332.7K
TOUse the Instant Data Scraper to quickly pull data from websites!
#scrape #data
@topclickmediasa
#Data Extraction Techniques
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128.9K
LOThere's a one line Python command that replaces hours of manual EDA work 📊
Most analysts start every project typing df.info, df.describe, checking duplicates, plotting histograms one by one. It's boring, slow, and easy to miss things.
Here's the smarter way:
Install ydata-profiling. Run one line of code on your dataframe. It automatically builds a full interactive HTML dashboard. Distributions, correlations, missing values, duplicates, all in one place.
The difference between junior and senior analysts isn't just skill. It's knowing which tools save you hours so you can focus on actual insights.
Comment "CODE" for the full script and save this before your next project 🎯
#PythonForDataScience #ExploratoryDataAnalysis #PandasProfiling #DataAnalyticsTips
@loresowhat
174.6K
JELet’s get DIRTY!! 😉 It’s gonna be 80% of your job!
Comment “clean” for my SQL skills cheat sheet, perfect for data projects and interview prep!
Save these 4 tips to handle messy datasets w/ ease 😮💨
• Clean strings: remove extra spaces, weird quotes, and fix casing
• Fix dates: standardize formats and remove stray characters
• Recode variables: turn “CA”, “California”, and “calif” into one consistent value
• Handle NULLs: don’t let missing data mess with your analysis
#data #datacleaning #sql
🏷️ data, data cleaning, sql, data analytics, data science
@jessramosdata
148.7K
MACleaning messy data is the bread and butter of a data analyst and data scientist job. This question often comes up in the technical portion of your interviews. The hiring manager is looking for a structured and thoughtful response that involve lots of communications with stakeholders throughout the process💕 you got this
#datascience #dataanalytics #breakintotech #careerindata #womenwhocode
@maggieindata
213.3K
PE9 data projects ideas instead of doomscrolling:
(And there is a repo with the data :) )
🤖 Machine Learning Projects
* Diabetes Classification
Build and compare classification models to show how data preprocessing, feature scaling, and hyperparameter tuning directly improve predictive performance.
* Heart Attack Prediction
Implement an end-to-end classification pipeline—from raw data to model evaluation—to demonstrate a realistic machine learning workflow.
* Medical Cost Prediction
Train a regression model to predict healthcare costs, emphasizing feature importance analysis and model optimization to explain what drives predictions.
🛠️ Data Engineering Projects
* NBA Player Statistics ETL Pipeline
Design an ETL pipeline that extracts player statistics, cleans and transforms the data, and stores it in a relational database for reliable downstream analysis.
* Real-Time & Batch Data Pipelines with Kafka
Build a scalable pipeline that processes streaming and batch data using Kafka, PostgreSQL, and Docker to demonstrate modern data flow architecture.
* Glassdoor Job Data Pipeline
Scrape job postings, clean and structure the raw data, and prepare it for analysis and visualization to showcase real-world data ingestion challenges.
📊 Data Analytics Projects
* Pokémon Dataset Analysis
Perform exploratory data analysis and feature engineering to uncover patterns in Pokémon characteristics such as types, stats, and legendary status.
* Automated EDA Tool Comparison
Benchmark AutoViz, SweetViz, and Pandas Profiling across multiple datasets to evaluate performance, resource usage, and practical trade-offs.
* Exploratory Job Market Analysis
Analyze cleaned job posting data to extract trends, key skills, and role distributions using visualizations and summary statistics.
👉🏻 Comment « data » to get the link to the repo and portfolio strategies!
#data #students #job
@penelope_data
3.2M
JAYou probably Googled “How to learn Data Analytics”…
And got 100 tabs open.
Courses, tools, bootcamps, blogs, YouTube videos,
each saying “Start here.”
But no one told you what not to do.
No one gave you a starter kit that actually made sense.
So I built.
And now I use this same plan to guide beginners I mentor.
Here’s the Data Analytics Starter Kit I wish everyone should have👇
1. Start with Excel
→ It’s not outdated, it’s underrated.
→ Master formulas, Pivot Tables, and charts.
2. Then SQL
→ Learn how to query real data.
→ SELECT, WHERE, GROUP BY, and JOIN. That’s 80% of your job.
3. Add one viz tool
→ Pick Tableau or Power BI.
→ Focus on storytelling, not fancy dashboards.
4. Forget 100-hour courses
Instead, build 3 small projects:
⤷ A Sales Dashboard in Excel
⤷ A Customer Retention Report in SQL
⤷ A Visual Story in Tableau
5. Use GitHub + LinkedIn
→ Document your projects.
→ Share your process.
→ Visibility builds credibility.
6. Give it 6–8 weeks
Learn 1 skill → Apply it → Move to the next.
If you're just starting out, don't chase 10 tools.
Build your foundation first.
Want my full Data Analytics Starter Kit with a roadmap, tool list, and project ideas?
Drop "Community" to join my community here.
#datavisualization #dataanalyst #datascience #data #sql #excel #python #career #careerswitch #trending #learning #interviewtips #india #metricminds
@jayenthakker
119.1K
VEIf you want to crack Data Science jobs in the next 30 days, here’s the three step process which you will follow which literally no one talks about.
.
.
.
#datascience #data #interview
@vee_daily19
247.3K
AMLearn how to implement these Data Cleaning methods using Python from the link in the bio!
#datascience #datasciencejobs #data #dataanalysis #dataanalytics #datascientist #machinelearning #artificialintelligence #ai #machinelearningprojects #datascienceprojects #amankharwal
@amankharwal.official
559.0K
CHThe best projects serve a real use case
Comment “data” for all the links and project descriptions
#tech #data #datascience #ml #explore
@chrisoh.zip
33.4K
DAIn my first years as a data scientist, I wasted hours on broken SQL, slow pandas scripts, messy Flask deployments, and “works on my machine” chaos.
These 4 tools fixed that:
• dbt → modular, documented SQL transformations
• Polars → faster, cleaner alternative to pandas
• FastAPI → quick, reliable model deployment
• Docker → consistent environments, no more deployment nightmares
If you’re just starting out, learning these early will save you months of frustration.
@data_pumpkin
7.9K
MASTOP CLEANING DATA MANUALLY (Use Power Query Instead)
Shoutout to Dell for providing me with the amazing Dell Pro Max so I can finally use Power BI 🫶🏼
#DellTech #DellProPrecision #NVIDIA #dataanalyst #dataanalysis
@marytheanalyst
1.2M
THHere’s a roadmap to help you go from a software engineer to a data scientist 👩💻 👇
If you’re tired of writing vanilla apps and want to build ML systems instead, this one’s for you.
Step 1 – Learn Python and SQL (not Java, C++, or JavaScript).
→ Focus on pandas, numpy, scikit-learn, matplotlib
→ For SQL: use LeetCode or StrataScratch to practice real-world queries
→ Don’t just write code—learn to think in data
Step 2 – Build your foundation in statistics + math.
→ Start with Practical Statistics for Data Scientists
→ Learn: probability, hypothesis testing, confidence intervals, distributions
→ Brush up on linear algebra (vectors, dot products) and calculus (gradients, chain rule)
Step 3 – Learn ML the right way.
→ Do Andrew Ng’s ML course (Deeplearning.ai)
→ Master the full pipeline: cleaning → feature engineering → modeling → evaluation
→ Read Elements of Statistical Learning or Sutton & Barto if you want to go deeper
Step 4 – Build 2–3 real, messy projects.
→ Don’t follow toy tutorials
→ Use APIs or scrape data, build full pipelines, and deploy using Streamlit or Gradio
→ Upload everything to GitHub with a clear README
Step 5 – Become a storyteller with data.
→ Read Storytelling with Data by Cole Knaflic
→ Learn to explain your findings to non-technical teams
→ Practice communicating precision/recall/F1 in simple language
Step 6 – Stay current. Never stop learning.
→ Follow PapersWithCode (it's now sun-setted, use huggingface.co/papers/trending, ArXiv Sanity, and follow ML practitioners on LinkedIn
→ Join communities, follow researchers, and keep shipping new experiments
-------
Save this for later.
Tag a friend who’s trying to make the switch.
[software engineer to data scientist, ML career roadmap, python for data science, SQL for ML, statistics for ML, data science career guide, ML project ideas, data storytelling, becoming a data scientist, ML learning path 2025]
@the.datascience.gal
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