#A Data Learn The Language

[rastronomicals]

9:24 PM EDT April 14, 2025:

The Mercury Program - "Fragile Or Possibly Extinct" From the album A Data Learn the Language (September 10, 2002)

Last song scrobbled from iTunes at Last.fm

[automaticclappingxboxterminator]

AUTOMATIC CLAPPING XBOX TERMINATOR GENISYS

[eddis-not-eeddis]

I got an hourly planner and instead of planning out my day, I just write in it if I’ve done something that I’m proud of or that I enjoy, and it’s amazing how much I’ve gotten done lately and how much fun I’ve had now that I’m trying to impress myself.

[kaurwreck]

y'all stop saying fyodor has never looked so sincerely angry before. he has.

I know this because, and this is not an exaggeration, the vast majority of my manga revisits are to enjoy his expressions of anger, disdain, and malcontent. i shit you not, several of my bsd meta posts wholly unrelated to fyodor were written because I happened to notice something else while flipping through to imbibe fedya's hissy fits. I don't reread the manga when I do this, just those scenes, unless something else catches my attention.

anyway, stop disrespecting my beloved pastime.

[max1461]

catch me working smarter not harder, sucker

Jokes on you, I learned 80% of the linguistics I know from reading grammars/random papers for the purpose of conlanging.

[phasesandplaylists]

Hello whomever might read this,

I've been wanting to post on here for a while. With 2025 around the corner, I decided to use Tumblr to track my goals and document my progress. Hopefully, I'll figure out how to relate that with Twitter(X) and whatever platform helps one land a job these days.

Basic info:

I'm a writer.

My name's Chisom and I'm 25 years (26 next year). I'm Nigerian and a University graduate with a BSc in Computer Science/Statistics. I live in Nigeria.

Other tidbits:

I listen to basically any type of music, but Pop is my main genre.

Fandoms I belong to: Directioner, Nctzen, Potterhead, etc.

I'm currently studying Data Analysis with every intention to land a job in the field next year. Most of my posts will be in regards to studying.

Languages spoken: English, Hausa, Igbo, Spanish (intermediate)

Currently studying: Korean, Swahili, Chinese. Korean takes priority currently.

Song I currently have on repeat:

Nice to meet you♥️

[chambersevidence]

Search Engines:

Search engines are independent computer systems that read or crawl webpages, documents, information sources, and links of all types accessible on the global network of computers on the planet Earth, the internet. Search engines at their most basic level read every word in every document they know of, and record which documents each word is in so that by searching for a words or set of words you can locate the addresses that relate to documents containing those words. More advanced search engines used more advanced algorithms to sort pages or documents returned as search results in order of likely applicability to the terms searched for, in order. More advanced search engines develop into large language models, or machine learning or artificial intelligence. Machine learning or artificial intelligence or large language models (LLMs) can be run in a virtual machine or shell on a computer and allowed to access all or part of accessible data, as needs dictate.

[dkettchen]

I'm applying to coding bootcamps (in my retraining efforts toward a stable career to fall back on whenever media industry is being an ass (aka their default state)) and this one is making me learn javascript as part of the application process, and I'm like just let me use my snake_case, you monsters ToT

[rastronomicals]

2:20 PM EST January 4, 2025:

The Mercury Program - "You Yourself Are Too Serious" From the album A Data Learn the Language (September 10, 2002)

Last song scrobbled from iTunes at Last.fm

[torokiseru]

January Hobby Recap Part 1

As I mentioned previously, I am working to spend more time on my hobbies and tracking that data. Here are the results of my first full month of data tracking.

Exercise - This is the one thing on the list that isn’t a hobby, but I am trying to make it become one. I had a great streak going there until I forgot one day.

Forestry - I bought native plants twice and removed Invasives seven times. Mostly Himalayan blackberry, a little bit of yellow archangel and English ivy. Kind of fell off a bit once we got freezing temps.

Learn Dutch - I was very successful at keeping this up daily. The amounts of time spent were variable. This was listening to a Dutch podcast nineteen times (shoutout to Een Beetje Nederlands), studying from my Dutch Reader twelve times, listening to Dutch music three times, watching a children’s show in Dutch two times, intentional speaking practice one time, and a movie in Dutch one time.

Look at birds - Currently it is often too dark for birds by the time I get home for work, but I was able to at least see some cool birds while commuting most days. There were a few days I missed because it was too dark on my commutes to and from work. I did make one dedicated trip to look for bald eagles.

Look at trees - This one was pretty easy for me to achieve daily because it can be done while driving and even in the dark. I was most excited about the appearance of hazelnut catkins and watching a snag in my driveway get demolished by a woodpecker.

Play a video game - Even though this was one of my lower ones, this was a hobby I was “stuck” on and hadn’t participated in for months before starting this data tracking. This is a huge improvement. This was nine days of playing Kingdom Hearts and four Pokémon Go outings.

[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!

[amphibifish]

i need to play minecraft bedwars

[innovatexblog]

How Large Language Models (LLMs) are Transforming Data Cleaning in 2024

Data is the new oil, and just like crude oil, it needs refining before it can be utilized effectively. Data cleaning, a crucial part of data preprocessing, is one of the most time-consuming and tedious tasks in data analytics. With the advent of Artificial Intelligence, particularly Large Language Models (LLMs), the landscape of data cleaning has started to shift dramatically. This blog delves into how LLMs are revolutionizing data cleaning in 2024 and what this means for businesses and data scientists.

The Growing Importance of Data Cleaning

Data cleaning involves identifying and rectifying errors, missing values, outliers, duplicates, and inconsistencies within datasets to ensure that data is accurate and usable. This step can take up to 80% of a data scientist's time. Inaccurate data can lead to flawed analysis, costing businesses both time and money. Hence, automating the data cleaning process without compromising data quality is essential. This is where LLMs come into play.

What are Large Language Models (LLMs)?

LLMs, like OpenAI's GPT-4 and Google's BERT, are deep learning models that have been trained on vast amounts of text data. These models are capable of understanding and generating human-like text, answering complex queries, and even writing code. With millions (sometimes billions) of parameters, LLMs can capture context, semantics, and nuances from data, making them ideal candidates for tasks beyond text generation—such as data cleaning.

To see how LLMs are also transforming other domains, like Business Intelligence (BI) and Analytics, check out our blog How LLMs are Transforming Business Intelligence (BI) and Analytics.

Traditional Data Cleaning Methods vs. LLM-Driven Approaches

Traditionally, data cleaning has relied heavily on rule-based systems and manual intervention. Common methods include:

Handling missing values: Methods like mean imputation or simply removing rows with missing data are used.

Detecting outliers: Outliers are identified using statistical methods, such as standard deviation or the Interquartile Range (IQR).

Deduplication: Exact or fuzzy matching algorithms identify and remove duplicates in datasets.

However, these traditional approaches come with significant limitations. For instance, rule-based systems often fail when dealing with unstructured data or context-specific errors. They also require constant updates to account for new data patterns.

LLM-driven approaches offer a more dynamic, context-aware solution to these problems.

How LLMs are Transforming Data Cleaning

1. Understanding Contextual Data Anomalies

LLMs excel in natural language understanding, which allows them to detect context-specific anomalies that rule-based systems might overlook. For example, an LLM can be trained to recognize that “N/A” in a field might mean "Not Available" in some contexts and "Not Applicable" in others. This contextual awareness ensures that data anomalies are corrected more accurately.

2. Data Imputation Using Natural Language Understanding

Missing data is one of the most common issues in data cleaning. LLMs, thanks to their vast training on text data, can fill in missing data points intelligently. For example, if a dataset contains customer reviews with missing ratings, an LLM could predict the likely rating based on the review's sentiment and content.

A recent study conducted by researchers at MIT (2023) demonstrated that LLMs could improve imputation accuracy by up to 30% compared to traditional statistical methods. These models were trained to understand patterns in missing data and generate contextually accurate predictions, which proved to be especially useful in cases where human oversight was traditionally required.

3. Automating Deduplication and Data Normalization

LLMs can handle text-based duplication much more effectively than traditional fuzzy matching algorithms. Since these models understand the nuances of language, they can identify duplicate entries even when the text is not an exact match. For example, consider two entries: "Apple Inc." and "Apple Incorporated." Traditional algorithms might not catch this as a duplicate, but an LLM can easily detect that both refer to the same entity.

Similarly, data normalization—ensuring that data is formatted uniformly across a dataset—can be automated with LLMs. These models can normalize everything from addresses to company names based on their understanding of common patterns and formats.

4. Handling Unstructured Data

One of the greatest strengths of LLMs is their ability to work with unstructured data, which is often neglected in traditional data cleaning processes. While rule-based systems struggle to clean unstructured text, such as customer feedback or social media comments, LLMs excel in this domain. For instance, they can classify, summarize, and extract insights from large volumes of unstructured text, converting it into a more analyzable format.

For businesses dealing with social media data, LLMs can be used to clean and organize comments by detecting sentiment, identifying spam or irrelevant information, and removing outliers from the dataset. This is an area where LLMs offer significant advantages over traditional data cleaning methods.

For those interested in leveraging both LLMs and DevOps for data cleaning, see our blog Leveraging LLMs and DevOps for Effective Data Cleaning: A Modern Approach.

Real-World Applications

1. Healthcare Sector

Data quality in healthcare is critical for effective treatment, patient safety, and research. LLMs have proven useful in cleaning messy medical data such as patient records, diagnostic reports, and treatment plans. For example, the use of LLMs has enabled hospitals to automate the cleaning of Electronic Health Records (EHRs) by understanding the medical context of missing or inconsistent information.

2. Financial Services

Financial institutions deal with massive datasets, ranging from customer transactions to market data. In the past, cleaning this data required extensive manual work and rule-based algorithms that often missed nuances. LLMs can assist in identifying fraudulent transactions, cleaning duplicate financial records, and even predicting market movements by analyzing unstructured market reports or news articles.

3. E-commerce

In e-commerce, product listings often contain inconsistent data due to manual entry or differing data formats across platforms. LLMs are helping e-commerce giants like Amazon clean and standardize product data more efficiently by detecting duplicates and filling in missing information based on customer reviews or product descriptions.

Challenges and Limitations

While LLMs have shown significant potential in data cleaning, they are not without challenges.

Training Data Quality: The effectiveness of an LLM depends on the quality of the data it was trained on. Poorly trained models might perpetuate errors in data cleaning.

Resource-Intensive: LLMs require substantial computational resources to function, which can be a limitation for small to medium-sized enterprises.

Data Privacy: Since LLMs are often cloud-based, using them to clean sensitive datasets, such as financial or healthcare data, raises concerns about data privacy and security.

The Future of Data Cleaning with LLMs

The advancements in LLMs represent a paradigm shift in how data cleaning will be conducted moving forward. As these models become more efficient and accessible, businesses will increasingly rely on them to automate data preprocessing tasks. We can expect further improvements in imputation techniques, anomaly detection, and the handling of unstructured data, all driven by the power of LLMs.

By integrating LLMs into data pipelines, organizations can not only save time but also improve the accuracy and reliability of their data, resulting in more informed decision-making and enhanced business outcomes. As we move further into 2024, the role of LLMs in data cleaning is set to expand, making this an exciting space to watch.

Large Language Models are poised to revolutionize the field of data cleaning by automating and enhancing key processes. Their ability to understand context, handle unstructured data, and perform intelligent imputation offers a glimpse into the future of data preprocessing. While challenges remain, the potential benefits of LLMs in transforming data cleaning processes are undeniable, and businesses that harness this technology are likely to gain a competitive edge in the era of big data.

[cynicalmusings]

oh god… i want to write for dan heng again…

[python-programming-language]

MIMO - My SQL Certificate ...

Post #94: MIMO, Learn To Code, My SQL Certificate, 2023.

[dkettchen]

Learning C for work, and, even having learnt C++ syntax before, it is truly a humbling experience after having gotten so used to python over the past half year 💀