Python Roadmap 🐍 (Beginner → Job)

📂 Syntax Basics
∟ Variables, Data Types
∟ Conditions & Loops

📂 Data Structures
∟ List, Tuple, Set, Dict
∟ Comprehensions

📂 Algorithms
∟ Searching & Sorting
∟ Recursion & Big-O

📂 OOP Concepts
∟ Class & Object
∟ Inheritance & Polymorphism

📂 Modules & Errors
∟ Import & pip
∟ try / except

📂 File Handling
∟ Read / Write Files
∟ CSV & JSON

📂 Networking
∟ APIs & Requests
∟ JSON Data

📂 Security Basics
∟ Password Hashing
∟ API Keys

📂 Practice & Projects
∟ Mini Programs
∟ Real Projects

∟✅ Job / Internship Ready

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Kandinsky 5.0 Video Lite and Kandinsky 5.0 Video Pro generative models on the global text-to-video landscape

🔘Pro is currently the #1 open-source model worldwide
🔘Lite (2B parameters) outperforms Sora v1.
🔘Only Google (Veo 3.1, Veo 3), OpenAI (Sora 2), Alibaba (Wan 2.5), and KlingAI (Kling 2.5, 2.6) outperform Pro — these are objectively the strongest video generation models in production today. We are on par with Luma AI (Ray 3) and MiniMax (Hailuo 2.3): the maximum ELO gap is 3 points, with a 95% CI of ±21.

Useful links
🔘Full leaderboard: LM Arena
🔘Kandinsky 5.0 details: technical report
🔘Open-source Kandinsky 5.0: GitHub and Hugging Face

🧠 DSA with Python Roadmap (Beginner → Interview)

📂 DSA Foundations
∟ What is DSA
∟ Time & Space Complexity

📂 Python for DSA
∟ Lists & Strings
∟ Set & Dictionary Usage

📂 Searching Algorithms
∟ Linear Search
∟ Binary Search

📂 Sorting Algorithms
∟ Bubble, Selection, Insertion
∟ Merge & Quick Sort

📂 Recursion Basics
∟ Base & Recursive Case
∟ Stack Memory

📂 Stack & Queue
∟ Stack using List
∟ Queue using Deque

📂 Linked List
∟ Singly & Doubly Linked List
∟ Slow & Fast Pointer

📂 Problem-Solving Patterns
∟ Two Pointer
∟ Sliding Window

📂 Hashing Techniques
∟ Frequency Count
∟ Subarray Problems

📂 Trees
∟ Binary Tree
∟ Tree Traversals

📂 Heap & Priority Queue
∟ Min / Max Heap
∟ Top-K Problems

📂 Graphs
∟ BFS & DFS
∟ Cycle Detection

📂 Dynamic Programming
∟ Memoization
∟ Tabulation

📂 Bit Manipulation
∟ Bitwise Operators
∟ Common Tricks

📂 Practice Strategy
∟ Beginner → Advanced Problems
∟ Revision & Mock Interviews

∟✅ Interview Ready

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✅ Python Coding Interview Questions 🐍💻

1️⃣ Q: Return the first duplicate in a list.

def first_duplicate(lst):
    seen = set()
    for x in lst:
        if x in seen:
            return x
        seen.add(x)
    return None

print(first_duplicate([3, 1, 3, 4, 2]))  # Output: 3

2️⃣ Q: Check whether a number is a palindrome.

def is_pal_num(n):
    return str(n) == str(n)[::-1]

print(is_pal_num(121))  # True  
print(is_pal_num(123))  # False

3️⃣ Q: Sort a dictionary by values.

def sort_by_value(d):
    return dict(sorted(d.items(), key=lambda x: x[1]))

print(sort_by_value({'a': 3, 'b': 1, 'c': 2}))  
Output: {'b': 1, 'c': 2, 'a': 3}

4️⃣ Q: Return all prime numbers in a given range.

def primes_upto(n):
    primes = []
    for num in range(2, n + 1):
        for i in range(2, int(num**0.5) + 1):
            if num % i == 0:
                break
        else:
            primes.append(num)
    return primes

print(primes_upto(10))  # [2, 3, 5, 7]

5️⃣ Q: Convert a list of numbers into a string.

def list_to_string(lst):
    return "".join(map(str, lst))

print(list_to_string([1, 2, 3]))  # Output: 123

💬 Double Tap ❤️ for Part-12

📖 Data Science Packages

Essential Python and SQL topics for data analysts 😄👇

Python Topics:

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:

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!

Python Resources - https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L

SQL Resources - https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v

Hope it helps :)

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

Share with credits: https://t.me/sqlproject

ENJOY LEARNING 👍👍