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SAData Scientist VS AI Engineer 🤑🤖 #tech #ai #data #fyp
@sajjaad.khader
#Data Engineer Vs Data Scientist
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THData Engineer vs AI Engineer.
Here’s what each role does, what they earn, and how to choose.
What You Actually Do:
Data Engineer: Pipelines and reliability. Ingest, transform, model, validate. If data breaks, everything downstream breaks. Building data foundations that analytics, ML, and product teams rely on.
AI Engineer: Models in production. RAG systems, agent evaluations. If the model is slow, wrong, or unsafe, you fix it. Building AI features like chat, search, copilot, automations that users actually touch.
Languages You Use:
Data Engineer: SQL all day, Python for pipelines, Scala or Java for Spark.
AI Engineer: Python for model workflows, TypeScript or JavaScript for APIs, some SQL.
Tech Stack:
Data Engineer: Snowflake, BigQuery, Redshift, dbt, Airflow, Kafka, Databricks, Spark, Monte Carlo.
AI Engineer: OpenAI, Anthropic, Gemini, LangChain, LangGraph, Pinecone, Weaviate, Fireworks AI, Ragas, LangSmith, Weights & Biases.
Salary Ranges (NYC/SF):
Data Engineer: $140K-$200K base, $170K-$240K total comp
AI Engineer: $160K-$230K base, $200K-$300K total comp (higher at AI-first companies with equity)
Interested in data and building scalable systems? Data engineering.
Like AI and want to work with models in production? AI engineering.
@the.datascience.gal
1.1M
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
39.1K
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
842.4K
SURepost to share with friends ♻️ Here’s how to become a data analyst in 2026 and beyond? 📈 The original video was 5 minutes long and I had to cut it down to 3 minutes because instagram. One part that got cut off was the job market. Should I post a part 2?
what are other skills that would you add to the list??
#dataanalysis #dataanalyst #sql #python
@sundaskhalidd
829.5K
BAAs a Data Scientist, you‘ll always feel like you know nothing🥲 #datascience #softwareengineering
@baniascodes
190.4K
JEComment “project” for my full video that breaks each of these projects down in detail with examples from my own work.
If you’re using the Titanic, Iris, or COVID-19 dataset for data analytics projects, STOP NOW! These are so boring and over used and scream “newbie”. You can find way more interesting datasets for FREE on public data sites and you can even make your own using ChatGPT or Claude!
Here are the 3 types of projects you need:
↳Exploratory Data Analysis (EDA): Exploring a dataset to uncover insights through descriptive statistics (averages, ranges, distributions) and data visualization, including analyzing relationships between variables
↳Full Stack Data Analytics Project: An end-to-end project that covers the entire data pipeline: wrangling data from a database, cleaning and transforming it. It demonstrates proficiency across multiple tools, not just one.
↳Funnel Analysis: Tracking users or items move from point A to point B, and how many make it through each step in between. This demonstrates a deeper level of business thinking by analyzing the process from beginning to end and providing actionable recommendations to improve it
Save this video for later + send to a data friend!
@jessramosdata
31.0K
YOWhere are all our data scientists at! 👀👇🏻
#young4stem #datascience #job #reel #stem #computerscience
@young4stem
2.5M
PR1. Netflix Show Clustering
Group similar shows using K-Means based on genre, rating, and duration.
Tech Stack: Python, Pandas, Scikit-learn, Seaborn
2. Spotify Audio Feature Analyzer
Analyze songs by tempo, energy and danceability using Spotify API.
Tech Stack: Python, Spotipy, Matplotlib, Plotly
3. YouTube Trending Video Analyzer
Discover what makes a video go viral.
Tech Stack: Python, Pandas, BeautifulSoup, Seaborn
4. Resume Scanner using NLP
Parse and rank resumes based on job description matching.
Tech Stack: Python, SpaCy, NLTK, Streamlit
5. Crypto Price Predictor
Predict BTC/ETH prices using historical data.
Tech Stack: Python, LSTM (Keras), Pandas, Matplotlib
6. Instagram Hashtag Recommender
Suggest hashtags based on image captions or niche.
Tech Stack: Python, NLP, TF-IDF, Cosine Similarity
7. Reddit Sentiment Tracker
Analyze community sentiment on hot topics using Reddit API.
Tech Stack: Python, PRAW, VADER, Plotly
8. AI Job Postings Dashboard
Scrape and visualize job trends by tech stack and location.
Tech Stack: Python, Selenium/BeautifulSoup, Streamlit
9. Airbnb Price Estimator
Predict listing prices based on location and amenities.
Tech Stack: Python, Scikit-learn, Pandas, XGBoost
10. Food Calorie Image Classifier
Estimate calories from food images using CNNs.
Tech Stack: Python, TensorFlow/Keras, OpenCV
Each project can be completed in 1-2 weekends.
#datascience #machinelearning #womeninstem #learningtogether #progresseveryday #tech #consistency #projects
@priyal.py
35.0K
TEHow much does a DATA SCIENTIST make? #viral #reels #techjobsin2minutes #amazon
@techjobsin2
109.7K
DAA lot of people use data analyst, data scientist, and data engineer interchangeably.
Understandably so because most of them don’t know the difference. They’re not the same not even close.Each role plays a very different part in how data actually creates value inside a company.
I tried a bit of everything while learning about data, and once I truly understood these roles, choosing the right path became much easier.
Each one needs a different mindset, different skills, and leads to very different day-to-day work.
Once this clicks, the confusion disappears.
[data, analyst, scientist, engineer, career, skills, roles, differences, clarity, mindset, learning, transition, professional, insights, guidance]
#datasciencejobs #datascientist #dataengineering
@datawithsai
121.7K
MAComment PROJECT to access my step-by-step Python tutorial that anyone can follow to build your very first geospatial dashboard web app! 🌍📊
A good number of portfolio projects is 3–5, and the types of projects you choose should reflect the kind of data role you’re going after.
A data analyst portfolio should look very different from a machine learning engineer one. Even within data science, a product/decision data scientist portfolio should focus on A/B testing and metrics storytelling—while an algorithm data scientist portfolio might highlight modeling and experimentation.
✨ Especially if you’re building your very first project, prioritize:
🌱 Real-world messiness (not polished Kaggle sets)
🌱 Business context and decision-making
🌱 Clear documentation (what you did and why)
🌱Visuals to help your work stand out
No one’s asking for perfection—they want to see how you think.
#datascienceportfolio #dataanalyst #learnpython #codingjourney #techcareers
@maggieindata
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