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SAUGC NET Data Interpretation Tricks 📊 | Paper 1 Handwritten Notes | DI in 2 Minutes
@sameera.fernaz
#Data Interpretation
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MAData Interpretation Tricks
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#mathsbymaruti #maths #math #di #ditricks #datainterpretation #mathskills #mathsteacher #teaching #teacher #education #instagramreels #reelsexplore #reelsitfeelsit #reelsgood #reelstrend #trending
@mathsbymaruti16
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EADATA INTERPRETATION DI TRICKS for All Competition Exam
#di #datainterpretation #ugcnet #ctet #uppcs
@easynotes4uonlinestudy_ugc_net
192.7K
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
842.9K
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
60.1K
PHStop suffering in silence. These tools will level up your analysis game!
Which one’s your fav?
Comment down👇🏻
#dataanalysis #phdlife #statsmadeeasy #dataanalysis #rstats #prism #researchtools #scientificreels #academiaa #phd #phdwithanjali #juliusai @try_julius.ai
@phdwithanjali
336.9K
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
33.2K
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
22.9K
MAReal analysis is a *scam* (not really, but hear me out) #math #realanalysis #numbertheory #padicnumbers #stem
@mathandcobb
27.9K
ARAprender Análisis de Datos antes de que termine el año es una decisión estratégica.
El Google Data Analytics Professional Certificate en coursera te enseña desde cero a trabajar con datos reales usando Excel, SQL y visualización, para tomar decisiones como lo haría un analista en una empresa.
Comenta COURSERA y te mando el link.
#DataAnalytics #Coursera #aprendertech
@arelis_reyes19
1.3M
CH@chithappens.co brings to you #simplystatistics
Comment below and let me know which topic do you want me to explain!!
#psychology #research #statistics
@chithappens.co
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
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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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