🚀 Data Science Roadmap 2026

📍 Phase 3: SQL for Data Science

📖 Topic 6 — GROUP BY

GROUP BY is used to group rows that have the same value in one or more columns.

It is mainly used when you want to perform calculations for each group.

For example:

• Total sales by region

• Average salary by department

• Number of customers by city

• Total orders by product

• Maximum transaction by category

🔹 Basic Syntax

SELECT column_name, aggregate_function(column_name)
FROM table_name
GROUP BY column_name;

Common aggregate functions:

• COUNT() → Count rows

• SUM() → Calculate total

• AVG() → Calculate average

• MIN() → Find minimum

• MAX() → Find maximum

🔹 Simple Example

Suppose we have:

department   salary
IT           60000
IT           80000
HR           50000
HR           70000
Finance      90000

Query:

SELECT department, AVG(salary) AS avg_salary
FROM employees
GROUP BY department;

Result:

department   avg_salary
IT           70000
HR           60000
Finance      90000

Instead of calculating one overall average, SQL calculates an average for each department.

🔹 GROUP BY with COUNT()

To count employees in each department:

SELECT department, COUNT(*) AS employee_count
FROM employees
GROUP BY department;

Result:

department   employee_count
IT           2
HR           2
Finance      1

🔹 GROUP BY with SUM()

Suppose you have sales data:

region   sales
North    10000
North    15000
South    20000
South    12000

Query:

SELECT region, SUM(sales) AS total_sales
FROM sales
GROUP BY region;

Result:

North   25000
South   32000

This is extremely common in business analytics.

🔹 GROUP BY with MAX()

Find the highest salary in each department:

SELECT department, MAX(salary) AS highest_salary
FROM employees
GROUP BY department;

🔹 GROUP BY with MIN()

Find the lowest salary in each department:

SELECT department, MIN(salary) AS lowest_salary
FROM employees
GROUP BY department;

🔹 GROUP BY with Multiple Columns

You can group by multiple columns.

SELECT department, job_title, COUNT(*) AS employee_count
FROM employees
GROUP BY department, job_title;

Here SQL creates groups based on:

department + job_title

For example:

IT        Analyst       5
IT        Developer     8
HR        Analyst       3
Finance   Manager       4

🔹 GROUP BY with WHERE

WHERE filters rows before grouping.

SELECT department, AVG(salary) AS avg_salary
FROM employees
WHERE salary > 50000
GROUP BY department;

🔹 GROUP BY with ORDER BY

You can sort grouped results.

For example, find departments with the highest total salary first:

SELECT department, SUM(salary) AS total_salary
FROM employees
GROUP BY department
ORDER BY total_salary DESC;

This is very useful for ranking groups.

🔹 GROUP BY vs DISTINCT

These two can sometimes look similar.

DISTINCT

SELECT DISTINCT department
FROM employees;

Used to find unique departments.

GROUP BY

SELECT department, COUNT(*)
FROM employees
GROUP BY department;

Used when you want to perform calculations for each department.

Simple rule:

DISTINCT → Give me unique values

GROUP BY → Give me groups so I can calculate something

🔹 Real-World Data Science Example

Imagine an e-commerce dataset.

You want to know the total revenue generated by each product category.

SELECT category,
       SUM(quantity * price) AS total_revenue
FROM sales
GROUP BY category
ORDER BY total_revenue DESC;

This gives you a category-level summary that can be used for:

• Revenue analysis

• Product performance

• Business reporting

• Feature creation

• Exploratory data analysis

🔹 Common Mistake

Consider:

SELECT department, employee_name, AVG(salary)
FROM employees
GROUP BY department;

This can cause an error in many SQL databases because employee_name is neither:

• included in GROUP BY, nor

• used inside an aggregate function.

A valid query would be:

SELECT department, AVG(salary)
FROM employees
GROUP BY department;

🔹 Important Rule

When using GROUP BY, selected columns generally need to be either:

1. Included in the GROUP BY, or

2. Wrapped inside an aggregate function.

Example:

SELECT department, COUNT(*)
FROM employees
GROUP BY department;

department → grouped column

COUNT(*) → aggregate

🔹 Interview Questions

1. What is GROUP BY?

GROUP BY groups rows with the same values so aggregate calculations can be performed for each group.

2. Which aggregate functions are commonly used with GROUP BY?

COUNT(), SUM(), AVG(), MIN(), and MAX().

3. What is the difference between WHERE and GROUP BY?

WHERE filters individual rows before grouping.

GROUP BY creates groups from the filtered rows.

4. Can GROUP BY use multiple columns?

Yes.

GROUP BY department, job_title;

5. Can GROUP BY be used without aggregate functions?

Yes, although DISTINCT is often clearer when the goal is simply to return unique combinations.

🎯 Practice Questions

1. Find the number of employees in each department.

2. Find the average salary for each department.

3. Find the total sales for each region.

4. Find the highest salary in each department.

5. Find the total revenue for each product category and sort categories from highest to lowest revenue.

🎯 Key Takeaways

• GROUP BY creates groups of similar rows.

• It is commonly used with aggregate functions.

• COUNT() counts.

• SUM() totals.

• AVG() calculates averages.

• MIN() finds the minimum.

• MAX() finds the maximum.

• WHERE filters rows before grouping.

• ORDER BY can sort grouped results.

• GROUP BY is one of the most important SQL concepts for Data Science and Analytics.

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🚀 Data Science Roadmap 2026

📍 Phase 3: SQL for Data Science

📖 Topic 7 — HAVING

HAVING is used to filter grouped results.

The most important thing to remember:

WHERE → filters rows

HAVING → filters groups

🔹 Why Do We Need HAVING?

Suppose you want to find departments that have more than 5 employees.

First, you need to group employees by department:

SELECT department, COUNT(*) AS employee_count
FROM employees
GROUP BY department;

But if you want only departments with more than 5 employees, use HAVING:

SELECT department, COUNT(*) AS employee_count
FROM employees
GROUP BY department
HAVING COUNT(*) > 5;

The grouping happens first, and then HAVING filters the groups.

🔹 Basic Syntax

SELECT column_name, aggregate_function(column_name)
FROM table_name
GROUP BY column_name
HAVING condition;

🔹 HAVING with COUNT()

Find departments having more than 10 employees:

SELECT department, COUNT(*) AS employee_count
FROM employees
GROUP BY department
HAVING COUNT(*) > 10;

🔹 HAVING with SUM()

Find regions where total sales exceed 100000:

SELECT region, SUM(sales) AS total_sales
FROM sales
GROUP BY region
HAVING SUM(sales) > 100000;

🔹 HAVING with AVG()

Find departments where the average salary is greater than 70000:

SELECT department, AVG(salary) AS avg_salary
FROM employees
GROUP BY department
HAVING AVG(salary) > 70000;

🔹 HAVING with MAX()

Find departments where the highest salary is greater than 150000:

SELECT department, MAX(salary) AS highest_salary
FROM employees
GROUP BY department
HAVING MAX(salary) > 150000;

🔹 WHERE vs HAVING

This is one of the most frequently asked SQL interview questions.

WHERE Filters individual rows.

SELECT *
FROM employees
WHERE salary > 50000;

It removes individual records that don't meet the condition.

HAVING

Filters groups after GROUP BY.

SELECT department, AVG(salary) AS avg_salary
FROM employees
GROUP BY department
HAVING AVG(salary) > 70000;

It removes entire groups that don't meet the condition.

🔹 Using WHERE and HAVING Together

You can use both.

Suppose you want departments where:

• Only employees earning more than 50000 are considered

• The department must have at least 5 such employees

SELECT department, COUNT(*) AS employee_count
FROM employees
WHERE salary > 50000
GROUP BY department
HAVING COUNT(*) >= 5;

Conceptually:

All employees
     ↓
WHERE
     ↓
Employees with salary > 50000
     ↓
GROUP BY
     ↓
Create department groups
     ↓
HAVING
     ↓
Keep groups with at least 5 employees

🔹 HAVING with ORDER BY

You can also sort the filtered groups.

SELECT department, SUM(salary) AS total_salary
FROM employees
GROUP BY department
HAVING SUM(salary) > 500000
ORDER BY total_salary DESC;

This finds departments whose total salary exceeds 500000 and displays the highest totals first.

🔹 HAVING Without GROUP BY

Some SQL databases allow HAVING without an explicit GROUP BY, treating the entire result as a single group.

For example:

SELECT COUNT(*) AS employee_count
FROM employees
HAVING COUNT(*) > 100;

This returns the result only if there are more than 100 employees.

For beginners, however, focus primarily on the common pattern:

GROUP BY → HAVING

🔹 Common Mistake

❌ Incorrect:

SELECT department, COUNT(*)
FROM employees
WHERE COUNT(*) > 5
GROUP BY department;

Aggregate functions such as COUNT() generally cannot be used in WHERE.

✅ Correct:

SELECT department, COUNT(*) AS employee_count
FROM employees
GROUP BY department
HAVING COUNT(*) > 5;

🔹 Real-World Data Science Example

Suppose you're analyzing customer transactions.

You want customers who have made at least 10 transactions:

SELECT customer_id,
       COUNT(*) AS transaction_count
FROM transactions
GROUP BY customer_id
HAVING COUNT(*) >= 10;

This can help identify:

• Frequent customers

• High-engagement users

• Active accounts

• Customers suitable for segmentation

Another example:

Find products generating more than 50000 in revenue:

SELECT product_id,
       SUM(quantity * price) AS revenue
FROM sales
GROUP BY product_id
HAVING SUM(quantity * price) > 50000
ORDER BY revenue DESC;

🔹 Interview Questions

What is HAVING?

HAVING filters groups created by GROUP BY.


What is the difference between WHERE and HAVING?

WHERE filters rows before grouping.

HAVING filters groups after grouping.


Can HAVING be used with aggregate functions?

Yes.

HAVING COUNT(*) > 5


Can WHERE and HAVING be used together?

Yes.

WHERE salary > 50000
GROUP BY department
HAVING COUNT(*) > 5

Why can't we normally use COUNT() in WHERE?

Because WHERE operates on individual rows before the aggregation is performed. HAVING is designed to filter aggregated groups.

🎯 Practice Questions

1. Find departments with more than 10 employees.

2. Find customers who have made at least 5 transactions.

3. Find regions where total sales exceed 100000.

4. Find departments where average salary is greater than 75000.

5. Find products whose total revenue exceeds 50000 and sort them from highest to lowest revenue.

🎯 Key Takeaways

• WHERE → Filters rows.

• GROUP BY → Creates groups.

• HAVING → Filters groups.

• WHERE is applied before grouping.

• HAVING is applied after grouping.

• HAVING is commonly used with COUNT(), SUM(), AVG(), MIN(), and MAX().

• WHERE + GROUP BY + HAVING is one of the most important SQL patterns for analytics.

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🚀 Data Science: Important Concepts You Should Know

Data Science combines statistics, programming, data analysis, machine learning, and business understanding to extract useful insights and make better decisions from data.

1. What is Data Science?

Data Science is the process of collecting, cleaning, analyzing, and modeling data to answer questions, discover patterns, and support decisions.

Example:

A company can use customer data to understand why customers leave, predict future sales, and identify high-value customers.

2. Data

Data is the raw information we work with.

Examples:

• Customer records

• Sales transactions

• Website activity

• Financial data

• Sensor readings

Data can be structured, semi-structured, or unstructured.

3. Dataset

A dataset is a collection of related data, usually organized into rows and columns.

Example:

Customer | Age | Income | Purchase
A        | 25  | 40K    | Yes
B        | 34  | 70K    | No

4. Variables

Variables are characteristics or attributes measured in the data.

Examples:

• Age

• Salary

• Department

• Sales

• Customer status

They can be numerical or categorical.

5. Data Collection

Data can come from:

• Databases

• APIs

• Surveys

• Excel files

• Websites

• Applications

• Sensors

• Business systems

Good analysis starts with reliable and relevant data.

6. Data Cleaning

Real-world data is rarely perfect.

Data cleaning involves handling:

• Missing values

• Duplicates

• Incorrect formats

• Invalid values

• Inconsistent categories

• Outliers

Garbage in → garbage out.

7. Missing Values

Missing values occur when information isn't available.

Example:

Customer | Age
A        | 25
B        | —
C        | 31

Possible approaches:

• Remove records

• Mean/median imputation

• Mode imputation

• Model-based imputation

The right method depends on the situation.

8. Exploratory Data Analysis — EDA

EDA is the process of exploring data before building models.

You investigate:

• Distributions

• Relationships

• Trends

• Outliers

• Missing values

• Patterns

EDA helps you understand what the data is telling you.

9. Descriptive Statistics

Descriptive statistics summarize data.

Important measures include:

• Mean

• Median

• Mode

• Minimum

• Maximum

• Range

• Variance

• Standard deviation

• Percentiles

10. Probability

Probability measures how likely an event is to occur.

It is important for:

• Risk analysis

• Statistical inference

• Machine learning

• Forecasting

• Decision-making

11. Statistics

Statistics helps us understand data and draw conclusions from samples.

Important concepts:

• Population vs sample

• Sampling

• Distributions

• Hypothesis testing

• Confidence intervals

• Correlation

• Regression

12. Data Visualization

Visualization turns data into charts and graphs that make patterns easier to understand.

Common charts:

• Bar chart

• Line chart

• Histogram

• Scatter plot

• Box plot

• Heatmap

Tools include:

• Power BI

• Tableau

• Excel

• Python

13. Correlation

Correlation measures the strength and direction of the relationship between variables.

Example:

As advertising spending increases, sales may also increase.

But remember:

Correlation does not automatically mean causation.

14. SQL

SQL is one of the most important skills for Data Science because much of the data lives in databases.

Important SQL concepts:

• SELECT

• WHERE

• GROUP BY

• JOIN

• CASE

• Subqueries

• CTEs

• Window Functions

15. Python

Python is widely used for Data Science.

Important libraries:

• NumPy → Numerical computing

• Pandas → Data manipulation

• Matplotlib → Visualization

• Seaborn → Statistical visualization

• Scikit-learn → Machine learning

16. Data Transformation

Data often needs to be transformed before analysis.

Examples:

• Changing data types

• Scaling numerical variables

• Encoding categories

• Creating calculated columns

• Aggregating data

17. Feature Engineering

Feature engineering creates useful variables from existing data.

Example:

Instead of using:

Purchase Date

Create:

Customer Tenure

Good features can improve both analysis and machine learning models.

18. Sampling

Sometimes you cannot analyze an entire population, so you work with a sample.

Common sampling methods:

• Random sampling

• Stratified sampling

• Systematic sampling

• Cluster sampling

A good sample should represent the population as accurately as possible.

19. Hypothesis Testing

Hypothesis testing helps determine whether evidence from data supports a particular claim.

Basic concepts:

• Null hypothesis

• Alternative hypothesis

• Significance level

• P-value

• Test statistic

Example:

"Did the new marketing campaign actually increase sales?"

20. Confidence Interval

A confidence interval provides a range of plausible values for a population parameter based on sample data.

Instead of saying:

"The average salary is exactly ₹8 lakh."

We might estimate a range around that value.

21. Regression

Regression is used to understand relationships between variables and/or predict numerical outcomes.

Example:

Predicting sales based on:

• Advertising

• Price

• Season

• Previous sales

22. Machine Learning

Machine Learning allows computers to learn patterns from data and make predictions.

Major categories:

Supervised Learning

Learns from labeled data.

Unsupervised Learning

Finds patterns in unlabeled data.

Reinforcement Learning

Learns through actions, rewards, and feedback.

23. Model Evaluation

A model needs to be evaluated using appropriate metrics.

For regression:

• MAE

• MSE

• RMSE

• R²

For classification:

• Accuracy

• Precision

• Recall

• F1 Score

• ROC-AUC

24. Overfitting

Overfitting happens when a model learns the training data too closely and performs poorly on new data.

The goal is generalization — performing well on unseen data.

25. Time Series

Time-series analysis works with data collected over time.

Examples:

• Daily sales

• Stock prices

• Monthly revenue

• Website traffic

Important concepts:

• Trend

• Seasonality

• Cycles

• Forecasting

• Lag

26. Natural Language Processing — NLP

NLP allows computers to work with human language.

Applications:

• Sentiment analysis

• Text classification

• Chatbots

• Text summarization

• Search

• Document analysis

27. Data Warehousing

A data warehouse stores integrated data from different sources for reporting and analytics.

Examples of concepts:

• ETL / ELT

• Fact tables

• Dimension tables

• Star schema

• Data marts

28. Big Data

Big Data refers to datasets that may be too large, fast, or complex for traditional approaches.

Common technologies include:

• Spark

• Databricks

• Cloud data platforms

• Distributed storage

29. Data Engineering

Data Engineering focuses on building systems that collect, transform, store, and deliver data.

A simplified flow:

Sources → Pipeline → Data Warehouse/Lake → Analytics

30. Data Science Lifecycle

A typical Data Science project follows:

Business Problem → Data Collection → Data Cleaning → EDA → Feature Engineering → Modeling → Evaluation → Deployment → Monitoring

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