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.

Machine Learning Glossary

Brief visual explanations of machine learning concepts with diagrams, code examples and links to resources for learning more.

Link: https://ml-cheatsheet.readthedocs.io/en/latest/index.html

#DataAnalytics #Python #SQL #RProgramming #DataScience #MachineLearning #DeepLearning #Statistics #DataVisualization #PowerBI #Tableau #LinearRegression #Probability #DataWrangling #Excel #AI #ArtificialIntelligence #BigData #DataAnalysis #NeuralNetworks #GAN #LearnDataScience #LLM #RAG #Mathematics #PythonProgramming #Keras

💻 Computer Graphics Handwritten Notes 📝 🌟

React ❤️ for more 📱

Build Data Analyst Portfolio in 1 month

Path 1 (More focus on SQL & then on Python)
👇👇

Week 1: Learn Fundamentals
Days 1-3: Start with online courses or tutorials on basic data analysis concepts.
Days 4-7: Dive into SQL basics for data retrieval and manipulation.
Free Resources: https://t.iss.one/sqlanalyst/74

Week 2: Data Analysis Projects
Days 8-14: Begin working on simple data analysis projects using SQL. Analyze the data and document your findings.

Week 3: Intermediate Skills
Days 15-21: Start learning Python for data analysis. Focus on libraries like Pandas for data manipulation.
Days 22-23: Explore more advanced SQL topics.

Week 4: Portfolio Completion
Days 24-28: Continue working on your SQL-based projects, applying what you've learned.
Day 29: Transition to Python for your personal project, applying Python's data analysis capabilities.
Day 30: Create a portfolio website showcasing your projects in SQL and Python, along with explanations and code.

Hope it helps :)

Python Libraries For Data Science

Scrap Image from bing using BeautifulSoup

import requests
from bs4 import BeautifulSoup as BSP

def split_url(url):
    return url.split('&')[0]

def get_image_urls(search_query):
    url = f"https://cn.bing.com/images/search?q={search_query}&first=1&cw=1177&ch=678"
    headers = {
        "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.3"
    }
    rss = requests.get(url, headers=headers)
    soup = BSP(rss.content, "html.parser")

    all_img = []
    for img in soup.find_all('img'):
        img_url = img.get('src2')
        if img_url and img_url.startswith('https://tse2.mm.bing.net/'):
            img_url = split_url(img_url)
            all_img.append(img_url)

    return all_img

print(get_image_urls("cat"))

sample response :

['https://tse2.mm.bing.net/th?q=Cat+Portrait', ...']