#Tech Data Analytics

[smarttechdata]

smarttechdata

I am alexpaul working for smarttechdata as PR consultant. With more than 6 year’s experience in PR and Digital Industry, helping teams to achieve goals by streamlining the process.

[turns-out-its-adhd]

AI exists and there's nothing any of us can do to change that.

If you have concerns about how AI is being/will be used the solution is not to abstain - it's to get involved.

Learn about it, practice utilising AI tools, understand it. Ignorance will not protect you, and putting your fingers in your ears going 'lalalala AI doesn't exist I don't acknowledge it' won't stop it from affecting your life.

The more the general population fears and misunderstands this technology, the less equipped they will be to resist its influence.

[digital-specialist]

Acadecraft Partners with Wadhwani Foundation's Government Digital Transformation Initiative to Develop eLearning Courses

[truetechreview]

Top 5 DeepSeek AI Features Powering Industry Innovation

Table of Contents1. The Problem: Why Legacy Tools Can’t Keep Up2. What Makes DeepSeek AI Unique?3. 5 Game-Changing DeepSeek AI Features (with Real Stories)3.1 Adaptive Learning Engine3.2 Real-Time Anomaly Detection3.3 Natural Language Reports3.4 Multi-Cloud Sync3.5 Ethical AI Auditor4. How These Features Solve Everyday Challenges5. Step-by-Step: Getting Started with DeepSeek AI6. FAQs: Your…

[matrixmasterassembly]

A Beginner’s Guide to Data Science: Skills and Tools You Need

Data science is transforming industries, offering tools and insights to solve complex problems. As a beginner, understanding the essential skills and tools can set you on the right path to success.

Key Skills to Start Your Journey To excel in data science, begin with foundational skills like:

Programming Languages: Python and R are essential for data manipulation and analysis.

Data Visualization: Tools like Tableau and matplotlib simplify the presentation of insights.

Machine Learning Basics: Learning algorithms such as regression and clustering is a great starting point.

Tools Every Beginner Should Know Starting with the right tools can make your learning process smoother:

Python and Jupyter Notebooks: Ideal for coding and data visualization.

SQL: Essential for querying and managing data stored in databases.

Tableau: A powerful tool for creating interactive and engaging dashboards.

Explore more about the essential skills and tools for data science beginners on Matrix Masters Assembly.

If you're ready to build your first data science project, learn how data visualization tools like Tableau and coding in Python can simplify complex data analysis. Discover practical steps to get started at Matrix Masters Assembly’s comprehensive guide.

[womaneng]

View this post on Instagram

A post shared by Zeynep Küçük Woman Engineer (@woman.engineer)

ou can become a data analyst ⤵️📈📊💯 Here’s what you need to do: - believe in yourself - learn Excel -learn SQL - learn Tableau - build Portfolio - update Linkedin - optimize Resume - Use Network -apply for jobs That’s the way. . . .

[xlsdesignt]

what u think, to much colour, or less?

https://sdesignt.threadless.com/

[vedangkadia]

🚀 Meta's New Ad Tools for Facebook & Instagram Are Here! 🎯

Meta just released exciting updates to its ad platforms, aimed at making your campaigns smarter and more effective. Here's a quick look at what’s new and why it matters:

1️⃣ Smarter Targeting with AI: Meta's AI-powered optimization helps you polish your ad targeting. Whether you're customizing ads for different audiences or adjusting campaigns, these tools are designed to reach the right people more efficiently.

2️⃣ New Incremental Attribution Model: This new attribution setting targets those who are more likely to convert after seeing your ad—customers who wouldn’t have taken action otherwise. Early tests show an average 20% increase in incremental conversions, ensuring your ads have a real impact.

3️⃣ Better Analytics Integration: Meta is simplifying connections with external analytics tools like Google Analytics and Adobe, providing a clearer view of how your campaigns perform across platforms. Now, you can track and understand the full customer journey, from paid social to SEO, all in one place.

✨ Key Features :

- Conversion Value Rules: Prioritize high-value customers without creating separate campaigns. Adjust your bids for different customer actions based on long-term value, so you can place higher bids on customers who offer more value over time—all within the same campaign.

- Incremental Attribution: Focus on “incremental conversions”—customers who wouldn’t have converted without seeing your ad. Early adopters have seen a 20% rise in these valuable conversions.

- Cross-Platform Analytics: Meta’s direct connections with analytics platforms allow you to merge data from different channels, giving you a holistic view of your ad performance. Early tests show a 30% increase in conversions when third-party analytics tools like Google Analytics are used alongside Meta ads.

These updates are about improving the precision and efficiency of your ad campaigns. Meta’s new AI-driven features help you achieve better results, make smarter decisions, and maximize the value of your ad spend.

💡 What to Do Now :

✔️ Review your current Meta ad strategy to ensure you're ready to take advantage of these tools.

✔️ Map out your customer journey to identify where these new features can add the most value.

✔️ Be prepared to test these updates as they roll out—early adopters are already seeing impressive gains.

How do you plan to use these new Meta tools? Share your thoughts in the comments below! 💬

📌Follow us on Social Media📌

📢 LinkedIn — Vedang Kadia — Amazon Associate | LinkedIn

📢 Quora — Vedang Kadia

📢 Tumblr — Untitled

📢 Medium — Vedang Kadia — Medium

[kookiesdayum]

I want to learn AWS from scratch, but I'm not familiar with it and unsure where to start. Can anyone recommend good resources for beginners? Looking for structured courses, tutorials, or hands-on labs that can help me build a strong foundation.

If you know any resources then plz let me know.

Thanks 🍬

[piqtescue]

so basically all of my timeline data in google- every step ive taken with every phone ive had since 2016, is going to be deleted as they migrate away from the web version.... big fuck you to Google. Huge, huge fuck you

ive got the JSON backup obviously, but fuck if I know or even want to implement that into a map-based environment- this literally makes me want to fucking piss on their entire business

[innonurse]

Researchers create AI tool to forecast cancer patients' responses to immunotherapy

- By InnoNurse Staff -

NIH scientists have developed an AI tool that uses routine clinical data to predict cancer patients' responses to immunotherapy, potentially aiding in treatment decisions.

Read more at National Institutes of Health (NIH)

///

Other recent news and insights

New analytical tool enhances comprehension of heritable human traits and diseases (University of Oslo/Medical Xpress)

[automatticdata]

From Support to Data Science and Analytics: My Journey at Automattic

“Is it possible to transform a role in customer support into a data science career?”  This question, which once seemed like a distant dream, became my career blueprint at Automattic. My journey from a Happiness Engineer in September 2014 to a data wrangler today is a tale of continuous evolution, learning, and adaptation. Starting in the dynamic world of customer support with team “Hermes” (an…

[digital-specialist]

Qatar Partners With Scale AI for AI-Powered Digital Transformation of Government Services

[truetechreview]

Ultimate Guide to DeepSeek AI for Business Growth

Table of Contents of DeepSeek AI for Business Growth1. Introduction: Why AI is Essential for Modern Business Growth2. What Is DeepSeek AI?3. Top 5 DeepSeek AI Tools for Scaling Businesses3.1 Demand Forecasting Engine3.2 Customer Lifetime Value (CLV) Predictor3.3 Automated Supply Chain Optimizer3.4 Dynamic Pricing Module3.5 Sentiment Analysis Hub4. How DeepSeek AI Reduces Costs and Boosts…

[vivekavicky12]

Foundations for Data Science Success: Essential Steps for Aspiring Professionals

Embarking on a journey into the world of data science is an exhilarating endeavor, promising the discovery of meaningful insights and contributions to cutting-edge advancements. However, achieving success in this dynamic field requires a robust skill set. In this article, we'll explore crucial prerequisites that will significantly enhance your prospects of securing a fulfilling position in data science.

Academic Background: Building a Solid Ground

A pivotal step in the pursuit of a data science career is cultivating the right educational foundation. Whether it's a degree in statistics, mathematics, computer science, or a specialized field like data science, acquiring foundational knowledge is crucial for comprehending the complexities of data analysis.

Programming Proficiency: Mastering the Language of Data

Proficiency in programming languages serves as a cornerstone for data science. Mastery of languages like Python and R is indispensable, given their widespread use in the field. Additionally, familiarity with essential libraries such as NumPy and pandas is vital for efficient data manipulation, a fundamental aspect of any data scientist's role.

Statistical Knowledge: Deciphering the Data Narrative

A profound grasp of statistical concepts lies at the core of data science. Understanding concepts like hypothesis testing, regression analysis, and probability theory equips data scientists with the tools needed to extract meaningful insights from intricate datasets.

Data Manipulation Skills: Crafting Clean and Usable Data

The ability to manipulate and clean data sets proficiently sets skilled data scientists apart. Learning techniques for handling missing values, addressing outliers, and utilizing tools like pandas for effective data formatting ensures that the data is in a usable state for in-depth analysis.

Data Visualization: Illuminating the Insights

Data visualization is the art of presenting complex findings in a clear and understandable manner. Familiarizing yourself with visualization tools such as Matplotlib, Seaborn, or ggplot2 enhances your capacity to communicate insights effectively, making data-driven decisions accessible to diverse audiences.

Communication Skills: Bridging the Technical Gap

Effective communication is a pivotal skill for any data scientist. The ability to convey complex findings to both technical and non-technical stakeholders is essential. Clear communication ensures that insights derived from data analysis are actionable and comprehensible across diverse teams.

Continuous Learning: Staying Ahead in a Dynamic Landscape

Data science is a swiftly evolving field with constant advancements in tools and techniques. Staying abreast of the latest trends through continuous learning is imperative. Engaging in online courses, webinars, and participating in the data science community are avenues for staying informed about industry developments.

Build a Robust Portfolio: Showcasing Your Expertise

A strong portfolio serves as your professional showcase. Showcasing real-world projects that highlight your problem-solving abilities and practical skills provides tangible evidence of your capabilities to potential employers.

In conclusion, by focusing on these essential prerequisites, you'll position yourself as a well-prepared candidate ready to navigate the intricate landscape of data science. While the journey may present challenges, a solid foundation and a commitment to continuous learning will enable you to confidently step into the dynamic and evolving world of data science, securing your first job with enthusiasm and expertise.