How to start your career in data analysis for freshers 😄👇

1. Learn the Basics: Begin with understanding the fundamental concepts of statistics, mathematics, and programming languages like Python or R.

Free Resources: https://news.1rj.ru/str/pythonanalyst/103

2. Acquire Technical Skills: Develop proficiency in data analysis tools such as Excel, SQL, and data visualization tools like Tableau or Power BI.

Free Data Analysis Books: https://news.1rj.ru/str/learndataanalysis

3. Gain Knowledge in Statistics: A solid foundation in statistical concepts is crucial for data analysis. Learn about probability, hypothesis testing, and regression analysis.
Free course by Khan Academy will help you to enhance these skills.

4. Programming Proficiency: Enhance your programming skills, especially in languages commonly used in data analysis like Python or R. Familiarity with libraries such as Pandas and NumPy in Python is beneficial. Kaggle has amazing content to learn these skills.

5. Data Cleaning and Preprocessing: Understand the importance of cleaning and preprocessing data. Learn techniques to handle missing values, outliers, and transform data for analysis.

6. Database Knowledge: Acquire knowledge about databases and SQL for efficient data retrieval and manipulation.
SQL for data analytics: https://news.1rj.ru/str/sqlanalyst

7. Data Visualization: Master the art of presenting insights through visualizations. Learn tools like Matplotlib, Seaborn, or ggplot2 for creating meaningful charts and graphs. If you are from non-technical background, learn Tableau or Power BI.
FREE Resources to learn data visualization: https://news.1rj.ru/str/PowerBI_analyst

8. Machine Learning Basics: Familiarize yourself with basic machine learning concepts. This knowledge can be beneficial for advanced analytics tasks.
ML Basics: https://news.1rj.ru/str/datasciencefun/1476

9. Build a Portfolio: Work on projects that showcase your skills. This could be personal projects, contributions to open-source projects, or challenges from platforms like Kaggle.
Data Analytics Portfolio Projects: https://news.1rj.ru/str/DataPortfolio

10. Networking and Continuous Learning: Engage with the data science community, attend meetups, webinars, and conferences. Build your strong Linkedin profile and enhance your network.

11. Apply for Internships or Entry-Level Positions: Gain practical experience by applying for internships or entry-level positions in data analysis. Real-world projects contribute significantly to your learning.
Data Analyst Jobs & Internship opportunities: https://news.1rj.ru/str/jobs_SQL

12. Effective Communication: Develop strong communication skills. Being able to convey your findings and insights in a clear and understandable manner is crucial.

Share with credits: https://news.1rj.ru/str/sqlspecialist

Hope it helps :)

Complete Roadmap to become a data scientist in 5 months

Free Resources to learn Data Science: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D

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 and Visualization
- Day 11-15: Pandas for data manipulation.
- Day 16-20: Data visualization with Matplotlib and Seaborn.

Week 5-6: Machine Learning Foundations
- Day 21-25: Introduction to scikit-learn.
- Day 26-30: Linear regression and logistic regression.

Work on Data Science Projects: https://news.1rj.ru/str/pythonspecialist/29

Week 7-8: Advanced Machine Learning
- Day 31-35: Decision trees and random forests.
- Day 36-40: Clustering (K-Means, DBSCAN) and dimensionality reduction.

Week 9-10: Deep Learning
- Day 41-45: Basics of Neural Networks and TensorFlow/Keras.
- Day 46-50: Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs).

Week 11-12: Data Engineering
- Day 51-55: Learn about SQL and databases.
- Day 56-60: Data preprocessing and cleaning.

Week 13-14: Model Evaluation and Optimization
- Day 61-65: Cross-validation, hyperparameter tuning.
- Day 66-70: Evaluation metrics (accuracy, precision, recall, F1-score).

Week 15-16: Big Data and Tools
- Day 71-75: Introduction to big data technologies (Hadoop, Spark).
- Day 76-80: Basics of cloud computing (AWS, GCP, Azure).

Week 17-18: Deployment and Production
- Day 81-85: Model deployment with Flask or FastAPI.
- Day 86-90: Containerization with Docker, cloud deployment (AWS, Heroku).

Week 19-20: Specialization
- Day 91-95: NLP or Computer Vision, based on your interests.

Week 21-22: Projects and Portfolios
- Day 96-100: Work on personal data science projects.

Week 23-24: Soft Skills and Networking
- Day 101-105: Improve communication and presentation skills.
- Day 106-110: Attend online data science meetups or forums.

Week 25-26: Interview Preparation
- Day 111-115: Practice coding interviews on platforms like LeetCode.
- Day 116-120: Review your projects and be ready to discuss them.

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 trends in data science.

Week 33-34: Accepting Offers
- Day 136-140: Evaluate job offers and negotiate if necessary.

Week 35-36: Settling In
- Day 141-150: Start your new data science job, adapt to the team, and continue learning on the job.

ENJOY LEARNING 👍👍

30-days learning plan to master web development, covering HTML, CSS, JavaScript, and foundational concepts 👇👇

### Week 1: HTML and CSS Basics
Day 1-2: HTML Fundamentals
- Learn the structure of HTML documents.
- Tags: <!DOCTYPE html>, <html>, <head>, <body>, <noscript>, <h1> to <h6>, <p>, <a>, <img>, <div>, <span>, <ul>, <ol>, <li>, <table>, <form>.
- Practice by creating a simple webpage.

Day 3-4: CSS Basics
- Introduction to CSS: Selectors, properties, values.
- Inline, internal, and external CSS.
- Basic styling: colors, fonts, text alignment, borders, margins, padding.
- Create a basic styled webpage.

Day 5-6: CSS Layouts
- Box model.
- Display properties: block, inline-block, inline, none.
- Positioning: static, relative, absolute, fixed, sticky.
- Flexbox basics.

Day 7: Project
- Create a simple multi-page website using HTML and CSS.

### Week 2: Advanced CSS and Responsive Design
Day 8-9: Advanced CSS
- CSS Grid.
- Advanced selectors: attribute selectors, pseudo-classes, pseudo-elements.
- CSS variables.

Day 10-11: Responsive Design
- Media queries.
- Responsive units: em, rem, vh, vw.
- Mobile-first design principles.

Day 12-13: CSS Frameworks
- Introduction to frameworks (Bootstrap, Tailwind CSS).
- Basic usage of Bootstrap.

Day 14: Project
- Build a responsive website using Bootstrap or Tailwind CSS.

### Week 3: JavaScript Basics
Day 15-16: JavaScript Fundamentals
- Syntax, data types, variables, operators.
- Control structures: if-else, switch, loops (for, while).
- Functions and scope.

Day 17-18: DOM Manipulation
- Selecting elements (getElementById, querySelector).
- Modifying elements (text, styles, attributes).
- Event listeners.

Day 19-20: Working with Data
- Arrays and objects.
- Array methods: push, pop, shift, unshift, map, filter, reduce.
- Basic JSON handling.

Day 21: Project
- Create a dynamic webpage with JavaScript (e.g., a simple to-do list).

### Week 4: Advanced JavaScript and Final Project
Day 22-23: Advanced JavaScript
- ES6+ features: let/const, arrow functions, template literals, destructuring.
- Promises and async/await.
- Fetch API for AJAX requests.

Day 24-25: JavaScript Frameworks/Libraries
- Introduction to React (components, state, props).
- Basic React project setup.

Day 26-27: Version Control with Git
- Basic Git commands: init, clone, add, commit, push, pull.
- Branching and merging.

Day 28-29: Deployment
- Introduction to web hosting.
- Deploy a website using GitHub Pages, Netlify, or Vercel.

Day 30: Final Project
- Combine everything learned to build a comprehensive web application.
- Include HTML, CSS, JavaScript, and possibly a JavaScript framework like React.
- Deploy the final project.

### Additional Resources
- HTML/CSS: MDN Web Docs, W3Schools.
- JavaScript: MDN Web Docs, Eloquent JavaScript.
- Frameworks/Libraries: Official documentation for Bootstrap, Tailwind CSS, React.
- Version Control: Pro Git book.

Practice consistently, build projects, and refer to official documentation and online resources for deeper understanding.

5 Free Web Development Courses by Udacity & Microsoft 👇👇

Intro to HTML and CSS

Intro to Backend

Intro to JavaScript

Web Development for Beginners

Object-Oriented JavaScript

Useful Web Development Books👇

Javanoscript for Professionals

Javanoscript from Frontend to Backend

CSS Guide

Best Web Development Resources

Web Development Resources
👇 👇
https://news.1rj.ru/str/webdevcoursefree

Join @free4unow_backup for more free resources.

ENJOY LEARNING 👍👍

Complete Roadmap to learn Machine Learning and Artificial Intelligence
👇👇

Week 1-2: Introduction to Machine Learning
- Learn the basics of Python programming language (if you are not already familiar with it)
- Understand the fundamentals of Machine Learning concepts such as supervised learning, unsupervised learning, and reinforcement learning
- Study linear algebra and calculus basics
- Complete online courses like Andrew Ng's Machine Learning course on Coursera

Week 3-4: Deep Learning Fundamentals
- Dive into neural networks and deep learning
- Learn about different types of neural networks like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs)
- Implement deep learning models using frameworks like TensorFlow or PyTorch
- Complete online courses like Deep Learning Specialization on Coursera

Week 5-6: Natural Language Processing (NLP) and Computer Vision
- Explore NLP techniques such as tokenization, word embeddings, and sentiment analysis
- Dive into computer vision concepts like image classification, object detection, and image segmentation
- Work on projects involving NLP and Computer Vision applications

Week 7-8: Reinforcement Learning and AI Applications
- Learn about Reinforcement Learning algorithms like Q-learning and Deep Q Networks
- Explore AI applications in fields like healthcare, finance, and autonomous vehicles
- Work on a final project that combines different aspects of Machine Learning and AI

Additional Tips:
- Practice coding regularly to strengthen your programming skills
- Join online communities like Kaggle or GitHub to collaborate with other learners
- Read research papers and articles to stay updated on the latest advancements in the field

Pro Tip: Roadmap won't help unless you start working on it consistently. Start working on projects as early as possible.

2 months are good as a starting point to get grasp the basics of ML & AI but mastering it is very difficult as AI keeps evolving every day.

Best Resources to learn ML & AI 👇

Learn Python for Free

Prompt Engineering Course

Prompt Engineering Guide

Data Science Course

Google Cloud Generative AI Path

Unlock the power of Generative AI Models

Machine Learning with Python Free Course

Machine Learning Free Book

Deep Learning Nanodegree Program with Real-world Projects

AI, Machine Learning and Deep Learning

Join @free4unow_backup for more free courses

ENJOY LEARNING👍👍

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://news.1rj.ru/str/sqlproject

ENJOY LEARNING 👍👍

Essential Tools, Frameworks, & Concepts in Java Programming

1. Core Java Concepts:

Object-Oriented Programming (OOP)

Exception Handling

Multithreading

Collections Framework

Generics

Java I/O (Input/Output)

Lambda Expressions and Streams (Java 8 and beyond)


2. Java Frameworks:

Spring Framework: Comprehensive framework for enterprise applications.

Hibernate: ORM (Object Relational Mapping) framework.

Apache Struts: For building web applications.

Play Framework: Lightweight and reactive web application framework.


3. Build Tools:

Maven

Gradle


4. Java Testing Frameworks:

JUnit

TestNG


5. Web Development with Java:

Servlets and JSP (Java Server Pages)

Spring MVC

Thymeleaf


6. Java for Microservices:

Spring Boot

Spring Cloud

Quarkus


7. Database Integration:

JDBC (Java Database Connectivity)

JPA (Java Persistence API)


8. IDEs for Java Development:

IntelliJ IDEA

Eclipse

NetBeans


9. Advanced Concepts:

JVM (Java Virtual Machine) Internals

Garbage Collection

Memory Management

Reflection API


10. Java for Cloud and Distributed Systems:

Apache Kafka

Apache Hadoop

Kubernetes (with Java apps)


11. Networking in Java:

Sockets and ServerSockets

RMI (Remote Method Invocation)


12. Popular Java Libraries:

Apache Commons

Google Guava

Jackson (for JSON parsing)


13. Java for Android Development:

Android Studio

Java SDK


Free books and courses to learn Java👇👇

https://imp.i115008.net/QOz50M

https://bit.ly/3hbu3Dg

https://imp.i115008.net/Jrjo1R

https://bit.ly/3BSHP5S

https://news.1rj.ru/str/Java_Programming_Notes

https://introcs.cs.princeton.edu/java/11cheatsheet/

Join @free4unow_backup for more free courses

ENJOY LEARNING👍👍

Learn for free ✅

HTML — https://www.w3schools.com/html
CSS — http://CSS-tricks.com
JavaScript — http://LearnJavaScript.online
DSA --- https://news.1rj.ru/str/dsabooks/21
Git, GitHub -- http://LearnGitBranching.js.org
React — http://React-tutorial.app
API — http://RapidAPI.com/comics
SQL — http://SQLbolt.com
Python -- https://news.1rj.ru/str/pythondevelopersindia/76
PHP --- https://bit.ly/3QkY3wW
ML -- https://developers.google.com/machine-learning/crash-course
AI -- http://microsoft.github.io/AI-For-Beginners

ENJOY LEARNING 👍👍

List of Top 12 Coding Channels on WhatsApp:

1. Python Programming:
https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L


2. Coding Resources:
https://whatsapp.com/channel/0029VahiFZQ4o7qN54LTzB17


3. Coding Projects:
https://whatsapp.com/channel/0029VazkxJ62UPB7OQhBE502


4. Coding Interviews:
https://whatsapp.com/channel/0029VammZijATRSlLxywEC3X


5. Java Programming:
https://whatsapp.com/channel/0029VamdH5mHAdNMHMSBwg1s


6. Javanoscript:
https://whatsapp.com/channel/0029VavR9OxLtOjJTXrZNi32


7. Web Development:
https://whatsapp.com/channel/0029VaiSdWu4NVis9yNEE72z


8. Artificial Intelligence:
https://whatsapp.com/channel/0029VaoePz73bbV94yTh6V2E


9. Data Science:
https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y


10. Machine Learning:
https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D


11. SQL:
https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v


12. GitHub:
https://whatsapp.com/channel/0029Vawixh9IXnlk7VfY6w43

ENJOY LEARNING 👍👍

Top 10 Programming Languages to learn in 2025 (With Free Resources to learn) :-

1. Python
- learnpython.org
- t.me/pythonfreebootcamp

2. Java
- learnjavaonline.org
- t.me/free4unow_backup/550

3. C#
- learncs.org
- w3schools.com

4. JavaScript
- learnjavanoscript.online
- t.me/javanoscript_courses

5. Rust
- rust-lang.org
- exercism.org

6. Go Programming
- go.dev
- learn-golang.org

7. Kotlin
- kotlinlang.org
- w3schools.com/KOTLIN

8. TypeScript
- Typenoscriptlang.org
- learntypenoscript.dev

9. SQL
- datasimplifier.com
- t.me/sqlanalyst

10. R Programming
- w3schools.com/r/
- r-coder.com

ENJOY LEARNING 👍👍

Artificial Intelligence (AI) Roadmap
|
|-- Fundamentals
| |-- Mathematics
| | |-- Linear Algebra
| | |-- Calculus
| | |-- Probability and Statistics
| |
| |-- Programming
| | |-- Python (Focus on Libraries like NumPy, Pandas)
| | |-- Java or C++ (optional but useful)
| |
| |-- Algorithms and Data Structures
| | |-- Graphs and Trees
| | |-- Dynamic Programming
| | |-- Search Algorithms (e.g., A*, Minimax)
|
|-- Core AI Concepts
| |-- Knowledge Representation
| |-- Search Methods (DFS, BFS)
| |-- Constraint Satisfaction Problems
| |-- Logical Reasoning
|
|-- Machine Learning (ML)
| |-- Supervised Learning (Regression, Classification)
| |-- Unsupervised Learning (Clustering, Dimensionality Reduction)
| |-- Reinforcement Learning (Q-Learning, Policy Gradient Methods)
| |-- Ensemble Methods (Random Forest, Gradient Boosting)
|
|-- Deep Learning (DL)
| |-- Neural Networks
| |-- Convolutional Neural Networks (CNNs)
| |-- Recurrent Neural Networks (RNNs)
| |-- Transformers (BERT, GPT)
| |-- Frameworks (TensorFlow, PyTorch)
|
|-- Natural Language Processing (NLP)
| |-- Text Preprocessing (Tokenization, Lemmatization)
| |-- NLP Models (Word2Vec, BERT)
| |-- Applications (Chatbots, Sentiment Analysis, NER)
|
|-- Computer Vision
| |-- Image Processing
| |-- Object Detection (YOLO, SSD)
| |-- Image Segmentation
| |-- Applications (Facial Recognition, OCR)
|
|-- Ethical AI
| |-- Fairness and Bias
| |-- Privacy and Security
| |-- Explainability (SHAP, LIME)
|
|-- Applications of AI
| |-- Healthcare (Diagnostics, Personalized Medicine)
| |-- Finance (Fraud Detection, Algorithmic Trading)
| |-- Retail (Recommendation Systems, Inventory Management)
| |-- Autonomous Vehicles (Perception, Control Systems)
|
|-- AI Deployment
| |-- Model Serving (Flask, FastAPI)
| |-- Cloud Platforms (AWS SageMaker, Google AI)
| |-- Edge AI (TensorFlow Lite, ONNX)
|
|-- Advanced Topics
| |-- Multi-Agent Systems
| |-- Generative Models (GANs, VAEs)
| |-- Knowledge Graphs
| |-- AI in Quantum Computing

Best Resources to learn ML & AI 👇

Learn Python for Free

Prompt Engineering Course

Prompt Engineering Guide

Data Science Course

Google Cloud Generative AI Path

Machine Learning with Python Free Course

Machine Learning Free Book

Artificial Intelligence WhatsApp channel

Hands-on Machine Learning

Deep Learning Nanodegree Program with Real-world Projects

AI, Machine Learning and Deep Learning

Like this post for more roadmaps ❤️

Follow & share the channel link with your friends: t.me/free4unow_backup

ENJOY LEARNING👍👍

Data Analytics Roadmap
|
|-- Fundamentals
|   |-- Mathematics
|   |   |-- Denoscriptive Statistics
|   |   |-- Inferential Statistics
|   |   |-- Probability Theory
|   |
|   |-- Programming
|   |   |-- Python (Focus on Libraries like Pandas, NumPy)
|   |   |-- R (For Statistical Analysis)
|   |   |-- SQL (For Data Extraction)
|
|-- Data Collection and Storage
|   |-- Data Sources
|   |   |-- APIs
|   |   |-- Web Scraping
|   |   |-- Databases
|   |
|   |-- Data Storage
|   |   |-- Relational Databases (MySQL, PostgreSQL)
|   |   |-- NoSQL Databases (MongoDB, Cassandra)
|   |   |-- Data Lakes and Warehousing (Snowflake, Redshift)
|
|-- Data Cleaning and Preparation
|   |-- Handling Missing Data
|   |-- Data Transformation
|   |-- Data Normalization and Standardization
|   |-- Outlier Detection
|
|-- Exploratory Data Analysis (EDA)
|   |-- Data Visualization Tools
|   |   |-- Matplotlib
|   |   |-- Seaborn
|   |   |-- ggplot2
|   |
|   |-- Identifying Trends and Patterns
|   |-- Correlation Analysis
|
|-- Advanced Analytics
|   |-- Predictive Analytics (Regression, Forecasting)
|   |-- Prenoscriptive Analytics (Optimization Models)
|   |-- Segmentation (Clustering Techniques)
|   |-- Sentiment Analysis (Text Data)
|
|-- Data Visualization and Reporting
|   |-- Visualization Tools
|   |   |-- Power BI
|   |   |-- Tableau
|   |   |-- Google Data Studio
|   |
|   |-- Dashboard Design
|   |-- Interactive Visualizations
|   |-- Storytelling with Data
|
|-- Business Intelligence (BI)
|   |-- KPI Design and Implementation
|   |-- Decision-Making Frameworks
|   |-- Industry-Specific Use Cases (Finance, Marketing, HR)
|
|-- Big Data Analytics
|   |-- Tools and Frameworks
|   |   |-- Hadoop
|   |   |-- Apache Spark
|   |
|   |-- Real-Time Data Processing
|   |-- Stream Analytics (Kafka, Flink)
|
|-- Domain Knowledge
|   |-- Industry Applications
|   |   |-- E-commerce
|   |   |-- Healthcare
|   |   |-- Supply Chain
|
|-- Ethical Data Usage
|   |-- Data Privacy Regulations (GDPR, CCPA)
|   |-- Bias Mitigation in Analysis
|   |-- Transparency in Reporting

Free Resources to learn Data Analytics skills👇👇

1. SQL

https://mode.com/sql-tutorial/introduction-to-sql

https://news.1rj.ru/str/sqlspecialist/738

2. Python

https://www.learnpython.org/

https://news.1rj.ru/str/pythondevelopersindia/873

https://bit.ly/3T7y4ta

https://www.geeksforgeeks.org/python-programming-language/learn-python-tutorial

3. R

https://datacamp.pxf.io/vPyB4L

4. Data Structures

https://leetcode.com/study-plan/data-structure/

https://www.udacity.com/course/data-structures-and-algorithms-in-python--ud513

5. Data Visualization

https://www.freecodecamp.org/learn/data-visualization/

https://news.1rj.ru/str/Data_Visual/2

https://www.tableau.com/learn/training/20223

https://www.workout-wednesday.com/power-bi-challenges/

6. Excel

https://excel-practice-online.com/

https://news.1rj.ru/str/excel_data

https://www.w3schools.com/EXCEL/index.php

Join @free4unow_backup for more free courses

Like for more ❤️

ENJOY LEARNING 👍👍

Machine Learning Roadmap
|
|-- Fundamentals
| |-- Mathematics
| | |-- Linear Algebra
| | |-- Calculus (Gradients, Optimization)
| | |-- Probability and Statistics
| | |-- Matrix Operations
| |
| |-- Programming
| | |-- Python (NumPy, Pandas, Scikit-learn)
| | |-- R (Optional for Statistical Modeling)
| | |-- SQL (For Data Extraction)
|
|-- Data Preprocessing
| |-- Data Cleaning
| |-- Feature Engineering
| | |-- Encoding Categorical Data
| | |-- Feature Scaling (Standardization, Normalization)
| | |-- Handling Missing Values
| |-- Dimensionality Reduction (PCA, LDA)
|
|-- Supervised Learning
| |-- Regression
| | |-- Linear Regression
| | |-- Polynomial Regression
| | |-- Ridge and Lasso Regression
| |-- Classification
| | |-- Logistic Regression
| | |-- Decision Trees
| | |-- Support Vector Machines (SVM)
| | |-- Ensemble Methods (Random Forest, Gradient Boosting, XGBoost)
|
|-- Unsupervised Learning
| |-- Clustering
| | |-- K-Means
| | |-- Hierarchical Clustering
| | |-- DBSCAN
| |-- Dimensionality Reduction
| | |-- Principal Component Analysis (PCA)
| | |-- t-SNE
| |-- Association Rules (Apriori, FP-Growth)
|
|-- Reinforcement Learning
| |-- Markov Decision Processes
| |-- Q-Learning
| |-- Deep Q-Learning
| |-- Policy Gradient Methods
|
|-- Model Evaluation and Optimization
| |-- Train-Test Split and Cross-Validation
| |-- Performance Metrics
| | |-- Accuracy, Precision, Recall, F1-Score
| | |-- ROC-AUC
| | |-- Mean Squared Error (MSE), R-squared
| |-- Hyperparameter Tuning
| | |-- Grid Search
| | |-- Random Search
| | |-- Bayesian Optimization
|
|-- Deep Learning
| |-- Neural Networks
| | |-- Perceptrons
| | |-- Backpropagation
| |-- Convolutional Neural Networks (CNN)
| | |-- Image Classification
| | |-- Object Detection (YOLO, SSD)
| |-- Recurrent Neural Networks (RNN)
| | |-- LSTM
| | |-- GRU
| |-- Transformers (Attention Mechanisms, BERT, GPT)
| |-- Tools and Frameworks (TensorFlow, PyTorch)
|
|-- Advanced Topics
| |-- Transfer Learning
| |-- Generative Adversarial Networks (GANs)
| |-- Reinforcement Learning with Neural Networks
| |-- Explainable AI (SHAP, LIME)
|
|-- Applications of Machine Learning
| |-- Recommender Systems (Collaborative Filtering, Content-Based)
| |-- Fraud Detection
| |-- Sentiment Analysis
| |-- Predictive Maintenance
| |-- Autonomous Vehicles
|
|-- Deployment of Models
| |-- Flask, FastAPI
| |-- Cloud Deployment (AWS SageMaker, Azure ML)
| |-- Containerization (Docker, Kubernetes)
| |-- Model Monitoring and Retraining

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Web Development Roadmap
|
|-- Fundamentals
| |-- Web Basics
| | |-- Internet and HTTP/HTTPS Protocols
| | |-- Domain Names and Hosting
| | |-- Client-Server Architecture
| |
| |-- HTML (HyperText Markup Language)
| | |-- Structure of a Web Page
| | |-- Semantic HTML
| | |-- Forms and Validations
| |
| |-- CSS (Cascading Style Sheets)
| | |-- Selectors and Properties
| | |-- Box Model
| | |-- Responsive Design (Media Queries, Flexbox, Grid)
| | |-- CSS Frameworks (Bootstrap, Tailwind CSS)
| |
| |-- JavaScript (JS)
| | |-- ES6+ Features
| | |-- DOM Manipulation
| | |-- Fetch API and Promises
| | |-- Event Handling
| |
|-- Version Control Systems
| |-- Git Basics
| |-- GitHub/GitLab
| |-- Branching and Merging
|
|-- Front-End Development
| |-- Advanced JavaScript
| | |-- Modules and Classes
| | |-- Error Handling
| | |-- Asynchronous Programming (Async/Await)
| |
| |-- Frameworks and Libraries
| | |-- React (Hooks, Context API)
| | |-- Angular (Components, Services)
| | |-- Vue.js (Directives, Vue Router)
| |
| |-- State Management
| | |-- Redux
| | |-- MobX
| |
|-- Back-End Development
| |-- Server-Side Languages
| | |-- Node.js (Express.js)
| | |-- Python (Django, Flask)
| | |-- PHP (Laravel)
| | |-- Ruby (Ruby on Rails)
| |
| |-- Database Management
| | |-- SQL Databases (MySQL, PostgreSQL)
| | |-- NoSQL Databases (MongoDB, Firebase)
| |
| |-- Authentication and Authorization
| | |-- JWT (JSON Web Tokens)
| | |-- OAuth 2.0
| |
|-- APIs and Microservices
| |-- RESTful APIs
| |-- GraphQL
| |-- API Security (Rate Limiting, CORS)
|
|-- Full-Stack Development
| |-- Integrating Front-End and Back-End
| |-- MERN Stack (MongoDB, Express.js, React, Node.js)
| |-- MEAN Stack (MongoDB, Express.js, Angular, Node.js)
| |-- JAMstack (JavaScript, APIs, Markup)
|
|-- DevOps and Deployment
| |-- Build Tools (Webpack, Vite)
| |-- Containerization (Docker, Kubernetes)
| |-- CI/CD Pipelines (Jenkins, GitHub Actions)
| |-- Cloud Platforms (AWS, Azure, Google Cloud)
| |-- Hosting (Netlify, Vercel, Heroku)
|
|-- Web Performance Optimization
| |-- Minification and Compression
| |-- Lazy Loading
| |-- Code Splitting
| |-- Caching (Service Workers)
|
|-- Web Security
| |-- HTTPS and SSL
| |-- Cross-Site Scripting (XSS)
| |-- SQL Injection Prevention
| |-- Content Security Policy (CSP)
|
|-- Specializations
| |-- Progressive Web Apps (PWAs)
| |-- Single-Page Applications (SPAs)
| |-- Server-Side Rendering (Next.js, Nuxt.js)
| |-- WebAssembly
|
|-- Trends and Advanced Topics
| |-- Web 3.0 and Decentralized Apps (dApps)
| |-- Motion UI and Animations
| |-- AI Integration in Web Apps
| |-- Real-Time Applications

Web Development Resources 👇👇

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Cloud Computing Roadmap
|
|-- Fundamentals
| |-- Introduction to Cloud Computing
| | |-- Cloud Models (IaaS, PaaS, SaaS)
| | |-- Cloud Deployment Models (Public, Private, Hybrid)
| | |-- Virtualization Concepts
|
|-- Cloud Platforms
| |-- Amazon Web Services (AWS)
| | |-- EC2, S3, IAM, Lambda
| | |-- AWS Networking and VPC
| | |-- AWS Elastic Beanstalk
| |
| |-- Microsoft Azure
| | |-- Azure Compute Services (VMs, Functions, App Services)
| | |-- Azure Storage (Blob, SQL)
| | |-- Azure Active Directory
| |
| |-- Google Cloud Platform (GCP)
| | |-- Compute Engine, App Engine
| | |-- Google Cloud Storage
| | |-- BigQuery
|
|-- Cloud Computing Services
| |-- Cloud Storage
| | |-- Object Storage (S3, Google Cloud Storage)
| | |-- Block Storage (EBS, Azure Disk Storage)
| |
| |-- Compute Resources
| | |-- Virtual Machines (VMs)
| | |-- Containers and Kubernetes
| | |-- Serverless Computing (Lambda, Azure Functions)
|
|-- Networking and Security
| |-- Cloud Networking
| | |-- Virtual Private Cloud (VPC)
| | |-- Load Balancers
| | |-- Cloud DNS
| |
| |-- Cloud Security
| | |-- Identity and Access Management (IAM)
| | |-- Encryption (In-Transit, At-Rest)
| | |-- Security Best Practices
|
|-- Cloud Automation and DevOps
| |-- Infrastructure as Code
| | |-- AWS CloudFormation
| | |-- Terraform
| |-- CI/CD in the Cloud
| | |-- Jenkins, GitLab CI, AWS CodePipeline
| |-- Containerization and Orchestration
| | |-- Docker
| | |-- Kubernetes
|
|-- Cloud Monitoring and Optimization
| |-- Monitoring Tools
| | |-- AWS CloudWatch
| | |-- Azure Monitor
| |-- Cost Management
| | |-- AWS Cost Explorer
| | |-- Google Cloud Billing
|
|-- Advanced Topics
| |-- AI and Machine Learning in Cloud
| | |-- AWS SageMaker
| | |-- Azure Machine Learning
| |-- Edge Computing
| | |-- AWS Greengrass
| |-- Cloud Security Architectures

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Big Data Roadmap
|
|-- Fundamentals
| |-- Introduction to Big Data
| | |-- Characteristics of Big Data (Volume, Velocity, Variety, Veracity, Value)
| | |-- Big Data vs. Traditional Data Processing
| |-- Mathematics and Programming for Big Data
| | |-- Basic Probability and Statistics
| | |-- Python (Pandas, NumPy)
| | |-- Java/Scala (Optional)
|
|-- Big Data Tools and Frameworks
| |-- Apache Hadoop
| | |-- Hadoop HDFS (Distributed File System)
| | |-- MapReduce
| | |-- Hadoop Ecosystem (Hive, Pig, HBase, etc.)
| |-- Apache Spark
| | |-- RDDs and DataFrames
| | |-- SparkSQL
| | |-- Spark Streaming
| | |-- MLlib (Machine Learning with Spark)
|
|-- Data Storage Solutions
| |-- Distributed Databases
| | |-- Apache HBase
| | |-- Cassandra
| | |-- Amazon DynamoDB
| |-- NoSQL Databases
| | |-- MongoDB
| | |-- Couchbase
| |-- Data Lakes
| | |-- Amazon S3
| | |-- Hadoop HDFS
|
|-- Data Processing Frameworks
| |-- Batch Processing
| | |-- Apache Hadoop MapReduce
| | |-- Apache Flink
| |-- Stream Processing
| | |-- Apache Kafka
| | |-- Apache Storm
| | |-- Apache Samza
|
|-- Data Analysis and Visualization
| |-- Data Analysis Tools
| | |-- Apache Hive
| | |-- Apache Drill
| |-- Data Visualization
| | |-- Apache Zeppelin
| | |-- Tableau (for big data)
| | |-- Power BI
|
|-- Cloud-Based Big Data Tools
| |-- Amazon Web Services (AWS)
| | |-- Amazon EMR
| | |-- AWS Redshift
| | |-- AWS Glue
| |-- Microsoft Azure
| | |-- Azure HDInsight
| | |-- Azure Synapse Analytics
| |-- Google Cloud
| | |-- Google BigQuery
| | |-- Google Dataflow
|
|-- Machine Learning with Big Data
| |-- Machine Learning Algorithms for Big Data
| | |-- Collaborative Filtering
| | |-- Dimensionality Reduction (PCA, LDA)
| |-- Apache Mahout
| | |-- Machine Learning on Hadoop
| |-- Deep Learning on Big Data
| | |-- TensorFlow on Spark
|
|-- Big Data Analytics
| |-- Real-Time Analytics
| | |-- Apache Kafka + Apache Storm
| | |-- Apache Flink
| |-- Predictive Analytics
| | |-- Time Series Forecasting
| | |-- Predictive Modeling with Spark MLlib
|
|-- Security and Privacy
| |-- Big Data Security
| | |-- Data Encryption
| | |-- Authentication and Authorization in Hadoop
| | |-- Secure Data Transmission
| |-- Privacy Concerns
| | |-- GDPR Compliance
| | |-- Anonymization and Data Masking
|
|-- Certifications
| |-- Cloudera Certified Associate (CCA)
| |-- Google Cloud Certified - Professional Data Engineer
| |-- Microsoft Certified: Azure Data Engineer

Cybersecurity Roadmap
|
|-- Fundamentals
| |-- Introduction to Cybersecurity
| | |-- Importance and Principles of Cybersecurity
| | |-- Types of Cybersecurity (Network, Information, Application, Cloud, etc.)
| | |-- Cybersecurity Threat Landscape (Malware, Phishing, Ransomware, etc.)
| |-- Network Security
| | |-- Firewalls and VPNs
| | |-- Intrusion Detection Systems (IDS)
| | |-- Intrusion Prevention Systems (IPS)
| | |-- Network Access Control
|
|-- Threats and Vulnerabilities
| |-- Types of Cyber Threats
| | |-- Malware (Viruses, Worms, Trojans, etc.)
| | |-- Phishing and Social Engineering
| | |-- Denial of Service (DoS) Attacks
| | |-- Insider Threats
| |-- Vulnerability Assessment
| | |-- Vulnerability Scanning
| | |-- Penetration Testing (Ethical Hacking)
| | |-- Security Audits and Assessments
|
|-- Encryption and Cryptography
| |-- Introduction to Cryptography
| | |-- Symmetric and Asymmetric Encryption
| | |-- Hashing Algorithms (SHA, MD5, etc.)
| | |-- Public Key Infrastructure (PKI)
| |-- Encryption Protocols
| | |-- SSL/TLS
| | |-- IPsec
|
|-- Identity and Access Management (IAM)
| |-- Authentication Mechanisms
| | |-- Password Policies and Multi-Factor Authentication (MFA)
| | |-- Biometric Authentication
| |-- Access Control Models
| | |-- Role-Based Access Control (RBAC)
| | |-- Attribute-Based Access Control (ABAC)
| | |-- Mandatory Access Control (MAC)
|
|-- Incident Response and Forensics
| |-- Incident Response Process
| | |-- Detection, Containment, Eradication, Recovery
| | |-- Incident Response Teams (CSIRT)
| |-- Digital Forensics
| | |-- Evidence Collection and Preservation
| | |-- Data Recovery
| | |-- Forensic Tools (Autopsy, EnCase, etc.)
|
|-- Security Operations
| |-- Security Monitoring
| | |-- Security Information and Event Management (SIEM)
| | |-- Log Management and Analysis
| | |-- Threat Intelligence
| |-- Security Operations Center (SOC)
| | |-- SOC Roles and Responsibilities
| | |-- Incident Management
|
|-- Cloud Security
| |-- Cloud Security Principles
| | |-- Shared Responsibility Model
| | |-- Data Protection in Cloud Environments
| |-- Cloud Security Tools
| | |-- Cloud Access Security Brokers (CASB)
| | |-- Security in Cloud Platforms (AWS, Azure, Google Cloud)
|
|-- Application Security
| |-- Secure Software Development
| | |-- Secure Coding Practices
| | |-- Software Development Life Cycle (SDLC)
| | |-- Secure Code Reviews
| |-- Web Application Security
| | |-- OWASP Top 10
| | |-- SQL Injection, Cross-Site Scripting (XSS), CSRF
|
|-- Compliance and Regulations
| |-- Cybersecurity Standards
| | |-- ISO/IEC 27001, NIST Cybersecurity Framework
| | |-- CIS Controls, SOC 2
| |-- Data Privacy Regulations
| | |-- GDPR
| | |-- HIPAA, CCPA, PCI DSS
|
|-- Advanced Topics
| |-- Advanced Persistent Threats (APT)
| | |-- Detection and Mitigation
| | |-- Threat Hunting
| |-- Blockchain Security
| | |-- Cryptographic Principles
| | |-- Smart Contracts and Security
| |-- IoT Security
| | |-- Securing IoT Devices
| | |-- Network Segmentation for IoT
|
|-- Emerging Trends
| |-- AI and Machine Learning in Cybersecurity
| | |-- AI-Based Threat Detection
| | |-- Automating Incident Response
| |-- Zero Trust Architecture
| | |-- Principles of Zero Trust
| | |-- Implementing Zero Trust in an Organization
|
|-- Soft Skills
| |-- Communication and Collaboration
| | |-- Reporting Security Incidents
| | |-- Collaboration with Other Departments
| |-- Ethical Hacking
| | |-- Red Teaming and Blue Teaming
| | |-- Bug Bounty Programs

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#cybersecurity

NoSQL Database Roadmap
|
| |-- Fundamentals
| |-- Introduction to NoSQL Databases
| | |-- What is NoSQL?
| | |-- Types of NoSQL Databases: Document, Key-Value, Column, Graph
| | |-- NoSQL vs. Relational Databases
|
|-- Types of NoSQL Databases
| |-- Document-Based Databases
| | |-- MongoDB
| | |-- CouchDB
| |-- Key-Value Databases
| | |-- Redis
| | |-- Riak
| |-- Column-Based Databases
| | |-- Cassandra
| | |-- HBase
| |-- Graph Databases
| | |-- Neo4j
| | |-- ArangoDB
|
|-- Data Modeling in NoSQL
| |-- Designing Schemas for NoSQL
| | |-- Understanding Data Structures in NoSQL
| | |-- Denormalization vs Normalization
| |-- Indexes and Queries
| | |-- Indexing in NoSQL
| | |-- Querying NoSQL Databases
|
|-- Scalability and Performance
| |-- Horizontal vs Vertical Scaling
| | |-- Sharding and Partitioning
| |-- Consistency and Availability
| | |-- CAP Theorem (Consistency, Availability, Partition Tolerance)
| | |-- Eventual Consistency
|
|-- Security and Backup
| |-- Authentication and Authorization
| | |-- Access Control in NoSQL Databases
| |-- Backup and Data Recovery
| | |-- Techniques for NoSQL Backup
|
|-- Tools and Frameworks
| |-- Data Access Libraries
| | |-- Mongoose (for MongoDB)
| | |-- Cassandra Driver
| |-- Cloud-based NoSQL Services
| | |-- Amazon DynamoDB
| | |-- Google Cloud Datastore
|
|-- Use Cases and Applications
| |-- Content Management Systems
| |-- Real-Time Applications
| |-- Social Networks
|
|-- Advanced Topics
| |-- Graph Processing with NoSQL
| |-- Time-Series Data in NoSQL Databases
| |-- Data Consistency Models
|
|-- Integration with Other Technologies
| |-- NoSQL with Hadoop and Spark
| |-- Integrating NoSQL with Relational Databases (Polyglot Persistence)

Internet of Things (IoT) Roadmap
|
| |-- Fundamentals
| |-- Introduction to IoT
| | |-- What is IoT?
| | |-- IoT Architecture and Components
| | |-- IoT Communication Protocols
| | |-- IoT Applications and Use Cases
|
|-- IoT Hardware
| |-- Sensors and Actuators
| | |-- Temperature Sensors
| | |-- Motion Sensors
| | |-- Humidity Sensors
| |-- Microcontrollers and Development Boards
| | |-- Arduino
| | |-- Raspberry Pi
| | |-- ESP8266/ESP32
|
|-- Networking in IoT
| |-- IoT Protocols
| | |-- MQTT
| | |-- CoAP
| | |-- HTTP
| |-- Low Power Networks
| | |-- LoRaWAN
| | |-- Zigbee
| | |-- NB-IoT
|
|-- IoT Data Processing
| |-- Edge Computing
| | |-- Edge Devices
| | |-- Edge Analytics
| |-- Cloud Platforms for IoT
| | |-- AWS IoT
| | |-- Google Cloud IoT
| | |-- Microsoft Azure IoT
|
|-- IoT Security
| |-- IoT Device Security
| | |-- Authentication and Authorization
| | |-- Encryption
| |-- Network Security
| | |-- VPN
| | |-- Firewalls
|
|-- IoT Software Development
| |-- IoT Programming Languages
| | |-- Python
| | |-- C/C++
| |-- IoT Frameworks
| | |-- ThingSpeak
| | |-- Node-RED
|
|-- IoT Applications and Projects
| |-- Smart Home
| | |-- Home Automation with IoT
| | |-- Smart Lighting
| |-- Healthcare IoT
| | |-- Wearables
| | |-- Remote Monitoring
| |-- Industrial IoT (IIoT)
| | |-- Predictive Maintenance
| | |-- Real-Time Monitoring
|
|-- Advanced Topics
| |-- Machine Learning for IoT
| | |-- Edge AI
| | |-- Data Prediction and Analysis
| |-- 5G and IoT
| | |-- High-Speed Connectivity
| | |-- Low Latency IoT Applications
| |-- Blockchain in IoT
| | |-- IoT Security and Privacy with Blockchain

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Complete Data Analytics Mastery: From Basics to Advanced 🚀

Begin your Data Analytics journey by mastering the fundamentals:
- Understanding Data Types and Formats
- Basics of Exploratory Data Analysis (EDA)
- Introduction to Data Cleaning Techniques
- Statistical Foundations for Data Analytics
- Data Visualization Essentials

Grasp these essentials in just a week to build a solid foundation in data analytics.

Once you're comfortable, dive into intermediate topics:
- Advanced Data Visualization (using tools like Tableau)
- Hypothesis Testing and A/B Testing
- Regression Analysis
- Time Series Analysis for Analytics
- SQL for Data Analytics

Take another week to solidify these skills and enhance your ability to draw meaningful insights from data.

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- Machine Learning for Data Analytics
- Predictive Analytics
- Big Data Analytics (Hadoop, Spark)
- Advanced Statistical Methods (Multivariate Analysis)
- Data Ethics and Privacy in Analytics

These advanced concepts can be mastered in a couple of weeks with focused study and practice.

Remember, mastery comes with hands-on experience:
- Work on a simple data analytics project
- Tackle an intermediate-level analysis task
- Challenge yourself with an advanced analytics project involving real-world data sets

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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.


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