#Data Science 2025

[codecrafted]

10 Biggest Data Science Trends to Watch in 2025 Data science is evolving faster than ever! From generative AI and real-time analytics to edge computing and ethical AI, 2025 is set to bring groundbreaking changes. 🌐💡

Whether you're a data enthusiast, professional, or just curious, this list breaks down the biggest trends reshaping how businesses and tech teams work with data. Learn about synthetic data, low-code tools, quantum computing’s potential, and more.

[ayjadasblogforeverything]

Cardinal-O-Mat Data Science

Soooo I wanted to learn something data sciency. And I stumbled over David Kriesel's Wahl-O-Mat analyses and wanted to do the same but different. I, like you maybe, have stumbled over the cardinal-o-mat. Logical conclusion: Let's do data science without mama (I'm so sorry!) but with the cardinal-o-mat! (Of course, I also have done this with the Wahl-O-Mat.)

The Cluster Heatmap

Here we have a cluster heatmap. (Isn't she pretty? Actually not so much, there's a lot of grey there...)

On the right side, you can see the names of the cardinals, and on the bottom, you can see the names of the theses. (I was too lazy to make them look pretty.) Red means disagree, blue means agree (for colorblindness reasons). Grey means either that the cardinal was explicitly neutral to the question or that there was simply no data on his position regarding the thesis in the cardinal-o-mat.

The theses are as follows (in order of appearance in the cardinal-o-mat):

I'll spare you and not list all the cardinals' names.

female_deacons: Women should be admitted to the diaconate.

same_sex: Same-sex couples should continue to be allowed to receive blessings outside of liturgical celebrations.

celibacy: Priestly celibacy should become voluntary.

vetus_ordo: The celebration of the Old Latin Mass should remain restricted for the sake of church unity.

vatican_china: The secret agreement between the Vatican and the People's Republic of China on the appointment of bishops should be upheld.

synodal_church: The Catholic Church should be a synodal church in which more emphasis is placed on participation, inclusion and joint decision-making.

climate_change: The Catholic Church should get involved in climate protection because it is committed to God's creation and the protection of the most vulnerable.

humanae_vitae: The Catholic Church should reconsider its position on contraception.

communion_unmarried: Divorced and remarried persons should be admitted to communion in individual cases.

german_synode: The German Synodal Way, aiming at reforming the doctrine of faith and morals, should be regarded positively overall.

covid: Church closures and vaccination recommendations during the Covid-19 pandemic were right.

islam: Interfaith dialogue with Islam is important.

What do we see here?

Roughly speaking, the closer two cardinals or two theses are shown in the map, the more similar they are, and the further apart in the map, the more dissimilar. Because of this closeness of similar cardinals/theses, we get these blocks of blue and red (kinda. I mean, it could be much worse.).

I want to emphasize that I did not sort this by hand. Rather it was sorted by an algorithm with respect to a certain metric (here the Jaccard metric), which measures the "distance" between the cardinals and theses. The method used is (divisive) hierarchical clustering. At each step, a cluster is divided into two subsets such that their distance is maximized. You can see these steps in the lines on the top and left side. This is called a dendrogram.

What do we learn from this?

Damn good question! The amount of things to learn is somewhat limited, if we look at the amount of neutrals and non-opinions, also considering I did not seperate those two.

Since this is a non-serious setting, I think we can reasonably infer that a cardinal that has spoken in favor of a couple of the theses is also generally more open to those he has not voiced an opinion on, and similarly for the conservative ones. If you look at it like this, then it becomes quite clear that the blue, so the generally more open minded cardinals are in the majority. I would have loved to have a cardinal-o-mat for the previous conclave, because I have the hypothesis that there, the conservative cardinals might have had the majority and I would love to test this.

Something I find funny is that one of the theses that is most liked is the synodal_church one, which is about participation and joint decision making. One of the least liked ones is the german_synode one (only one agreement, thx Marx my homie), which tries to do exactly the participation and joint decision making.

I don't know what else we learn from this, I just think that a cluster heatmap is a neat way of visualising the positions of the cardinals wrt to the theses and since it is somewhat sorted, we learn something about their relation with each other.

If you can explain to me why there is this red block in the left bottom corner, please do! I thought it might have something to do with the metric I used but the map always looks similar or worse.

Also, maybe someone can explain to me which metric to use when.

[creativeronica]

I'm creating designs that stand against tyranny. This specific design will show that you protest the fascist takeover of the United States of America. RESIST. RESIST. RESIST. NEVER RELENT. The fascists won't.

[delicatelysublimeforester]

Don’t Let the City Nature Challenge End Without You: Get Out, Observe, and Have Fun!

[feralparsnip]

one of the things you will not hear commonly on the internet is that it is like. fine to be an athiest and also a pagan or whatever. like learning about blavatsky et al & finding materialism did change how i relate to my practices a Lot. a lot. and i do think we have a responsibility to for example understand the history and mechanisms of orientalism, etc, bc these 'outside of organized religion' religious practices have an ugly and exploitative history and we have to look at that if we are to salvage anything meaningful from the project of paganism. i mean this to you so sincerely. if you think of yourself as a witch this post is for you. i lived there for ten years i'm not throwing stones from outside the house here

but last night i lit my candles on my little altar to death and it's like. what she means to me has changed as i come to understand the world. i think she's the progressive force, in truth. i am trying to love the world enough to see it for true

but the other other interesting thing to me is that. seeing the world more clearly doesn't change what i'm getting out of the experience very much. and i guess i thought it would.

i think if we were to dismantle the systems of power wherein religion is used for control (and believe me, paganism is not exempt from these pressures due to it's largely decentralized nature), that religion might just be like. dance. like you can get fancy with it or not but it moves you because it moves You. like it may one day just be a kind of art

anyway. the gods don't have to speak to you. in truth i don't think they are speaking to anybody because i don't think they are real. and if you're looking at paganism or 'witchery' or whatever and wanting to start or expand your practice. i am telling you directly that you don't have to use it to escape from the world. it is so tempting to find some magical system to simplify your model of the world but you don't actually have to use this to hide. it can be the place you love the world enough to see it for true. or love yourself enough to see what you need. take the parts that work and discard the parts that don't & remember you need some chemicals to cast fireball in real life.

[knowledge4power]

[ajeenkyadypatiluniversity]

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[academic-corner-of-unicattl]

Mastering Marketing: Emotion-Driven Brands, Data-Powered Insights & Behavioral Change

Coca‑Cola “New Coke” Case & the Birth of the Garrison Group

Mistake diagnosed: Focused on “taste” not emotion, so New Coke failed emotionally.

Solution: Built an in‑house “think tank” to study emotional branding → Paul Garrison spun this out as the Garrison Group consultancy.

Core Marketing Concepts

Marketing = sell more stuff to more people, for more money, more often, more efficiently.

Marketing vs. Communications

Marketing sets the target, the brand associations, and the desired behavior (i.e. the “why” and “who”).

Communications is how you convey those associations (ads, social media, reels, etc.).

4 Functions of Business: production, finance, HR, marketing—all aim to make money, but marketing’s toolset centers on emotion + behavior.

Brand = the cluster of functional + emotional associations a large group holds in their heads.

Positioning is done by consumers’ perceptions, not by marketers—our role is to influence it.

Needs, Wants & Demand

Need = a fundamental human requirement (functional or emotional).

Want = culturally shaped expression of a need (e.g. quick breakfast → šmav atka vs. burger vs. ramen).

Demand = wants backed by willingness & ability to pay.

Marketing cycle:

Influence associations (branding)

→ drive behavior (habits)

→ generate revenue

→ reinvest in branding

Correct approach:

Define target—e.g. 60+ German couples (have money, time, crave new experiences)

Identify insights/pain points (e.g. safety, comfort, cultural immersion)

Craft communications to build desired associations → drive bookings

Key takeaway: Always lead with target + insight → desired associations → communications → behavior → revenue.

[codingbrushup]

Top 10 Data Science Tools You Should Learn in 2025

Best Tools for Data Science are evolving fast, and if you want to stay ahead in 2025, it’s time to upgrade your toolkit. Whether you’re just starting out or already deep into data projects, using the right tools can make your work smoother, smarter, and a lot more fun. With powerful no-code platforms, AI-driven automation, and cloud-based collaboration, the Future of Data Science Tools is all about speed and simplicity. So, whether you’re brushing up your skills or diving into new ones, these Must-Have Tools for Data Scientists are your ticket to staying competitive this year.

1. Python — Still the King of the Jungle

If you haven’t started with Python yet, 2025 is your cue. It’s powerful, readable, and has libraries for nearly everything. Tools like Pandas, NumPy, and Scikit-learn make it your go-to for analytics, modeling, and more. Python is basically the heartbeat of the Best Tools for Data Science ecosystem — and yes, that’s the first mention (just four to go!).

2. R — For the Love of Stats and Visuals

R is like that friend who’s always great with numbers and loves making beautiful plots. It’s perfect for statistical analysis and data visualization. Plus, if you’re into research or academic work, R might just be your best buddy. In the world of Popular Data Science Tools, R continues to hold its own, especially when paired with RStudio.

3. Jupyter Notebooks — Your Data Diary

Jupyter makes it fun to play with code and data in real-time. You can document your thinking, share notebooks with others, and even run visualizations inline. Think of it as your interactive coding journal. It’s easily one of the Top Data Science Tools 2025 and continues to be a favorite for experimentation.

4. SQL — Old But Gold

You can’t really skip SQL if you’re serious about data. It’s been around forever, and that’s because it works. Databases power everything — and being able to query them quickly makes SQL a non-negotiable tool. Every data scientist needs it in their toolkit — it’s a staple in any list of Must-Have Tools for Data Scientists.

5. Power BI — Dashboard Like a Pro

Want to impress your team with interactive dashboards? Power BI is Microsoft’s ace in the business analytics world. It’s user-friendly, integrates well with other Microsoft products, and is super powerful. Among the Data Science Software 2025, Power BI is shining brightly as a great tool for storytelling with data.

6. Tableau — Turning Data into Visual Gold

If you’re a visual thinker, Tableau will win your heart. Drag, drop, and make stunning dashboards in no time. It’s a favorite in the Best Tools for Data Science collection (that’s two now!). Business teams love it, and so should you if you’re serious about communicating insights clearly.

7. Apache Spark — For Big Data Firepower

When your dataset is way too big for Excel and even Python starts to lag, Spark comes in to save the day. Apache Spark lets you handle massive amounts of data in a distributed computing environment. It’s fast, powerful, and a favorite in the world of Future of Data Science Tools.

8. Git and GitHub — Version Control Like a Boss

Messy code history? No more. Git lets you keep track of every change, while GitHub is your team’s central code-sharing spot. It’s not just for developers — every modern data scientist should know Git. You’ll find it featured in every list of Learn Data Science Tools resources.

9. Google Colab — Cloud Notebooks Made Easy

Google Colab is like Jupyter, but in the cloud, and with free GPU access! You don’t even need to install anything. Just log in and start coding. It’s part of the Best Tools for Data Science toolkit (we’re at three now!) and great for remote collaboration.

10. AutoML Tools — Because Smart Tools Save Time

Why code every model from scratch when tools like Google AutoML, H2O.ai, and DataRobot can automate the heavy lifting? These platforms are evolving fast and are key players in the Future of Data Science Tools. Embrace automation — it’s not cheating, it’s smart!

Final Thoughts — Brush Up, Stay Ahead

The tools you use can define how far and how fast you grow as a data scientist. Whether you’re focused on big data, beautiful dashboards, or building machine learning models, knowing the Best Tools for Data Science (we’re at four!) gives you a serious edge.

And hey, if you’re ready to really power up your skills, the team over at Coding Brushup has some fantastic resources for getting hands-on experience with these tools. They’re all about helping you stay sharp in the fast-changing world of data science.

So go ahead and start experimenting with these Top Data Science Tools 2025. Mastering even a few of them can supercharge your data career — and yes, here’s that final SEO magic: one more mention of the Best Tools for Data Science to wrap it up.

[pier-carlo-universe]

rande successo per la Conferenza Esri Italia 2025: due giornate tra intelligenza artificiale, digital twin e la presentazione del libro sui viaggi di Papa Francesco. Scopri di più su Alessandria today.

[satnara]

IIT Madras BS in Data science, Electronic Systems without JEE ( last Date to register: 20th May)

#iitmadras

#datascience

#datascientist #datasciencetraining

#datasciencecourse

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[delicatelysublimeforester]

Saskatoon’s City Nature Challenge 2025: A Celebration of Nature, Community, and Curiosity

[detroitography]

Mapping and Mobilizing the Vote: Fertile Ground in Detroit

by: Jeffrey Herbstman, PhD, data scientist Voter Mobilization efforts should focus on a simple equation that indicates the potential available voters in each U.S. census block. The Voter Participation Rate (VPR) as defined as the Voter Totals / Voting Age Citizens located in each block indicates the untapped quantity of voters available in each tract. Using reliable data sets for each of the…

[krmangalam121]

B.Sc. in Data Science: Course Details, Eligibility Criteria & Syllabus

B.Sc. in Data Science is a 3-year undergraduate course designed for individuals who aspire to obtain insights from structured and unstructured data. K.R. Mangalam University has crafted this programme to teach candidates about statistics, machine learning and big data science. Students who have a strong programming background benefit from this course. Overall, the main objective is to develop familiar professionals who have extensive knowledge of the existing and advanced technological tools and are actively interested in carrying out investigation and synthesis with computer-oriented solutions. 

Why Pursue a B.Sc. in Data Science at K.R. Mangalam University?

Over the years, K.R. Mangalam University has been recognised as the best university in Delhi NCR for B.Sc. in Data Science due to the following reasons:

Industry Experience: Top data science experts from the industry arrive at the university to guide the students about real-world case studies. 

Rich Curriculum: Renowned industry experts and faculty members have teamed up together to develop detailed study modules. 

Career Guidance: Students get to attend career counselling sessions and receive 100% placement assistance. 

Well-Equipped Laboratories: KRMU boasts highly sophisticated laboratories which consist of cutting-edge apparatus and software. 

B.Sc. Data Science Course Details

Students keen on pursuing a B.Sc in Data Science from K.R. Mangalam University must have a look course highlights below. This will give you a clear idea about the fee structure and other details associated with the course. 

The B.Sc. (Hons.) Data Science is a full-time undergraduate programme with a duration of 3 years. To be eligible, candidates must have passed 10+2 or an equivalent examination from a recognised board with a minimum of 50% aggregate, with Mathematics, Computer Science, or Information Technology as a compulsory subject. The annual programme fee is ₹1,35,000 (as of 30th April 2025). This course equips students with analytical, statistical, and programming skills essential for data-driven decision-making. Graduates are well-prepared for roles in various industries, with top recruiters including Deloitte, IBM, Amazon, Accenture, The Times Group, and TATA Cliq Luxury.

B.Sc. Data Science Syllabus

There are various B.Sc. Data Science subjects that students need to pursue while studying this course at KRMU. Here are some of them.

Fundamentals of Web Technologies

Matlab Programming

Essentials of Data Science

Introduction to Discrete Structures

Introduction to Data Structures

Fundamentals of Machine Learning

Fundamentals of Algorithm Design & Analysis

Introduction to Database Management Systems

Computer Organisation and Architecture

B.Sc. Data Science Admission 2025

You need to be thorough with the admission procedure if you’re interested in pursuing this programme at K.R. Mangalam University in 2025. The admission procedure is as follows: 

Visit www.krmangalam.edu.in to apply for a B.Sc. in Data Science course.

Fill up the application form.

Complete the payment procedure.

Sit for the KREE entrance test.

Go for the faculty-led interview. 

If selected, you will receive the admission offer.

Enrol for B.Sc. in Data Science at KRMU. 

Conclusion

Selecting an appropriate university for a B.Sc. in Data Science requires careful planning and consideration of multiple factors. Although, there are so many universities in Delhi-NCR, K.R. Mangalam University is still the most preferred option amongst all. With modern amenities, an extensive curriculum and state-of-the-art infrastructure, it has become an excellent choice for aspiring data scientists. 

Frequently Asked Questions 

What is the full form of a B.Sc in Data Science?

B.Sc in Data Science stands for Bachelor of Science in Data Science.

Why should I pursue a B.Sc in Data Science from K.R. Mangalam University?

KRMU is known for picking up the best talent and preparing them for the future-ready professional. Henceforth, it’s the best decision to pursue this course from here. 

Can I study B.Sc in Data Science after the 12th?

Students looking forward to pursuing a career in data science can pursue this course after higher secondary school. 

What kind of skills are required to pursue this programme?

You need to demonstrate strong analytical and problem-solving skills to succeed in this programme. 

[techtrendslive]

USDSI's Data Science Professional Salary Guide 2025 is all about what's hot and what's not in the current data science industry worldwide. Know the latest data science professionals’ salaries, market insights, and other exciting facts and figures. Download our Data Science Salary Guide 2025 Today