Data Science Roadmap
|
|-- Fundamentals
| |-- Mathematics
| | |-- Linear Algebra
| | |-- Calculus
| | |-- Probability and Statistics
| |
| |-- Programming
| | |-- Python
| | |-- R
| | |-- SQL
|
|-- Data Collection and Cleaning
| |-- Data Sources
| | |-- APIs
| | |-- Web Scraping
| | |-- Databases
| |
| |-- Data Cleaning
| | |-- Missing Values
| | |-- Data Transformation
| | |-- Data Normalization
|
|-- Data Analysis
| |-- Exploratory Data Analysis (EDA)
| | |-- Descriptive Statistics
| | |-- Data Visualization
| | |-- Hypothesis Testing
| |
| |-- Data Wrangling
| | |-- Pandas
| | |-- NumPy
| | |-- dplyr (R)
|
|-- Machine Learning
| |-- Supervised Learning
| | |-- Regression
| | |-- Classification
| |
| |-- Unsupervised Learning
| | |-- Clustering
| | |-- Dimensionality Reduction
| |
| |-- Reinforcement Learning
| | |-- Q-Learning
| | |-- Policy Gradient Methods
| |
| |-- Model Evaluation
| | |-- Cross-Validation
| | |-- Performance Metrics
| | |-- Hyperparameter Tuning
|
|-- Deep Learning
| |-- Neural Networks
| | |-- Feedforward Networks
| | |-- Backpropagation
| |
| |-- Advanced Architectures
| | |-- Convolutional Neural Networks (CNN)
| | |-- Recurrent Neural Networks (RNN)
| | |-- Transformers
| |
| |-- Tools and Frameworks
| | |-- TensorFlow
| | |-- PyTorch
|
|-- Natural Language Processing (NLP)
| |-- Text Preprocessing
| | |-- Tokenization
| | |-- Stop Words Removal
| | |-- Stemming and Lemmatization
| |
| |-- NLP Techniques
| | |-- Word Embeddings
| | |-- Sentiment Analysis
| | |-- Named Entity Recognition (NER)
|
|-- Data Visualization
| |-- Basic Plotting
| | |-- Matplotlib
| | |-- Seaborn
| | |-- ggplot2 (R)
| |
| |-- Interactive Visualization
| | |-- Plotly
| | |-- Bokeh
| | |-- Dash
|
|-- Big Data
| |-- Tools and Frameworks
| | |-- Hadoop
| | |-- Spark
| |
| |-- NoSQL Databases
| |-- MongoDB
| |-- Cassandra
|
|-- Cloud Computing
| |-- Cloud Platforms
| | |-- AWS
| | |-- Google Cloud
| | |-- Azure
| |
| |-- Data Services
| |-- Data Storage (S3, Google Cloud Storage)
| |-- Data Pipelines (Dataflow, AWS Data Pipeline)
|
|-- Model Deployment
| |-- Serving Models
| | |-- Flask/Django
| | |-- FastAPI
| |
| |-- Model Monitoring
| |-- Performance Tracking
| |-- A/B Testing
|
|-- Domain Knowledge
| |-- Industry-Specific Applications
| | |-- Finance
| | |-- Healthcare
| | |-- Retail
|
|-- Ethical and Responsible AI
| |-- Bias and Fairness
| |-- Privacy and Security
| |-- Interpretability and Explainability
|
|-- Communication and Storytelling
| |-- Reporting
| |-- Dashboarding
| |-- Presentation Skills
|
|-- Advanced Topics
| |-- Time Series Analysis
| |-- Anomaly Detection
| |-- Graph Analytics
| |-- *PH4N745M*
└-- Comments
|-- # Single-line comment (Python)
└-- /* Multi-line comment (Python/R) */

Useful AI courses for free: 📱🤖

𝟭. Prompt Engineering Basics:
https://skillbuilder.aws/search?searchText=foundations-of-prompt-engineering&showRedirectNotFoundBanner=true

𝟮. ChatGPT Prompts Mastery:
https://deeplearning.ai/short-courses/chatgpt-prompt-engineering-for-developers/

𝟯. Intro to Generative AI:
https://cloudskillsboost.google/course_templates/536

𝟰. AI Introduction by Harvard:
https://pll.harvard.edu/course/cs50s-introduction-artificial-intelligence-python/2023-05

𝟱. Microsoft GenAI Basics:
https://linkedin.com/learning/what-is-generative-ai/generative-ai-is-a-tool-in-service-of-humanity

𝟲. Prompt Engineering Pro:
https://learnprompting.org

𝟳. Google’s Ethical AI:
https://cloudskillsboost.google/course_templates/554

𝟴. Harvard Machine Learning:
https://pll.harvard.edu/course/data-science-machine-learning

𝟵. LangChain App Developer:
https://deeplearning.ai/short-courses/langchain-for-llm-application-development/

𝟭𝟬. Bing Chat Applications:
https://linkedin.com/learning/streamlining-your-work-with-microsoft-bing-chat

𝟭𝟭. Generative AI by Microsoft:
https://learn.microsoft.com/en-us/training/paths/introduction-to-ai-on-azure/

𝟭𝟮. Amazon’s AI Strategy:
https://skillbuilder.aws/search?searchText=generative-ai-learning-plan-for-decision-makers&showRedirectNotFoundBanner=true

𝟭𝟯. GenAI for Everyone:
https://deeplearning.ai/courses/generative-ai-for-everyone/

React ♥️ for more

✅ 100 Days Artificial Intelligence Roadmap – 2025 🤖🚀

📍 Days 1–10: Python for AI
– Install Python, Jupyter
– Learn Python basics & data structures
– Numpy & Pandas for data wrangling

📍 Days 11–20: Math & Statistics Foundations
– Linear algebra: vectors, matrices
– Probability, statistics, distributions
– Understand data normalization, scaling

📍 Days 21–30: Data Exploration & Visualization
– Data cleaning basics
– Use Matplotlib, Seaborn for visuals
– Explore and summarize datasets

📍 Days 31–40: SQL & Databases
– Learn SQL queries (SELECT, JOIN, GROUP BY)
– Practice extracting data from relational databases

📍 Days 41–50: Core Machine Learning
– Supervised & unsupervised learning
– Scikit-learn basics (classification, regression, clustering)
– Model evaluation/metrics

📍 Days 51–60: Advanced ML & Projects
– Feature engineering & selection
– Hyperparameter tuning, cross-validation
– Complete ML mini-projects

📍 Days 61–70: Deep Learning Foundations
– Neural networks overview
– Use TensorFlow or PyTorch
– Build & train simple neural networks

📍 Days 71–80: Specialization – NLP / Computer Vision
– Basics of NLP or Image recognition
– Preprocessing, embeddings, CNN/RNN basics
– Work on a small domain project

📍 Days 81–90: MLOps & Deployment
– Version control with Git
– Model deployment basics (Flask/FastAPI)
– Track experiments, monitor models

📍 Days 91–100: GenAI, Trends & Capstone
– Explore Generative AI (LLMs, image generation)
– Ethics, prompt engineering
– Complete a capstone project, share on GitHub/portfolio

📚 React ❤️ for more!

✅ Data Science Fundamental Concepts You Should Know 📊🧠

1️⃣ Data Collection
Gathering raw data from various sources like databases, APIs, or web scraping for analysis.

2️⃣ Data Cleaning & Preprocessing
Preparing data by handling missing values, removing duplicates, correcting errors, and formatting for analysis.

3️⃣ Exploratory Data Analysis (EDA)
Using statistics and visualization to understand data patterns, trends, and detect outliers.

4️⃣ Statistical Inference
Drawing conclusions about populations using sample data through hypothesis testing, confidence intervals, and p-values.

5️⃣ Data Visualization
Creating charts and graphs (bar, line, scatter, histograms) to communicate insights clearly using tools like Matplotlib, Seaborn, or Tableau.

6️⃣ Feature Engineering
Transforming raw data into meaningful features that improve model performance, such as scaling, encoding and creating new variables.

7️⃣ Machine Learning Basics
Building predictive models by training algorithms on data:
⦁ Supervised Learning (regression, classification)
⦁ Unsupervised Learning (clustering, dimensionality reduction)

8️⃣ Model Evaluation
Assessing model accuracy using metrics like accuracy, precision, recall, F1 score (classification) and RMSE, MAE (regression).

9️⃣ Model Deployment
Putting your trained model into production so it can make real-time predictions or support decision-making.

🔟 Big Data & Tools
Handling large datasets using technologies like Hadoop, Spark, and databases such as SQL/NoSQL.

1️⃣1️⃣ Programming & Libraries
Essential coding skills in Python or R, with libraries like Pandas, NumPy, Scikit-learn for analysis and modeling.

1️⃣2️⃣ Data Ethics & Privacy
Ensuring responsible use of data, respecting privacy laws (GDPR), and avoiding biases in models.

💡 Tap ❤️ for more!

Famous programming languages and their frameworks


1. Python:

Frameworks:
Django
Flask
Pyramid
Tornado

2. JavaScript:

Frameworks (Front-End):
React
Angular
Vue.js
Ember.js
Frameworks (Back-End):
Node.js (Runtime)
Express.js
Nest.js
Meteor

3. Java:

Frameworks:
Spring Framework
Hibernate
Apache Struts
Play Framework

4. Ruby:

Frameworks:
Ruby on Rails (Rails)
Sinatra
Hanami

5. PHP:

Frameworks:
Laravel
Symfony
CodeIgniter
Yii
Zend Framework

6. C#:

Frameworks:
.NET Framework
ASP.NET
ASP.NET Core

7. Go (Golang):

Frameworks:
Gin
Echo
Revel

8. Rust:

Frameworks:
Rocket
Actix
Warp

9. Swift:

Frameworks (iOS/macOS):
SwiftUI
UIKit
Cocoa Touch

10. Kotlin:
- Frameworks (Android):
- Android Jetpack
- Ktor

11. TypeScript:
- Frameworks (Front-End):
- Angular
- Vue.js (with TypeScript)
- React (with TypeScript)

12. Scala:
- Frameworks:
- Play Framework
- Akka

13. Perl:
- Frameworks:
- Dancer
- Catalyst

14. Lua:
- Frameworks:
- OpenResty (for web development)

15. Dart:
- Frameworks:
- Flutter (for mobile app development)

16. R:
- Frameworks (for data science and statistics):
- Shiny
- ggplot2

17. Julia:
- Frameworks (for scientific computing):
- Pluto.jl
- Genie.jl

18. MATLAB:
- Frameworks (for scientific and engineering applications):
- Simulink

19. COBOL:
- Frameworks:
- COBOL-IT

20. Erlang:
- Frameworks:
- Phoenix (for web applications)

21. Groovy:
- Frameworks:
- Grails (for web applications)

✅10 Most Useful SQL Interview Queries (with Examples) 💼

1️⃣ Find the second highest salary:

SELECT MAX(salary)  
FROM employees  
WHERE salary < (SELECT MAX(salary) FROM employees);

2️⃣ Count employees in each department:

SELECT department, COUNT(*)  
FROM employees  
GROUP BY department;

3️⃣ Fetch duplicate emails:

SELECT email, COUNT(*)  
FROM users  
GROUP BY email  
HAVING COUNT(*) > 1;

4️⃣ Join orders with customer names:

SELECT c.name, o.order_date  
FROM customers c  
JOIN orders o ON c.id = o.customer_id;

5️⃣ Get top 3 highest salaries:

SELECT DISTINCT salary  
FROM employees  
ORDER BY salary DESC  
LIMIT 3;

6️⃣ Retrieve latest 5 logins:

SELECT * FROM logins  
ORDER BY login_time DESC  
LIMIT 5;

7️⃣ Employees with no manager:

SELECT name  
FROM employees  
WHERE manager_id IS NULL;

8️⃣ Search names starting with ‘S’:

SELECT * FROM employees  
WHERE name LIKE 'S%';

9️⃣ Total sales per month:

SELECT MONTH(order_date) AS month, SUM(amount)  
FROM sales  
GROUP BY MONTH(order_date);

🔟 Delete inactive users:

DELETE FROM users  
WHERE last_active < '2023-01-01';

✅ Tip: Master subqueries, joins, groupings & filters – they show up in nearly every interview!

💬 Tap ❤️ for more!

Ever wondered what the difference is between a Data Analyst and a Data Scientist? Both roles are in high demand, but they tackle data in different ways.

How to enter into Data Science

👉Start with the basics: Learn programming languages like Python and R to master data analysis and machine learning techniques. Familiarize yourself with tools such as TensorFlow, sci-kit-learn, and Tableau to build a strong foundation.

👉Choose your target field: From healthcare to finance, marketing, and more, data scientists play a pivotal role in extracting valuable insights from data. You should choose which field you want to become a data scientist in and start learning more about it.

👉Build a portfolio: Start building small projects and add them to your portfolio. This will help you build credibility and showcase your skills.

FREE FREE FREE

10 Books on Data Science & Data Analysis will be posted on this channel daily basis

Book 1. Python for Data Analysis

Publisher: O'Reilly

wesmckinney.com/book/

Give it a like if you want me to continue ❤️

2. Fundamentals of Data Visualization

Publisher: O'Reilly

clauswilke.com/dataviz/

Like for more ❤️

3. Hands-On Data Visualization

Publisher: O'Reilly

handsondataviz.org

10 Data Science Books

4. R for Data Science

Publisher: O'Reilly

🖇️ r4ds.hadley.nz

10 Data Science Books

5. Data Science at the Command Line

Publisher: O'Reilly

jeroenjanssens.com/dsatcl/

10 Data Science Books

6. Introduction to Probability for Data Science

Publisher: Michigan University

probability4datascience.com