Time-traveling in R Skip ahead (or rewind!) days with ease using lubridate & timetk. Add weeks, months, or even years! Master dates, analyze time series, and share your tricks! Let's conquer time together! #RStats#dataanalysis
FreeCodeCamp released a new course for data analysis with Python 🐍using astronomical data. The course covers the foundations of data analysis, focusing on the following:
✅ Python core commands
✅ Functions
✅ Working with tabular data
✅ Data visualization
🚀 Dive into the world of statistical magic with TidyDensity's bootstrap_stat_plot()! 📊✨ Uncover the nuances of your data using R with this powerful function. 🤓
Example 1 explores central tendency, visualizing bootstrapped means, mins, maxes, and standard deviations. Example 2 customizes the plot for a cleaner look. 💡
My TidyDensity package just got a major upgrade, powered by the blazing-fast data.table.
⚡️ And the best part? You get the speed boost no matter what format you choose.
Ready to experience the difference?
1.install.packages("TidyDensity")
2. Pick your output format: .return_tibble = TRUE for tibbles, .return_tibble = FALSE for data.tables.
3. Dive into your data
Python Pandas Tips 🚀 by Kimberly Fessel is a list of short pandas tutorials that focus on basic pandas 🐼 operations such as:
✅ Reading flat files (e.g., CSV, Excel, etc.)
✅ Columns and rows manipulation
✅ Handle missing and duplicate values
✅ Changing columns attributes
#Janelia is offering a 12-day bootcamp designed to demonstrate how biological queries and hypotheses steer experimental designs on various #microscopy platforms and across length scales from molecules to small animals:
Question: someone I know is doing a data science project for university, and needs to scrape some tabular data from a web site to perform analysis on as an assignment.
Is there anything open source or GNOME-related that is publicly listed as tabular data somewhere that could be interesting for them to analyze? Ideally something with at least 100 data points and multiple columns per data point, if that makes sense.
'In a paper published in JAMA Ophthalmology on 9 November1, the authors used GPT-4... paired with Advanced Data Analysis (ADA), a model that incorporates the programming language Python and can perform statistical analysis and create data visualizations. The AI-generated data compared the outcomes of two surgical procedures and indicated — wrongly — that one treatment is better than the other.'
As a little teaser for my upcoming #rstats#dplyr online course, I'll be releasing a free video series on related topics on the Statistics Globe YouTube channel during the next few days!
👉 new plot type: Q-Q plot
👉 new plot type: Lollipop plot
👉 new plot type: KDE plot
👉 ODS import support @libreoffice
👉 more functions in the function editor
👉 extended search&replace
...and much more!
I know this is a long-shot, but it occurred to me as I prepare for yet another shift at a retail store giving it literally everything I've got and then some (and ending up with a sleep deficit and feeling so exhausted I can barely crawl to bed) for a whopping $15/hr, I figured it wouldn't hurt to ask if anyone has any leads for a #DataAnalyst position.
It turns out a great deal of my prior experience was of similar activities. I'm working on my "google certificate" as a data analyst but tbh progress has been incredibly slow as I deal with one life issue after another. This is also costly for me since there's a monthly fee through the 3rd-party provider hosting the google courses.
Yes.. I'm asking to skip ahead to the part where I'm no longer earning starvation wages anymore.
In a nutshell, a Bland-Altman plot shows the differences between two measurements against their means. It's a powerful tool for quality control and validation, widely used in various industries, including healthcare.
Step 1: Load your data.
Step 2: Perform Principal Component Analysis (PCA).
Step 3: Calculate the variance explained.
Step 4: Create a stunning scree plot.
Step 5: Interpret the plot to find the "elbow."
Step 6: Decide how many components to retain.
Step 7: Apply your decision and get insights!