโs Goal
โ Understand Power BI basics
โ Learn workflow
โ Understand Power Query & DAX
โ Learn dashboard concepts
Power BI Resources: https://whatsapp.com/channel/0029Vai1xKf1dAvuk6s1v22c
๐ฌ Tap โค๏ธ for more!
๐ Power BI is one of the most popular Business Intelligence BI tools used for:
โ Data visualization
โ Dashboard creation
โ Business reporting
It is widely used by:
โ Data Analysts
โ Business Analysts
โ Data Scientists
๐น 1. What is Power BI?
Power BI is a Microsoft tool used to transform raw data into:
๐ Interactive dashboards
๐ Reports
๐ Visual insights
๐ฅ 2. Components of Power BI
โ Power BI Desktop
๐ Used to create reports & dashboards.
โ Power BI Service
๐ Cloud platform for sharing reports online.
โ Power BI Mobile
๐ Access dashboards on mobile devices.
๐น 3. Power BI Workflow โญ
Data โ Cleaning โ Modeling โ Visualization โ Dashboard โ Sharing
๐น 4. Connecting Data Sources
Power BI can connect with:
โ Excel
โ SQL Database
โ CSV Files
โ APIs
โ Cloud services
๐น 5. Power Query Data Cleaning
Used for:
โ Removing duplicates
โ Changing data types
โ Filtering rows
โ Merging data
๐ Similar to data cleaning in Pandas.
๐น 6. Data Modeling
๐ Relationships between tables.
Examples:
โ One-to-Many
โ Many-to-One
๐ฅ 7. Visualizations in Power BI
Popular visuals:
โ Bar Chart
โ Line Chart
โ Pie Chart
โ Table
โ KPI Cards
โ Maps
๐น 8. DAX Data Analysis Expressions
DAX is the formula language of Power BI.
Example:
Total Sales = SUM(Sales[Amount])
๐น 9. Why Power BI is Important?
โ Highly demanded skill
โ Used in real companies
โ Important for dashboards & reporting
โ Great for storytelling with data
๐ฏ Todayโs Goal
โ Understand Power BI basics
โ Learn workflow
โ Understand Power Query & DAX
โ Learn dashboard concepts
Power BI Resources: https://whatsapp.com/channel/0029Vai1xKf1dAvuk6s1v22c
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Build a Career in Data Science & AI with a job-focused curriculum designed by industry experts.
โ Learn from IIT Alumni & Top Industry Professionals
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โค1
What is Power BI mainly used for?
Anonymous Quiz
2%
A) Web development
1%
B) Game development
96%
C) Data visualization and reporting
1%
D) Mobile app testing
โค1
Which company developed Power BI?
Anonymous Quiz
5%
A) Google
82%
B) Microsoft
2%
C) Amazon
11%
D) IBM
โค1
Which component of Power BI is mainly used to create reports?
Anonymous Quiz
2%
A) Power BI Mobile
17%
B) Power BI Service
64%
C) Power BI Desktop
17%
D) Power Query
โค1
What is DAX in Power BI?
Anonymous Quiz
26%
A) Database tool
24%
B) Visualization tool
49%
C) Formula language
1%
D) Cloud storage
โค1
Which Power BI feature is mainly used for data cleaning and transformation?
Anonymous Quiz
60%
A) Power Query
24%
B) DAX
13%
C) Dashboard
3%
D) KPI Card
โค1
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โ
Dashboard Design Principles ๐๐จ
๐ Creating dashboards is not just about charts.
A good dashboard should be:
โ Clear
โ Interactive
โ Easy to understand
โ Business-focused
๐น 1. What is a Dashboard?
A dashboard is a visual interface that shows:
๐ KPIs
๐ Charts
๐ Business insights
๐ Used for decision-making.
๐ฅ 2. Goals of a Good Dashboard
โ Show important insights quickly
โ Reduce confusion
โ Help users take action
๐น 3. Key Dashboard Principles โญ
โ Keep It Simple
โ Too many visuals = confusion
โ Use only important charts
โ Use Proper Chart Types
Purpose : Best Chart
Comparison : Bar Chart
Trends : Line Chart
Distribution : Histogram
Percentage : Pie Chart
โ Maintain Visual Hierarchy
๐ Important KPIs should appear at the top.
Example:
โ Revenue
โ Profit
โ Customer Count
๐น 4. Use Consistent Colors โญ
โ Same color for same category
โ Avoid too many bright colors
Example:
๐ข Profit
๐ด Loss
๐น 5. Add Filters & Interactivity
Use:
โ Slicers
โ Drill-through
โ Dropdown filters
๐ Helps users explore data.
๐น 6. Dashboard Layout Best Practices
Top Section
๐ KPIs & summary cards
Middle Section
๐ Main charts
Bottom Section
๐ Detailed tables
๐น 7. Common Dashboard Mistakes โ
โ Too much data
โ Wrong chart selection
โ Poor color choices
โ Cluttered layout
๐น 8. Storytelling with Data โญ
A dashboard should answer:
โ What happened?
โ Why did it happen?
โ What should we do next?
๐น 9. Why Dashboard Design Matters?
โ Better business decisions
โ Improved user experience
โ Professional reporting
๐ฏ Todayโs Goal
โ Learn dashboard principles
โ Understand chart selection
โ Learn layout & storytelling
Power BI Resources: https://whatsapp.com/channel/0029Vai1xKf1dAvuk6s1v22c
๐ฌ Tap โค๏ธ for more!
๐ Creating dashboards is not just about charts.
A good dashboard should be:
โ Clear
โ Interactive
โ Easy to understand
โ Business-focused
๐น 1. What is a Dashboard?
A dashboard is a visual interface that shows:
๐ KPIs
๐ Charts
๐ Business insights
๐ Used for decision-making.
๐ฅ 2. Goals of a Good Dashboard
โ Show important insights quickly
โ Reduce confusion
โ Help users take action
๐น 3. Key Dashboard Principles โญ
โ Keep It Simple
โ Too many visuals = confusion
โ Use only important charts
โ Use Proper Chart Types
Purpose : Best Chart
Comparison : Bar Chart
Trends : Line Chart
Distribution : Histogram
Percentage : Pie Chart
โ Maintain Visual Hierarchy
๐ Important KPIs should appear at the top.
Example:
โ Revenue
โ Profit
โ Customer Count
๐น 4. Use Consistent Colors โญ
โ Same color for same category
โ Avoid too many bright colors
Example:
๐ข Profit
๐ด Loss
๐น 5. Add Filters & Interactivity
Use:
โ Slicers
โ Drill-through
โ Dropdown filters
๐ Helps users explore data.
๐น 6. Dashboard Layout Best Practices
Top Section
๐ KPIs & summary cards
Middle Section
๐ Main charts
Bottom Section
๐ Detailed tables
๐น 7. Common Dashboard Mistakes โ
โ Too much data
โ Wrong chart selection
โ Poor color choices
โ Cluttered layout
๐น 8. Storytelling with Data โญ
A dashboard should answer:
โ What happened?
โ Why did it happen?
โ What should we do next?
๐น 9. Why Dashboard Design Matters?
โ Better business decisions
โ Improved user experience
โ Professional reporting
๐ฏ Todayโs Goal
โ Learn dashboard principles
โ Understand chart selection
โ Learn layout & storytelling
Power BI Resources: https://whatsapp.com/channel/0029Vai1xKf1dAvuk6s1v22c
๐ฌ Tap โค๏ธ for more!
โค4
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โค4
What is the main purpose of a dashboard?
Anonymous Quiz
2%
A) Writing code
3%
B) Data storage
93%
C) Visualizing business insights
1%
D) Creating databases
โค1
Which chart is best for showing trends over time?
Anonymous Quiz
9%
A) Pie Chart
20%
B) Histogram
66%
C) Line Chart
6%
D) Scatter Plot
โค1