#Data Science Training

Comment ‘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

This 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

If 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

Here’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]

Ep44- Stop learning everything!! Are you learning everything in data analytics?? that’sthe biggest mistake and the reason people stay stuck with out getting a job. Interviews don’t test random topics. They test specific skills. Right tools and project scenario based knowledge. As an experienced data analyst with over 8 years of experience i have created a detailed pdf from my data analyst journey on which topics needs to be covered. Which needs to be ignored. How to prepare your own project based portfolio. Answer questions with right tools and skill. Below are the details included in pdf. ✔️ What to learn (and what to skip) ✔️ Skills interviewers actually ask ✔️ Role-wise roadmap (Fresher → Job ready) ✔️ Project clarity + interview direction This is only for serious learners. Hence i made it as a paid one which costs a minimal fee. Follow and comment EP-44. I’ll send you the link directly. [data analytics, journey, road map, data analyst, jobs] #dataanalyst #journey #roadmap #skills #growth

Here is a full roadmap on how to get started with Data Science. Comment “DATA” for the full roadmap pdf. #datascience #machinelearning #coding #ai #university

Data Science + AI = Easy Mode 🔥 #ai#code#datascience#python#programming

Data Science Course ! #datascience #dataanalytics #datascientist #education #degreestudents #jobs #skills

Comment “Data Science” to know more! #datascience#tech

Learn data science fundamentals for free! STEM background recommened but not required for most courses. - free data science courses? - best way to learn data science online? - how do I learn data science? #ai #datascience #learning #sabrinaramonov

People are spending ₹10,000–₹20,000 on data analytics courses…👇🏻✅🤑🤯 but most of that content is already available for free. The real problem is not lack of resources… 👉 it’s lack of practice and direction Platforms like: 👉 Kaggle (real-world datasets + projects) 👉 YouTube (structured learning if used right) 👉 GitHub (real project exposure) can actually teach you more than most paid courses — if you use them properly. Don’t just keep learning… 👉 start building. Save this if you’re serious about your data career. #DataAnalytics #LearnDataAnalytics #TechCareers #CareerGrowth #dataanalyst

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