#AI tools for data science

[olivergisttv]

Top Tools for Data Scientists in 2025: What You Must Master to Stay Relevant

The data science profession is facing a massive toolchain shake-up. What worked in 2020 is now considered outdated. With the rise of LLMs, local-first analytics, and AI-native workflows, companies are rethinking their entire data stack. If you’re a data scientist—or hoping to become one—this is your survival guide. Here’s a deep dive into the cutting-edge tools, obsolete tech to avoid, and career…

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

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Know about AI in full detailed.

[codecrafted]

Remote Data Science Jobs Are Booming: Here’s How to Prepare

In the ever-evolving digital economy, data has become the backbone of decision-making across every industry. From e-commerce giants and fintech startups to healthcare and manufacturing sectors, organizations are increasingly relying on data science to unlock hidden patterns, predict outcomes, and guide strategic planning. What’s truly revolutionary, however, is how this once location-dependent…

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Hexaware Partners with Abluva to Launch Secure Agentic AI Solutions for the Life Sciences Industry

Hexaware Technologies, a leading global IT services provider, has announced a strategic partnership with Abluva, an innovator in agentic AI security, to deliver secure and governed Generative AI (GenAI) solutions tailored for the Life Sciences industry. This collaboration is aimed at helping pharmaceutical companies, clinical research organizations (CROs), and healthcare enterprises deploy…

[pangaeax]

How Agentic AI Is Transforming the Future of Data Science and Analytics

We’re entering a new era in data science—one where AI doesn’t just respond to our prompts but acts on its own. Enter Agentic AI, the next-generation intelligence model that’s already transforming how organizations collect, process, and utilize data.

In traditional AI systems, most processes rely heavily on human input: analysts feed in the data, guide the model training, build dashboards, and trigger reports. But with the rise of Agentic AI, these time-consuming tasks are now being performed autonomously—often before you even realize they’re needed.

So, what exactly is Agentic AI?

Agentic AI refers to intelligent software agents capable of autonomous goal setting, contextual decision-making, and real-time action execution. Think of it as an AI that not only follows instructions—but also knows what to do, when, and why—all without constant human supervision.

In data analytics, this means your AI systems can ingest new data in real time, clean and structure it on the fly, train the most relevant models, and deliver insights to decision-makers through APIs or messages—before anyone asks for them.

Here’s why this is a big deal:

✅ Speed & Efficiency: Agentic AI significantly shortens decision-making cycles. A 2024 McKinsey study showed companies using autonomous AI in analytics made decisions 30–50% faster.

✅ Scalability: These agents can handle massive, multi-source datasets across business units without scaling issues.

✅ Accuracy: They reduce the margin for human error by automating repetitive tasks like data wrangling and model testing.

✅ Context Awareness: Agentic AI systems adjust to environmental and data changes dynamically, making them far more reliable in fast-paced industries.

Real-world applications are already here. In healthcare, agents monitor patient vitals and trigger alerts. In retail, AI adjusts pricing and promotions in real time. In finance, autonomous systems rebalance portfolios and detect fraud proactively.

And no, Agentic AI is not replacing human data scientists—it’s elevating them. By handling the heavy lifting, these systems allow professionals to focus on strategy, ethical governance, and deeper data interpretation.

Whether you're a business leader aiming to optimize your analytics workflows or a freelance data analyst seeking a competitive edge, understanding Agentic AI is no longer optional—it’s essential.

Want to dive deeper into how this works, what tools it complements, and what challenges to watch for?

Read this blog to explore the full guide: 🔗 https://www.pangaeax.com/2025/06/20/agentic-ai-in-data-science-analytics/

[learnerworld]

Code Smarter, Not Harder: Top AI Assistants in 2025

If you work in tech, you already know the drill: there’s always something new to catch up on — a library, a tool, a syntax update, or a surprise function you suddenly need to implement. It feels like a never-ending race, isn’t it?

The pressure to “keep up” is real. But here’s the shift: AI assistants are now helping us learn faster, grow more confidently, and adapt without burning out.

Let’s admit it — as humans, we have limits. Memory fades. Context-switching drains us. And no, we don’t have to be a walking storage device who remembers every syntax rule or function signature.

Instead, we need to act smart. Let these polite, efficient AI assistants help us code smarter — and think clearer. Many of these are free to start with, and you can always upgrade later if needed.

In this article, we’ll explore today’s top AI coding assistants, compare their unique strengths, and help you decide which tools are best suited for your workflow.

Why AI Coding Assistants Matter

AI coding assistants can now suggest, refactor, and even debug code in real time — transforming how developers write software

These tools:

·        Boost productivity by reducing repetitive coding tasks.

·        Improve code quality with intelligent suggestions.

·        Enhance collaboration by integrating with development environments.

·        Reduce errors by detecting vulnerabilities and optimizing code.

Top AI Coding Assistants in 2025

1. GitHub Copilot

Best for: General-purpose coding, multi-language support.

Key Features: Code completion, function suggestions, debugging, documentation generation.

USP: Most widely adopted AI coding assistant, integrated into VS Code & JetBrains.

Limitations: May occasionally generate incorrect or outdated code.

Data Privacy: Copilot for Business does not use code for training; personal usage may contribute.

Recently, GitHub Copilot Agent was also released — an upgrade over the original Copilot, offering more autonomous task execution, chat-based interactions, and deeper IDE integration for navigating code, running commands, and making decisions with minimal prompts.

2. Amazon CodeWhisperer

Best for: AWS-based development.

Key Features: Code completion, security vulnerability detection, AWS SDK integration.

USP: An assistant optimized for cloud-native applications.

Limitations: Less effective outside AWS ecosystem.

Data Privacy: Does not use customer code for training.

3. ChatGPT (Not specifically a Coding assistant but quite popular among learners for coding help)

Best for: Learning, debugging, code explanations.

Key Features: Conversational programming, multi-language support, code generation.

USP: Great for prototyping and explaining complex concepts.

Limitations: Not IDE-integrated, lacks real-time coding assistance.

Data Privacy: OpenAI may use interactions for training unless opted out.

Other Tools:

DeepCode (now part of Snyk Code) is widely used for security-focused static analysis. While I haven’t used it hands-on yet, developers praise its ability to catch subtle vulnerabilities early in the dev cycle.

Sourcery is gaining traction among Python developers for its ability to auto-refactor and improve code readability. It integrates with VSCode and PyCharm, and early user reviews highlight its value in maintaining clean codebases.

Cursor AI is positioned as a “Copilot alternative with deep file awareness.” While I’ve only explored it as such, it appears to focus heavily on project-wide understanding and autonomous generation.

Tabnine and Windsurf (formerly CodiumAI) have made strong cases for privacy-conscious and team-based AI development, respectively.

Where caution is still needed:

· Code quality and correctness: Code quality and correctness: AI can be confidently wrong. Sometimes, I get code that looks perfect, but fails because it uses a function that does not even exist. You need to review and test the code (It will help you there too!)

· Security blind spots: Most tools aren’t security-aware by default. They might generate code that works, but doesn’t sanitize inputs or handle edge cases.

· Enterprise concerns: Teams are still wary of using AI-generated code from tools trained on public repositories. Licensing, IP ownership, and data privacy are legitimate considerations.

Final Thoughts

AI coding assistants are revolutionizing software development, making coding faster, smarter, and more efficient. Whether you're a seasoned developer or just starting out, leveraging these tools can enhance your workflow and boost productivity.

The future is not AI versus developers — it’s developers with AI, building better software together.  The question now is how to adopt them responsibly, not whether to use them at all.

Have you explored any AI coding assistants yet? Which one do you use, and what’s your experience with it?

If you haven’t tried one, have you heard about them? What’s holding you back — trust, accuracy, privacy, or just not the right time? Drop your thoughts in the comments!

[olivergisttv]

Data Science in 2025: The Shocking Tools That Will Make or Break Your Career

The data science field in 2025 is no longer what it used to be. With AI-native tooling, vector search pipelines, and local-first compute on the rise, a seismic shift is underway. 📉 40% of traditional data science tasks are now automated 📈 Roles demand real-time, AI-integrated, and app-ready solutions ❗ And if you’re still clinging to legacy tools like Pandas and Jupyter? You’re not just…

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Saskatoon’s Wild Stats

[igmpi]

Explore IGMPI’s Big Data Analytics program, designed for professionals seeking expertise in data-driven decision-making. Learn advanced analytics techniques, data mining, machine learning, and business intelligence tools to excel in the fast-evolving world of big data.

[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.

[youthchronical]

Scientists from India, UK join hands to research healthy brain aging

The Centre for Brain Research (CBR) at the Indian Institute of Science (IISc) and the UK Dementia Research Institute (UK DRI) on Friday launched an international partnership that will create an interconnected research ecosystem to accelerate scientific understanding and innovation in brain health. According to CBR, by integrating expertise and cutting-edge technologies including blood-based…

[pangaeax]

These modern recruitment AI tools are reshaping the hiring process, offering a smarter, faster, and more accurate way to evaluate candidates. Whether you’re a tech recruiter at an enterprise or sourcing freelancers from a platform like Pangaea X, understanding how these AI assessments work can transform your hiring strategy.

[classroomlearning]

BTech CSE: Your Gateway to High-Demand Tech Careers

Apply now for admission and avail the Early Bird Offer

In the digital age, a BTech in Computer Science & Engineering (CSE) is one of the most sought-after degrees, offering unmatched career opportunities across industries. From software development to artificial intelligence, the possibilities are endless for CSE graduates.

Top Job Opportunities for BTech CSE Graduates

Software Developer: Design and develop innovative applications and systems.

Data Scientist: Analyze big data to drive business decisions.

Cybersecurity Analyst: Safeguard organizations from digital threats.

AI/ML Engineer: Lead the way in artificial intelligence and machine learning.

Cloud Architect: Build and maintain cloud-based infrastructure for global organizations.

Why Choose Brainware University for BTech CSE?

Brainware University provides a cutting-edge curriculum, hands-on training, and access to industry-leading tools. Our dedicated placement cell ensures you’re job-ready, connecting you with top recruiters in tech.

👉 Early Bird Offer: Don’t wait! Enroll now and take the first step toward a high-paying, future-ready career in CSE.

Your journey to becoming a tech leader starts here!

[aiinsight47]

Build AI Tools for Money: Simple Developers Guide

Learn how developers can create and sell AI tools or APIs for profit. Discover steps to identify problems, build solutions, monetize effectively, and grow your AI business.

The world of artificial intelligence (AI) is booming, and developers are uniquely positioned to take advantage of this trend. Creating and selling AI tools or APIs is one of the most lucrative ways to turn your coding skills into a business. Whether you’re solving a niche problem or building tools for broader use, there are endless opportunities in this space. Table of Contents What Are AI Tools…

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Bridgetown Research Secures $19M Series A to Revolutionize AI-Driven Business Research

New Post has been published on https://thedigitalinsider.com/bridgetown-research-secures-19m-series-a-to-revolutionize-ai-driven-business-research/

Bridgetown Research Secures $19M Series A to Revolutionize AI-Driven Business Research

Strategic business decisions have long been constrained by time and resources, but Bridgetown Research is changing the game. The AI decision science startup today announced a $19 million Series A funding round led by Lightspeed and Accel, with participation from a leading research university. This capital will fuel Bridgetown’s mission to deploy AI-powered business research agents capable of transforming how private equity firms, consulting teams, and corporate strategists analyze markets and make high-stakes decisions.

AI-Powered Research Agents: A Paradigm Shift

Traditional business research is slow, expensive, and often constrained by the availability of experts who have limited exposure to repeated cases. Bridgetown Research aims to change this by automating fundamental research and analysis through AI-driven agents. These agents autonomously gather, analyze, and synthesize primary and secondary data at a scale and speed previously unimaginable.

A standout feature of Bridgetown’s AI suite is its voice bots—sophisticated AI agents trained to recruit and interview industry experts. By collecting primary data—direct insights from seasoned professionals—these agents provide businesses with proprietary intelligence that goes beyond publicly available information. The AI agents also leverage alternative data sources, including web-crawled insights and structured datasets from industry partners, to create a comprehensive analytical framework.

A Visionary Team at the Helm

Bridgetown Research was founded by Harsh Sahai, a former Amazon machine learning leader and McKinsey & Co. strategist who recognized that most business analyses are repetitive and thus ripe for automation. His team consists of experts from McKinsey, Bain, Amazon, and top technology startups, bringing together deep experience in AI, strategy consulting, and data science.

Harsh Sahai, CEO & Founder of Bridgetown Research, “We are excited to be a catalyst for change. By working with private equity firms, management consultants, and corporate teams, we are helping them make strategic decisions faster and more effectively. This, in turn, drives demand for advisory and information services, creating a powerful network effect. For every $1 we generate, we enable over $10 in advisory and information services revenue—we’re building an ecosystem where everyone thrives.”

Beyond Standard AI: Structuring Business Intelligence

Unlike many AI tools that merely search and summarize information using large language models (LLMs), Bridgetown Research’s AI agents go much further. Business decisions often require a combination of proprietary intelligence, structured market frameworks, and repeatable methodologies—something Harsh Sahai refers to as ontologies. These structured frameworks allow businesses to systematically evaluate markets, competitors, and opportunities, ensuring that decision-making is not only fast but also auditable and repeatable.

Harsh Sahai, “Bridgetown Research is the only AI-driven company using agents to gather primary data and systematically extract patterns to generate original insights.”

Market Adoption & Early Success

Bridgetown Research initially focused on private equity deal screening and diligence, a high-value use case where speed and accuracy are critical. Today, top-tier PE and VC firms use Bridgetown’s AI agents to screen investment opportunities in just 24 hours—compared to the weeks it would traditionally take. By eliminating the bottleneck of manual research, these firms can focus on decision-making rather than just gathering data.

Beyond investing, Bridgetown’s AI research agents are revolutionizing other business applications. For corporate clients, its voice-of-customer surveys enable companies to speak with hundreds of respondents simultaneously, delivering insights within days instead of months. This level of efficiency is unheard of in traditional market research, making Bridgetown an indispensable tool for decision-makers.

Backing from Lightspeed and Accel

Venture firms Lightspeed and Accel see immense potential in Bridgetown’s AI-driven approach.

Anagh Prasad, Investor at Accel, “AI is causing widespread disruptions across enterprise functions, and Bridgetown Research is at the forefront of this transformation. Executives using Bridgetown’s platform describe it as having a team of top-tier consultants at their fingertips. We are excited to partner with Harsh and the team as they define a new category of applied AI.”

Ishaan Preet Singh, Investor at Lightspeed, “Strategic decisions define a company’s trajectory. The research and analysis behind these decisions have historically been slow and fragmented. Bridgetown Research is changing that—offering executives and investors an order of magnitude more information, at a pace that was once impossible. This is just the beginning.”

Future Plans: Scaling AI-Driven Decision Science

As global markets become more complex, the demand for high-quality, fast, and cost-effective decision intelligence continues to rise. With this funding, Bridgetown Research plans to expand the capabilities of its AI agents to cover a broader range of industries and business problems. The company will also strengthen its partnerships with domain-specific intelligence providers, further enhancing the quality of insights available to its clients.