September 2026 Bootcamp | Python with Data Science Syllabus

Join Now: September Bootcamp Registration

PHASE 1 — Python Foundations (Day 1–5)

• Day 1: Python & DS Foundations — Setup, Syntax, Data Types
• Day 2: Operators & Conditionals — if-elif-else, Logic Building
• Day 3: Data Structures — Lists, Dictionaries, Nested Data
• Day 4: Loops & Comprehensions — for/while, List & Dict Comprehension
• Day 5: Functions — Args, Lambda, map/filter/zip 

PHASE 2 — Advanced Python (Day 6–7)

• Day 6: Exception & File Handling — Try-Except, CSV/JSON, Paths
• Day 7: OOP Essentials — Classes, Objects, Constructors

PHASE 3 — NumPy (Day 8–9)

• Day 8: NumPy Fundamentals — Arrays, Indexing, Reshaping
• Day 9: NumPy for Computing — Vectorization, Broadcasting, Stats

PHASE 4 — Pandas (Day 10–13)

• Day 10: Pandas Fundamentals — Series, DataFrame, Reading Data
• Day 11: Data Selection — loc/iloc, Filtering, Sorting
• Day 12: Data Cleaning — Missing Values, Duplicates, Outliers
• Day 13: Advanced Pandas — GroupBy, Merge, Pivot 

 PHASE 5 — Data Visualization (Day 14–15)

• Day 14: Matplotlib — Line, Bar, Scatter, Histogram
• Day 15: Seaborn — Heatmap, Pairplot 

PHASE 6 — Statistics (Day 16–17)

• Day 16: Descriptive Statistics — Mean, Variance, Std Dev
• Day 17: Probability & Inferential Stats — Hypothesis Testing, p-value

PHASE 7 — Exploratory Data Analysis (Day 18–19)

• Day 18: EDA Fundamentals — Univariate, Bivariate Analysis
• Day 19: Complete EDA Workflow — Real Dataset 

PHASE 8 — Machine Learning (Day 20–21)

• Day 20: ML Foundations — Regression, KNN, Train-Test Split
• Day 21: ML Evaluation — Trees, Random Forest 

FINAL CAPSTONE (Day 22)

• Day 22: Capstone Project — End-to-End Data Science Project

Join Now: September Bootcamp Registration

Whatsapp Community: https://chat.whatsapp.com/LPxRJGoCr3hIFCu2nrBmHE

Read More

Statistics for Data Science with Python

 

Introduction

Statistics is the backbone of data-science: before you build predictive models or draw insights, you must understand your data, summarise it, reason about uncertainty and draw valid inferences. The “Statistics for Data Science with Python” course offers a focused path into this world — teaching you how to use Python to perform descriptive statistics, visualise data, understand probability distributions, carry out hypothesis tests, regression and more. If you’re looking to strengthen your foundation in statistics and tie it directly to Python and data-science workflows, this is a very relevant course.

Join Now: Statistics for Data Science with Python

---

Why This Course Matters

• In a data-rich world, knowing how to analyse data is not enough — you must understand what the statistical numbers mean, how distributions behave, when a result is reliable.

• Python is the main language used in many data-science workflows; this course marries statistical thinking with code implementation — giving you practical, applicable skills.

• Many data-science roles expect you to be comfortable with not just “running models”, but also exploring data, summarising it, detecting relationships, and validating statements. This course addresses those expectations.

• It serves as a bridge: from using statistics in spreadsheets or ad-hoc reports to integrating statistical methods in Python notebooks, preparing you for more advanced data-science, machine-learning and big-data work.

---

What You’ll Learn

Here’s a breakdown of the content you can expect, and how it builds your knowledge and skill-set:

Module 1: Course Introduction & Python Statistical Tools

The course begins by orienting you to the goals, tools and environment. You’ll be introduced to Python-based tools (Pandas, NumPy, Matplotlib) for statistical use-cases, setting up a foundation for the remaining modules.

Module 2: Descriptive Statistics

You’ll learn how to summarise data using measures of central tendency (mean, median, mode), measures of dispersion (variance, standard deviation, range), and explore how data behaves at different levels of measurement. You’ll practise computing these statistics in Python and visualising them.

Module 3: Data Visualisation

This module emphasises how to represent data graphically: histograms, box-plots, scatterplots, time-series plots — and how your statistical summaries and visualisations work together to reveal patterns, outliers, correlations.

Module 4: Probability Distributions

You’ll dive into probability: random variables, probability distributions (normal, binomial, Poisson, etc.), and how they underpin statistical reasoning. You’ll also learn how to use Python to calculate probabilities, expected values, cumulative distributions and visualise these distributions.

Module 5: Inferential Statistics & Hypothesis Testing

The core of statistical insight: you’ll learn about sample vs population, confidence intervals, hypothesis tests (t-tests, ANOVA), p-values, statistical significance. You’ll use Python to conduct these tests and interpret what the results mean in context.

Module 6: Regression & Correlation

You’ll explore relationships between variables: correlation (Pearson’s, Spearman’s), simple and multiple regression analysis — building models to estimate and interpret relationships, making predictions, and understanding limitations (assumptions, residuals, goodness-of-fit).

Final Project & Application

To consolidate your learning, you’ll apply what you’ve learned on a dataset: performing descriptive statistics, visualisation, hypothesis testing, regression modelling — all coded in Python. This helps you see the end-to-end workflow from raw data to insight.

---

Who Should Take This Course?

• Beginners in data science who know basic Python and want to deepen their statistical understanding.

• Data analysts who are comfortable with spreadsheets but want to transition into statistical analytics with Python.

• Programmers or engineers who want to add statistical reasoning to their skill-set — not just coding, but interpreting data and drawing conclusions.

• Students or career-changers aiming to build a strong foundation before diving into machine learning or advanced analytics.

If you are completely new to programming or statistics, you might find some of the mathematical/statistical conceptual parts challenging — but the course is designed to be accessible and builds progressively.

---

How to Get the Most Out of It

• Install and use the tools: Make sure you have Python, Jupyter Notebook (or equivalent), and libraries like Pandas, NumPy, Matplotlib installed so you can code along.

• Code actively: When you see an example of computing a mean or running a t-test, type it yourself, experiment with different data, change parameters, observe how results change.

• Work with your own data: After completing a module, pick a small dataset (maybe something from your domain) and apply the techniques: compute descriptive stats, visualise, run hypothesis tests. That personalises learning.

• Interpret results: Don’t just compute numbers — ask: “What does this mean? Why is this distribution this shape? What do the p-value and confidence interval tell me about the hypothesis?”

• Document your learning: Keep a notebook or script with your explorations, what you changed, what you observed. This becomes a mini-portfolio of your statistical work.

• Reflect on assumptions and limitations: For example, regression assumes linearity, normality of residuals, etc. Visualise residuals, test these assumptions, think critically.

• Link to further work: After you finish, think: how will I use these statistics in modelling, prediction, reporting? This helps you move toward full data-science workflows.

---

What You’ll Walk Away With

After completing the course you will:

• Be comfortable computing and interpreting descriptive statistics in Python.

• Understand probability distributions, how they relate to real data, and how to calculate probabilities and expectations in code.

• Be able to perform inferential statistical testing (e.g., t-test, ANOVA) and explain what the results mean practically.

• Build regression and correlation models in Python, interpret results, check model assumptions and limitations.

• Have a practical workflow from raw data → summary statistics → visualisation → statistical test/model → interpretation.

• Be better prepared for roles in data analysis, data science, analytics reporting and for further study in machine learning.

---

Join Now: Statistics for Data Science with Python

Conclusion

“Statistics for Data Science with Python” is a solid and practical course for anyone aiming to build statistical competence within a data-science context. It doesn’t just teach you Python commands — it teaches you how to think statistically, how to interpret data, how to draw valid conclusions. Whether you aim to move into analytics, data science or a domain where data drives decisions, this course gives you a strong foundation.

Read More

 

🚀 Day 65/150 – Count Consonants in a String in Python

Consonants are all alphabet characters except vowels (a, e, i, o, u).

In Python, we can count consonants using loops, conditions, list comprehensions, and functions.

Let’s explore different methods 👇

🔹 Method 1 – Using for Loop

text = "Python Programming" count = 0 for ch in text.lower(): if ch.isalpha() and ch not in "aeiou": count += 1 print("Consonant Count:", count)

✅ Output

Consonant Count: 13

📌 Checks whether the character is an alphabet and not a vowel.

🔹 Method 2 – Taking User Input

text = input("Enter a string: ")

count = 0

for ch in text.lower():
    if ch.isalpha() and ch not in "aeiou":
        count += 1

print("Consonant Count:", count)

📌 Allows users to enter any string dynamically.

🔹 Method 3 – Using List Comprehension

text = "Python Programming" count = sum([1 for ch in text.lower() if ch.isalpha() and ch not in "aeiou"]) print("Consonant Count:", count)

✅ Output

Consonant Count: 13

📌 Cleaner and shorter Pythonic approach.

🔹 Method 4 – Using Function

def count_consonants(text): count = 0 for ch in text.lower(): if ch.isalpha() and ch not in "aeiou": count += 1 return count print(count_consonants("Python Programming"))

✅ Output

13

📌 Best method for reusable programs.

 Key Takeaways

✅ isalpha() ignores spaces and symbols
✅ not in "aeiou" helps identify consonants
✅ List comprehensions make code compact
✅ Functions improve code reusability

Read More

Convex Optimization: Algorithms and Complexity (Foundations and Trends in Machine Learning)(Free PDF)

 

Introduction

Convex Optimization is an important area of mathematical optimization with strong connections to Machine Learning, statistics, and computational mathematics. It focuses on designing efficient methods for finding optimal solutions to problems where the objective and constraints have suitable convex structure.

Sébastien Bubeck’s Convex Optimization: Algorithms and Complexity is a monograph published in Foundations and Trends in Machine Learning, Volume 8, in 2015. It presents the theory behind major optimization algorithms while emphasizing their computational complexity and relevance to machine learning.

Downloaf the PDF : https://arxiv.org/abs/1405.4980

Understanding Convex Optimization

Convex optimization studies problems where the structure of the objective function and feasible region allows optimization methods to provide strong guarantees.

Convexity is particularly valuable because it helps distinguish globally optimal solutions from merely local solutions. This makes convex problems especially attractive in machine learning and other computational applications.

Convexity in Machine Learning

Optimization is at the heart of many machine learning methods. Training a model generally involves minimizing a loss function or maximizing an objective.

When the resulting optimization problem has convex structure, mathematical theory can provide useful guarantees about convergence, efficiency, and solution quality.

Black-Box Optimization

The work begins with the fundamental theory of black-box optimization, where algorithms access information about an objective through an abstract computational model.

This framework provides a way to compare optimization methods based on how efficiently they can approach an optimal solution.

Gradient-Based Methods

Gradient-based optimization methods use information about how an objective function changes to guide the search toward better solutions.

The monograph discusses both standard and accelerated gradient descent, which are fundamental techniques in modern optimization and machine learning.

Cutting-Plane Methods

Cutting-plane methods progressively reduce the region in which an optimal solution can exist.

The work discusses several approaches, including the center-of-gravity method, ellipsoid method, and Vaidya's cutting-plane method. These methods demonstrate how geometric information can be used to solve convex optimization problems efficiently.

Non-Euclidean Optimization

Optimization does not always need to operate under standard Euclidean geometry. The book explores non-Euclidean approaches such as Frank-Wolfe, mirror descent, and dual averaging.

These methods are particularly relevant when the geometry of the optimization problem or the structure of the data makes traditional gradient-based approaches less suitable.

Structured Optimization

Many practical machine learning problems contain additional structure that can be exploited during optimization.

The work introduces techniques including FISTA, which is useful for problems combining smooth and non-smooth components, as well as saddle-point methods and interior-point methods.

Stochastic Optimization

Large-scale machine learning often makes it expensive to process an entire dataset during every optimization step.

Stochastic optimization addresses this challenge by working with partial or randomly selected information. Topics covered include stochastic gradient descent, mini-batches, random coordinate descent, and sublinear optimization methods.

Optimization Complexity

A major focus of the work is complexity. Optimization algorithms are not evaluated only by whether they eventually find a solution, but also by how efficiently they approach the desired accuracy.

Complexity analysis helps compare algorithms and understand the computational resources required as problem dimensions and accuracy requirements change.

Convex Relaxation

Convex relaxation provides a way to replace difficult optimization problems with related convex problems that are easier to solve.

This idea is particularly useful for certain combinatorial optimization problems and can provide practical approximations to otherwise difficult computational tasks.

Importance in Modern Machine Learning

Optimization forms a fundamental layer underneath many machine learning techniques. Understanding optimization algorithms helps explain how models are trained, why certain methods converge faster than others, and how computational efficiency can be improved.

Convex optimization also provides theoretical foundations for understanding the behavior and limitations of optimization procedures.

Hard Copy: Convex Optimization: Algorithms and Complexity (Foundations and Trends in Machine Learning)

Downloaf the PDF : https://arxiv.org/abs/1405.4980

Conclusion

Convex Optimization: Algorithms and Complexity provides a theoretical foundation for understanding optimization algorithms and their computational efficiency. The work moves from basic convex optimization and black-box models toward gradient methods, cutting-plane techniques, non-Euclidean optimization, structured optimization, stochastic methods, and convex relaxation.

For Machine Learning, convex optimization is especially important because it connects mathematical theory, algorithm design, computational complexity, and model training. Understanding these foundations provides a deeper perspective on why optimization algorithms work and how they can be designed to solve increasingly large and complex problems.

Read More

Python Coding Challenge - Question with Answer (ID 290826)

Code Explanation:

Line 1: Creating the List

x = [0, 0, 5, 1/0]

A list x is being created.

It contains 0, 0, 5, and 1/0.

But 1/0 causes a ZeroDivisionError immediately.

Therefore, the list is never successfully created.

Line 2: Calling any()

print(any(x))

This line is never executed.

Python stops at the previous line because division by zero is invalid.

So any() does not get a chance to check the values.

Output

ZeroDivisionError: division by zero

Books: Python Projects for Real-World Applications

Read More

🚀 DAY 105/150 – Number guessing game

 

🚀 Day 105/150 – Number Guessing Game in Python

A Number Guessing Game is a simple and fun Python project that helps beginners understand **random numbers, user input, conditional statements, loops, and comparison operators**.

In this post, we'll explore three different ways to build a Number Guessing Game in Python — from a basic version to a version with hints and limited attempts.

Method 1 – Basic Guess

The simplest version generates a random number between 1 and 10 and asks the user to guess it.

import random n = random.randint(1, 10) g = int(input("Guess: ")) print("Correct!" if g == n else "Wrong!")

Sample Output

Guess: 7

Correct!

Explanation

`import random` imports Python's random module.

`random.randint(1, 10)` generates a random integer between 1 and 10.

`input()` takes the user's guess.

`int()` converts the entered value into an integer.

The conditional expression checks whether the guess is equal to the generated number.

If both numbers match, `"Correct!"` is displayed; otherwise, `"Wrong!"` is displayed.

This is the easiest version and is perfect for understanding the basic concept.

Method 2 – Guess With Hint

We can make the game more interactive by telling the player whether their guess is too high or too low.

import random n = random.randint(1, 10) g = int(input("Guess: ")) if g < n: print("Too Low!") elif g > n: print("Too High!") else: print("Correct!")

Sample Output

Guess: 4

Too Low!

Explanation

The program first generates a random number between 1 and 10.

The player enters a guess.

If the guess is smaller than the secret number, `"Too Low!"` is displayed.

If the guess is greater than the secret number, `"Too High!"` is displayed.

If neither condition is true, the guess must be correct, so `"Correct!"` is displayed.

This version introduces **if, elif, and else**, making the game more interactive.

Method 3 – Number Guessing Game With 3 Attempts

We can make the game more challenging by giving the player only three attempts.

import random n = random.randint(1, 10) for i in range(3): g = int(input("Guess: ")) if g == n: print("🎉 Correct!") break else: print("❌ Game Over!")

Sample Output

Guess: 3

Guess: 8

Guess: 6

🎉 Correct!

Explanation

`random.randint(1, 10)` generates the secret number.

`for i in range(3)` allows the player to make a maximum of three guesses.

Inside the loop, the user enters a guess.

If the guess matches the secret number, `"🎉 Correct!"` is displayed.

The `break` statement immediately stops the loop when the correct answer is found.

The `else` block belongs to the `for` loop. It executes only when the loop finishes all three attempts without encountering `break`.

If the player fails all three attempts, `"❌ Game Over!"` is displayed.

This version introduces an important Python concept: **`for...else`**.

---

Comparison of Methods

| Method                  | Best For                               |

| ---------------          | -------------------------------------- |

| Basic Guess           | Understanding random numbers and input |

| Guess With Hint     | Learning conditional statements        |

| 3 Attempts              | Practicing loops and `break`           |

🔥 Key Takeaways

* `random.randint()` can be used to generate a random number.

* `input()` allows the player to enter a guess.

* `if`, `elif`, and `else` help compare the user's guess with the secret number.

* `for` loops can be used to provide multiple attempts.

* `break` stops the game when the correct number is guessed.

* Python's `for...else` can detect when all attempts are completed without success.

* Number Guessing Game is a great beginner project for practicing Python fundamentals.

🚀 **Stay tuned for Day 106 of the #150DaysOfPython series!**

Read More

 

🚀 Day 100/150 – Decorator Example in Python

A decorator is a special function in Python that allows you to add extra functionality to another function without modifying its original code. Decorators are commonly used for logging, authentication, timing functions, and more.

In this post, we'll explore four common examples of decorators in Python.

---

Method 1 – Basic Decorator

Create a simple decorator that prints a message before calling a function.

def decorator(func):
    def wrapper():
        print("Before the function is called")
        func()
    return wrapper

@decorator
def greet():
    print("Hello, World!")

greet()

Output

Before the function is called 

Hello, World!

Explanation

• decorator() accepts a function as an argument.

• wrapper() adds extra functionality before calling the original function.

• @decorator applies the decorator to greet().

• Calling greet() actually executes wrapper().

---

Method 2 – Decorator with Function Arguments

Decorators can also work with functions that take parameters.

def decorator(func):
    def wrapper(name):
        print("Welcome!")
        func(name)
    return wrapper

@decorator
def greet(name):
    print("Hello,", name)

greet("Alice")










Output

Welcome!
Hello, Alice

Explanation

• wrapper(name) accepts the argument passed to greet().

• It prints a welcome message before calling the original function.

• The original function receives the same argument.

---

Method 3 – Decorator that Executes Code Before and After

A decorator can execute code both before and after the original function.

def decorator(func):
    def wrapper():
        print("Starting...")
        func()
        print("Finished!")
    return wrapper

@decorator
def task():
    print("Task is running")

task()

Output

Starting... 

Task is running 

Finished!

Explanation

• The decorator prints "Starting...".

• It then calls the original function.

• After the function finishes, it prints "Finished!".

• This is useful for logging and monitoring function execution.

---

Method 4 – Taking User Input

Use a decorator with a function that accepts user input.

def decorator(func):
    def wrapper(name):
        print("Welcome to Python!")
        func(name)
    return wrapper

@decorator
def greet(name):
    print("Hello,", name)

name = input("Enter your name: ")

greet(name)

Sample Input

Sam

Output

Welcome to Python! 

Hello, Sam

Explanation

• The user enters a name.

• The decorator displays a welcome message.

• The original function greets the user using the entered name.

---

Comparison of Methods

Method	Best For
Basic Decorator	Understanding how decorators work
Decorator with Arguments	Functions that accept parameters
Before and After Execution	Logging and monitoring
User Input	Interactive programs

---

🔥 Key Takeaways

• A decorator adds extra functionality to a function without changing its original code.

• Decorators are created using functions that return another function.

• The @decorator syntax is used to apply a decorator.

• Decorators can work with functions that have parameters.

• They are commonly used for logging, authentication, timing, caching, and validation.

Stay tuned for Day 101 of the #150DaysOfPython series! 🚀

Read More