https://topmate.io/coding/898340

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⌨️ Grammar Correction using Python

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Important Sorting Algorithms-

Bubble Sort: Bubble Sort is the most basic sorting algorithm, and it works by repeatedly swapping adjacent elements if they are out of order.

Merge Sort: Merge sort is a sorting technique that uses the divide and conquer strategy.

Quicksort: Quicksort is a popular sorting algorithm that performs n log n comparisons on average when sorting an array of n elements. It is a more efficient and faster sorting algorithm.

Heap Sort: Heap sort works by visualizing the array elements as a special type of complete binary tree known as a heap.

Important Searching Algorithms-

Binary Search: Binary search employs the divide and conquer strategy, in which a sorted list is divided into two halves and the item is compared to the list’s middle element. If a match is found, the middle element’s location is returned.

Breadth-First Search(BFS): Breadth-first search is a graph traversal algorithm that begins at the root node and explores all neighboring nodes.

Depth-First Search(DFS): The depth-first search (DFS) algorithm begins with the first node of the graph and proceeds to go deeper and deeper until we find the goal node or node with no children.

#coding

Here is the list of few projects (found on kaggle). They cover Basics of Python, Advanced Statistics, Supervised Learning (Regression and Classification problems) & Data Science

Please also check the discussions and notebook submissions for different approaches and solution after you tried yourself.

1. Basic python and statistics

Pima Indians :- https://www.kaggle.com/uciml/pima-indians-diabetes-database
Cardio Goodness fit :- https://www.kaggle.com/saurav9786/cardiogoodfitness
Automobile :- https://www.kaggle.com/toramky/automobile-dataset

2. Advanced Statistics

Game of Thrones:-https://www.kaggle.com/mylesoneill/game-of-thrones
World University Ranking:-https://www.kaggle.com/mylesoneill/world-university-rankings
IMDB Movie Dataset:- https://www.kaggle.com/carolzhangdc/imdb-5000-movie-dataset

3. Supervised Learning

a) Regression Problems

How much did it rain :- https://www.kaggle.com/c/how-much-did-it-rain-ii/overview
Inventory Demand:- https://www.kaggle.com/c/grupo-bimbo-inventory-demand
Property Inspection predictiion:- https://www.kaggle.com/c/liberty-mutual-group-property-inspection-prediction
Restaurant Revenue prediction:- https://www.kaggle.com/c/restaurant-revenue-prediction/data
IMDB Box office Prediction:-https://www.kaggle.com/c/tmdb-box-office-prediction/overview

b) Classification problems

Employee Access challenge :- https://www.kaggle.com/c/amazon-employee-access-challenge/overview
Titanic :- https://www.kaggle.com/c/titanic
San Francisco crime:- https://www.kaggle.com/c/sf-crime
Customer satisfcation:-https://www.kaggle.com/c/santander-customer-satisfaction
Trip type classification:- https://www.kaggle.com/c/walmart-recruiting-trip-type-classification
Categorize cusine:- https://www.kaggle.com/c/whats-cooking

4. Some helpful Data science projects for beginners

https://www.kaggle.com/c/house-prices-advanced-regression-techniques

https://www.kaggle.com/c/digit-recognizer

https://www.kaggle.com/c/titanic

5. Intermediate Level Data science Projects

Black Friday Data : https://www.kaggle.com/sdolezel/black-friday

Human Activity Recognition Data : https://www.kaggle.com/uciml/human-activity-recognition-with-smartphones

Trip History Data : https://www.kaggle.com/pronto/cycle-share-dataset

Million Song Data : https://www.kaggle.com/c/msdchallenge

Census Income Data : https://www.kaggle.com/c/census-income/data

Movie Lens Data : https://www.kaggle.com/grouplens/movielens-20m-dataset

Twitter Classification Data : https://www.kaggle.com/c/twitter-sentiment-analysis2

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Python Detailed Roadmap 🚀

📌 1. Basics
◼ Data Types & Variables
◼ Operators & Expressions
◼ Control Flow (if, loops)

📌 2. Functions & Modules
◼ Defining Functions
◼ Lambda Functions
◼ Importing & Creating Modules

📌 3. File Handling
◼ Reading & Writing Files
◼ Working with CSV & JSON

📌 4. Object-Oriented Programming (OOP)
◼ Classes & Objects
◼ Inheritance & Polymorphism
◼ Encapsulation

📌 5. Exception Handling
◼ Try-Except Blocks
◼ Custom Exceptions

📌 6. Advanced Python Concepts
◼ List & Dictionary Comprehensions
◼ Generators & Iterators
◼ Decorators

📌 7. Essential Libraries
◼ NumPy (Arrays & Computations)
◼ Pandas (Data Analysis)
◼ Matplotlib & Seaborn (Visualization)

📌 8. Web Development & APIs
◼ Web Scraping (BeautifulSoup, Scrapy)
◼ API Integration (Requests)
◼ Flask & Django (Backend Development)

📌 9. Automation & Scripting
◼ Automating Tasks with Python
◼ Working with Selenium & PyAutoGUI

📌 10. Data Science & Machine Learning
◼ Data Cleaning & Preprocessing
◼ Scikit-Learn (ML Algorithms)
◼ TensorFlow & PyTorch (Deep Learning)

📌 11. Projects
◼ Build Real-World Applications
◼ Showcase on GitHub

📌 12. ✅ Apply for Jobs
◼ Strengthen Resume & Portfolio
◼ Prepare for Technical Interviews

Like for more ❤️💪

🔰 Deep Python Roadmap for Beginners 🐍

Setup & Installation 🖥⚙️
• Install Python, choose an IDE (VS Code, PyCharm)
• Set up virtual environments for project isolation 🌎

Basic Syntax & Data Types 📝🔢
• Learn variables, numbers, strings, booleans
• Understand comments, basic input/output, and simple expressions ✍️

Control Flow & Loops 🔄🔀
• Master conditionals (if, elif, else)
• Practice loops (for, while) and use control statements like break and continue 👮

Functions & Scope ⚙️🎯
• Define functions with def and learn about parameters and return values
• Explore lambda functions, recursion, and variable scope 📜

Data Structures 📊📚
• Work with lists, tuples, sets, and dictionaries
• Learn list comprehensions and built-in methods for data manipulation ⚙️

Object-Oriented Programming (OOP) 🏗👩‍💻
• Understand classes, objects, and methods
• Dive into inheritance, polymorphism, and encapsulation 🔍

React "❤️" for Part 2

Python Cheatsheet 🚀

1️⃣ Variables & Data Types

x = 10 (Integer)

y = 3.14 (Float)

name = "Python" (String)

is_valid = True (Boolean)

items = [1, 2, 3] (List)

data = (1, 2, 3) (Tuple)

person = {"name": "Alice", "age": 25} (Dictionary)


2️⃣ Operators

Arithmetic: +, -, *, /, //, %, **

Comparison: ==, !=, >, <, >=, <=

Logical: and, or, not

Membership: in, not in


3️⃣ Control Flow

If-Else:

if age > 18:
print("Adult")
elif age == 18:
print("Just turned 18")
else:
print("Minor")

Loops:

for i in range(5):
print(i)
while x < 10:
x += 1


4️⃣ Functions

Defining & Calling:

def greet(name):
return f"Hello, {name}"
print(greet("Alice"))

Lambda Functions: add = lambda x, y: x + y


5️⃣ Lists & Dictionary Operations

Append: items.append(4)

Remove: items.remove(2)

List Comprehension: [x**2 for x in range(5)]

Dictionary Access: person["name"]


6️⃣ File Handling

Read File:

with open("file.txt", "r") as f:
content = f.read()

Write File:

with open("file.txt", "w") as f:
f.write("Hello, World!")


7️⃣ Exception Handling

try:
result = 10 / 0
except ZeroDivisionError:
print("Cannot divide by zero!")
finally:
print("Done")

8️⃣ Modules & Packages

Importing:

import math
print(math.sqrt(25))

Creating a Module (mymodule.py):

def add(x, y):
return x + y

Usage: from mymodule import add


9️⃣ Object-Oriented Programming (OOP)

Defining a Class:

class Person:
def init(self, name, age):
self.name = name
self.age = age
def greet(self):
return f"Hello, my name is {self.name}"

Creating an Object: p = Person("Alice", 25)


🔟 Useful Libraries

NumPy: import numpy as np

Pandas: import pandas as pd

Matplotlib: import matplotlib.pyplot as plt

Requests: import requests

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ENJOY LEARNING 👍👍

Python Cheatsheet 🚀

1️⃣ Variables & Data Types

x = 10 (Integer)

y = 3.14 (Float)

name = "Python" (String)

is_valid = True (Boolean)

items = [1, 2, 3] (List)

data = (1, 2, 3) (Tuple)

person = {"name": "Alice", "age": 25} (Dictionary)


2️⃣ Operators

Arithmetic: +, -, *, /, //, %, **

Comparison: ==, !=, >, <, >=, <=

Logical: and, or, not

Membership: in, not in


3️⃣ Control Flow

If-Else:

if age > 18:
print("Adult")
elif age == 18:
print("Just turned 18")
else:
print("Minor")

Loops:

for i in range(5):
print(i)
while x < 10:
x += 1


4️⃣ Functions

Defining & Calling:

def greet(name):
return f"Hello, {name}"
print(greet("Alice"))

Lambda Functions: add = lambda x, y: x + y


5️⃣ Lists & Dictionary Operations

Append: items.append(4)

Remove: items.remove(2)

List Comprehension: [x**2 for x in range(5)]

Dictionary Access: person["name"]


6️⃣ File Handling

Read File:

with open("file.txt", "r") as f:
content = f.read()

Write File:

with open("file.txt", "w") as f:
f.write("Hello, World!")


7️⃣ Exception Handling

try:
result = 10 / 0
except ZeroDivisionError:
print("Cannot divide by zero!")
finally:
print("Done")

8️⃣ Modules & Packages

Importing:

import math
print(math.sqrt(25))

Creating a Module (mymodule.py):

def add(x, y):
return x + y

Usage: from mymodule import add


9️⃣ Object-Oriented Programming (OOP)

Defining a Class:

class Person:
def init(self, name, age):
self.name = name
self.age = age
def greet(self):
return f"Hello, my name is {self.name}"

Creating an Object: p = Person("Alice", 25)


🔟 Useful Libraries

NumPy: import numpy as np

Pandas: import pandas as pd

Matplotlib: import matplotlib.pyplot as plt

Requests: import requests

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ENJOY LEARNING 👍👍

Here's a concise cheat sheet to help you get started with Python for Data Analytics. This guide covers essential libraries and functions that you'll frequently use.


1. Python Basics
- Variables:
x = 10
y = "Hello"

- Data Types:
  - Integers: x = 10
  - Floats: y = 3.14
  - Strings: name = "Alice"
  - Lists: my_list = [1, 2, 3]
  - Dictionaries: my_dict = {"key": "value"}
  - Tuples: my_tuple = (1, 2, 3)

- Control Structures:
  - if, elif, else statements
  - Loops: 
  

    for i in range(5):
        print(i)
    

  - While loop:
  

    while x < 5:
        print(x)
        x += 1
    

2. Importing Libraries

- NumPy:

  import numpy as np
  

- Pandas:

  import pandas as pd
  

- Matplotlib:

  import matplotlib.pyplot as plt
  

- Seaborn:

  import seaborn as sns
  

3. NumPy for Numerical Data

- Creating Arrays:

  arr = np.array([1, 2, 3, 4])
  

- Array Operations:

  arr.sum()
  arr.mean()
  

- Reshaping Arrays:

  arr.reshape((2, 2))
  

- Indexing and Slicing:

  arr[0:2]  # First two elements
  

4. Pandas for Data Manipulation

- Creating DataFrames:

  df = pd.DataFrame({
      'col1': [1, 2, 3],
      'col2': ['A', 'B', 'C']
  })
  

- Reading Data:

  df = pd.read_csv('file.csv')
  

- Basic Operations:

  df.head()          # First 5 rows
  df.describe()      # Summary statistics
  df.info()          # DataFrame info
  

- Selecting Columns:

  df['col1']
  df[['col1', 'col2']]
  

- Filtering Data:

  df[df['col1'] > 2]
  

- Handling Missing Data:

  df.dropna()        # Drop missing values
  df.fillna(0)       # Replace missing values
  

- GroupBy:

  df.groupby('col2').mean()
  

5. Data Visualization

- Matplotlib:

  plt.plot(df['col1'], df['col2'])
  plt.xlabel('X-axis')
  plt.ylabel('Y-axis')
  plt.title('Title')
  plt.show()
  

- Seaborn:

  sns.histplot(df['col1'])
  sns.boxplot(x='col1', y='col2', data=df)
  

6. Common Data Operations

- Merging DataFrames:

  pd.merge(df1, df2, on='key')
  

- Pivot Table:

  df.pivot_table(index='col1', columns='col2', values='col3')
  

- Applying Functions:

  df['col1'].apply(lambda x: x*2)
  

7. Basic Statistics

- Descriptive Stats:

  df['col1'].mean()
  df['col1'].median()
  df['col1'].std()
  

- Correlation:

  df.corr()
  

This cheat sheet should give you a solid foundation in Python for data analytics. As you get more comfortable, you can delve deeper into each library's documentation for more advanced features.

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If I Were to Start My Data Science Career from Scratch, Here's What I Would Do 👇

1️⃣ Master Advanced SQL

Foundations: Learn database structures, tables, and relationships.

Basic SQL Commands: SELECT, FROM, WHERE, ORDER BY.

Aggregations: Get hands-on with SUM, COUNT, AVG, MIN, MAX, GROUP BY, and HAVING.

JOINs: Understand LEFT, RIGHT, INNER, OUTER, and CARTESIAN joins.

Advanced Concepts: CTEs, window functions, and query optimization.

Metric Development: Build and report metrics effectively.


2️⃣ Study Statistics & A/B Testing

Descriptive Statistics: Know your mean, median, mode, and standard deviation.

Distributions: Familiarize yourself with normal, Bernoulli, binomial, exponential, and uniform distributions.

Probability: Understand basic probability and Bayes' theorem.

Intro to ML: Start with linear regression, decision trees, and K-means clustering.

Experimentation Basics: T-tests, Z-tests, Type 1 & Type 2 errors.

A/B Testing: Design experiments—hypothesis formation, sample size calculation, and sample biases.


3️⃣ Learn Python for Data

Data Manipulation: Use pandas for data cleaning and manipulation.

Data Visualization: Explore matplotlib and seaborn for creating visualizations.

Hypothesis Testing: Dive into scipy for statistical testing.

Basic Modeling: Practice building models with scikit-learn.


4️⃣ Develop Product Sense

Product Management Basics: Manage projects and understand the product life cycle.

Data-Driven Strategy: Leverage data to inform decisions and measure success.

Metrics in Business: Define and evaluate metrics that matter to the business.


5️⃣ Hone Soft Skills

Communication: Clearly explain data findings to technical and non-technical audiences.

Collaboration: Work effectively in teams.

Time Management: Prioritize and manage projects efficiently.

Self-Reflection: Regularly assess and improve your skills.


6️⃣ Bonus: Basic Data Engineering

Data Modeling: Understand dimensional modeling and trade-offs in normalization vs. denormalization.

ETL: Set up extraction jobs, manage dependencies, clean and validate data.

Pipeline Testing: Conduct unit testing and ensure data quality throughout the pipeline.

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Important Topics You Should Know to Learn Python 👇

Lists, Strings, Tuples, Dictionaries, Sets – Learn the core data structures in Python.

Boolean, Arithmetic, and Comparison Operators – Understand how Python evaluates conditions.

Operations on Data Structures – Append, delete, insert, reverse, sort, and manipulate collections efficiently.

Reading and Extracting Data – Learn how to access, modify, and extract values from lists and dictionaries.

Conditions and Loops – Master if, elif, else, for, while, break, and continue statements.

Range and Enumerate – Efficiently loop through sequences with indexing.

Functions – Create functions with and without parameters, and understand *args and **kwargs.

Classes & Object-Oriented Programming – Work with init methods, global/local variables, and concepts like inheritance and encapsulation.

File Handling – Read, write, and manipulate files in Python.


Free Resources to learn Python👇👇

👉 Free Python course by Google

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👉 Udacity Intro to Python course

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👉Python Cheatsheet

https://t.iss.one/pythondevelopersindia/262?single

👉 Practice Python

https://www.pythonchallenge.com/

👉 Kaggle

https://kaggle.com/learn/intro-to-programming
https://kaggle.com/learn/python

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ENJOY LEARNING 👍👍

🚀 Complete Roadmap to Become a Data Scientist in 5 Months

📅 Week 1-2: Fundamentals
✅ Day 1-3: Introduction to Data Science, its applications, and roles.
✅ Day 4-7: Brush up on Python programming 🐍.
✅ Day 8-10: Learn basic statistics 📊 and probability 🎲.

🔍 Week 3-4: Data Manipulation & Visualization
📝 Day 11-15: Master Pandas for data manipulation.
📈 Day 16-20: Learn Matplotlib & Seaborn for data visualization.

🤖 Week 5-6: Machine Learning Foundations
🔬 Day 21-25: Introduction to scikit-learn.
📊 Day 26-30: Learn Linear & Logistic Regression.

🏗 Week 7-8: Advanced Machine Learning
🌳 Day 31-35: Explore Decision Trees & Random Forests.
📌 Day 36-40: Learn Clustering (K-Means, DBSCAN) & Dimensionality Reduction.

🧠 Week 9-10: Deep Learning
🤖 Day 41-45: Basics of Neural Networks with TensorFlow/Keras.
📸 Day 46-50: Learn CNNs & RNNs for image & text data.

🏛 Week 11-12: Data Engineering
🗄 Day 51-55: Learn SQL & Databases.
🧹 Day 56-60: Data Preprocessing & Cleaning.

📊 Week 13-14: Model Evaluation & Optimization
📏 Day 61-65: Learn Cross-validation & Hyperparameter Tuning.
📉 Day 66-70: Understand Evaluation Metrics (Accuracy, Precision, Recall, F1-score).

🏗 Week 15-16: Big Data & Tools
🐘 Day 71-75: Introduction to Big Data Technologies (Hadoop, Spark).
☁️ Day 76-80: Learn Cloud Computing (AWS, GCP, Azure).

🚀 Week 17-18: Deployment & Production
🛠 Day 81-85: Deploy models using Flask or FastAPI.
📦 Day 86-90: Learn Docker & Cloud Deployment (AWS, Heroku).

🎯 Week 19-20: Specialization
📝 Day 91-95: Choose NLP or Computer Vision, based on your interest.

🏆 Week 21-22: Projects & Portfolio
📂 Day 96-100: Work on Personal Data Science Projects.

💬 Week 23-24: Soft Skills & Networking
🎤 Day 101-105: Improve Communication & Presentation Skills.
🌐 Day 106-110: Attend Online Meetups & Forums.

🎯 Week 25-26: Interview Preparation
💻 Day 111-115: Practice Coding Interviews (LeetCode, HackerRank).
📂 Day 116-120: Review your projects & prepare for discussions.

👨‍💻 Week 27-28: Apply for Jobs
📩 Day 121-125: Start applying for Entry-Level Data Scientist positions.

🎤 Week 29-30: Interviews
📝 Day 126-130: Attend Interviews & Practice Whiteboard Problems.

🔄 Week 31-32: Continuous Learning
📰 Day 131-135: Stay updated with the Latest Data Science Trends.

🏆 Week 33-34: Accepting Offers
📝 Day 136-140: Evaluate job offers & Negotiate Your Salary.

🏢 Week 35-36: Settling In
🎯 Day 141-150: Start your New Data Science Job, adapt & keep learning!

🎉 Enjoy Learning & Build Your Dream Career in Data Science! 🚀🔥