Pretty self explanatory comic. "If I speak I am in big trouble", as they say

Python for Data Analysis- (Free Course) https://www.coursejoiner.com/free-udemy/python-for-data-analysis-free-course/


@code_it_now

Squid Game Season 2 now streaming on Netflix with 7 Episodes🥳🥳🥳

Humanity cries here😣😣

It says a lot

አንዳንዴ ግን የግቢ ሽንት ቤት ላይ እንዴት ቢጠቀሙ ነዉ እንደዚ ሚያበላሹት 😫😫

specially AAU 4K

I haven't seen any campus so far which is why I said 4k.😁😁

Deemmm 151321 view with in 10 month😎 this is insane 😁😁



#telemetrio2024
@code_it_now

🚀 Make Internships Easier!

Was your first internship hard to find? Let’s change that for others! 🌟

If you’ve interned at a tech company or are a company hiring interns, fill out this quick form

Your input will help create a guide for students to find internships faster and smarter. Deadline: 1 week! Results will be shared.

🙏 Can’t fill out the form? Share this post to help it reach someone who can!

#internship #opportunity #tech #help #impact

Check out "Hasset"
https://play.google.com/store/apps/details?id=com.idealogic.hasset

R Programming – R Programming Language Beginners to Pro https://coursecouponclub.com/matplotlib-for-python-developers-beginners/

@code_it_now

R is a programming language and environment primarily used for statistical computing, data analysis, and graphical representation. It is widely used among statisticians, data scientists, and researchers for its powerful data manipulation capabilities and extensive libraries.

Key Features of R:

1. Statistical Analysis: R provides a wide range of statistical techniques, including linear and nonlinear modeling, time-series analysis, classification, clustering, and more.

2. Data Visualization: R has strong graphical capabilities, allowing users to create high-quality plots and visualizations. Libraries like ggplot2 are popular for creating complex graphics.

3. Data Manipulation: Packages like dplyr and tidyr make it easy to manipulate and clean data, transforming it into a format suitable for analysis.

4. Extensive Package Ecosystem: R has a rich ecosystem of packages available through CRAN (Comprehensive R Archive Network), Bioconductor (for bioinformatics), and GitHub.

5. Open Source: R is free to use and has a large community of users who contribute to its development and provide support.

6. Integration with Other Languages: R can be integrated with other programming languages like C++, Python, and Java, allowing for more complex applications.

7. Reproducible Research: Tools like R Markdown allow users to create dynamic reports that combine code, output, and narrative text, promoting reproducibility in research.

Basic Syntax:

Here are some basic examples of R syntax:

1. Creating a Vector:
  

   my_vector <- c(1, 2, 3, 4, 5)
   

2. Data Frame Creation:
  

   my_data <- data.frame(
       Name = c("Alice", "Bob", "Charlie"),
       Age = c(25, 30, 35)
   )
   

3. Basic Plotting:
  

   plot(my_data$Age, main="Age Plot", xlab="Index", ylab="Age", col="blue")
   

4. Using ggplot2 for Advanced Visualization:
  

   library(ggplot2)
   ggplot(my_data, aes(x=Name, y=Age)) + geom_bar(stat="identity")
   

5. Statistical Modeling:
  

   model <- lm(Age ~ Name, data=my_data)
   summary(model)
  

Statistics about Internet in Ethiopia

https://www.statista.com/map/africa/ethiopia/internet

@code_it_now