#R Data Analysis

Stop 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

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

The Secret to Understanding Correlation Coefficients #statistics #math #datascience #correlation #Manim Master the Pearson Correlation Coefficient in seconds! This video breaks down the complex world of statistics by visualizing how 'r' values change across different scatter plots. From strong positive correlations (+0.95) to strong negative correlations (-0.95), you will see exactly how data points align with the line of best fit.

Comment “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!

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

FREE Data Analytics learning resources. Seriously, start here before paying for any courses. These are FREE & a great introduction for any skill you want to learn. - SQL: https://www.youtube.com/watch?v=7S_tz1z_5bA - Excel: https://www.youtube.com/watch?v=pCJ15nGFgVg - Tableau: https://www.youtube.com/watch?v=aHaOIvR00So - Python: https://www.youtube.com/watch?v=LHBE6Q9XlzI #dataanalytics #dataanalyst #datascience #womenintech #aiengineering #techcareers

watch this if you want to become a data analyst in 2026, these are my top simple tips 📊 1. Learn SQL: its the tool you’ll use to get data from databases, and then use to analyse business performance 2. Learn Excel or something similar: it’s great for ad hoc analysis and building engaging charts and diagrams 3. Get familiar with a reporting tool, you don’t need to be great at this just an understanding is fine 4. The core skills are communicating your insights clearly and understanding business metrics Save this and come back to it when you’re planning what to learn, I have links on my profile for courses/guides for each of these aspects!

Data Interpretation Mind Map + Important Formulas 📊 | UGC NET Paper 1 Quick Revision

Comment "DATA" for the links. You Will Never Struggle With Data Science Again 📌 Learn the most important foundations with these beginner-friendly resources: 1️⃣ Learn Python for Data Science – FreeCodeCamp’s full beginner course 2️⃣ Essence of Linear Algebra – 3Blue1Brown’s visual, intuitive playlist 3️⃣ Statistics – A Full Lecture (2025) – step-by-step breakdown of core stats concepts Stop feeling overwhelmed by Python, statistics, or linear algebra. These tutorials simplify the fundamentals of Data Science with clear explanations, visuals, and real-world examples. Whether you’re preparing for a career in Data Science, getting into machine learning, or just curious about data analysis, this is the fastest way to finally understand how it all fits together. Save this post, share it, and turn confusion into clarity with Python, Stats, and Linear Algebra for Data Science 📊

1. QUALIFY + ROW_NUMBER() Lets you rank rows and filter results in the same query — perfect for grabbing the most recent or top record without subqueries. 2. LAG / LEAD Used to look at the previous or next row — great for comparing changes over time (day-over-day, month-over-month). 3. CTE (WITH clause) Creates a temporary, named query so you can break complex SQL into clean, readable steps. #data #analyst #dayinthelife #dadlife #sql

I won’t be mad if you copy this entire roadmap… #dataanalyst #dataanalysis #dataanalytics #data #analyst #techjobs #breakintotech #wfh #workfromhome #wfhjobs #remotejobs #remotework #excel #sql #tableau #python

#rprogramming #pythonprogramming #heatmap #datavisualisation #gene #dataanalytics