#R Data Science Tutorials

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

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

1️⃣ “R for Data Science” by Wickham et al. is widely recommended across stats forums as one of the best books to learn hands on R programming. The is available online for free at www.r4ds.hadley.nz 2️⃣ HarvardX Data Science R Basics is free to audit assumes no prior knowledge and teaches you foundational programming concepts and operations (it doesn’t get into statistical modeling yet). 3️⃣ “An Introduction to Statistical Learning with Applications in R” by James et al. can be a little bit more technical and advanced (as it actually covers statistical topics), but comes with great real-life R coding examples.The PDF of the book is available online for free at www.statlearning.com 4️⃣ www.rscreencasts.com has a long list of screencast videos by data scientist David Robinson where he shares real-world examples of live data analyses in R, including how to approach analysis, what packages and methods he uses, as well as general R tricks and tips. 5️⃣ If you prefer more interactive learning, you might enjoy swirl (swirlstats.com) that teaches you R programming interactively inside the R console, no reading books or watching courses required. ❓Any other good recs? Drop them in the comments! #rprogramming #rstudio #datascientist #womenintech #womeninstem

What is Data Science? 🤖📊 It’s literally where human intelligence meets computer science — a field where we actually predict the future using data. 🔮 Companies study graphs, maps, past trends, and millions of data points to understand what might happen next… because yes, history repeats itself. Election agencies even pay millions for prediction models before the results are out. 🗳️📈 And tech companies? They track your behaviour to recommend products, personalize your apps, and show ads you’re most likely to click. 🎯📱 If you want to enter the world of Data Science, here are the 3 skills you NEED: 1️⃣ Mathematics — statistics & probability 2️⃣ Programming — Python or R for analysis & visualization 3️⃣ Machine Learning Algorithms — including regressions 🤝🤖 Comment “Data Science + your favourite company” and I’ll send you a full beginner-friendly roadmap! Follow @podus.app for more tech breakdowns, coding insights, and career guides. 🚀✨ #datascience #machinelearning #pythonprogramming #techcontent #aicommunity #programminglife #learnpython #datavisualization #techfacts #techreels #codingreels #aiml #artificialintelligence #bigdata #datatrends #datascientist #analytics #mlalgorithms #statistics #probability #codinglife #techcreator #techguide #computerscience #techlearning #futuretech #programmingtutorial #dataanalysis #reelsinstagram #podus

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

A tip if you’re trying to learn R ⬇️ SWIRL is a package within R Studio that has tutorials so you can “learn R within R.” I did the R Programming course as an assignment a year or so ago and now use it to refresh my memory about basic terms and codes within R. It also looks like there are quite a few “courses” within SWIRL that are not just for beginners if you already know some R and want to advance - although I haven’t tried them yet 😄 Share this with your friends who might find this useful since R is surprisingly necessary for a lot of majors and academic fields 👩🏼‍💻 #rprogramming #collegetips #gradschool #womeninstem #r

My second most asked question is always how I learnt R     I taught myself mainly by just trial and error (I have to actually physically do something I can’t just watch videos as I don’t take it in) so I started with the very basics.   I think it’s so easy to overdo and feel like you need to know how to do everything or a lot of things at the start. Stick to simple things like understanding the R studio interface and loading packages and other basic commands     (after this most things I learnt were googling very specifically what I needed to do and adding the command to a ‘useful command’ list I have)    Next:  Following a vignette from start to finish (one that would be similar to what I would soon need) I then would go through and click on functions to look at the arguments (this tells you all the parameters for the function) and how I can change them if needed!    Finally try swirl it’s so easy to just load directly in the terminal and you learn as you go!        What’s your top tips?    I also have so many more so make sure you follow!      #phd #student #coding #rprogramming #university #tipsandtricks

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]

🖥️ RStudio has been my favourite program to run R. ✨ It has lots of features to make coding in R fun 1) Code autocompletion 2) Beautiful themes 3) Run bash, python, git and many more 4) Preview plots and tables ✨ Follow me for tutorials on how I use R and RStudio to analyze transcriptome data ❤️ Give this reel a like 🔖 Save this reel

3 data science projects you can do in a weekend. If you’re learning data science, one of the best ways to improve is by working through real examples. Here are three Kaggle notebooks you can explore: • Loan Prediction – predicting whether a loan gets approved based on applicant data. • Bank Churn – analyzing which customers are likely to leave a bank. • House Price Prediction – estimating house prices from property features. You can study the notebooks and also try solving the same problems yourself using the same datasets. It’s a great way to practice and see different ways people approach the same problem. Comment “DATA” and I’ll send you the notebooks. #coding #datascience #university

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