#data engineering vs data science

[dreamsoft4u]

Understanding the differences between Data Science vs Data Analysis vs Data Engineering is essential for making the right choices in building your data strategy. All three roles have varying benefits, and it is crucial to understand what strategy is needed and when to build your business.

[ajay--sharma]

Data Scientist vs. Data Engineer: Key Differences & Career Insights

Data is everywhere in today's world. Whatever apps we use or whatever websites we visit, everything is based on data. But have you ever thought about who does all this magic behind data? There are two very important roles: Data Scientists and Data Engineers. Though they deal with data, their jobs are completely different.

So, let's break it down in basic terms and go through the main key differences between a Data scientist vs Data engineer to help you decide which path might be right for you.

Difference Between Data Engineer And Data Scientist

In Data engineer vs Data scientist think of a Data Engineer as the person who designs the foundation and building of structures to allow data to flow well. Their job involves making sure data gets collected stored, and kept ready to analyze. A Data Scientist, however, works like a detective to use data to uncover patterns, trends, and insights that help businesses make smart choices.

Educational Requirements

In India, skill sets are more crucial than degree requirements for becoming a successful Data Scientist vs Data Engineer. You may observe many successful Data Scientists and Data Engineers from various educational backgrounds who are excelling in this industry, but if becoming a data scientist or data engineer is your ultimate aim, then studying data analytics courses in college can undoubtedly help you become the greatest.

Educational qualification to become a Data Scientist. 

1. You will need a bachelor's degree in Computer Science, Mathematics, Statistics, or Engineering to start a career in data science. To get better opportunities you can also go for a master's and PHD degree.

2. If you have already completed your degree in another stream and then chose data analysis as your career short-duration data analyst course can help you with that.

3. Familiarity with data analysis, SQL, Excel, and software such as Tableau or Power BI are also important to be a good data scientist. 

4. A good understanding of statistics and probability is important for analyzing data correctly.

Educational qualification to become a Data Engineer. 

1. A bachelor's degree in Computer Science, Information Technology, or Engineering.

2. Strong programming skills in languages like Python, Java, or Scala. You also need to know databases and data processing frameworks. 

3. Tools: Experience with cloud platforms (AWS, Google Cloud) and managing data pipelines (such as Apache Kafka or Hadoop) is important for designing efficient data systems.

4. The ability to solve problems and design systems that store and process large amounts of data efficiently.

Career Tips: Getting a Job as a Data Science VS Data Engineering

Here are some tips that can help you get jobs in data science vs data engineering. 

Learn the Basics

Start by learning the basics of programming, databases, and math. These are the foundation for both data engineer vs data scientist. Once you understand these, you'll be able to learn more advanced topics easily.

Online Courses

There are many free online courses that teach skills like Python, SQL, and machine learning. Take advantage of these courses to learn at your own pace and build your knowledge step by step.

Hands-On Practice

Theory is important, but nothing beats practical experience. Work on small projects, such as analyzing datasets or building basic machine learning models. Use platforms like Kaggle to practice with real-world datasets. 

Internships

Look for internships or part-time jobs related to data. Even a small role can help you gain experience and make your resume stronger in the data engineer vs data scientist vs data analyst field. You’ll also learn a lot by working in a real-world environment.

Stay Updated

The tech world moves quickly, so it’s important to keep learning new tools and techniques related to data engineer vs data analyst. Stay up-to-date to stay competitive in your field.

Networking

Connect with others in the field. Join online communities, attend meetups, or follow experts on social media. Networking can help you learn from others and lead to job opportunities in the data engineer vs data scientist field.  

How to Enhance Your Job Application

To enhance your job application in data science vs data engineering, follow these simple tips. 

Tailor Your Resume: Customize your resume to match the job you're applying for. Highlight your skills and experience that are most relevant to the role.

Write a Strong Cover Letter: Write a short, personalized cover letter and mention why you want the job and why you’re a great fit.

Showcase Your Achievements: Focus on your accomplishments, not just your responsibilities. Use numbers to show your impact if possible.

Keep it short and simple: Avoid big words or complicated sentences. Make everything easy to read and straightforward. 

Proofread: Always look for errors. You may look careless to interviewers if one simple mistake occurs.

Be honest: Never exaggerate and write what you can not explain. Employers value honesty most of all.

Comparison Of Salary: Data Engineer VS Data Scientist

Both Data Engineers and Data Scientists have high-paying jobs, but data scientist vs data engineer salaries can vary a bit. In India, the average data science engineer salary is between ₹9,00,000 and ₹22,00,000, depending on their skills like machine learning and analytics, which are in high demand. If you're a senior Data Scientist, you could earn ₹25,00,000 or even more. Additionally, if you're considering a career as a data analyst, the data analyst job salary typically ranges between ₹4,00,000 and ₹8,00,000, depending on experience and expertise.

On the other hand, a Data Engineer's salary is usually between ₹7,00,000 and ₹15,00,000, depending on the type of work and experience. If you have knowledge of cloud computing and big data tools, your salary can be higher. Also, these are the estimates, it also depends on the company you are working with and the knowledge you have gained. 

Career Growth and Pathways

The career growth you can expect as a Data engineer vs Data analyst or scientist. 

Career Pathway for Data Engineers.

Starting Out: You might begin as a junior data engineer or a data analyst. This helps you understand how to handle data and learn the tools.

Mid-Level: After gaining some experience, you can become a senior data engineer. At this stage, you’ll take on bigger projects and maybe even manage teams.

Top-Level: With more experience, you could become a lead data engineer or even a chief data officer. These roles involve more decision-making and overseeing larger data systems for the company.

Career Pathway for Data Scientists.

Starting Out: Most people start as junior data scientists or data analysts. This is where you learn how to work with data and do basic analysis.

Mid-Level: As you gain experience, you can become a senior data scientist. You’ll work on bigger problems, analyze more complex data, and might even guide junior team members.

Top-Level: At this level, you can become a lead data scientist or a machine learning engineer. These positions involve using advanced techniques to solve difficult problems and sometimes creating new tools for analysis.

Conclusion

If you're looking to master Python, Analytics Shiksha is the perfect choice. Their Super30 Analytics course, part of their data analytics courses online, offers comprehensive training in data analysis and job preparation. Don’t miss out—secure your spot today, as only 30 seats are available!

The opportunities in both fields are immense, and with the right skills, you can make your mark in the data world.

Be sure to enroll and reserve your spot in our Super30 data analytics course to advance your skills. This program is built around problem-solving techniques and allows you to work with real data. 

Frequently Asked Questions: Data Engineer vs. Data Scientist

Can Data Scientists Transition with Data Analytics Courses to Become Data Engineers?

Yes, by increasing knowledge of coding, databases, and system design through data analytics courses, a data scientist can easily become a data engineer.

Which is Better: Data Scientist Data Engineer vs Data Scientist?

It depends on what you enjoy doing to solve problems and make predictions or to build the infrastructure and tools to store and process data. If you love analyzing data and creating models, a data scientist role might be better. If you prefer working on technology and systems, a data engineer job could be the right fit for you.

What is The Difference Between Data Engineer and Data Scientist

A data engineer works on systems that collect, store, and process data. They make sure the data is clean and ready for analysis. A data scientist analyzes the data, creates models, and discovers insights to help businesses solve problems. In a nutshell, engineers prepare the data, while scientists analyze it. To excel in either role, enrolling in data analytics courses can help you build the necessary skills and expertise.

[mkcecollege]

Data Science and Engineering Driving industry Innovations

The integration of data science and engineering is revolutionizing industries, enabling smarter decision-making, process optimization, and predictive capabilities. At M.Kumaraswamy College of Engineering (MKCE), students are equipped to harness data science to solve complex challenges and drive innovation. By combining theoretical knowledge with practical applications, MKCE prepares students to optimize processes in manufacturing, healthcare, transportation, energy, and urban planning. The curriculum includes courses on machine learning, big data analytics, and programming, alongside hands-on projects and internships. MKCE’s focus on industry collaborations ensures students stay ahead of emerging trends like AI, IoT, and digital twins. This interdisciplinary approach empowers students to lead in data-driven industries and shape the future of engineering.

To Know More : https://mkce.ac.in/blog/data-science-and-engineering-driving-innovation-across-industries/

[iimtgroup1234]

Artificial Intelligence & Data Science vs Computer Science & Engineering Career Differences

Students who graduate from B.Tech artificial intelligence institutes in India have an abundance of options for further study and employment in the public and commercial sectors. Because of their academic relevance and growing demand for graduates from these schools, the best colleges in India for a B.Tech in artificial intelligence and data science provide a fairly lucrative scope and earning potential.

Read more:

[tripta-123]

Data Engineer vs Data Scientist vs Data Analyst

Learn the key differences between Data Engineer, Data Scientist, and Data Analyst here. Know the primary role and in which area they have the expertise to choose wisely which profile is more suitable according to your current IT skills.

[vlruso]

Retro-Engineering a Database Schema: GPT vs. Bard vs. LLama2 (Episode 2)

Exciting news! In my latest blog post, I dive into the world of database retro-engineering and compare the performance of three AI models: GPT, Bard, and the new player on the block, LLama-2. 🚀 This article discusses how LLama-2 analyzes a dataset and suggests a database schema with separate tables for different categories. It successfully identifies categorical and confidential columns, providing valuable insights for data analysis. 💡 Curious about the results? Click the link below to read the full blog post and learn about Llama-2's performance and areas for improvement. 📖 [Read more here](https://ift.tt/SosADt0) Don't miss out on the latest trends in database retro-engineering! Stay informed and unlock valuable insights for your data-driven projects. #DataScience #AI #DatabaseRetroEngineering List of Useful Links: AI Scrum Bot - ask about AI scrum and agile Our Telegram @itinai Twitter -  @itinaicom

[gippity]

Trump and Palantir Forge a Pan-Government Surveillance State, Empowering Tech Oligarchs and Silencing Critics

Source article for analysis: https://newrepublic.com/post/195904/trump-palantir-data-americans

1. Narrative Framing

Simplicity & “Common-Sense” Appeal The administration casts cross‐agency data‐sharing as an efficiency and “government modernization” measure, flattening complex privacy and constitutional concerns into a feel-good story about bureaucratic streamlining. This preloads the conclusion that any objection is mere technophobia or red tape, rather than a debate over surveillance power.

Binary Framing (“Security vs. Chaos”) By emphasizing “national security” and “public safety,” critics are implicitly positioned as indifferent to immigrant crime or terrorism, pressuring dissenters to choose between safety and liberty—an either-or that forecloses nuanced policy discussion.

2. Emotional Engineering

Fear & Resentment References to “enforcing the March executive order,” “punish his critics,” and fears of immigrant targeting stoke anxiety about arbitrary state power. This fear is then channeled into loyalty among “true patriots” who trust the administration to wield that power wisely.

Pride & Tribal Bonding Invoking a “war on inefficiency” and naming a “far-right billionaire” ally provides a rallying narrative for supporters who see themselves as part of an inner circle, engendering pride in being on the “winning team.”

3. Pipeline On-Ramps & Ecosystem Mapping

Soft Entry via “Modernization” Pitches around “data modernization” and “innovation” serve as gateway content—memes and soundbites in tech-oriented outlets gradually introduce audiences to more radical surveillance proposals.

Content Funnel

Friendly tech press (“efficiency gains”)

Conservative opinion pieces (“keep America safe”)

Policy white papers and FOIA-leaked memos (“full database blueprints”)

Private sector deep dives (Palantir user groups, DOD contractor briefings)

4. Dog Whistles & Euphemisms

“National Security” Sanitized language for mass surveillance and immigrant tracking.

“Data-Driven Governance” A euphemism that hides the indiscriminate collection of personal information under the veneer of neutral analytics.

“Government Efficiency” Code for centralizing power and reducing agency-specific safeguards that currently protect civil liberties.

5. Archetypes & Mythos

Tech-Militarist Savior Casting Peter Thiel and Alex Karp as modern “warrior-lords” of data who will “defend” America—evoking the warrior archetype that simplifies identity into a battle of “us vs. them.”

Fallen Homeland Narrative Suggests America’s institutions are backward and corrupt, needing a techno-strongman to resurrect core values—mirroring the “rise-from-ruin” mythos common in alt-right rhetoric.

6. Strategic Impact Assessment

Real-World Mobilization This intel could be used to silence dissidents (through audits, visa denials, or targeted prosecutions), chill protest activity, and surveil immigrant communities disproportionately.

Beneficiaries & Victims Tech oligarchs (Thiel, Musk) and the Trump political machine gain concentrated power; critics, immigrants, student activists, and labor organizers become object lessons.

7. Vibe Warfare & Identity Signals

Stoic Realist Aesthetic Dark, angular visuals of data centers and code screens reinforce a mood of uncompromising techno-authority.

“Based” Tech Patriotism Pittings of “innovation bros” vs. “liberal elites,” using jargon (“Foundry,” “Grok”) as in-group markers to foster parasocial loyalty among tech-savvy conservatives.

8. Epistemic Booby Traps & Self-Sealing Logic

“If you have nothing to hide…” Pre-emptively discredits objections by labeling them paranoia or disloyalty, barring dissenting evidence from being taken seriously.

Data as Truth Presents analytics as inherently objective, making any critique of methodology or oversight seem “anti-science.”

9. Irony Shielding & Tone Drift

Tech-Bro Irony Occasional self-deprecating jokes about “big brother” memes allow participants plausible deniability (“We’re just goofing, who doesn’t love tech?”), while the surveillance machinery locks in.

Memetic Alchemy Use of playful GIFs or “dank” one-liners about “tracking your ex’s Starbucks habit” masks the seriousness of mass data collection.

[viridianriver]

∘◦✩◦∘ My Long Posts ∘◦✩◦∘

Nature, The Outdoors, Travel

Lazy Girl's Guide to Houseplants

How to survive in the wilderness for dirt cheap (+added info in reblogs)

How to stay safe traveling solo (Minus the classism that usually creeps into these articles)

Engineering & Machines

Sewing Machines & Planned Obsolescence

Queer Girl's Tips For Surviving Engineering

Engineering Job Interview Tips

2023 USA Railway Projects!!

Using your art to train an AI is theft! Here's how to fight back!

Sustainability & Anti Consumption

Sustainable Shopping - Alternatives to Corporate Stores

Shopping at corporations only when they're taking a loss

No Corporations November

Intro & Week 1

Week 2

Week 3

Week 4

Summary

SIKE YOU THOUGHT I STOPPED? NOPE IT'S NO CORPORATIONS 2024!!!

Tech & Computer Science

ChatGPT & Bias in "AI"

The Airbnb-Owned Tech Startup - Data Mining Tumblr Users' Mental Health Crises for "Content"

Cybersecurity & "Smart" Devices

Cop Robo Dogs

"AI" & The Meaning Of Intelligence

Dude... The Matrix is real?

Titan Submarine Disaster

Systems Engineering

Human Factors Engineering

Corporate Negligence & Regulation Dodging

Detailed Disaster Timeline

A Better Designed Submarine

Miscellaneous Opinions

Extinction Bursts & Misogyny?

Want to write a realistic sci-fi story about "AI"?

Get Crabs! Spread Crabs! (Fundraising vs Advertising-Based Social Media)

Machine Learning / "AI" Failure Modes

Politics & Economics

USA politics rant - We're not well represented by a 2 party system

Charitable Trust Donations are Not That Charitable? (+added info in reblogs)

Natural Gas & The 2023 Attacks on Gaza

The economy doing well isn't helping us

Debunking Finance Myths

STONKS

What is the Middle Class really? (I think it's propoganda)

Health, Wellness, and The Body

Science based skincare that doesn't focus on products / brands!

Antivaccers and an abusive Medical Industry

How to engage in activism without burning yourself out

Feet, and the damage modern shoes cause

Recipes For Dumbasses

Very Extra Pancakes

Soup is Easy?

Asks

Resources to learn about economics?

How to clean/sanitize thrifted stuff!

how to get shit done when you've got executive dysfunction

AI Bubble?

[educationmore]

Python for Beginners: Launch Your Tech Career with Coding Skills

Are you ready to launch your tech career but don’t know where to start? Learning Python is one of the best ways to break into the world of technology—even if you have zero coding experience.

In this guide, we’ll explore how Python for beginners can be your gateway to a rewarding career in software development, data science, automation, and more.

Why Python Is the Perfect Language for Beginners

Python has become the go-to programming language for beginners and professionals alike—and for good reason:

Simple syntax: Python reads like plain English, making it easy to learn.

High demand: Industries spanning the spectrum are actively seeking Python developers to fuel their technological advancements.

Versatile applications: Python's versatility shines as it powers everything from crafting websites to driving artificial intelligence and dissecting data.

Whether you want to become a software developer, data analyst, or AI engineer, Python lays the foundation.

What Can You Do With Python?

Python is not just a beginner language—it’s a career-building tool. Here are just a few career paths where Python is essential:

Web Development: Frameworks like Django and Flask make it easy to build powerful web applications. You can even enroll in a Python Course in Kochi to gain hands-on experience with real-world web projects.

Data Science & Analytics: For professionals tackling data analysis and visualization, the Python ecosystem, featuring powerhouses like Pandas, NumPy, and Matplotlib, sets the benchmark.

Machine Learning & AI: Spearheading advancements in artificial intelligence development, Python boasts powerful tools such as TensorFlow and scikit-learn.

Automation & Scripting: Simple yet effective Python scripts offer a pathway to amplified efficiency by automating routine workflows.

Cybersecurity & Networking: The application of Python is expanding into crucial domains such as ethical hacking, penetration testing, and the automation of network processes.

How to Get Started with Python

Starting your Python journey doesn't require a computer science degree. Success hinges on a focused commitment combined with a thoughtfully structured educational approach.

Step 1: Install Python

Download and install Python from python.org. It's free and available for all platforms.

Step 2: Choose an IDE

Use beginner-friendly tools like Thonny, PyCharm, or VS Code to write your code.

Step 3: Learn the Basics

Focus on:

Variables and data types

Conditional statements

Loops

Functions

Lists and dictionaries

If you prefer guided learning, a reputable Python Institute in Kochi can offer structured programs and mentorship to help you grasp core concepts efficiently.

Step 4: Build Projects

Learning by doing is key. Start small:

Build a calculator

Automate file organization

Create a to-do list app

As your skills grow, you can tackle more complex projects like data dashboards or web apps.

How Python Skills Can Boost Your Career

Adding Python to your resume instantly opens up new opportunities. Here's how it helps:

Higher employability: Python is one of the top 3 most in-demand programming languages.

Better salaries: Python developers earn competitive salaries across the globe.

Remote job opportunities: Many Python-related jobs are available remotely, offering flexibility.

Even if you're not aiming to be a full-time developer, Python skills can enhance careers in marketing, finance, research, and product management.

If you're serious about starting a career in tech, learning Python is the smartest first step you can take. It’s beginner-friendly, powerful, and widely used across industries.

Whether you're a student, job switcher, or just curious about programming, Python for beginners can unlock countless career opportunities. Invest time in learning today—and start building the future you want in tech.

Globally recognized as a premier educational hub, DataMites Institute delivers in-depth training programs across the pivotal fields of data science, artificial intelligence, and machine learning. They provide expert-led courses designed for both beginners and professionals aiming to boost their careers.

Python Modules Explained - Different Types and Functions - Python Tutorial

[oliviabutsmart]

Physics Friday #5: The Wonderful World of Programming Paradigms

Welcome to the first actual post on the dedicated blog! This will be continuing on from what I started over on my main account @oliviax727. But don't worry, I'll still repost this post over there.

Preamble: Wait! I thought this was Physics!

Education level: Primary School (Y5/6)

Topic: Computer Languages (Comp Sci)

So you may be thinking how this is relevant to physics, well it's not. But really, other adjacent fields: computer science, chemistry, science history, mathematics etc. Are really important to physics! The skills inform and help physicists make informed decisions on how to analyse theoretical frameworks, or to how physics can help inform other sciences.

I may do a bigger picture post relating to each science or the ways in which we marry different subjects to eachother, but what is important is that some knowledge of computer science is important when learning physics, or that you're bound to learn some CS along the way.

Also I can do what I want, bitch.

Introduction: What is a Programming Language?

You may have come across the term 'programming paradigm' - especially in computer science/software engineering classes. But what is a programming paradigm really?

Computers are very powerful things, and they can do quite a lot. Computers are also really dumb. They can't do anything unless if we tell them what to do.

So until our Sky-net machine overlords take control and start time-travelling to the past, we need to come up with ways to tell them how to do things.

Pure computer speak is in electrical signals corresponding to on and off. Whereas human speak is full of sounds and text.

It is possible for either one to understand the other (humans can pump electrical signals into a device and computers can language model). But we clearly need something better.

This is where a programming language comes in. It's basically a language that both the computer and the human understands. So we need a common language to talk to them.

It's like having two people. One speaks Mandarin, the other speaks English. So instead of making one person learn the other's language, we create a common language that the two of them can speak. This common language is a synthesis of both base languages.

But once we have an idea of how to communicate with the computer, we need to consider how we're going to talk to it:

How are we going to tell it to do things?

What are we going to ask it to do?

How will we organise and structure our common language?

This is where a programming paradigm comes in - a paradigm is a set of ideas surrounding how we should communicate with a device. It's really something that can truly only be understood by showing examples of paradigms.

Imperative vs. Declarative

The main two paradigms, or really categories of paradigms, are the imperative vs. declarative paradigm.

Imperative programming languages are quite simple: code is simply a set of instructions meant to tell the computer specifically what to do. It is about process, a series of steps the computer can follow to get some result.

Declarative programming languages are a bit more vapid: code is about getting what you want. It's less about how you get there and more about what you want at the end.

As you can see imperative programs tell the computer how to do something whereas declarative programs are about what you want out.

Here's an example of how an imperative language may find a specific name in a table of company data:

GET tableOfEmployees; GET nameToFind SET i = 0; WHILE i < tableOfEmployees.length: IF tableOfEmployees[i].firstName == nameToFind THEN: RETURN tableOfEmployees[i] AND i; ELSE: i = i + 1; RETURN "employee does not exist";

And here's that same attempt but in a declarative language:

FROM tableOfEmployees SELECT * WHERE firstName == INPUT(1);

Note that these languages aren't necessarily real languages, just based on real-life ones. Also please ignore the fact I used arrays of structures and databases in exactly the same way.

We can see the difference between the two paradigms a lot more clearly now. In the imperative paradigm, every step is laid out clear as day. "Add one to this number, check if this number is equal to that one".

Under the declarative paradigm, not only is the text shorter, we also put all of the instructions about how to do a task under the rug, we only care about what we want.

With all this, we can see an emerging spectrum of computer paradigms. From languages that are more computer-like, to languages that are more English-like. This is the programming languages' level:

Lower level languages are more likely to be imperative, as the fundamental construction of the computer relies on a series of instructions to be executed in order.

The lowest level, the series of electrical signals and circuitry called microcode is purely imperative in a sense, as everything is an instruction. Nothing is abstracted and everything is reduced to it's individual components.

The highest level, is effectively English. It's nothing but "I want this", "I'd like that". All of the processes involved are abstracted in favour of just the goal. It is all declarative.

In the middle we have most programming languages, what's known as the "high level languages". They are the best balance of abstraction of reduction, based on what you need to use the language for.

It's important that we also notice that increasingly higher-level and increasingly more declarative the language gets, the more specific the purpose of the language becomes.

Microcode and machine code can be used for effectively any purpose, they are the jack-of-all trades. Whereas something like SQL is really good at databases, but I wouldn't use it for game design.

As long as a language is Turing-complete, it can do anything any computer can do, what's important is how easy it is to program the diverse range of use-cases. Assembly can do literally anything, but it's an effort to program. Python can do the same, but it's an effort to run.

Imperative Paradigms: From the Transistor to the Website

As mentioned previously, the imperative paradigm is less a stand-alone paradigm but a group of paradigms. Much like how the UK is a country, but is also a collection of countries.

There are many ways in order to design imperative languages, for example, a simple imperative language from the 80's may look a lot like assembly:

... ADD r1, 1011 JMZ F313, r1

The last statement JMZ, corresponds to a "Jump to the instruction located at A if the value located at B is equal to zero" what it's effectively saying is a "Repeat step 4" or "Go to question 5" type of thing.

Also known as goto statements, these things are incredibly practical for computers, because all it requires is moving some electrical signals around the Registers/RAM.

But what goto statement is used as in code, is really just a glorified "if x then y". Additionally, these statements get really irritating when you want to repeat or recurse over instructions multiple times.

The Structured Paradigm

Thus we introduce the structured paradigm, which simply allows for control structures. A control structure is something that, controls the flow of the programs' instructions.

Control structures come in many forms:

Conditionals (If X then do Y otherwise do Z)

Multi-selects (If X1 then do Y1, if X2 then do Y2 ...)

Post-checked loops (Do X until Y happens)

Pre-checked loops (While Y, do X)

Counted Loops (For i = A to B do X)

Mapped Loops (For each X in Y, do Z)

These control structures are extra useful, as they have the added benefit of not having to specify what line you have to jump to every time you update previous instructions. They may also include more "safe" structures like the counted or mapped loop, which only executes a set amount of time.

But we still have an issue: all our code is stuffed into one file and it's everywhere, we have no way to seperate instructions into their own little components that we might want to execute multiple times. Currently, out only solution is to either nest things in far too many statements or use goto statements.

The Procedural Paradigm

What helps is the use of a procedure. Procedures are little blocks of code that can be called as many times as needed. They can often take many other names: commands, functions, processes, branches, methods, routines, subroutines, etc.

Procedures help to organise code for both repeated use and also it makes it easier to read. We can set an operating standard of "one task per subroutine" to help compartmentalise code.

Object-Oriented Code

Most of these basic programming languages, especially the more basic ones, include the use of data structures. Blocks of information that holds multiple types of information:

STRUCT Person: Name: String Age: Integer Phone: String Gender: String IsAlive: Boolean

But these structures often feel a bit empty. After all, we may want to have a specific process associated uniquely with that person.

We want to compartmentalise certain procedures and intrinsically tie them to an associated structure, preventing their use from other areas of the code.

Like "ChangeGender" is something we might not want to apply to something that doesn't have a gender, like a table.

We may also want to have structures that are similar to 'Person' but have a few extra properties like "Adult" may have a bank account or something.

What we're thinking of doing is constructing an object, a collection of BOTH attributes (variables) AND methods (procedures) associated with the object. We can also create new objects which inherit the properties of others.

Object oriented programming has been the industry standard for decades now, and it's incredibly clear as to why - it's rather useful! But as time marches forward, we've seen the popularisation of a new paradigm worthy of rivaling this one ...

Declarative Paradigms: The World of Logic

Declarative languages certainly help abstract a lot of information, but that's not always the case, sometimes the most well known declarative languages are very similar feature-wise to imperative paradigms. It's just a slight difference in focus which is important.

Functional Programming Languages

Whereas the object oriented language treats everything, or most things, like objects. A functional language uses functions as it's fundamental building block.

Functional languages rely on the operation of, well, functions. But functions of a specific kind - pure functions. A pure function is simply something that doesn't affect other parts of the computer outside of specifically itself.

A pure function is effectively read-only in it's operation - strictly read-only. The most practical-for-common-use functional languages often allow for a mixture of pure and impure functions.

A functional language is declarative because of the nature of a function - the process of how things work are abstracted away for a simple input -> output model. And with functional purity, you don't have to worry about if what takes the input to the output also affects other things on the computer.

Functional languages have been around for quite a while, however they've been relegated to the world of academia. Languages like Haskell and Lisp are, like most declarative languages, very restrictive in their general application. However in recent years, the use of functional programming has come quite common.

I may make a more opinionated piece in the future on the merits of combining both functional and object-oriented languages, and also a seperate my opinions on a particular functional language Haskell - which I have some contentions with.

Facts and Logic

The logic paradigm is another special mention of declarative languages, they focus on setting a series of facts (i.e. true statements):

[Billy] is a [Person]

Rules (i.e. true statements with generality):

If [A] is [Person] then [A] has a [Brain]

And Queries:

Does [Billy] have a [Brain]?

Logical languages have a lot more of a specific purpose, meant for, well, deductive/abductive logical modelling.

We can also use what's known as Fuzzy logic which is even more higher-level, relying on logic that is inductive or probabilistic, i.e. conclusions don't necessarily follow from the statements.

Visual and Documentation Languages

At some point, we start getting so high level, that the very components of the language start turning into something else.

You may have used a visual language before, for example, Scratch. Scratch is a declarative language that abstracts away instructions in-favour of visual blocks that represent certain tasks a computer can carry out.

Documentation languages like HTML, Markdown, CSS, XML, YML, etc. Are languages that can barely even be considered programming languages. Instead, they are methods of editing documents and storing text-based data.

Languages that don't even compile (without any significant effort)

At some point, we reach a point where languages don't even compile necessarily.

A metalanguage, is a language that describes language. Like EBNF, which is meant to describe the syntaxing and lexical structures of lower-level languages. Metalanguages can actually compile, and are often used in code editors for grammar checking.

Pseudocode can often be described as either imperative or declarative, focused on emulating programs in words. What you saw in previous sections are pseudocode.

Diagrams fall in this category too, as they describe the operation of a computer program without actually being used to run a computer.

Eventually we reach the point where what were doing is effectively giving instructions or requesting things in English. For this, we require AI modelling for a computer to even begin to interpret what we want it to interpret.

Esoteric Paradigms

Some paradigms happen to not really fall in this range form low to high level. Because they either don't apply to digital computing or exist in the purely theoretical realm.

Languages at the boundaries of the scale can fall into these classes, as microcode isn't really a language if it's all physical. And pseudocode isn't really a language if it doesn't even compile.

There are also the real theoretical models like automata and Turing machine code, which corresponds to simplified, idealised, and hypothetical machines that operate in ways analogous to computers.

Shells and commands also exist in this weird zone. Languages like bash, zsh, or powershell which operate as a set of command instructions you feed the computer to do specific things. They exist in the region blurred between imperative and declarative at the dead centre of the scale. But often their purpose is more used as a means to control a computer's operating system than anything else.

Lastly, we have the languages which don't fit in our neat diagram because they don't use digital computers in a traditional manner. These languages often take hold of the frontiers of computation:

Parallel Computing

Analog Computing

Quantum Computing

Mechanical Computing

Conclusion

In summary, there's a lot of different ways you can talk to computers! A very diverse range of paradigms and levels that operate in their own unique ways. Of course, I only covered the main paradigms, the ones most programmers are experienced in. And I barely scratched the surface of even the most popular paradigms.

Regardless, this write-up was long as well. I really wish I could find a way to shorten these posts without removing information I want to include. I guess that just comes with time. This is the first computer science based topic. Of course, like any programmer, I have strong opinions over the benefits of certain paradigms and languages. So hopefully I didn't let opinions get in the way of explanations.

Feedback is absolutely appreciated! And please, if you like what you see, consider following either @oliviabutsmart or @oliviax727!

Next week, I'll finish off our three-part series on dark matter and dark energy with a discussion of what dark energy does, and what we think it is made of!

[cyberstudious]

what's it like studying CS?? im pretty confused if i should choose CS as my major xx

hi there!

first, two "misconceptions" or maybe somewhat surprising things that I think are worth mentioning:

there really isn't that much "math" in the calculus/arithmetic sense*. I mostly remember doing lots of proofs. don't let not being a math wiz stop you from majoring in CS if you like CS

you can get by with surprisingly little programming - yeah you'll have programming assignments, but a degree program will teach you the theory and concepts for the most part (this is where universities will differ on the scale of theory vs. practice, but you'll always get a mix of both and it's important to learn both!)

*: there are some sub-fields where you actually do a Lot of math - machine learning and graphics programming will have you doing a lot of linear algebra, and I'm sure that there are plenty more that I don't remember at the moment. the point is that 1) if you're a bit afraid of math that's fine, you can still thrive in a CS degree but 2) if you love math or are willing to be brave there are a lot of cool things you can do!

I think the best way to get a good sense of what a major is like is to check out a sample degree plan from a university you're considering! here are some of the basic kinds of classes you'd be taking:

basic programming courses: you'll knock these out in your first year - once you know how to code and you have an in-depth understanding of the concepts, you now have a mental framework for the rest of your degree. and also once you learn one programming language, it's pretty easy to pick up another one, and you'll probably work in a handful of different languages throughout your degree.

discrete math/math for computer science courses: more courses that you'll take early on - this is mostly logic and learning to write proofs, and towards the end it just kind of becomes a bunch of semi-related math concepts that are useful in computing & problem solving. oh also I had to take a stats for CS course & a linear algebra course. oh and also calculus but that was mostly a university core requirement thing, I literally never really used it in my CS classes lol

data structures & algorithms: these are the big boys. stacks, queues, linked lists, trees, graphs, sorting algorithms, more complicated algorithms… if you're interviewing for a programming job, they will ask you data structures & algorithms questions. also this is where you learn to write smart, efficient code and solve problems. also this is where you learn which problems are proven to be unsolvable (or at least unsolvable in a reasonable amount of time) so you don't waste your time lol

courses on specific topics: operating systems, Linux/UNIX, circuits, databases, compilers, software engineering/design patterns, automata theory… some of these will be required, and then you'll get to pick some depending on what your interests are! I took cybersecurity-related courses but there really are so many different options!

In general I think CS is a really cool major that you can do a lot with. I realize this was pretty vague, so if you have any more questions feel free to send them my way! also I'm happy to talk more about specific classes/topics or if you just want an answer to "wtf is automata theory" lol

[vidumali]

Why I Love Studying at Sabaragamuwa University

🌿 Hey Tumblr fam! I just wanted to take a moment to share something close to my heart — my experience at Sabaragamuwa University of Sri Lanka, a place that’s more than just classrooms and assignments. It's where I found peace, passion, and purpose. 💚

🌄 A Hidden Gem in the Hills

Imagine studying on a campus surrounded by misty hills, green forests, and natural waterfalls. Sounds dreamy, right? Well, that’s exactly what SUSL in Belihuloya feels like. The air is fresh, the environment is peaceful, and nature literally whispers encouragement while you study. 😌🍃

📌 Location: Belihuloya, Sri Lanka 🔗 Official Website of SUSL

💻 My Faculty: Computing

As a proud student of the Faculty of Computing, I can honestly say that SUSL is more than qualified when it comes to academic excellence. 💯

Our professors are not just knowledgeable—they actually care. We work on cool projects, explore real-world tech, and even get support for internships and future careers.

?

👩‍💻 Tech, Talent & Tenacity

You might be surprised, but SUSL is seriously catching up with the tech world.

Let me break it down for you—our Faculty of Computing is organized into three departments, and each one opens up different futures:

🖥️ Department of Computing and Information Systems (CIS)

A great fit if you're interested in IT infrastructure, system design, software, and business applications

You learn how tech supports and transforms businesses, governments, and society

🛠️ Department of Software Engineering (SE)

Perfect if you love to build software from the ground up

Focuses on software architecture, testing, DevOps, and full development lifecycles

📊 Department of Data Science (DS)

The department of the future! 🌐

Teaches you how to work with big data, machine learning, AI, statistics, and more

If you like solving puzzles with data, this is your world

No matter which path you choose, you’ll get:

Modern course content aligned with global tech trends

Hands-on labs and access to real tools (GitHub, Python, VS Code, cloud platforms, etc.)

Internships with leading IT companies

Final-year projects that are often built with startups or community needs in mind

Some of my seniors are now working at top companies, others are doing research abroad—that’s the kind of transformation this faculty creates. 🙌

For more information: click here

🫶 Why SUSL Feels Like Home

Here’s a little list of what I adore about life here:

Friendly community – always someone to help you out

Calm campus – no traffic noise, just birds and waterfalls

Opportunities – tons of events, workshops, clubs

Affordable – both the university and the area are budget-friendly

Balance – education + mental wellness = perfect combo

🌐 Not Just a University – A Lifestyle

Sabaragamuwa University doesn't just prepare you for a career; it shapes you as a human being. It’s not all books and exams—we grow, we laugh, we support each other.

Whether you’re into tech, social sciences, management, or agriculture, there’s a faculty that fits your vibe.

✨ Learn more about SUSL here

💬 Final Thoughts

If you're thinking about studying in Sri Lanka, or even just curious about a different kind of university experience, I highly recommend checking out Sabaragamuwa University. It changed my life in the best way.

💚 Tag a friend who needs to hear about this gem! 📥 DM me if you want tips about the application process or student life here!

[chacusha]

The Orville thoughts

Okay, basically, most of my reaction to watching the first few minutes of this show is "How is this legal?!" Like, people weren't kidding when they said this show is more Star Trek than modern Star Trek...

(I haven't seen this many Star Trek alumni in one place since Disney's Gargoyles! :V Seriously, my face when I saw Penny Johnson Jerald!)

More thoughts:

This show really GETS the value of opening episodes with senior officers engaging in some wacky off-duty bullshit before getting called to the bridge.

This show also GETS the value of showing events happening in bright, clear lighting. Even if this makes the cheesiness of the costumes, locations, and special effects more apparent, it also makes for clearer action too, so very much worth it overall IMO.

It is so bro-y, though. So, so bro-y. I think I might have died during the "astrology sucks" episode.

Also, I feel like Kelly Grayson's character is like the embodiment of the Cool Girl archetype (bro version)? Effortlessly pretty, always willing to get drunk and party (never in an ugly or unappealing sort of way, though), always on Ed's (quite bro-y) level when it comes to humor and hobbies and interests (you know, except for that One Time She Betrayed Him).

Also, there is so much weird "women are emotional and men are logical" and other gender essentialism going on in this show that I'm not even going to go near that... Other than to note that a lot of the issues around the depiction of Moclan culture and the relationship between the Federation Union and Moclus in the show suffers from an ultimately patriarchal sort of approach to feminism.

I might be a bit feral for Claire and Isaac's relationship, though. A lot of the elements of The Orville seem to be a direct reference to Star Trek (especially TNG). As part of that, Isaac is clearly a Data expy (except with a robot superiority complex and therefore very much NOT The Nicest Boy, unlike how Data is), but I appreciate the differences here -- that they weren't afraid to give the Data expy character a romantic plotline (quite similar to the Data episode "In Theory," which ultimately shied away from having Data successfully be in a romantic relationship). I appreciate this plotline as the path not taken there!

Overall, I think this show is surprsingly good at doing sincere emotional moments (which is unexpected given Seth Macfarlane's oeuvre), but terrible at doing politics/philosophical debates/legal drama. (Like, some of the arguments made during these sci-fi issue debates are often so bad/shallow/missing the point, it's a bit cringeworthy.) Which is quite ironic given above-mentioned weird gender essentialism going on in the writing of the show!

That said, even though I would say the politics of The Orville is only OK (I would describe it as trying to live up to Star Trek but distinctly "Reddit atheist" in its aesthetic and political leanings), at least The Orville TRIES to do philosophical debate plotlines (i.e. episodes where the whole conflict/source of tension is an ethical puzzle and one that isn't a painfully easy "obviously good position vs. obviously evil position" ethical "debate" such as "is genocide good? please discuss" or "which is better: doing science or waging war? discuss"), which is something that modern Trek seems to have kind of given up on. And often the politics here, even if not particularly great, still have a distinctly progressive lean, which is better than shows like Picard, for example.

Sometimes it's hard for me to tell what in this show is meant to be a sudden change in creative vision, hastily executed, and what is meant to be a purposeful reference to TNG's (own hastily-executed) writing. For example, the black officer at the conn who suddenly gets promoted to chief of engineering? The show deciding they needed to switch directions with this character and give him more to do, or is John LaMarr a big reference to Geordi La Forge? The female security officer getting suddenly put on a bus -- a reference to what happened to Tasha Yar, or an indication of behind-the-scenes conflict? (Whatever the intention is, the writing here, while hasty, is still overall better done than those bumpy early parts of TNG.)

Another good thing about this show is that it has a good, very likable/charming mauve shirt cast, which it treats pretty well. Which, again, is more than what can be said about a lot of modern Trek, which either has very flat and boring mauve shirts, or kills them off for cheap drama (or, frequently, both, which is hilarious -- like, sure, maybe this mauve shirt dying would have some emotional weight on this show if they literally had a personality or were given anything interesting to do before this episode, but they weren't, so... 🤷).

I was looking at reviews this show got, and apparently it was quite poorly reviewed in the first season, but got better reception in later seasons. I guess this makes sense as the first season was kind of stuck in that weird area between irony-filled parody, fawning homage, and just trying earnestly to bring more Star Trek-type entertainment into the world. People seemed to think it found its footing by jettisoning some of the edgier and irreverent parody aspects in favor of straightforward earnesty, but I also kind of wonder if what happened was more like as Star Trek shows started getting worse around it (Discovery declining in quality; Picard just... being Picard....; Strange New Worlds being distinctively, like, just OK; Lower Decks being fine while avoiding serious/philosophical plotlines; Prodigy also being fine but for kids), having something that didn't shy away from the aesthetics, sci-fi worldbuilding + cosmopolitics, and self-contained plots of TNG felt refreshing? Like The Orville seemed to find its niche largely by just keeping going with what it was doing, while nuTrek failed to fill or offer anything in that highly-coveted niche aside from the perfectly passable but somewhat bland SNW.

So yeah, overall, this show falls quite short of meeting the bar of "more 90s Star Trek for you to watch," but it benefits from sincerely trying to be that, including not being afraid to do entirely new worldbuilding and political-balance-of-power within its own new universe. Or trying to create plots that tackle social issues not handled before by 90s Star Trek shows. In that sense, it keeps quite well with the original spirit of Star Trek even if it doesn't quite get there. There are some updates here as well due to being made several decades later, like more casual depictions of LGBT relationships, more variety in the alien designs, and more smoothness to the writing in general (like more coherent episode plots and character arcs). Overall, I felt the show very worth watching although quite "pointy" in quality (i.e. does some things really, really great, while doing others very poorly) and just very refreshing in the current nuTrek environment. (But with Discovery regaining its footing, maybe that will change!)

[shivanidigital]

Deep Seek vs. ChatGPT: Which AI Tool is Best for Your Needs?

The world of artificial intelligence (AI) is rapidly evolving, and two major players have emerged in the space of intelligent search and communication: Deep Seek and ChatGPT. While both are powerful AI tools, they serve different purposes and offer unique features. Choosing the right tool for your needs depends on the specific use case and goals you're trying to achieve.

In this blog, we will explore what each AI tool offers, how they differ, and help you decide which is best for your needs.

What is Deep Seek?

Deep Seek is an advanced AI-driven search tool that focuses on information retrieval. It helps users find highly relevant, deep, and specialized content from a wide range of sources. Unlike traditional search engines, which rely on basic keyword matching and links, Deep Seek uses AI to understand context, relevance, and the specific needs of the user. It's designed to deliver more precise and in-depth results, making it ideal for those looking for detailed answers, niche knowledge, or specialized data.

Key Features of Deep Seek:

Advanced Search Capabilities: It allows you to search beyond surface-level results and dive deeper into databases, articles, and scientific papers.

Context-Aware Results: Deep Seek understands the context of your query, delivering more relevant results tailored to your needs.

Specialized Search: Great for researchers, students, and professionals who require specialized knowledge from specific fields like medicine, science, and law.

Data Aggregation: It collects information from a variety of reputable sources to present you with a comprehensive overview of your search topic.

What is ChatGPT?

ChatGPT, on the other hand, is a conversational AI developed by OpenAI, designed to understand and generate human-like text based on prompts it receives. Unlike traditional search engines or specialized tools like Deep Seek, ChatGPT excels in engaging users in natural conversations, answering questions, and providing helpful explanations in real-time. It’s more about interaction than just information retrieval, making it great for tasks that require contextual conversation or support.

Key Features of ChatGPT:

Conversational AI: ChatGPT excels in holding conversations with users, answering questions, and helping with a variety of tasks ranging from casual queries to professional writing and coding.

Contextual Understanding: It uses advanced natural language processing to understand the context of questions, providing detailed, accurate responses.

Content Generation: Ideal for creating blog posts, writing assistance, coding help, and brainstorming ideas.

Wide-Ranging Applications: It can assist with nearly any topic, whether it’s education, customer support, coding, or even personal inquiries.

Deep Seek vs. ChatGPT: The Key Differences

1. Purpose and Use Case

Deep Seek: Primarily a search tool aimed at retrieving highly relevant, deep information across various databases and sources. It is more focused on finding specific answers from existing knowledge repositories, which is perfect for researchers and specialized tasks.

ChatGPT: Designed for interactive communication, answering questions, and generating text content. It is ideal for conversations, content creation, customer support, and problem-solving. ChatGPT excels in providing personalized, engaging dialogue.

2. Information Retrieval vs. Conversational AI

Deep Seek: Think of Deep Seek as an intelligent search engine. It’s best suited for users who need to pull in specific data or research across various industries or academic fields.

ChatGPT: ChatGPT is a conversational assistant. It’s perfect for getting immediate answers, having a back-and-forth discussion, and even generating creative content like blog posts, summaries, or stories.

3. Search Scope

Deep Seek: The tool dives deep into niche databases, specialized articles, journals, and scientific resources. It can search through academic papers, industry reports, and trusted data sources to give users a detailed, highly relevant result.

ChatGPT: While ChatGPT doesn’t pull data from specific databases or scholarly resources, it generates answers based on a broad understanding of general knowledge. It’s better suited for general knowledge and doesn’t specialize in retrieving highly detailed, authoritative sources like Deep Seek.

4. Accuracy and Context

Deep Seek: Deep Seek’s AI is designed to prioritize accuracy and relevance in search results. Its goal is to provide users with precise data and context that is directly related to their queries, especially in specialized areas.

ChatGPT: ChatGPT is very good at understanding the context of your questions and providing useful answers, but its knowledge is based on the data it was trained on up until its last update. It can generate highly accurate responses in most cases, but it might not always provide the most up-to-date or specialized information.

5. Interactivity and Engagement

Deep Seek: Offers minimal interactivity; its main strength lies in delivering relevant, curated search results based on your queries.

ChatGPT: It thrives on interactivity and can engage in continuous conversations, assist with a wide range of tasks, and adjust its responses based on the flow of the conversation.

Which One is Best for You?

Now that we understand what each tool offers, let’s break down which AI is best for specific needs.

When to Choose Deep Seek:

For In-Depth Research: If you're conducting detailed research in specific fields like science, technology, law, or academia, Deep Seek is the better choice. It allows you to explore deeper, more specialized content.

For Niche Knowledge: If your query requires information from specialized databases or scholarly sources, Deep Seek can provide highly tailored and relevant results.

For Accurate and Comprehensive Results: When you need to gather detailed data from credible sources, Deep Seek’s precision and context awareness can save you time.

When to Choose ChatGPT:

For Conversational Assistance: If you need quick answers, personalized responses, or just want to have a conversation, ChatGPT is the ideal tool.

For Content Generation: ChatGPT is fantastic for helping you write blog posts, articles, or even generating code. It can also assist with brainstorming, outlining, and drafting creative ideas.

For Task Automation: Whether it's summarizing content, explaining complex concepts, or assisting with basic tasks, ChatGPT provides helpful real-time support.

For Casual Inquiries: ChatGPT works great for everyday questions, like ��What’s the weather today?” or “What’s the best movie to watch this weekend?”

Conclusion

Both Deep Seek and ChatGPT offer exceptional capabilities, but they cater to different needs. If you require detailed, specialized search results for in-depth research or academic work, Deep Seek is your go-to tool. On the other hand, if you're looking for an interactive assistant to engage in conversations, generate content, or help with a variety of tasks, ChatGPT is a more versatile choice.

Ultimately, the decision comes down to what you need: Deep Seek for accurate, data-driven search, or ChatGPT for interactive, conversational AI that can assist with a wide range of tasks. Both tools are incredibly powerful in their own right, and knowing when to use each will ensure you get the most out of them.

[littleeyesofpallas]

I always really liked, in Death Note, that when they introduces M and N as L's two competing protégée, they just took a list of L's traits and quirks and split them down the middle: L was both logical and intuitive, passionate about justice and personally enraptured by solving puzzles, sat like a child, played with his food, and had an insatiable sweet tooth? Mellow gets the passion, the justice bordering on vengeance, the intuition, and the sweet tooth. Near gets the childlike demeanor, the play habits, the cold logic, and personal investment in puzzles for puzzles sake. And together they make one functional L.

I want that for the Batfam. Bruce has his giant obnoxious hyper competent repertoire, but instead of every Batkid being just a mini version of Bruce, give them each a SUPER distinct specialty. Something actually discernably different from their peers, rather than just slapping the same onesize fit all batbrand competency on all of them. The martial arts expert, the ace parkour/acrobat, the detective, the gadgeteer, the batcomputer data analyst, the criminal profiler, the vigilante, the master of disguise, the urban legend, the avenger, the ninja/assassin, etc... Obviously theyre each well rounded and versed in all these archetypes, but each one ought to have a specialized category where if it ever really came down to its, they could beat Bruce in a contest of that one set of skills. (Except Damian. Damian should just be a tiny Bruce with maybe a bit of an ethics problem, and the promise of being better than Bruce at EVERYTHING, just given a bit of time to grow into it while Bruce loses a bit of edge to old age)

(and frankly i really just want this for a bunch of hero families -the WWfam could have different fields of specialty from diplomacy to archeology and magic artifacts, to mythological beast tamer, to proper soldier and commander, etc...; the Flashfam could have radically different approaches to what elements of how they approach processing at superhuman speed and their atomic level of finite control; the Arrowfam could have a whole spread of survivalist vs hunter/tracker vs sport archer vs esoteric historical martial arts, etc skills to set one another apart.- but that's several whole other cans of worms...)

Dick is far and apart the best in the air, fastest maneuvering both around obstacles and on the run, in and out of the fray, sheer 0 to 60 from ground to air on a grappling line, fastest all around reaction time, and contender for most well honed raw athleticism right next to Cass.

Babs has the information network, the batcomputer sciences, and organizational and tactical perspective that comes uniquely from not just being a bat kid but from being so closely acquainted with GCPD's structures and systems. When it comes to cross referencing and pinpointing precise information, Babs is unmatched by a substantial margin.

Jason has the I Am Vengeance, I Am The Night down. The raw passion for crime fighting, and indeed the fixation on crime specifically. He and Steph are the most personally acquainted and invested in Gotham's underworld and the actual humans working, living, struggling and thriving in it.

Ill be honest I m actually never quite sure where the hell either Helena fits into this structure... Bertinelli feels like she should fit a niche almost too similar to Jason's, but lacking in the fanfavorite melodrama of being a dead robin. And Wayne ought rightly to fit a role not dissimilar to Damian or just Bruce himself... Consider this one unresolved...

Tim is of course is everyones favorite ace boy detective. I feel like theres always a temptation to make him Babs' equal in the tech department but outside of the laughable 90s hackerkid aesthetic I just dont see it. He's great at trivia and detective's intuition, and of course his near shamanic level of insight into Gotham itself.... He does strike me as one of the family's top gadgeteers; not a full blown engineer like Luke, but quick to pick up and make unconventional use of existing tech and hardware, matched and even surpassed in that respect only by...

Steph, who as Cluemaster's kid and one time potential protégée has had a thorough talent for tinkering and sabotage from and early age. Maybe she cant tap into the same depth of trivia or strictest logical deductions that some of the more thorough bred bats can, but no one can pinpoint the most vital areas, or dismantle a deathtrap more quickly than Steph, both by way of knowing the mechanics, but also by way of intimating a super villain's psychology and behavior. Where someone like Tim or even Bruce might fixate on knowing the exact layout or schematics or logistics of a hideout, a machine, or a plan before taking action to dismantle it, Steph knows at a glance where the most volatile parts of a machine or a plan are so that even if she doesn't have the time or the specific knowledge to work out every detail of what it does and how, she can figure out how to break a mechanism or topple a plan at its most central pillar(s) of support. I've always wanted her to be essentially the family's espionage expert, right next to Catwoman's breaking and entering expertise.(Ric Grayson eat your dman heartout)

Then there's Cass, and obviously, at her peak she's the family's (and frankly the world's) top martial artist. But even with her first language of combat fluency stripped down/away she's more than a match for anyone else in the family.

Luke kind of predictably taking his dads role as engineer, utilizing the full extent of the high end bat arsenal unlike really anyone else. People joke about comparing batman and Ironman but really if anyone should have a shtick comparable to Ironman it's Batwing. The rest of the bat fam can operate and maybe maintain the vehicles and hardware fine, but no one can design, upgrade, and see through the actual fabrication process of the tech better than Luke...

...Runner up in this same category is Harper, who is lacking in the straight up manufacturing department but built her whole vigilante arsenal by juryrigging salvaged battech.

Ill be honest, a bit like Huntress, I dont fully known what to make of Kate. Her background is unique among the batfamily as having been strictly military. She has a penchant for the noirsy side of the detective shtick, but thats more of a genre thing than a skill set; what does it really mean for her shtick inworld? I dont really have an answer...

Duke is a bit of a weird case in that the obvious answers are all baked right into his existing profile: he's the meta, he works in the light, he's supposedly more above board and less broody. And that all feels fine, but it also feels like he doesn't actually fit into the whole schema of batskills at all as a result. I would like to see him use his experience with We Are Robin to create a kind of PR or outreach branch of the bat fam. Like, a bat that Gotham can actually sort of get to know and learn to trust, beyond believing in a boogeyman or not. Like, i dunno, give him some airtime with Vicky Vale or Jack Ryder...

And I already mention Damian is just small Bruce waiting to grow into his dad's shoes. The whole benefit of being ras' heir is that he's the one kid who doesnt need Batman to train him. He can fight and think and ostensibly even gadget without Bruce, what Bruce gives him is what Ras and Talia cant: A moral compass. Plus it's a fun change of pace for Bruce to have a protégé where he isn't filling the time with teaching combat and shit to, so that he really has no choice but to learn to connect with Damian emotionally, for both Damian's sake and his own.

I'm not touching Gotham Girl with a 20ft pole...

Did I forget anyone??

People like Owlman, and Ghostmaker are all just the same shtick... Bruce's same character build stats but without the ethics. The Talons are cool but skillswise still just sort of amount to Nightwing knockoffs, ala the whole Owlman origins of the Court of Owls plot in the first place. Similarly the Batman Inc crew are just discount batmen; same basic skill tree but lower point values, so to speak. Not really worth investigating. (unrelated but has Catman fought Green Arrow before? I feel like they could have a really cool survivalist rivalry thing going on)

Not counting people like Clown hunter, or Harley, or Scarlet, or Raptor as parts of the family...

OH! Jace! Boy what a weird case. I wish we got more of an actual motive from him. It honestly doesn't feel like he has a particularly good reason to even be a vigilante, least of all a direct successor to Batman of all things. And I wish they hadn't just shipped him off to NYC... Other than that his shtick feels super weird in the scope of the general bat repertoire. Like Kate, it's weird that his background is really just (para)military rather than the more eclectic spread that Bruce has made the standard bat regimen.