Data Science & Machine Learning

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num1 = int(input("Enter first number: "))
num2 = int(input("Enter second number: "))
print(num1 + num2)

Output:

30

🔹 12. Real-World Example

salary = float(input("Enter your monthly salary: "))
annual_salary = salary * 12
print(f"Your annual salary is {annual_salary}")

🎯 Practice Questions  1. Take your name as input and print a welcome message.  2. Take two integers as input and print their sum.  3. Take a student's marks as input and print them using an f-string.  4. Take the radius of a circle as input and calculate the area.  5. Take your birth year as input and calculate your approximate age.  🎯 Key Takeaways ✅ Use print() to display output ✅ Use input() to accept user input ✅ input() always returns a string ✅ Convert input using int() or float() when needed ✅ Use f-strings for clean and readable output formatting  Double Tap ❤️ For More

🚀 Data Science Roadmap 2026 📘 Phase 1: Programming Fundamentals 🐍 Topic 3: Python Input & Output In the previous lesson, you learned about Python Operators. Now it's time to learn how Python interacts with users by taking input and displaying output. Input and Output (I/O) are fundamental concepts because almost every real-world program accepts input, processes it, and produces meaningful output. 🔹 1. What is Input & Output? A Python program generally follows three steps: Input → Process → Output Example: • User enters two numbers Input • Python adds them Process • Sum is displayed Output 🔹 2. Displaying Output Python uses the print() function to display information on the screen. Example

print("Hello, Data Science!")

Output

Hello, Data Science!

🔹 3. Printing Variables You can print variables along with text.

name = "Deepak"
print(name)

Output:

Deepak

Or:

name = "Deepak"
print("Welcome", name)

Output:

Welcome Deepak

🔹 4. Taking User Input Python uses the input() function to receive input from users.

name = input("Enter your name: ")
print("Hello", name)

Example Output:

Enter your name: Deepak
Hello Deepak

🔹 5. Important Note ⭐ The input() function always returns a string, even if the user enters a number.

age = input("Enter age: ")
print(type(age))

Output:

<class 'str'>

🔹 6. Converting Input to Integer To perform mathematical operations, convert the input using int().

age = int(input("Enter your age: "))
print(age + 5)

Example:

Enter your age: 25
30

🔹 7. Taking Decimal Input Use float() for decimal numbers.

price = float(input("Enter price: "))
print(price)

🔹 8. Taking Multiple Inputs You can take multiple inputs in a single line.

name, city = input("Enter your name and city: ").split()
print(name)
print(city)

Example Input:

Deepak Mumbai

Output:

Deepak
Mumbai

🔹 9. Formatting Output Using f-Strings ⭐ Recommended

name = "Deepak"
age = 25
print(f"My name is {name} and I am {age} years old.")

Output:

My name is Deepak and I am 25 years old.

Using .format()

name = "Deepak"
print("Welcome {}".format(name))

🔹 10. Example Program

name = input("Enter your name: ")
age = int(input("Enter your age: "))

print(f"Hello {name}")
print(f"Next year you will be {age + 1} years old.")

Example Output:

Enter your name: Deepak
Enter your age: 25
Hello Deepak
Next year you will be 26 years old.

🔹 11. Common Mistake

num1 = input("Enter first number: ")
num2 = input("Enter second number: ")
print(num1 + num2)

Input:

10
20

Output:

1020

Why? Because both values are strings. Correct way:

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result = 10 + 5 * 2
print(result)

Output:

20  

Multiplication is performed before addition. Use parentheses to change the order.

result = (10 + 5) * 2
print(result)

Output:

30

🔹 10. Real-World Example

salary = 60000
bonus = 5000
total_salary = salary + bonus
is_high_salary = total_salary > 50000
print(total_salary)
print(is_high_salary)

Output:

65000  
True

🎯 Key Takeaways ✅ Operators perform calculations and comparisons. ✅ Arithmetic operators are used for mathematical operations. ✅ Comparison operators return True or False. ✅ Logical operators help combine multiple conditions. ✅ Membership operators check if a value exists in a sequence. ✅ Identity operators check whether two variables refer to the same object.  Double Tap ❤️ For More

🚀 Data Science Roadmap 2026 📘 Phase 1: Programming Fundamentals 🐍 Topic 2: Python Operators In the previous lesson, you learned about Variables & Data Types. Now it's time to learn how Python performs calculations, comparisons, and logical operations using operators. Operators are one of the most fundamental concepts in Python. You'll use them in almost every program, from simple calculations to complex Machine Learning algorithms. 🔹 1. What are Operators? Operators are special symbols used to perform operations on variables and values. Example:

a = 10
b = 5
print(a + b)

Output:

15  

Here, "+" is an operator that adds two numbers. 🔹 2. Types of Operators in Python Python has several types of operators: ✅ Arithmetic Operators ✅ Comparison Operators ✅ Assignment Operators ✅ Logical Operators ✅ Membership Operators ✅ Identity Operators 🔹 3. Arithmetic Operators ⭐ Used for mathematical calculations. Operators: • ** + Addition**: 10 + 5 = 15 • - Subtraction: 10 - 5 = 5 • ** Multiplication*: 10 * 5 = 50 • / Division: 10 / 5 = 2.0 • // Floor Division: 10 // 3 = 3 • % Modulus (Remainder): 10 % 3 = 1 • ** Exponent: 2 ** 3 = 8 Example:

a = 10
b = 3
print(a + b)
print(a - b)
print(a * b)
print(a / b)
print(a // b)
print(a % b)
print(a ** b)

🔹 4. Comparison Operators ⭐ Used to compare two values. The result is always True or False. Operators: • == Equal to • != Not Equal to • > Greater than • < Less than • >= Greater than or Equal to • <= Less than or Equal to Example:

x = 20
y = 10
print(x > y)
print(x == y)
print(x != y)

Output:

True  
False  
True

🔹 5. Assignment Operators Used to assign values to variables.

x = 10
x += 5
print(x)

Output:

15

Other assignment operators:

x -= 2  
x *= 3  
x /= 2

🔹 6. Logical Operators ⭐ Used to combine multiple conditions. and Returns True only if both conditions are True.

age = 25
print(age > 18 and age < 30)

Output:

True

or Returns True if at least one condition is True.

print(age < 18 or age < 30)

Output:

True

not Reverses the result.

print(not(age > 18))

Output:

False

🔹 7. Membership Operators Used to check whether a value exists in a sequence. in

fruits = ["Apple", "Banana", "Mango"]
print("Apple" in fruits)

Output:

True

not in

print("Orange" not in fruits)

Output:

True

🔹 8. Identity Operators Used to check whether two variables refer to the same object. is

a = [1, 2]
b = a
print(a is b)

Output:

True

is not

x = [1, 2]
y = [1, 2]
print(x is not y)

Output:

True

🔹 9. Operator Precedence Python follows the PEMDAS/BODMAS rule while evaluating expressions. Example:

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<class 'int'>

🔹 11. Type Conversion (Casting) Sometimes you need to convert one data type into another. String → Integer

age = "25"
print(int(age))

Integer → Float

marks = 95
print(float(marks))

Float → Integer

price = 199.99
print(int(price)) # Output: 199

Integer → String

number = 100
print(str(number))

🔹 12. Multiple Variable Assignment Assign multiple variables in one line.

x, y, z = 10, 20, 30

Assign the same value to multiple variables.

a = b = c = 100

🔹 13. Dynamic Typing Python is dynamically typed. This means a variable can store different data types at different times.

x = 10
x = "Data Science"
print(x) 
# Output: Data Science

🔹 14. Best Practices ✅ Use meaningful variable names.

student_name = "Rahul"
monthly_salary = 50000

Instead of:

a = "Rahul"
b = 50000

Follow the snake_case naming convention. Examples: customer_name, total_sales, average_salary 🔹 15. Real-World Example

name = "Rohit"
age = 25
salary = 65000.50
is_employee = True

print(name)
print(age)
print(salary)
print(is_employee)

Output:

Rohit
25
65000.5
True

🎯 Key Takeaways ✅ Variables are used to store data. ✅ Python automatically detects data types. ✅ The most common data types are: int, float, str, bool, complex ✅ Use type() to check a variable's data type. ✅ Use meaningful variable names and follow the snake_case naming convention. Mastering variables and data types is the first step toward becoming a successful Data Scientist. Every machine learning model, data analysis project, and AI application starts with understanding how data is stored and managed in Python. Double Tap ❤️ For More ----- 1.31 ₽ · /balance_help

🚀 Data Science Roadmap 2026 📘 Phase 1: Programming Fundamentals 🐍 Topic 1: Python Basics – Variables & Data Types Welcome to the Complete Data Science Roadmap! 🎉 Over the coming lessons, we'll learn everything you need to become a job-ready Data Scientist—from Python and SQL to Machine Learning, Deep Learning, Generative AI, and MLOps. Today, we're starting with the first and most important topic of the roadmap: Python Basics – Variables & Data Types. Python is the most widely used programming language in Data Science because it is easy to learn, highly readable, and supported by powerful libraries such as NumPy, Pandas, Matplotlib, Scikit-learn, TensorFlow, and PyTorch. Before building machine learning models or analyzing data, you must understand how Python stores and manages data. Every Python program begins with variables and data types, making them the foundation of your Data Science journey. 🔹 1. What is Python? Python is a high-level, interpreted programming language used for: ✅ Data Science ✅ Machine Learning ✅ Artificial Intelligence ✅ Data Analysis ✅ Automation ✅ Web Development 🔹 2. What is a Variable? A variable is a named container used to store data in memory. Think of a variable like a labeled box. You store information inside the box, and whenever you need that information later, you simply use the label (variable name). For example:

name = "Aman"
age = 25
salary = 175000

Here: • "name" stores a string. • "age" stores an integer. • "salary" stores a numeric value. 🔹 3. Rules for Naming Variables ✅ Valid Rules • Must begin with a letter or underscore ("_") • Can contain letters, numbers, and underscores • Variable names are case-sensitive Examples:

student_name = "Rahul"
marks = 90
age2 = 24

❌ Invalid Examples

2name = "Rahul"
student name = "Rahul"
class = 10

Why? • Cannot start with a number • Spaces are not allowed • "class" is a reserved Python keyword 🔹 4. What are Data Types? A data type tells Python what kind of value a variable stores. Python automatically detects the data type when you assign a value. Data Types in Python: int: Whole numbers Example: 25 float : Decimal numbers Example: 99.99 str: Text Example: "Python" bool: True or False complex: Complex numbers Example: 3+4j 🔹 5. Integer (int) Stores whole numbers.

age = 25
print(age)
print(type(age))

Output:

25
<class 'int'>

🔹 6. Float (float) Stores decimal numbers.

price = 199.99
print(price)
print(type(price))

Output:

199.99
<class 'float'>

🔹 7. String (str) Stores text.

name = "Suresh"
print(name)
print(type(name))

Output:

Deepak
<class 'str'>

Strings can be written using either single (' ') or double (" ") quotes. 🔹 8. Boolean (bool) Boolean values are used for decision-making. They can store only two values: True or False

is_student = True
print(type(is_student))

Output:

<class 'bool'>

🔹 9. Complex Numbers Python also supports complex numbers.

number = 3 + 4j
print(type(number))

Output:

<class 'complex'>

Although rarely used in Data Science, they are useful in scientific and mathematical computations. 🔹 10. Checking the Data Type Use the type() function.

salary = 50000
print(type(salary))

Output:

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Essential Python and SQL topics for data analysts 😄👇 Python Topics: Python Resources - @pythonanalyst 1. Data Structures    - Lists, Tuples, and Dictionaries    - NumPy Arrays for numerical data 2. Data Manipulation    - Pandas DataFrames for structured data    - Data Cleaning and Preprocessing techniques    - Data Transformation and Reshaping 3. Data Visualization    - Matplotlib for basic plotting    - Seaborn for statistical visualizations    - Plotly for interactive charts 4. Statistical Analysis    - Descriptive Statistics    - Hypothesis Testing    - Regression Analysis 5. Machine Learning    - Scikit-Learn for machine learning models    - Model Building, Training, and Evaluation    - Feature Engineering and Selection 6. Time Series Analysis    - Handling Time Series Data    - Time Series Forecasting    - Anomaly Detection 7. Python Fundamentals    - Control Flow (if statements, loops)    - Functions and Modular Code    - Exception Handling    - File SQL Topics: SQL Resources - @sqlanalyst 1. SQL Basics - SQL Syntax - SELECT Queries - Filters 2. Data Retrieval - Aggregation Functions (SUM, AVG, COUNT) - GROUP BY 3. Data Filtering - WHERE Clause - ORDER BY 4. Data Joins - JOIN Operations - Subqueries 5. Advanced SQL - Window Functions - Indexing - Performance Optimization 6. Database Management - Connecting to Databases - SQLAlchemy 7. Database Design - Data Types - Normalization Remember, it's highly likely that you won't know all these concepts from the start. Data analysis is a journey where the more you learn, the more you grow. Embrace the learning process, and your skills will continually evolve and expand. Keep up the great work! Share with credits: https://t.me/sqlspecialist Hope it helps :)

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📍 Phase 12: Deep Learning (Week 18–19) • Neural Networks • Perceptron • Activation Functions • Backpropagation • TensorFlow • Keras • PyTorch • CNN Basics • RNN Basics • LSTM Basics 📍 Phase 13: Generative AI & LLMs (Week 20) • Transformers • Attention Mechanism • Large Language Models (LLMs) • Prompt Engineering • Retrieval-Augmented Generation (RAG) • Embeddings • Vector Databases • AI Agents • LangChain • LlamaIndex 📍 Phase 14: Model Deployment (Week 21) • Flask • FastAPI • Streamlit • Docker Basics • REST APIs • Model Serialization (Pickle, Joblib) 📍 Phase 15: MLOps (Week 22) • ML Pipelines • Model Versioning • Experiment Tracking (MLflow) • CI/CD for ML • Model Monitoring • Data Drift • Model Retraining 📍 Phase 16: Cloud for Data Science (Week 23) • AWS Basics • Amazon S3 • Amazon SageMaker • Azure ML • Google Vertex AI • Databricks Basics 📍 Phase 17: Git & GitHub (Week 24) • Git Basics • Branching • Merging • Pull Requests • GitHub Portfolio 📍 Phase 18: Data Science Projects (Week 25–26) Build at least 10 end-to-end projects, such as: • House Price Prediction • Customer Churn Prediction • Credit Card Fraud Detection • Loan Approval Prediction • Sales Forecasting • Movie Recommendation System • Sentiment Analysis • Employee Attrition Prediction • Image Classification • End-to-End RAG Chatbot 📍 Phase 19: Portfolio Building • GitHub Profile • Project Documentation • Technical Blog Writing • Resume Optimization • LinkedIn Optimization • Kaggle Profile 📍 Phase 20: Interview Preparation • Python Interview Questions • SQL Interview Questions • Statistics Questions • Machine Learning Questions • Case Studies • Coding Round • Business Problem Solving • Mock Interviews 🎯 Double Tap ❤️ For Detailed Explanation ----- 1.23 ₽ · /balance_help