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Google, IBM and Harvard University are sponsoring Free courses for Python
𝟭. 𝗖𝗦𝟱𝟬𝗣 𝗳𝗿𝗼𝗺 𝗛𝗮𝗿𝘃𝗮𝗿𝗱 𝗨𝗻𝗶𝘃𝗲𝗿𝘀𝗶𝘁𝘆
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Flask Projects: https://lnkd.in/eqnf7h-W
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1. Learn Multithreading, Multiprocessing, Asyncio: https://lnkd.in/e_99Jiwp
2. Gunicorn & Nginx with Flask: https://lnkd.in/eWxgTNdB
3. TDD with Python & Flask: https://lnkd.in/eMjweHuZ
4. Basic RDBMS: https://lnkd.in/ebkPd8-q
5. Learn SQL: https://sqlbolt.com/ & W3Schools.com
6. PostgreSQL with Python: https://lnkd.in/esKUqNdt
7. Flask App with PostgreSQL: https://lnkd.in/eTzpcwNc
4️⃣ 𝗠𝗼𝗻𝘁𝗵 𝟰: 𝗣𝗼𝗹𝗶𝘀𝗵 𝘆𝗼𝘂𝗿 𝘀𝗸𝗶𝗹𝗹𝘀 𝗮𝗻𝗱 𝗽𝗿𝗲𝗽𝗮𝗿𝗲 𝗳𝗼𝗿 𝘁𝗵𝗲 𝗷𝗼𝗯 𝗺𝗮𝗿𝗸𝗲𝘁
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FREE RESOURCES TO LEARN
🔰 SQL
http://online.stanford.edu/courses/soe-ydatabases0005-databases-relational-databases-and-sql
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🔰Statistics and R
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Free Courses/ Certifications for a full-stack career in 2023:
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6. Databases
https://freecodecamp.org/learn/relational-database/
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Complete SQL road map
👇👇
1.Intro to SQL
• Definition
• Purpose
• Relational DBs
• DBMS
2.Basic SQL Syntax
• SELECT
• FROM
• WHERE
• ORDER BY
• GROUP BY
3. Data Types
• Integer
• Floating-Point
• Character
• Date
• VARCHAR
• TEXT
• BLOB
• BOOLEAN
4.Sub languages
• DML
• DDL
• DQL
• DCL
• TCL
5. Data Manipulation
• INSERT
• UPDATE
• DELETE
6. Data Definition
• CREATE
• ALTER
• DROP
• Indexes
7.Query Filtering and Sorting
• WHERE
• AND
• OR Conditions
• Ascending
• Descending
8. Data Aggregation
• SUM
• AVG
• COUNT
• MIN
• MAX
9.Joins and Relationships
• INNER JOIN
• LEFT JOIN
• RIGHT JOIN
• Self-Joins
• Cross Joins
• FULL OUTER JOIN
10.Subqueries
• Subqueries used in
• Filtering data
• Aggregating data
• Joining tables
• Correlated Subqueries
11.Views
• Creating
• Modifying
• Dropping Views
12.Transactions
• ACID Properties
• COMMIT
• ROLLBACK
• SAVEPOINT
• ROLLBACK TO SAVEPOINT
13.Stored Procedures
• CREATE PROCEDURE
• ALTER PROCEDURE
• DROP PROCEDURE
• EXECUTE PROCEDURE
• User-Defined Functions (UDFs)
14.Triggers
• Trigger Events
• Trigger Execution and Syntax
15. Security and Permissions
• CREATE USER
• GRANT
• REVOKE
• ALTER USER
• DROP USER
16.Optimizations
• Indexing Strategies
• Query Optimization
17.Normalization
• 1NF(Normal Form)
• 2NF
• 3NF
• BCNF
18.Backup and Recovery
• Database Backups
• Point-in-Time Recovery
19.NoSQL Databases
• MongoDB
• Cassandra etc...
• Key differences
20. Data Integrity
• Primary Key
• Foreign Key
21.Advanced SQL Queries
• Window Functions
• Common Table Expressions (CTEs)
22.Full-Text Search
• Full-Text Indexes
• Search Optimization
23. Data Import and Export
• Importing Data
• Exporting Data (CSV, JSON)
• Using SQL Dump Files
24.Database Design
• Entity-Relationship Diagrams
• Normalization Techniques
25.Advanced Indexing
• Composite Indexes
• Covering Indexes
26.Database Transactions
• Savepoints
• Nested Transactions
• Two-Phase Commit Protocol
27.Performance Tuning
• Query Profiling and Analysis
• Query Cache Optimization
------------------ END -------------------
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Confused about which field to dive into—Front-End Development (FE), Back-End Development (BE), Machine Learning (ML), or Blockchain?
Here's a concise breakdown of each, designed to clarify your options:
### Front-End Development (FE)
Key Skills:
- HTML/CSS: Fundamental for creating the structure and style of web pages.
- JavaScript: Essential for adding interactivity and functionality to websites.
- Frameworks/Libraries: React, Angular, or Vue.js for efficient and scalable front-end development.
- Responsive Design: Ensuring websites look good on all devices.
- Version Control: Git for managing code changes and collaboration.
Career Prospects:
- Web Developer
- UI/UX Designer
- Front-End Engineer
### Back-End Development (BE)
Key Skills:
- Programming Languages: Python, Java, Ruby, Node.js, or PHP for server-side logic.
- Databases: SQL (MySQL, PostgreSQL) and NoSQL (MongoDB) for data management.
- APIs: RESTful and GraphQL for communication between front-end and back-end.
- Server Management: Understanding of server, network, and hosting environments.
- Security: Knowledge of authentication, authorization, and data protection.
Career Prospects:
- Back-End Developer
- Full-Stack Developer
- Database Administrator
### Machine Learning (ML)
Key Skills:
- Programming Languages: Python and R are widely used in ML.
- Mathematics: Statistics, linear algebra, and calculus for understanding ML algorithms.
- Libraries/Frameworks: TensorFlow, PyTorch, Scikit-Learn for building ML models.
- Data Handling: Pandas, NumPy for data manipulation and preprocessing.
- Model Evaluation: Techniques for assessing model performance.
Career Prospects:
- Data Scientist
- Machine Learning Engineer
- AI Researcher
### Blockchain
Key Skills:
- Cryptography: Understanding of encryption and security principles.
- Blockchain Platforms: Ethereum, Hyperledger, Binance Smart Chain for building decentralized applications.
- Smart Contracts: Solidity for developing smart contracts.
- Distributed Systems: Knowledge of peer-to-peer networks and consensus algorithms.
- Blockchain Tools: Truffle, Ganache, Metamask for development and testing.
Career Prospects:
- Blockchain Developer
- Smart Contract Developer
- Crypto Analyst
### Decision Criteria
1. Interest: Choose an area you are genuinely interested in.
2. Market Demand: Research the current job market to see which skills are in demand.
3. Career Goals: Consider your long-term career aspirations.
4. Learning Curve: Assess how much time and effort you can dedicate to learning new skills.
Each field offers unique opportunities and challenges, so weigh your options carefully based on your personal preferences and career objectives.
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Complete Roadmap to learn SQL in 2024 👇👇
1. Basic Concepts
- Understand databases and SQL.
- Learn data types (INT, VARCHAR, DATE, etc.).
2. Basic Queries
- SELECT: Retrieve data.
- WHERE: Filter results.
- ORDER BY: Sort results.
- LIMIT: Restrict results.
3. Aggregate Functions
- COUNT, SUM, AVG, MAX, MIN.
- Use GROUP BY to group results.
4. Joins
- INNER JOIN: Combine rows from two tables based on a condition.
- LEFT JOIN: Include all rows from the left table.
- RIGHT JOIN: Include all rows from the right table.
- FULL OUTER JOIN: Include all rows from both tables.
5. Subqueries
- Use nested queries for complex data retrieval.
6. Data Manipulation
- INSERT: Add new records.
- UPDATE: Modify existing records.
- DELETE: Remove records.
7. Schema Management
- CREATE TABLE: Define new tables.
- ALTER TABLE: Modify existing tables.
- DROP TABLE: Remove tables.
8. Indexes
- Understand how to create and use indexes to optimize queries.
9. Views
- Create and manage views for simplified data access.
10. Transactions
- Learn about COMMIT and ROLLBACK for data integrity.
11. Advanced Topics
- Stored Procedures: Automate complex tasks.
- Triggers: Execute actions automatically based on events.
- Normalization: Understand database design principles.
12. Practice
- Use platforms like LeetCode, HackerRank, or learnsql for hands-on practice.
Here are some free resources to learn & practice SQL 👇👇
Udacity free course- https://imp.i115008.net/AoAg7K
SQL For Data Analysis: https://t.me/sqlanalyst
For Practice- https://stratascratch.com/?via=free
SQL Learning Series: https://t.me/sqlspecialist/567
Top 10 SQL Projects with Datasets: https://t.me/DataPortfolio/16
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Hi Guys,
Here are some of the telegram channels which may help you in data analytics journey 👇👇
SQL: https://t.me/sqlanalyst
Power BI & Tableau: https://t.me/PowerBI_analyst
Excel: https://t.me/excel_analyst
Python: https://t.me/dsabooks
Jobs: https://t.me/jobs_SQL
Data Science: https://t.me/datasciencefree
Artificial intelligence: https://t.me/machinelearning_deeplearning
Data Engineering: https://t.me/sql_engineer
Hope it helps :)
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Python Roadmap
|
|-- Fundamentals
| |-- Basics of Programming
| | |-- Introduction to Python
| | |-- Setting Up Development Environment (IDE: PyCharm, VSCode, etc.)
| |
| |-- Syntax and Structure
| | |-- Basic Syntax
| | |-- Variables and Data Types
| | |-- Operators and Expressions
|
|-- Control Structures
| |-- Conditional Statements
| | |-- If-Else Statements
| | |-- Elif Statements
| |
| |-- Loops
| | |-- For Loop
| | |-- While Loop
| |
| |-- Exception Handling
| | |-- Try-Except Block
| | |-- Finally Block
| | |-- Raise and Custom Exceptions
|
|-- Functions and Modules
| |-- Defining Functions
| | |-- Function Syntax
| | |-- Parameters and Arguments
| | |-- Return Statement
| |
| |-- Lambda Functions
| | |-- Syntax and Usage
| |
| |-- Modules and Packages
| | |-- Importing Modules
| | |-- Creating and Using Packages
|
|-- Object-Oriented Programming (OOP)
| |-- Basics of OOP
| | |-- Classes and Objects
| | |-- Methods and Constructors
| |
| |-- Inheritance
| | |-- Single and Multiple Inheritance
| | |-- Method Overriding
| |
| |-- Polymorphism
| | |-- Method Overloading (using default arguments)
| | |-- Operator Overloading
| |
| |-- Encapsulation
| | |-- Access Modifiers (Public, Private, Protected)
| | |-- Getters and Setters
| |
| |-- Abstraction
| | |-- Abstract Base Classes
| | |-- Interfaces (using ABC module)
|
|-- Advanced Python
| |-- File Handling
| | |-- Reading and Writing Files
| | |-- Working with CSV and JSON Files
| |
| |-- Iterators and Generators
| | |-- Creating Iterators
| | |-- Using Generators and Yield Statement
| |
| |-- Decorators
| | |-- Function Decorators
| | |-- Class Decorators
|
|-- Data Structures
| |-- Lists
| | |-- List Comprehensions
| | |-- Common List Methods
| |
| |-- Tuples
| | |-- Immutable Sequences
| |
| |-- Dictionaries
| | |-- Dictionary Comprehensions
| | |-- Common Dictionary Methods
| |
| |-- Sets
| | |-- Set Operations
| | |-- Set Comprehensions
|
|-- Libraries and Frameworks
| |-- Data Science
| | |-- NumPy
| | |-- Pandas
| | |-- Matplotlib
| | |-- Seaborn
| | |-- SciPy
| |
| |-- Web Development
| | |-- Flask
| | |-- Django
| |
| |-- Automation
| | |-- Selenium
| | |-- BeautifulSoup
| | |-- Scrapy
|
|-- Testing in Python
| |-- Unit Testing
| | |-- Unittest
| | |-- PyTest
| |
| |-- Mocking
| | |-- unittest.mock
| | |-- Using Mocks and Patches
|
|-- Deployment and DevOps
| |-- Containers and Microservices
| | |-- Docker (Dockerfile, Image Creation, Container Management)
| | |-- Kubernetes (Pods, Services, Deployments, Managing Python Applications on Kubernetes)
|
|-- Best Practices and Advanced Topics
| |-- Code Style
| | |-- PEP 8 Guidelines
| | |-- Code Linters (Pylint, Flake8)
| |
| |-- Performance Optimization
| | |-- Profiling and Benchmarking
| | |-- Using Cython and Numba
| |
| |-- Concurrency and Parallelism
| | |-- Threading
| | |-- Multiprocessing
| | |-- Asyncio
|
|-- Building and Distributing Packages
| |-- Creating Packages
| | |-- setuptools
| | |-- Creating environment setup
| |
| |-- Publishing Packages
| | |-- PyPI
| | |-- Versioning and Documentation
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JavaScript Roadmap
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|-- Fundamentals
| |-- Basics of Programming
| | |-- Introduction to JavaScript
| | |-- Setting Up Development Environment (IDE: VSCode, Sublime Text, etc.)
| |
| |-- Syntax and Structure
| | |-- Basic Syntax
| | |-- Variables (var, let, const) and Data Types
| | |-- Operators and Expressions
|
|-- Control Structures
| |-- Conditional Statements
| | |-- If-Else Statements
| | |-- Switch Case
| |
| |-- Loops
| | |-- For Loop
| | |-- While Loop
| | |-- Do-While Loop
| | |-- For...in and For...of Loops
| |
| |-- Exception Handling
| | |-- Try-Catch Block
| | |-- Finally Block
| | |-- Throwing Errors
|
|-- Functions and Scope
| |-- Defining Functions
| | |-- Function Declarations
| | |-- Function Expressions
| | |-- Arrow Functions
| |
| |-- Parameters and Arguments
| | |-- Default Parameters
| | |-- Rest and Spread Operators
| |
| |-- Scope
| | |-- Global and Local Scope
| | |-- Hoisting
| | |-- Closures
|
|-- Object-Oriented Programming (OOP)
| |-- Basics of OOP
| | |-- Objects and Properties
| | |-- Methods
| |
| |-- Prototypes and Inheritance
| | |-- Prototype Chain
| | |-- Inheritance with Prototypes
| |
| |-- Classes
| | |-- Class Syntax
| | |-- Constructors
| | |-- Inheritance (extends and super)
| |
| |-- Encapsulation
| | |-- Private and Public Members (using # for private)
|
|-- Advanced JavaScript
| |-- Asynchronous JavaScript
| | |-- Callbacks
| | |-- Promises
| | |-- Async/Await
| |
| |-- Event Loop
| | |-- Understanding the Event Loop
| | |-- Microtasks and Macrotasks
|
|-- Data Structures
| |-- Arrays
| | |-- Array Methods (map, filter, reduce, etc.)
| | |-- Array Manipulation
| |
| |-- Objects
| | |-- Creating and Manipulating Objects
| | |-- Object Methods (keys, values, entries)
| |
| |-- Sets and Maps
| | |-- Working with Sets
| | |-- Working with Maps
|
|-- Browser APIs
| |-- Document Object Model (DOM)
| | |-- Selecting Elements
| | |-- Manipulating Elements
| | |-- Event Handling
| |
| |-- Fetch API
| | |-- Making HTTP Requests
| | |-- Handling Responses
| |
| |-- Web Storage
| | |-- LocalStorage and SessionStorage
|
|-- Libraries and Frameworks
| |-- jQuery
| | |-- Basics of jQuery
| | |-- DOM Manipulation with jQuery
| |
| |-- React
| | |-- Components and JSX
| | |-- State and Props
| | |-- Lifecycle Methods
| |
| |-- Angular
| | |-- Components and Templates
| | |-- Services and Dependency Injection
| | |-- Routing
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| |-- Vue
| | |-- Vue Instance
| | |-- Templates and Directives
| | |-- Vue Router
|
|-- Build Tools and Module Bundlers
| |-- NPM and Yarn
| | |-- Package Management
| | |-- Scripts and Dependencies
| |
| |-- Webpack
| | |-- Module Bundling
| | |-- Loaders and Plugins
| |
| |-- Babel
| | |-- Transpiling JavaScript
| | |-- Using Presets and Plugins
|
|-- Testing in JavaScript
| |-- Unit Testing
| | |-- Jest (Setup, Writing Tests, Mocking)
| | |-- Mocha and Chai
| |
| |-- End-to-End Testing
| | |-- Cypress
| | |-- Selenium WebDriver
|
|-- Deployment and DevOps
| |-- Continuous Integration/Continuous Deployment (CI/CD)
| | |-- GitHub Actions
| | |-- Travis CI
| |
| |-- Containers and Microservices
| | |-- Docker (Dockerfile, Image Creation, Container Management)
| | |-- Kubernetes (Pods, Services, Deployments, Managing JavaScript Applications on Kubernetes)
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R Programming Roadmap
|
|-- Fundamentals
| |-- Basics of Programming
| | |-- Introduction to R
| | |-- Setting Up Development Environment (RStudio)
| |
| |-- Syntax and Structure
| | |-- Basic Syntax
| | |-- Variables and Data Types
| | |-- Operators and Expressions
|
|-- Control Structures
| |-- Conditional Statements
| | |-- If-Else Statements
| |
| |-- Loops
| | |-- For Loop
| | |-- While Loop
| | |-- Repeat Loop
| |
| |-- Exception Handling
| | |-- Try-Catch Block
| | |-- Warnings and Errors
|
|-- Functions and Scope
| |-- Defining Functions
| | |-- Function Syntax
| | |-- Parameters and Arguments
| | |-- Return Statement
| |
| |-- Scope
| | |-- Global and Local Scope
| | |-- Environments
|
|-- Data Structures
| |-- Vectors
| | |-- Creating Vectors
| | |-- Vectorized Operations
| |
| |-- Lists
| | |-- Creating and Manipulating Lists
| |
| |-- Matrices
| | |-- Creating Matrices
| | |-- Matrix Operations
| |
| |-- Data Frames
| | |-- Creating Data Frames
| | |-- Manipulating Data Frames
| |
| |-- Factors
| | |-- Creating and Using Factors
|
|-- Data Manipulation
| |-- dplyr
| | |-- Select, Filter, Arrange, Mutate, Summarize
| | |-- Piping (%>%)
| |
| |-- tidyr
| | |-- Gather and Spread
| | |-- Separate and Unite
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|-- Data Visualization
| |-- Base R Graphics
| | |-- Plot, Hist, Boxplot, Barplot
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| |-- ggplot2
| | |-- Grammar of Graphics
| | |-- Creating Plots (Scatter, Line, Bar, Histogram)
| | |-- Customizing Plots (Themes, Labels, Legends)
|
|-- Statistical Analysis
| |-- Descriptive Statistics
| | |-- Mean, Median, Mode
| | |-- Standard Deviation, Variance
| |
| |-- Inferential Statistics
| | |-- Hypothesis Testing (t-tests, ANOVA)
| | |-- Correlation and Regression Analysis
|
|-- Advanced R
| |-- Date and Time
| | |-- Working with Dates and Times
| | |-- lubridate Package
| |
| |-- String Manipulation
| | |-- Stringr Package
| | |-- Regular Expressions
|
|-- Programming Concepts
| |-- Apply Family of Functions
| | |-- lapply, sapply, tapply, vapply
| |
| |-- Debugging
| | |-- Debugging Tools (browser, debug, trace)
| |
| |-- Object-Oriented Programming (OOP)
| | |-- S3 and S4 Systems
| | |-- Reference Classes (R5)
|
|-- Libraries and Packages
| |-- CRAN and Bioconductor
| | |-- Installing and Using Packages
| |
| |-- Popular Packages
| | |-- Data Manipulation (dplyr, tidyr)
| | |-- Data Visualization (ggplot2, lattice)
| | |-- Machine Learning (caret, randomForest)
|
|-- Reporting and Documentation
| |-- RMarkdown
| | |-- Creating RMarkdown Documents
| | |-- Including Code Chunks
| | |-- Generating Reports (HTML, PDF, Word)
|
|-- Deployment and Reproducibility
| |-- Version Control with Git
| | |-- Integrating RStudio with GitHub
| |
| |-- Reproducible Research
| | |-- Workflow Practices
| | |-- Using renv for Package Management
|
|-- Working with Big Data
| |-- Data.table Package
| | |-- Efficient Data Manipulation
| |
| |-- SparkR
| | |-- Using Apache Spark with R
| | |-- Handling Large Datasets
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