36.3K
YOWhere are all our data scientists at! 👀👇🏻
#young4stem #datascience #job #reel #stem #computerscience
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349.4K
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
14.3K
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
111.2K
CHHere is a full roadmap on how to get started with Data Science. Comment “DATA” for the full roadmap pdf.
#datascience #machinelearning #coding #ai #university
@chrispathway
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
558.0K
CHThe best projects serve a real use case
Comment “data” for all the links and project descriptions
#tech #data #datascience #ml #explore
@chrisoh.zip
14.2K
RAData Scientist Roadmap
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#reels #viral #trendingreels #newcollection
#viralvideos #reelsvideo #reelsinstagram #shorts #trending #viralreels
@random_code_83
42.3K
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
50.7K
DEData Science Roadmap from a Googler❤️
Recently I spoke to several friends here in the Bay Area, one of them is a data scientist at google, some are data scientists at walmart, and a few others working in California!
Based on their 4-10 years of experience in the field, I have designed a beginner friendly roadmap:
✅Covering 4 Month Timeline
✅Topics to cover and their resources
✅Frequently asked questions
#datascience #google #softwareengineer #indiansinusa #jobsearch
@devshree.17
1.8M
SHConfused between becoming a Data Scientist or an AI Engineer?
Both roles are powerful—but require different skills, tools, and thinking.
Comment “Roles” and I’ll send you a detailed roadmap for both 🚀
Got questions or feeling stuck? Drop your doubts in the comments—I’ll personally help you get clarity and move forward on your journey.
#datascientist #datascience #ai #aiengineer #careergrowth
@shailjamishra__
17.2K
COData Science is art paired with logic 😊.
In my soft girl in tech era 💕. Calm lights, chaotic datasets
#datascience #stem #womeninstem
#study #computerscience #coding #programming #tech #explore #ai #python #dev #tools #study #datascientist #data #design #software #codinglife #programmer #datascience #build #learning #growth #technology #information
@codewithboi
960.2K
CHI didn’t come from a technical background. No coding, no deep math. But little by little, these are the steps that helped me break into Data Science & Machine Learning ⬇️
1. Start small with Python → I focused on the very basics first (loops, functions, simple algorithms).
2. Build up the math slowly → Statistics and probability were way more useful in the beginning than trying to jump straight into deep learning.
3. Do tiny projects early → Cleaning messy datasets, making visualizations, or trying out a simple sentiment analysis taught me more than just reading theory.
4. Use free resources first → FreeCodeCamp, Kaggle, YouTube, and MOOCs gave me a foundation. Later I used platforms like DataCamp once I knew what I needed.
5. Consistency > intensity → I wasn’t grinding 10 hours a day. I just showed up for 1–2 hours almost every day and that’s what really made the difference.
6. Share your progress → Putting projects on GitHub and LinkedIn helped way more than I expected. It’s how people actually saw what I was learning.
If you’re not from a tech background: you don’t need to be born with it, you just need to build it one step at a time.
#datascience #coding #machinelearning #cs #studygram #motivation #selfimprovement #study #polymath #stem #inspiration #studywithme #success #mindset #grind #learning #studymotivation #finance #university #student #aesthetic
@chrispathway
✨ #Data Scientists Discovery Guide
Instagram hosts 247K posts under #Data Scientists, creating one of the platform's most vibrant visual ecosystems. This massive collection represents trending moments, creative expressions, and global conversations happening right now.
The massive #Data Scientists collection on Instagram features today's most engaging videos. Content from @shailjamishra__, @the.datascience.gal and @chrispathway and other creative producers has reached 247K posts globally. Filter and watch the freshest #Data Scientists reels instantly.
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