🧠 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

❤️ React for more

✅ 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 👍👍

Are you looking to become a machine learning engineer? The algorithm brought you to the right place! 📌

I created a free and comprehensive roadmap. Let's go through this thread and explore what you need to know to become an expert machine learning engineer:

Math & Statistics

Just like most other data roles, machine learning engineering starts with strong foundations from math, precisely linear algebra, probability and statistics.

Here are the probability units you will need to focus on:

Basic probability concepts statistics
Inferential statistics
Regression analysis
Experimental design and A/B testing Bayesian statistics
Calculus
Linear algebra

Python:

You can choose Python, R, Julia, or any other language, but Python is the most versatile and flexible language for machine learning.

Variables, data types, and basic operations
Control flow statements (e.g., if-else, loops)
Functions and modules
Error handling and exceptions
Basic data structures (e.g., lists, dictionaries, tuples)
Object-oriented programming concepts
Basic work with APIs
Detailed data structures and algorithmic thinking

Machine Learning Prerequisites:

Exploratory Data Analysis (EDA) with NumPy and Pandas
Basic data visualization techniques to visualize the variables and features.
Feature extraction
Feature engineering
Different types of encoding data

Machine Learning Fundamentals

Using scikit-learn library in combination with other Python libraries for:

Supervised Learning: (Linear Regression, K-Nearest Neighbors, Decision Trees)
Unsupervised Learning: (K-Means Clustering, Principal Component Analysis, Hierarchical Clustering)
Reinforcement Learning: (Q-Learning, Deep Q Network, Policy Gradients)

Solving two types of problems:
Regression
Classification

Neural Networks:
Neural networks are like computer brains that learn from examples, made up of layers of "neurons" that handle data. They learn without explicit instructions.

Types of Neural Networks:

Feedforward Neural Networks: Simplest form, with straight connections and no loops.
Convolutional Neural Networks (CNNs): Great for images, learning visual patterns.
Recurrent Neural Networks (RNNs): Good for sequences like text or time series, because they remember past information.

In Python, it’s the best to use TensorFlow and Keras libraries, as well as PyTorch, for deeper and more complex neural network systems.

Deep Learning:

Deep learning is a subset of machine learning in artificial intelligence (AI) that has networks capable of learning unsupervised from data that is unstructured or unlabeled.

Convolutional Neural Networks (CNNs)
Recurrent Neural Networks (RNNs)
Long Short-Term Memory Networks (LSTMs)
Generative Adversarial Networks (GANs)
Autoencoders
Deep Belief Networks (DBNs)
Transformer Models

Machine Learning Project Deployment

Machine learning engineers should also be able to dive into MLOps and project deployment. Here are the things that you should be familiar or skilled at:

Version Control for Data and Models
Automated Testing and Continuous Integration (CI)
Continuous Delivery and Deployment (CD)
Monitoring and Logging
Experiment Tracking and Management
Feature Stores
Data Pipeline and Workflow Orchestration
Infrastructure as Code (IaC)
Model Serving and APIs

Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624

Credits: https://t.me/datasciencefun

Like if you need similar content 😄👍

Hope this helps you 😊

Generate Barcode using Python 👆