23.8K
AFDemo included - protect yourself and use AI to empower your understanding!
@africantechbro.ai
#Data Extraction Techniques 2026
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TOUse the Instant Data Scraper to quickly pull data from websites!
#scrape #data
@topclickmediasa
9.8K
AA🌐 DAY 4/7: Web Scraper - Extract Data from ANY Website Automatically!
Tired of manually copying data from websites? Today we’re building a web scraper that does it automatically! This is HUGE for data analysis! 📊
🧠 THE LOGIC:
Every website is built with HTML (just structured text):
1️⃣ We send a request to the website
2️⃣ Website sends back HTML (all the code)
3️⃣ We parse through that HTML looking for specific tags
4️⃣ Extract just the data we want (prices, quotes, titles, etc.)
5️⃣ Save or analyze the data
It’s like using Ctrl+F, but automated and MUCH more powerful! 🔍
💻 THE CODE:
Using `requests` and `BeautifulSoup`:
✓ Fetch the webpage HTML
✓ Parse the HTML structure
✓ Find specific elements (by tag, class, id)
✓ Extract text, links, images, whatever you need
✓ Process hundreds of pages in seconds!
🎯 REAL-WORLD USE:
• Price monitoring (track when items go on sale!)
• Job listing aggregation (all jobs in one place)
• News article collection (research/analysis)
• Product review analysis (sentiment tracking)
• Real estate listings (find best deals)
• Research data collection (academic projects)
• Competitor analysis (market research)
💡 PRO TIP: Companies pay $50-100/hour for web scraping services! This skill is VALUABLE! Add it to your resume! 💰
⚠️ ETHICAL SCRAPING: Always check a website’s robots.txt file and Terms of Service. Respect rate limits and don’t overload servers!
🔥 DID YOU KNOW: Google’s entire business started with web scraping! They crawl billions of pages daily to index the internet!
📌 SAVE this - web scraping is one of the most in-demand Python skills!
💬 Comment what website YOU would scrape! Drop ideas below! 👇
👥 Tag a data analyst friend who needs this!
Tomorrow: Day 5 - AI Chatbot! Build your own conversational AI! 🤖
#python #webscraping #datascience #pythonprogramming #automation
learnpython pythontutorial pythoncode pythonprojects dataanalysis
codinglife programming pythondeveloper 100daysofcode webdev
pythonlearning codingisfun developer pythonbeginner tech
bigdata machinelearning analytics research buildinpublic
@aartii.py
71.2K
DA“AI in Analytics - Data Extraction Edition”
Save & Share this reel. Follow @datawithashok for more!
@datawithashok
14.6K
ASThis is the EXACT order I would learn Data Science in 2026.
Hi 😊 my name is Dawn. I’ve been a Data Scientist at Meta, Patreon and other startups. And have coached 20+ clients into landing their dream Data jobs in the past year.
1️⃣ Learn SQL
SQL is a must-have skill for every data professional because it’s the primary way you get data OUT of a database.
It’s also a very easy coding language to learn, so I would start there.
Use Interview Master to learn and practice SQL (link in bio):
→ Learn SQL: www.interviewmaster.ai/content/sql
→ Practice SQL: www.interviewmaster.ai/home
2️⃣ Start building Product Sense & Business Sense
Product sense & business sense basically means you know how to use Data to solve real problems.
I would start building this “soft” skill early because (1) it takes time to really learn this, and (2) as you’re learning Stats and Python, you already have context on how these might be used in the real world.
I found the book: Cracking the PM Career to be super helpful before I landed my first Data Science job.
3️⃣ Learn Statistics
How much Stats do you need for Data Science? Just the foundations, but you need to know it really really well.
→ Descriptive statistics
→ Common distributions
→ Probability and Bayes’ Theorem
→ Basic Machine Learning models
→ Experimentation concepts
→ A/B experiment design
Check out Stanford’s Introduction to Statistics, which is free on Coursera.
4️⃣ Learn Python
Python is the #1 skill for Data Scientists in 2025, but I put it 4th on this list because I find that it builds on skills 1-3.
I learned Python on my own using DataCamp’s Python Data Fundamentals (link in bio).
5️⃣ Use AI-assisted coding tools
Many data scientists are already using tools, like Claude Code & Cursor, to 2x their productivity.
And also many companies are evaluating you on your use of AI during interviews.
#datascience #datascientist
@askdatadawn
22.4K
SIThinking about applying to dataannotation.tech?
Here’s what you need to know before you waste time.
I’ve applied twice and still haven’t passed their test. And I’m not the only one—Reddit and YouTube comments are full of people saying the same.
But if you do get in?
The work is real.
You’re annotating text—basically labeling or correcting AI-generated answers.
It’s not hard once you know what to do, but passing the initial test? That’s the tricky part.
Most people say the pay is decent for side income—nothing crazy—but flexible and remote.
Searches for “data annotation” are blowing up right now because it feels like easy money. But you have to pass the exam. No shortcuts. No guarantees.
I’ll share what I’ve learned from the process so far and what I’ve found online about the kind of work they assign.
Let me know if you’ve passed the test or if you want me to keep documenting this journey.
@side.hustlereview
42.5K
WO📍How to prepare for Data Scientist role in 2026 🚀
CORE SKILLS YOU MUST MASTER: Programming You must be fluent in:
● Python
● NumPy
● Pandas
● Scikit-learn
Writing clean, readable, bug free code
Data transformations without IDE help
Expect:
● Data cleaning
● Feature extraction
● Aggregations
● Writing logic heavy code
SQL
Almost every Data Science role tests SQL. You should be comfortable with:
● Joins - inner, left, self
● Window functions
● Grouping & aggregations
● Subqueries
● Handling NULLs
Statistics & Probability:
● Probability distributions
● Hypothesis testing
● Confidence intervals
● A/B testing
● Correlation vs causation
● Sampling bias
Machine Learning Fundamentals. You must know:
● Supervised vs Unsupervised learning
● Regression & Classification
● Bias Variance tradeoff
● Overfitting / Underfitting
Evaluation metrics:
● Accuracy
● Precision / Recall
● F1-score
● ROC-AUC
● RMSE
FEATURE ENGINEERING & DATA UNDERSTANDING:
● This is where strong candidates stand out.
● Handling missing data
● Encoding categorical variables
● Feature scaling
● Outlier treatment
CORE SKILLS YOU MUST MASTER: Programming You must be fluent in:
● Python
● NumPy
● Pandas
● Scikit-learn
Writing clean, readable, bug free code
Data transformations without IDE help
Expect:
● Data cleaning
● Feature extraction
● Aggregations
● Writing logic heavy code
SQL
Almost every Data Science role tests SQL. You should be comfortable with:
● Joins - inner, left, self
● Window functions
● Grouping & aggregations
● Subqueries
● Handling NULLs
Statistics & Probability:
● Probability distributions
● Hypothesis testing
● Confidence intervals
● A/B testing
● Correlation vs causation
● Sampling bias
Machine Learning Fundamentals. You must know:
● Supervised vs Unsupervised learning
● Regression & Classification
● Bias Variance tradeoff
● Overfitting / Underfitting
Evaluation metrics:
● Accuracy
● Precision / Recall
● F1-score
● ROC-AUC
● RMSE
+++ for more look at the comment
#datascientist #aiengineer #softwareengineer #datascience #dataengineer
@woman.engineer
565.8K
CHThe best projects serve a real use case
Comment “data” for all the links and project descriptions
#tech #data #datascience #ml #explore
@chrisoh.zip
28.8K
AVRaw data is messy. Real work starts after loading it.
• Handle nulls
• Fix data types
• Remove garbage values
• Validate again
This is where actual analysis begins.
#DataAnalysis #EDA #Python #PowerBI
@avneet.builds
132.6K
KRVisualizing the architecture of intelligence. 🕸️✨
Every neural network is built on the same fundamental concept: Layers.
🟡 Input Layer: Receives the raw data (pixels, text, numbers).
🟢 Hidden Layers: Where the magic happens—processing features and finding patterns.
🟠 Output Layer: Delivers the final prediction or decision.
From the simple Perceptron to the complex loops of an RNN, these structures are the blueprints for how machines learn. 📐
#NeuralNetworks #MachineLearning #DeepLearning #DataScience #AI #Education #Visualized
@kreggscode
1.8M
MAI won’t be mad if you copy this entire roadmap…
#dataanalyst #dataanalysis #dataanalytics #data #analyst #techjobs #breakintotech #wfh #workfromhome #wfhjobs #remotejobs #remotework #excel #sql #tableau #python
@marytheanalyst
350.2K
SUComment ‘Projects’ to get 5 Data Scientist Project ideas and a plan 👩🏻💻
♻️ repost to share with friends. Here is how to become a data scientist in 2026 and beyond 📈 the original video was 4 min Andi had to cut it down to 3 because instagram. Should I do a part 3v
what are other skills that you would add to the list and let me know what I should cover in the next video 👩🏻💻
#datascientist #datascience #python #machinelearning #sql #ai
@sundaskhalidd
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