#data 📊

[isportz]

iSportz Learning Management System empowers its members to compete not just with their bodies but also their brains thanks to the gamified learning approach. Learn more

[xtruss]

According to the data compiled by Islamic Relief, this year’s Ramadan will be the toughest ever as more than 600 Million People in Muslim-Majority Countries will mark the Holy Month of Ramadan without enough food. A third of those people are already facing severe Hunger and Malnutrition due to a Fatal Combination of Conflict, Climate Change and Inequality.

[nazninakther]

7 Secrets Every Aspiring Data Scientist Should Know

In recent years, the field of data science has exploded in popularity, driven by the increasing availability of data and the growing demand for professionals who can analyze and interpret this information. As businesses and organizations strive to make data-driven decisions, the role of the data scientist has become more critical than ever. For those looking to break into this exciting field, several key insights and strategies can help pave the way to a successful career. Here are seven secrets every aspiring data scientist should know.

Master the Fundamentals Before diving into the more glamorous aspects of data science, it’s essential to have a strong grasp of the fundamentals. This includes understanding basic concepts in statistics, probability, and linear algebra. A solid foundation in these areas will not only help you understand the algorithms and models you’ll be working with but also enable you to troubleshoot and refine them effectively.

Statistics and Probability Understanding statistical measures like mean, median, variance, and standard deviation is crucial. Probability theory helps in understanding distributions, hypothesis testing, and statistical significance, which are integral to data analysis.

Linear Algebra Linear algebra is the backbone of many data science algorithms. Concepts like vectors, matrices, eigenvalues, and eigenvectors are essential for understanding complex models, especially in machine learning and deep learning.

Develop Strong Programming Skills Proficiency in programming is a must for any data scientist. Python and R are the most popular languages in the field due to their extensive libraries and frameworks that support data manipulation, analysis, and visualization.

Python Python is favored for its simplicity and readability. Libraries such as NumPy, pandas, Scikit-learn, and TensorFlow are instrumental for data manipulation, statistical modeling, and machine learning.

R is particularly strong in statistical analysis and visualization. Packages like ggplot2, dplyr, and caret are powerful tools for creating detailed and informative visualizations and performing robust statistical analyses.

Gain Practical Experience While theoretical knowledge is important, hands-on experience is invaluable. Engaging in practical projects, internships, and competitions can help solidify your understanding and provide tangible proof of your skills.

Kaggle Competitions Participating in Kaggle competitions can provide exposure to real-world data problems and allow you to apply and test your skills against a global community.

Internships and Projects Internships offer practical experience and insights into the workings of the industry. Working on real-world projects, either through internships or independently, helps in understanding the end-to-end process of data science workflows.

Understand Data Cleaning and Preparation A significant portion of a data scientist’s time is spent on data cleaning and preparation. Raw data is often messy, incomplete, or inconsistent, and must be cleaned and transformed before analysis.

Data cleaning involves handling missing values, correcting errors, and ensuring consistency. Techniques include imputation, outlier detection, and normalization.

Data Transformation Data transformation includes scaling, encoding categorical variables, and feature engineering. Properly prepared data is crucial for building accurate and reliable models.

Learn Machine Learning Algorithms Machine learning is at the heart of data science. Understanding various machine learning algorithms and knowing when and how to apply them is critical.

Supervised Learning Supervised learning algorithms, such as linear regression, decision trees, and support vector machines, are used

when you have unlabeled data. These algorithms identify patterns and structures in the data.

Deep Learning Deep learning, a subset of machine learning, uses neural networks with many layers (hence "deep"). It is particularly effective for tasks like image and speech recognition.

Cultivate Communication Skills Data scientists must be able to communicate their findings effectively to stakeholders who may not have a technical background. Strong communication skills are essential for explaining complex concepts clearly and concisely.

Data Visualization Data visualization is a powerful tool for communicating insights. Tools like Tableau, Power BI, and Matplotlib can help you create compelling visualizations that tell a story with data.

presentation and Storytelling Being able to present your findings in a compelling narrative is crucial. Focus on clarity, conciseness, and relevance to your audience’s needs.

Stay Curious and Keep Learning The field of data science is constantly evolving, with new techniques, tools, and algorithms being developed regularly. A successful data scientist must stay curious and committed to continuous learning.

Online Courses and Certifications Platforms like Coursera, edX, and Udacity offer courses and certifications in data science and related fields. Continuous learning helps you stay updated with the latest trends and technologies.

Research and Reading Following academic research papers, blogs, and forums can provide insights into cutting-edge developments. Engaging with the data science community through conferences and meetups can also be beneficial.

Conclusion Embarking on a career in data science can be both exciting and challenging. By mastering the fundamentals, developing strong programming skills, gaining practical experience, understanding data cleaning and preparation, learning machine learning algorithms, cultivating communication skills, and committing to continuous learning, you can position yourself for success in this dynamic field. Remember, the journey of a data scientist is a marathon, not a sprint. Stay curious, keep learning, and enjoy the process of uncovering insights from data.

Additional Resources for Aspiring Data Scientists To help you on your journey, here are some additional resources that can provide valuable knowledge and experience:

Books "Python Data Science Handbook" by Jake VanderPlas "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurélien Géron "Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville Online Courses "Machine Learning" by Andrew Ng on Coursera "Data Science MicroMasters" by UC San Diego on edX "Deep Learning Specialization" by Andrew Ng and team on Coursera Communities and Forums Kaggle: Participate in competitions and discussions Stack Overflow: Ask questions and share knowledge Data Science Reddit: Engage with a community of data enthusiasts Practical Steps to Get Started Build a Portfolio: Start by working on small projects and gradually take on more complex ones. Create a portfolio showcasing your work on platforms like GitHub. Network: Attend meetups, conferences, and webinars to connect with other data professionals. Networking can open doors to job opportunities and collaborations. Seek Mentorship: Find a mentor who can provide guidance and feedback. A mentor can help you navigate the challenges of starting a career in data science. Stay Updated: Follow influential data scientists on social media, subscribe to newsletters, and regularly read blogs to keep up with the latest trends and best practices.

Data science is a field that offers endless opportunities to learn and grow. Whether you are just starting out or looking to advance your career, the key is to stay motivated and persistent. Embrace challenges as learning opportunities and always strive to improve your skills. With dedication and the right approach, you can achieve success in the exciting world of data science.

[trixiesol-blog]

[natures-uprise]

[acrosstobear]

mclaren Crunching the data ahead of tomorrow. 📊

[gendercensus]

New zine for sale!

"What are your pronouns?"

A very specific guide to talking about trans people with confidence and respect

It's aiming to be a beginner-to-advanced guide for allies, with an informative and nerdy tone. It explains:

Third-person, personal, singular pronouns (with established examples)

Why learning new pronouns is harder than learning new names

Why singular they always has plural verbs (always "you are" and not "you is", even when talking about one person)

How to use they/them for nonbinary people (including themselves vs. themself)

How to mess up gracefully (with a focus on making it more comfortable for the trans person)

What neopronouns are

This zine is 36 sides of A5, with 120 gsm 100% recycled paper pages and 100% recycled card cover, handstitch-bound.

It's informed by my 12 years or so of running the Gender Census and gathering data from tens of thousands of nonbinary and gender-divergent people, so this might be the closest I've gotten to official Gender Census merch!

How to buy

Here are some purchase links for one copy:

UK 1st class, £6.35 - £4 for one copy, plus UK first class postage £2.35 (1-2 days)

UK 2nd class, £5.85 - £4 for one copy, plus UK first class postage £1.85 (2-4 days)

Outside UK, £7.20 - £4 for one copy, plus postage to anywhere outside of the UK £3.20 (5-7 working days)

Update 2024-06-20: They’re now available through our new online shop!

If you'd like to buy more than one copy then please do email me for a quote. It's £4 GBP per copy, plus P&P from the UK to wherever you are. I can take payment by bank transfer or PayPal. You can message me here on Tumblr or, more reliably, email me: hello@gendercensus.com (And if you are curious to see what else I've got in stock crafts-wise, you can check out my "things for sale" page here.)

And a quick reminder that the annual survey is currently open until 13th June 2024 - 38,000 participants and counting!

Thank you for your attention, folks. Now back to the usual statistical enthusiasm. ✨📊

[rythyme]

Introducing the Thai Drama AO3 Trends Dashboard! (Beta) 🇹🇭

Over the last several weeks or so I've been building an auto-scraping setup to get AO3 stats on Thai Drama fandoms. Now I finally have it ready to share out!

Take a look if you're interested and let me know what you think :)

(More details and process info under the cut.)

Main Features

This dashboard pulls in data about the quantity of Thai Drama fics over time.

Using filters, it allows you to break that data down by drama, fandom size, air date, and a select number of MyDramaList tags.

You can also see which fandoms have had the most new fics added on a weekly basis, plus the growth as a percentage of the total.

My hope is that this will make it easier to compare Thai Drama fandoms as a collective and pick out trends that otherwise might be difficult to see in an all-AO3 dataset.

Process

Okay -- now for the crunchy stuff...

Scraping 🔎

Welcome to the most over-complicated Google Sheets spreadsheet ever made.

I used Google Sheets formulas to scrape certain info from each Thai Drama tag, and then I wrote some app scripts to refresh the data once a day. There are 5 second breaks between the refreshes for each fandom to avoid overwhelming AO3's servers.

Archiving 📁

Once all the data is scraped, it gets transferred to a different Archive spreadsheet that feeds directly into the data dashboard. The dashboard will update automatically when new data is added to the spreadsheet, so I don't have to do anything manually.

Show Metadata 📊

I decided to be extra and use a (currently unofficial) MyDramaList API to pull in data about each show, such as the year it came out and the MDL tags associated with it. Fun! I might pull in even more info in the future if the mood strikes me.

Bonus - Pan-Fandom AO3 Search

Do you ever find it a bit tedious to have like, 15 different tabs open for the shows you're currently reading fic for?

While making this dash, I also put together this insane URL that basically serves as a "feed" for any and all new Thai drama fics. You can check it out here! It could be useful if you like checking for new fics in multiple fandoms at once. :)

Other Notes

Consider this dashboard the "beta" version -- please let me know if you notice anything that looks off. Also let me know if there are any fandoms missing! Thanks for checking it out!

The inspiration for this dashboard came from @ao3-anonymous 's AO3 Fandom Trend Analysis Dashboard, which I used as a jumping off point for my own data dash. Please give them some love <3

[destinationtoast]

Come join the Fandom Data Projects community! (You don't need to have a project or any relevant background... just curiosity 🤓)

Apparently I can't reblog the community post I made outside the community, so to quote myself:

Hello, fans of fandom data science, fandom research, fandom stats, fandom surveys, fandom data visualization, and everything related! 🪭📊📈📋📓🎉🎉🎉 I run a blog called @toastystats , and I love fandom data! I am starting this community for folks with a personal or academic curiosity about fans/fanworks and a desire to answer questions with data 🧑‍��. All of the following are welcome here: * Sharing questions about fandom and brainstorming ways to gather relevant data; * Sharing analyses & insights; * Trading tips on how to gather or analyze data; * Chatting about methods; * Asking for volunteers to participate in surveys or help gather data; * Anything else related!

Learners and lurkers are welcome. Drama and discourse are not; please be thoughtful and generous in how you participate in the group, and try not to stir controversy. (That's not to say there aren't valid fandom research topics that involve controversies -- but the goal of this space is to focus on people helping each other with research and learning in a low stress environment.)

(Honestly I'm starting this partly because I'm curious about the Tumblr community feature, and I like to learn by trying things. 🤓 We'll see how this goes.)

[isportz]

Simply put down your head and perform

iSportz CTMS enables centralized data management and saves time with automated scheduling, roster building, and fan engagement.

Learn more about our Club and Team Management System (CTMS)

[xtruss]

The Countries Most Addicted To Screen Time, Mapped

— Jared Russo | Digg.Com |

You'll never guess which country is the most obsessed with spending time online, just glued to their phones and desktops. The internet is addicting.

The most significant thing to happen to the human race this millennium (so far) has been the proliferation and explosion of the internet, particularly through smart phones. Being glued to devices that are small computers with screens bigger than most hands is how many people interact with the world, work at their jobs, order food, meet their loved ones and manage their finances.

But who is the most addicted to their screens? And is that mostly just scrolling through TikTok?

Smartick gathered data from DataReportal.com on digital behavior to put together maps showing who's the most online, and, to a lesser extent, the most online.

Key Findings:

South Africa 🇿🇦 spends the most time on desktop and mobile, averaging nearly 10 hours a day per internet user. Brazil 🇧🇷, Philippines 🇵🇭, Argentina 🇦🇷 and Colombia also average more than nine hours a day.

South Africa 🇿🇦 also wins the award for most internet usage via a computer, with almost 4.5 hours per day. Russia 🇷🇺, 🇧🇷, Argentina 🇦🇷 and Columbia 🇨🇴 were also quite close the four hour mark.

The Philippines 🇵🇭 spends the most time on their phones, averaging more than 5.5 hours per day, per user, followed closely by Brazil 🇧🇷, Thailand 🇹🇭, South Africa 🇿🇦 and Indonesia 🇮🇩.

[carlandoscars]

mclaren: Crunching the data ahead of tomorrow. 📊

[skittlebits]

Reblog so we get more data! 📊

[hugheses]

NHL: Data secured. 📊

Jack Hughes (@/jhugh86) paid a visit to the NHL EDGE IQ powered by @/awscloud booth at #NHLAllStar weekend and this is what he learned. 👀

[umseb]

Sebastian Vettel dives into SailGP data 📊⛵️