#r programing language

[kijetesantakalu-lanpan]

computer nerds when the boring office service requires an internet connection to function: 🤮

computer nerds when the programming language requires an internet connection to function: 🤩

[house-of-mirrors]

VERY bad post alert. Sometimes I get the urge to encode a horrid little random generator in Jupyter notebook and tonight I've made quite literally the worst possible one:

This has 85 characters (what I could think of off the top of my head), 12 tropes, and 13 locations. And yes, it's poly inclusive. Buckle your seatbelt. Make peace with yourself. Let's try it.

Gonna tell the grandkids this was storycrafting!

I create a completely random generator and it still gives us another HD tie-in! It's like everything evolving into crabs but with this accursed ambition!

I am full of indescribable dread

More highlights under the cut (You've been warned)

69 nice- wait a minute

THE NUMBER

This is the most jarring one tbh, I'm so sorry Frank let me go correct the code and put you down as "AND Frank" right away, do not separate

Sure why not

I want to go home now

Alas I press on...

This is probably already on AO3 somewhere

POINTS

I mean Fires already works everyone into the grave...

I can't take much more of this

Omg #cancelled

I'm tired now. Goodbye. Will be recovering from the psychic damage all weekend

[education43]

What Are the Qualifications for a Data Scientist?

In today's data-driven world, the role of a data scientist has become one of the most coveted career paths. With businesses relying on data for decision-making, understanding customer behavior, and improving products, the demand for skilled professionals who can analyze, interpret, and extract value from data is at an all-time high. If you're wondering what qualifications are needed to become a successful data scientist, how DataCouncil can help you get there, and why a data science course in Pune is a great option, this blog has the answers.

The Key Qualifications for a Data Scientist

To succeed as a data scientist, a mix of technical skills, education, and hands-on experience is essential. Here are the core qualifications required:

1. Educational Background

A strong foundation in mathematics, statistics, or computer science is typically expected. Most data scientists hold at least a bachelor’s degree in one of these fields, with many pursuing higher education such as a master's or a Ph.D. A data science course in Pune with DataCouncil can bridge this gap, offering the academic and practical knowledge required for a strong start in the industry.

2. Proficiency in Programming Languages

Programming is at the heart of data science. You need to be comfortable with languages like Python, R, and SQL, which are widely used for data analysis, machine learning, and database management. A comprehensive data science course in Pune will teach these programming skills from scratch, ensuring you become proficient in coding for data science tasks.

3. Understanding of Machine Learning

Data scientists must have a solid grasp of machine learning techniques and algorithms such as regression, clustering, and decision trees. By enrolling in a DataCouncil course, you'll learn how to implement machine learning models to analyze data and make predictions, an essential qualification for landing a data science job.

4. Data Wrangling Skills

Raw data is often messy and unstructured, and a good data scientist needs to be adept at cleaning and processing data before it can be analyzed. DataCouncil's data science course in Pune includes practical training in tools like Pandas and Numpy for effective data wrangling, helping you develop a strong skill set in this critical area.

5. Statistical Knowledge

Statistical analysis forms the backbone of data science. Knowledge of probability, hypothesis testing, and statistical modeling allows data scientists to draw meaningful insights from data. A structured data science course in Pune offers the theoretical and practical aspects of statistics required to excel.

6. Communication and Data Visualization Skills

Being able to explain your findings in a clear and concise manner is crucial. Data scientists often need to communicate with non-technical stakeholders, making tools like Tableau, Power BI, and Matplotlib essential for creating insightful visualizations. DataCouncil’s data science course in Pune includes modules on data visualization, which can help you present data in a way that’s easy to understand.

7. Domain Knowledge

Apart from technical skills, understanding the industry you work in is a major asset. Whether it’s healthcare, finance, or e-commerce, knowing how data applies within your industry will set you apart from the competition. DataCouncil's data science course in Pune is designed to offer case studies from multiple industries, helping students gain domain-specific insights.

Why Choose DataCouncil for a Data Science Course in Pune?

If you're looking to build a successful career as a data scientist, enrolling in a data science course in Pune with DataCouncil can be your first step toward reaching your goals. Here’s why DataCouncil is the ideal choice:

Comprehensive Curriculum: The course covers everything from the basics of data science to advanced machine learning techniques.

Hands-On Projects: You'll work on real-world projects that mimic the challenges faced by data scientists in various industries.

Experienced Faculty: Learn from industry professionals who have years of experience in data science and analytics.

100% Placement Support: DataCouncil provides job assistance to help you land a data science job in Pune or anywhere else, making it a great investment in your future.

Flexible Learning Options: With both weekday and weekend batches, DataCouncil ensures that you can learn at your own pace without compromising your current commitments.

Conclusion

Becoming a data scientist requires a combination of technical expertise, analytical skills, and industry knowledge. By enrolling in a data science course in Pune with DataCouncil, you can gain all the qualifications you need to thrive in this exciting field. Whether you're a fresher looking to start your career or a professional wanting to upskill, this course will equip you with the knowledge, skills, and practical experience to succeed as a data scientist.

Explore DataCouncil’s offerings today and take the first step toward unlocking a rewarding career in data science! Looking for the best data science course in Pune? DataCouncil offers comprehensive data science classes in Pune, designed to equip you with the skills to excel in this booming field. Our data science course in Pune covers everything from data analysis to machine learning, with competitive data science course fees in Pune. We provide job-oriented programs, making us the best institute for data science in Pune with placement support. Explore online data science training in Pune and take your career to new heights!

[revasserium]

his rich + self-aware combination topped the CAKE. yeah… he’s like… so so so so so funny. i also love how he’s associated with purple because i love love purple. speaking of kag’s VA, do u play the game in chinese or jpn dub? i assume jpn dub with cn text since u mentioned being able to understand the game in cn. also, u studied in florence? was it also for art or a student exchange sort of thing? — @anonymilk

anonymilk i'm saving ur prev ask as a request u__u imma write that shit BINCH I AM SO HYPE.

i uh -- play each character w/ a dif voice pack bc i'm UNWELL i mean -- i just... have a thing for voices? so i went through the official tot website and like.... listened to all the va's and picked the ones that i liked the best:

marius (JPN)

artem (S-CH)

luke (T-CH)

vyn (KR)

and the ensemble cast speak jpn bc... i'm writing this game off as my jpn language practice.... bc yeah... that's totally what i play it for. uh-huh.

[misterradio]

instead of saying god's green earth programs woukd say something like "what on flynn's great grid..."

[ineffablemossy]

On my shelf at work. Clearly one of his finer works. Has generated enormous quantities of low grade evil from scientists everywhere.

[aianddata]

10 Smart R Programming Tips to become Better R Programmer

Coding is the process by which a programmer converts tasks from human-readable logic to machine-readable language. The reason behind coding being so popular is that there are so many ways to do the same thing that programmers don’t know the right choice anymore.

As a result, each programmer has his/her own style in writing implementations to the same part of an algorithm.

Writing code can sometimes be the most difficult and time-consuming part of any project. If the code is written in such a way that it is hard to change or requires a lot of work for every small update, then the investments will keep on piling up and more and more issues will crop up as the project progresses.

A good and well-written code is reusable, efficient and written cleverly by a smart programmer. This is what differentiates programmers from each other.

So, here are some tips to becoming a SMART coder:

Table of contents:

Writing codes for Programmer, Developer, and Even for A Layman

Knowing how to improve the code

Writing robust code

When to use shortcuts and when not to use

Reduce effort through code reuse

Write  planned out code

Active memory management

Remove redundant tasks

Learn to adapt

Peer review

1. Writing Codes for Programmer, Developer, and Even for A Layman

Though codes are primarily written for the machine to understand. They should be structured and well organized for other developers or for any layman to understand. In reality, codes should be written for all the three.

Those who keep this fact in mind are one step ahead of other coders while those who are able to make sure everyone can understand their code are miles ahead than their struggling friends.

Good programmers always document their codes and make use of IDE. I will use R language to explain the concept. Using IDE such as Rstudio makes it easier to write code quickly.

The main advantage available in almost all IDE is the auto-completion feature which suggests the function or command when part of it is written.

IDE is also known to suggest the syntax of the selected functions which saves time. Rstudio IDE environment also displays environment variables alongside with some basic details of each variable.

Documentation is another ability which differentiates good programmers from the rest.

Let’s look at this viewpoint using an example. Say you read the following code:

Code snippet 1

# Code snippet 1

a=16

b=a/2

c=(a+b)/2

Code snippet 2

# Code snippet 2

# store the max memory size

a=16

# taking half of the maximum memory as the minimum memory

b=a/2

# taking mean of maximum and minimum memory as the recommended memory

c=(a+b)/2

Code snippet 3

# Code snippet 3

# store the max memory size

max_mem=16

# taking half of the maximum memory as the minimum memory

min_mem=max_mem/2

# taking mean of maximum and minimum memory as the recommended memory

mean_mem=(max_mem+min_mem)/2

The difference in documentation is highlighted in these three code snippets and this is just a simple demonstration of code understandability.

The first code is difficult to understand. It just sets the values of three variables. There are no comments and the variable names do not explain anything.

The second code snippet explains that ‘a’ is the maximum memory, ‘b’ is the minimum memory and ‘c’ is the mean of the two.

Without the comments in code snippet 2, no one can understand whether the calculation for ‘c’ is correct or not.

The third code is a step further with the variables representing what is stored in them.

The third code is the easiest to understand even though all the three codes perform similar tasks. Moreover, when the variables are used elsewhere, the variables used in the third snippet are self-explanatory and will not require a programmer to search in the code for what they store until an error occurs in the code.

2. Knowing how to Improve

R has multiple ways to achieve a task. Each of the possibilities comes from using more memory, faster execution or different algorithm/logic.

Whenever possible, good programmers make this choice wisely.

R has the feature to execute code in parallel. Lengthy tasks such as fitting models can be executed in parallel, resulting in time-saving. Other tasks can also be executed faster based on the logic and packages used.

As an illustration, the following code snippets reflects the same task, one with sqldf package and another with dplyr package.

These practices are foundational not only for efficient programming, but also for building scalable AI and machine learning solutions.

Using sqldf version

# Using sqldf version

install.packages(“sqldf”)

library(sqldf)

Out_df=sqldf(“select * from table_a left outer join table_b on table_a.var_x=table_b.var_x”)

Using dplyr version

# Using dplyr version

install.packages(“dplyr”)

library(dplyr)

Out_df=left_join(table_a,table_b)

I personally prefer the dplyr version whenever possible. However, there are some differences between the outputs.

The dplyr version will look at all variables with the same name and join using them. If there is more than one such variable, I need to use them by field. Moreover, left join using dplyr will not keep both copies of the variable used to join tables whereas sqldf does.

One advantage of sqldf is that sqldf is not case sensitive and can easily join tables even if the variable names in the two tables are completely different. However, it is slower than dplyr.

3. Writing Robust Code

While writing code, you can make the code simple but situation specific or write a generic code. One such way in which programmers write simple but situation-specific code is by ‘Hard Coding’.

It is the term given to fixing values of variables and is never recommended.

For example, dividing the sum of all salaries in a 50,000-row salary data by 50,000 rather than dividing the sum of that sum with the number of rows may seem to make the same sense but have a different meaning in programming.

If the data changes with the change in the number of rows, the number 50,000 needs to be searched and updated. If the programmer misses making the small change, all the work goes down the drain. On the other hand, the latter approach automatically does the task and is a robust method.

Another popular programming issue quite specific to languages such as R is Code Portability. Codes running on one computer may not work on another because the other computer does not have some packages installed or has outdated packages.

Such cases can be handled by checking for installed packages first and then installing them. These tasks can be collectively called as robust programming and make the code error free.

Using an illustration for checking and installing/updating h2o package.

# If h2o package is already loaded, unload it and uninstall

if (“package:h2o” %in% search()) { detach(“package:h2o”, unload=TRUE) }

# Checking

if (“h2o” %in% rownames(installed.packages())) { remove.packages(“h2o”) }

# Next, we download packages that H2O depends on.

# methods

if (! (“methods” %in% rownames(installed.packages()))) { install.packages(“methods”) }

# statmod

if (! (“statmod” %in% rownames(installed.packages()))) { install.packages(“statmod”) }

# stats

if (! (“stats” %in% rownames(installed.packages()))) { install.packages(“stats”) }

# graphics

if (! (“graphics” %in% rownames(installed.packages()))) { install.packages(“graphics”) }

# Rcurl

if (! (“RCurl” %in% rownames(installed.packages()))) { install.packages(“RCurl”) }

# jsonlite

if (! (“jsonlite” %in% rownames(installed.packages()))) { install.packages(“jsonlite”) }

# tools

if (! (“tools” %in% rownames(installed.packages()))) { install.packages(“tools”) }

# utils

if (! (“utils” %in% rownames(installed.packages()))) { install.packages(“utils”) }

# Finally install and load h2o package

install.packages(“h20”)

library(h2o)

4. When to Use Shortcuts and When Not to

Using shortcuts may be tempting in the pursuit of writing code swiftly but the right practice is to know when to use them.

For instance, shortcut keys are something which is really helpful and can always be used. Using Ctrl+L in windows clears the console output screen, Using Ctrl+Shift+C in windows comments and un-comments all selected lines of code in one go are my favorite shortcuts in Rstudio.

Another shortcut is writing code for fixing code temporarily or writing faulty fixes which are not desired.

Here are some of the examples of faulty fixes.

This code changes a particular column name without checking its existing name

# This code changes a particular column name without checking its existing name

colnames(data_f)[5]=”new_name”

This removes certain columns using a number. This may remove important ones and code may give the error if the number of columns less than 10 in this case.

# This removes certain columns using a number. This may remove important ones and code may give error if the number of columns are less than 10 in this case

data_f=data_f[,1:4,6:10]

This converts a value to numeric without checking if it actually has all numbers. If the value does not contain numbers, it may produce NAs by coercion

# This converts a value to numeric without checking if it actually has all numbers. If the value does not contain numbers, it may produce NAs by coercion

Num_val=”123″

The following converts Num_val to 123 correctly

# The following converts Num_val to 123 correctly

Num_val=as.numeric(Num_val)

char_val=”A_Name”

The following issues a warning and converts Num_val to NA as it is not a number

# The following issues a warning and converts Num_val to NA as it is not a number

char_val=as.numeric(char_val)

5. Reduce Effort Through Code Reuse

When you start writing a code, you don’t need to waste time if a particular piece of logic has already been written for you. Better known as “Code Re-use”, you can always use your own code you previously wrote or even google to reach out the large R community.

Don’t be afraid to search. Looking up for already implemented solutions online is very helpful in learning the methods prevalent for similar situations and the pros and cons associated with them.

Even when it becomes necessary to reinvent the wheel, the existing solutions can serve as a benchmark to test your new solution. An equally important part of writing code is to make your own code reusable.

Here are two snippets which highlight reusability.

Code which needs to be edited before resuing it

# Code which needs to be edited before reusing it

for(i in 1:501)             {

df[,i]=as.numeric(df[,i])

}

Code which can be reused with lesser editing

# Code which can be reused with lesser editing

for(i in 1:ncol(df))      {

df[,i]=as.numeric(df[,i])

}

6. Write  Planned Out Code

Writing code on the fly may be a cool-to-have skill but not helpful for writing efficient codes. Coding is most efficient when you know what you are writing.

Always plan and write your logic on a piece of paper before implementing it. Inculcating the habit of adding tabs and spaces and basic formatting as you code is another time-saving skill for a good programmer.

For instance, every new ‘if’, ‘for’ or ‘while’ statement can be followed by tabs so that indentation is clearly visible. Although optional, such actions separate out blocks of code and helpful in identifying breakpoints as well as debugging.

A more rigorous but helpful approach is to write code using functions and modules and explaining every section with examples in comments or printing progress inside loops and conditions. Ultimately it all depends on the programmer how he/she chooses to document and log in the code.

7. Active Memory Management

Adding memory handling code is like handling a double-edged sword. It may not be useful for small-scale programs due to a slowdown in execution speed but nevertheless a great skill to have for writing scalable code.

In Rstudio, removing variables and frames when they are no longer required with the rm() function, garbage collection using gc() command and selecting the relevant features and data for proceeding are ways to manage memory.

Adjusting RAM usage with memory.limit() and setting parallel processing are also tasks for managing your memory usage. Remember! Memory management goes hand in hand with data backup.

It only takes a few seconds create and store copies of data. It should be done to ensure that data loss does not occur if backtracking is required.

Have a look at this example snippet which stores the master data and then frees up memory.

# dividing master dataset into train and test with ratio 7:3

library(dplyr)

train<-sample_frac(master_data, 0.7

train_ind<-as.numeric(rownames(train))

test<-master_data[-train_ind,]

# saving backup of master_data and removing unneeded data

write.csv(master_data,”master_data.csv”)

rm(master_data)

rm(train_ind)

gc()<span style="font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen-Sans, Ubuntu, Cantarell, 'Helvetica Neue', sans-serif; font-size: 16px; background-color: #ffffff;"> </span>

8. Remove Redundant Tasks

Sometimes programmers do some tasks repeatedly or forget to remove program code without knowing it.

Writing separate iterations for each data manipulation step, leaving libraries loaded even after they are no longer required, not removing features until the last moment, multiple joins and queries,etc. are some examples of redundancy lurking in your code.

While these happen somewhat naturally as more and more changes are made and new logic is added. It is a good practice to look at existing code and adjust your new lines to save runtime.

Redundancy can slow your code so much that removing it can do wonders in execution speed.

# Redundant code

# Takes about 0.5 seconds for iris data   

for(i in 1:ncol(df))      {

            df[,i]=as.numeric(df[,i])

}

for(i in 1:ncol(df))      {

            #storing missing values per column in mis vector

            mis[i]=length(which(is.na(df[,i])))

}

#Better implementation (implementations faster than the one below also exist)

#Gives a similar output but takes about 0.3 seconds for iris data - 35% improvement

for(i in 1:ncol(df))      {

            df[,i]=as.numeric(df[,i])

            #storing missing values per column in mis vector

          mis[i]=length(which(is.na(df[,i])))

}

9. Learn to Adapt

No matter how good a programmer you are, you can always be better! This tip is not related to typical coding practices but teamwork. Sharing and understanding codes from peers, Reading codes online (such as from repositories).

setting yourself up to date with books and blogs and learning about new technologies and packages which are released for R are some ways to learn.

Being flexible and adaptive to new methods and keeping yourself up to date with what’s happening in the analytics industry today can help you in avoiding becoming obsolete with old practices.

10. Peer Review

The code you write may be straightforward for you but very complex for everyone else. How will you know that? The only way is to know what others think about it.

Code review is thus the last but not the least in terms of importance for better coding. Ask people to go through your code and be open to suggested edits. You may come across situations when some code you thought is written beautifully can be replaced with more efficient code.

Code review is a process which helps both the coder and reviewer as it is a way of helping each other to improve and move forward.

The Path is Not So Difficult: Conclusion

Becoming a good programmer is no easy feat but becoming better at programming as you progress is possible. Though it will take time, persevering to add strong programming habits will make you a strong member in every team’s arsenal.

These tips are just the beginning and there may be more ways to improve. The knowledge to always keep improving will take you forward and let you taste the sweet results of being a hi-tech programmer.

In the rapidly changing analytics world, staying with the latest tools and techniques is a priority and being good at R programming can be a prime factor towards your progress in your analytics career.

So go out there and make yourself acquainted with the techniques of becoming better at R programming.

This article was originally published at Perceptive Analytics.

Perceptive Analytics partners with businesses to unlock value in data and drive innovation. With two decades of experience, we’ve delivered results for 100+ clients worldwide. Our expertise includes Tableau development services, Chatbot Consulting, and Power BI development services.

[underthehedge]

Going through the notes seems insane to me because like...in my experience the average ecologist nowadays is at least a mid-range coder. Geneticists as well, you can't do bioinformatics/genomics without coding.

WHAT DO YOU MEAN YOU'RE DOING IT ALL BY HAND??? LIKE BACKSPACING OUT EVERY LETTER BEFORE YOU SEARCH THE STRING???

String identified: AT A ' G T A A??? ACACG T TT AC T TG???

Closest match: Crassostrea gigas strain QD chromosome 2 Common name: Pacific oyster

[judecsi]

ok I am locking in on my Spanish learning today👆👆👆

[jhkinfotech]

R, a programming language developed in the early 1990s by Ross Ihaka and Robert Gentleman, was specifically designed for statistical analysis and data visualization. It quickly gained traction within the academic and research communities due to its powerful capabilities in statistical modeling and its open-source nature. R has become synonymous with statistical computing, providing an extensive set of tools and packages to perform complex data analysis.

Python, on the other hand, was created in the late 1980s by Guido van Rossum and released in 1991. Its design philosophy emphasizes simplicity and readability, making it an accessible language for programmers of all skill levels. Python has evolved into one of the most versatile programming languages, widely used in various domains, including web development, machine learning, data science, and automation. Its ease of use and broad applicability make it a favorite among both novice and professional developers.

[futuretechwords]

The Rise Of R Programming Language: Where And Why To Use?

The Rise of R Programming Language: Top 6 uses

In the ever-expanding landscape of programming languages, R has emerged as a powerhouse for data analysis, statistical computing, and machine learning. Its versatility and robust capabilities have propelled its rise to prominence across various industries and domains.

Unleashing the Potential of R:

1. Data Analysis and Visualization: R's extensive library of packages, including ggplot2 and dplyr, empowers analysts to manipulate data and create stunning visualizations with ease.

2. Statistical Computing: With built-in functions for statistical modeling and hypothesis testing, R is the preferred choice for statisticians and researchers worldwide.

3. Machine Learning: R's machine learning packages, such as caret and randomForest, enable developers to build predictive models and uncover patterns in data.

4. Bioinformatics: R is widely used in bioinformatics for analyzing genomic data, DNA sequencing, and protein structure prediction.

5. Finance: In finance, R is employed for risk modeling, portfolio optimization, and algorithmic trading strategies.

6. Social Sciences: Researchers leverage R for survey analysis, experimental design, and sentiment analysis in social sciences.

7. Healthcare: From clinical trials to epidemiological studies, R plays a pivotal role in analyzing healthcare data and improving patient outcomes.

8. Marketing and Advertising: Marketers utilize R for customer segmentation, campaign optimization, and sentiment analysis on social media data.

Why Choose R?

1. Open Source: R is open-source and free to use, making it accessible to a wide range of users, from students to seasoned professionals.

2. Rich Ecosystem: R boasts a vibrant community and extensive package ecosystem, providing users with a wealth of resources and tools for their projects.

3. Interactivity and Reproducibility: R's interactive environment allows for iterative exploration and analysis, while its scripting capabilities facilitate reproducible research and collaboration.

4. Integration with Other Languages: R seamlessly integrates with other programming languages like Python and SQL, enabling users to leverage the strengths of different tools within their workflows.

As industries increasingly rely on data-driven insights to make informed decisions, the demand for skilled R programmers continues to soar. Whether you're a data scientist, researcher, or industry professional, mastering R opens doors to a world of opportunities in data analytics and beyond.

For an in-depth exploration of the rise of R programming language, visit FutureTech Words. Unlock the potential of R today!

[xi-off]

did i ttell yyall bout that time i accidentally took a quantum physics class . u should hear it. it says more abt me than my mbti ever will

my first deadly yet obvious mistake was letting my cousin* help me put my schedule together. in my defense it was my first semester ever at uni and i was taking any and all help i could get. "ur doin premed u might as well take this chem class in case u need it for ur major later" he says. "ok" i say.

*this is the one notorious for building bombs in his kitchen sink. yes he was 2 semesters from getting his bachelors in chemical engineering b4 deciding it was boring and then swapping to computer science for funsies. why do you ask

so yeah the class is named some benign thing like "intro to chemistry principles" with a large footnote that its only required for a handful of STEM degrees, but it therefore covers any and every intro chem credit u will ever need. so im like awesomesauce. might as well since this uni is notorious for idiot credit transfer policies 👍

first week or two is also fairly benign. prof mentions the class is gonna b pretty intense due to the material itself being pretty intense, this isnt really an intro course so hopefully u took ap chem, and im like sure its a 4 credit class. i didnt take ap chem in high school bc our chem teacher Sucked (2/15 ap chem kids my year got a 3 and everyone else failed) so im a little nervous but prepared to hate myself the rest of the semester. pretty cool. chugging along. i dont actually have to teach myself as much basic chem as i thought bc most of its pretty intuitive but im waiting for the other shoe to drop

add/drop deadline passes. my schedule is now set in stone

everything was still fine for a bit. but as per The Rules, somewhere around the 2nd of 4 midterms stuff starts going off the rails and im like. bestie WHAT is happening.u want me modeling WHAT in this janky software from the 90s that responds if and only if it feels like it? wtf is a pi orbital? wtf is hilbert space??? (pause) ARE WE DOING QUANTUM MECHANICS in my INTRO TO CHEM CLASS

(also side note im taking 17 credit hours this semester. the other classes included calc 2 which sucks fat nuts despite the fact im taking it for the second time…its been like 2 years bc i took it in high school… and japanese 101 which ended up being worse than the ACCIDENTAL QUANTUM PHYSICS class in many ways)

so yeah i cried a lot. i got like a 60 on my final and scraped out with a B-. somehow even with Also A B- in my calc class my gpa didnt drop below my scholarship minimum of 3.5 until i failed illustration 101 later. and then i got really disabled. and then covid happened. and now ive been on academic probation for like . hang on doing math. 3 years. and also havent been able to get that resolved to take classes that entire time. and i need to go get that figured out so i can apply to another school UUUUUUGGGHHHHHHH f my gay baka life

tldr: stay in school to draw yuri on ur notes or jesus from bible will put u on academic probation for 3 years

[besttraininginstitute]

Understanding The Advantages And Disadvantages of R Programming

R programming, renowned for statistical computing, offers a rich ecosystem of packages for data analysis. Its open-source nature facilitates collaborative development and extensive community support. With versatile visualization tools, R is ideal for exploring and presenting data insights. However, its steeper learning curve may pose a challenge for beginners compared to more user-friendly languages. Despite this, R remains a preferred choice for statisticians, data scientists, and researchers due to its robust statistical capabilities.

[stackslip]

most of all i'm tired to see a group of people who talk big game about ableism and how much it affects them then immediately turn around and base a solid 75 to 80% of their arguments against a new technology (that to some is genuinely an assistive technology! like it or not!) on the idea of the Natural Brain And Natural Body that is Pure and Good, and how their brains and souls are perfect and unrotten bc they're not idiot stupid r*tard brain-damaged baby losers who need help for BASIC tasks such as

applying for a job

communicating in a more efficient/socially acceptable/gramatically correct manner

summarizing texts or ideas in easy and simple to understand language

breaking down basic tasks to easier and more manageable ones

tasks that are soooo easy any baby could do it, and anyone who can't on their own is just a stupid stupid brain-rotten baby /s

idk maybe it's because i actually spend time with people with cognitive/intellectual disabilities or learning disabilities, as well as refugees and second language speakers who are incredibly disadvantaged in their interactions in the west, that i can't take this rhetoric anymore; especially from people who talk a big game about how the barriers they face because of their autism/adhd/anxiety and chronic illness, but then turn around and point and laugh and talk nastily about folks who for some reason or another struggle with communication, executive function, understanding/comprehension (whether in reading or writing or simple cognition of the thing at hand) and more.

you don't have to use genAI, sure. genAI like all current technologies can also be incredibly harmful to disabled people and mirrors the same systemic ableism and racism that exist. that's without going into data/privacy and how chatgpt and LLMs use them! but that's not the conversation from Principled Anti AI Posters. no, the conversation is that a disabled person who used midjourney or even a self-written genAI art program is a lazy bastard who's probably fat and languishing in a basement somewhere, and anyone who's used it to write an email or translate something is some disgusting baby loser with a rotten brain. and shockingly none of this rhetoric has translated into actionable actions against the tech industry or a better understanding of capitalism, nvm compassion for disabled people who aren't aspie supremacist adjacent losers--instead it's just resulted in harassment campaigns and 20 something year olds sobbing because they believe their future and art as a whole is destroyed forever and doomed. so really, how effective is your anti "brain rotting" genAI posting, or is it just cope because you refuse to acknowledge the grim reality and what could--should be done?

[novy2sirius]

hobbies that you should try

the 5th house in astrology represents hobbies so your 5h ruler can tell about the types of hobbies that would be best for u. there can be more interpretations, but these r just some examples 🩷🎀🧸 ©novy2sirius

ꨄ︎ 5h ruler in the 1h: running, hiking, biking, table tennis, cooking, air hockey, camping, archery, boxing, bodybuilding

ꨄ︎ 5h ruler in the 2h: singing, gardening, clothing making, cooking/baking, karaoke

ꨄ︎ 5h ruler in the 3h: journaling/scrapbooking, writing in a diary, computer programming/coding, web designing, digital art, 3D printing, photography, juggling

ꨄ︎ 5h ruler in the 4h: theatre/acting, baking, interior designing, knitting, crocheting

ꨄ︎ 5h ruler in the 5h: chess, video games, theatre/acting, hula hooping, babysitting

ꨄ︎ 5h ruler in the 6h: fishing, gardening, hunting, camping, horseback riding

ꨄ︎ 5h ruler in the 7h: dancing, playing instruments, acting, clothing making, painting

ꨄ︎ 5h ruler in the 8h: puzzles, sudoku, word searches, magic tricks, card games, playing men for their money

ꨄ︎ 5h ruler in the 9h: photography, learning languages, astrology, filmmaking, meditation

ꨄ︎ 5h ruler in the 10h: ur main hobby could be ur career, running a fanpage, reading abt history

ꨄ︎ 5h ruler in the 11h: computer programming/coding, web designing, digital art, filmmaking, 3D printing

ꨄ︎ 5h ruler in the 12h: playing instruments, writing/producing music, painting, astrology, filmmaking, meditation, magic tricks