#Dataengineer

[kittu800]

[praveennareshit]

📢 FREE MASTERCLASS

🔷 Azure Data Engineering with Data Factory 🗓️ 19th June | 🕢 7:30 AM IST 👨‍🏫 Trainer: Mr. Venkat Reddy 🔗 https://tr.ee/vepeQC

📌 Learn Data Pipelines, Azure Integration & Real-Time Projects

[nschool]

The Role of Data Science in Healthcare and Diagnosis

Data science is changing many areas, and healthcare is one of the most important ones. Today, healthcare uses data science to help doctors find diseases early, make better decisions, and create treatments that fit each patient. Hospitals, clinics, and researchers have a lot of health data, like patient records, test results, and information from devices like fitness trackers. Data science helps to understand all this data and use it to improve health and save lives.

Why Healthcare Needs Data Science

Healthcare creates huge amounts of data every day. Each patient has a medical history, lab tests, prescriptions, and other information. But this data is often spread out and not easy to use. Data science helps by analyzing this data and finding useful patterns.

Using tools like machine learning and statistics, data scientists find important information that can help doctors and nurses make faster and better decisions. This means patients get the right care at the right time.

How Data Science Helps Healthcare

1. Finding Diseases Early

One of the biggest ways data science helps is by spotting diseases early. Doctors use data science models trained on thousands of medical images and patient data to find signs of diseases like cancer or heart problems before they become serious.

For example, some AI tools can look at breast cancer scans and find tiny changes that a doctor might miss. This helps catch cancer early when treatment is easier and more effective.

2. Predicting Health Problems

Data science can also predict which patients might get sick or need extra care. Hospitals use this to plan treatment and avoid emergencies.

For example, data models can predict if a patient might develop a serious infection like sepsis. If the model alerts the doctors early, they can start treatment sooner and save the patient’s life.

3. Making Treatment Personal

Every person is different, so one treatment might not work for everyone. Data science helps by studying a patient’s genes, lifestyle, and past treatments to suggest the best medicine or therapy for them.

In cancer treatment, for example, doctors use genetic data to choose the drugs that will work best for a patient’s specific type of cancer. This approach is called “precision medicine.”

4. Helping Doctors Read Medical Images

Reading X-rays, MRIs, or CT scans takes time and skill. Data science uses AI to help doctors by quickly analyzing these images and pointing out problems.

For example, AI can find small lung nodules on a chest X-ray, which could be early signs of lung cancer. This helps doctors make faster and more accurate diagnoses.

5. Finding New Medicines

Creating new drugs takes a long time and costs a lot of money. Data science can speed up this process by predicting which chemicals might work as medicines.

During the COVID-19 pandemic, data science helped researchers understand the virus and find possible treatments faster than ever before.

Tools Used in Healthcare Data Science

Healthcare data science uses many computer tools to do its work:

Python and R: These programming languages help analyze data and build models.

TensorFlow and PyTorch: These tools help create AI programs that learn from data.

Tableau and Power BI: These help make charts and graphs to show data clearly.

Cloud platforms like AWS and Azure: These provide places to store and process big amounts of data quickly.

Together, these tools help doctors and data scientists work as a team to improve health care.

Challenges of Using Data Science in Healthcare

Even though data science is very helpful, there are some challenges:

Privacy: Patient data is very private. It must be kept safe and only used in the right ways.

Data Quality: Sometimes data is incomplete or wrong, which can lead to mistakes.

Understanding AI: Doctors need to know how AI makes decisions to trust it, but sometimes AI is hard to understand.

Fairness: If data is biased, AI might make unfair decisions that hurt some patients.

Healthcare providers, data scientists, and regulators must work together to solve these problems carefully.

What the Future Looks Like

The future of healthcare will rely even more on data science. Some examples include:

AI assistants helping with mental health support.

Wearable devices that monitor health and alert doctors in emergencies.

Hospitals using data to manage patient care and resources better.

Digital models of patients that test treatments before trying them in real life.

As technology improves and more data becomes available, healthcare will become faster, safer, and more personal.

Conclusion

Data science is changing healthcare in many good ways. It helps find diseases early, predicts health risks, personalizes treatments, helps doctors read medical images, and speeds up drug discovery. These improvements come from using data and technology together.

[nit2023]

Django Online Training - NareshIT

Django Online Training - NareshIT

Are you looking to build powerful web applications using Python? Then it’s time to explore Django, one of the most popular and robust web frameworks available today. Whether you're a beginner in web development or looking to upgrade your skills, a structured Django course can help you unlock new career opportunities in the field offull-stack development.

What is Django? Django is a high-level Python web framework that encourages rapid development and clean, pragmatic design. Built by experienced developers, it handles much of the hassle of web development, so you can focus on writing your app without needing to reinvent the wheel.

Why Learn Django? Here are some compelling reasons to learn Django:

Fast Development: Django’s built-in features, such as the admin interface, authentication system, and ORM, help you build and scale web applications quickly.

Secure by Default: Django includes built-in protection against many security threats like SQL injection, cross-site scripting (XSS), and cross-site request forgery (CSRF).

Versatile: Django is suitable for building everything from simple websites to large-scale enterprise applications.

Community Support: Being open-source, Django has a strong community that continually contributes to its growth and updates.

What Will You Learn in a Django Course?

A well-structured Django course will cover:

Basics of Python for web development

Django architecture and installation

URL routing and view handling

Working with templates and static files

Forms and validations

Connecting to databases and using Django ORM

Building REST APIs with Django Rest Framework (DRF)

Deployment strategies for Django applications

Best Django Online Training:

If you are looking for Django Online Training, NareshIT offers a comprehensive and practical course that helps you master Django from scratch. The course is designed by industry experts and includes hands-on projects to solidify your learning.

👉 For More Information: Django Online Training at NareshIT

Final Thoughts: Learning Django opens up a wide range of opportunities in web development, backend programming, and full-stack roles. With the right training and guidance, you can become a job-ready Django developer in just a few weeks. Start your journey today with a reliable and industry-approved Django Online Training course.

[icedq-toranainc]

iceDQ Training: Learn ETL Testing and Data Governance

Manual testing won’t cut it in today’s high-volume data environments. The Introduction to iceDQ v1.0 course introduces you to a smarter way to manage quality across your entire data stack.

What’s inside the course:

🧠 Deep dive into the logic of rule creation

🔄 Understand execution and test cycles

📊 Learn to audit BI dashboards and warehouse data

🛠️ Discover scalable data QA strategies

Ideal for:

QA professionals tired of writing SQL scripts for validation

Analysts needing reliable reporting

Developers building ETL pipelines

Businesses requiring continuous data monitoring

Top skills you’ll gain:

Automated data validation

Data observability implementation

Data governance fundamentals

📚 Let automation handle your data quality. Start the Introduction to iceDQ v1.0 Course today and streamline your testing.

[azuretrainingsin]

Top Azure Data Engineering Tools You Should Know! Are you ready to master data engineering with Azure tools? These powerful tools will help you process, store, and analyze massive datasets with ease! Want to become an Azure Data Engineer? Join Azure Trainings and take your skills to the next level!

Call: +91 98824 98844 Learn More: www.azuretrainings.in

[jadgest]

Guía esencial para aquellos que aspiran a convertirse en ingenieros de datos en la actualidad

Ya no se limita a construir pipelines de datos, sino que requiere un perfil más amplio y profundo en diversas áreas. Los puntos clave son:* Amplio conocimiento en plataformas cloud: AWS, Azure y GCP son fundamentales.* Experiencia en ETL: Extracción, transformación y carga de datos, incluyendo el uso de herramientas específicas de cada plataforma.* Modelado de datos: Diseño de esquemas…

[jvminstitute]

🎉 Congratulations to Mr. Karan from JVM Institute for getting placed as a Data Engineer- Data Science Division in the PRIMOTECH with a stellar package of ₹9 LPA! 🚀 Your hard work and determination have paid off—this is just the beginning of your incredible journey!

[datascienceschool]

Dreaming of a career in Data Engineering? 🚀 Start your journey by mastering key fundamentals like databases and ETL, strengthening your programming skills in Python or Java, gaining hands-on experience with cloud platforms, and working with real-world datasets. Practice coding and system design on platforms like LeetCode to ace your interviews.

[simple-logic]

Tech is evolving, and so should you! 🚀

Explore the Top 10 Tech Professions You Must Master in 2025 to stay ahead in the game.

📚 From AI to Cloud Computing, find out the skills that will define the future of tech.

👉 Read the full blog here: https://simplelogic-it.com/blogs/top-10-tech-professions-you-must-master/

📌 For more insights, visit: https://simplelogic-it.com/

[ketul99]

2024 Guide: 10 Must-Have Skills for Hiring a Databricks Data Engineer Associate

Find the top 10 skills employers should prioritize when hiring a Databricks Data Engineer Associate in 2024. Build a future-ready team.

[nit2023]

[craigbrownphd]

JEP 472: Prepare to Restrict the Use of JNI in JDK 24

#Technology #Tech #Infrastructure #DataArchitecture #DataDriven #DataEngineering https://www.infoq.com/news/2024/07/jep-472-restrict-jni-jdk-24/?utm_campaign=infoq_content&utm_source=dlvr.it&utm_medium=tumblr&utm_term=Architecture%20%26%20Design

[dicecamp]

Data Engineering Using SQL Server Free Course

Learn SSIS, SSAS, SSRS for data modeling, visualization, and advanced analytics in this Course and Get Certificate.

Link : https://dicecamp.com/data-engineering-using-sql-server-

[alex-merced-web-data]

RECENT APACHE ICEBERG LAKEHOUSE CONTENT

—-Recent Articles—-

- Iceberg hybrid lakehouses: https://bit.ly/3VkCfV8

- How iceberg optimizes queries: https://lnkd.in/eF5mNuF4

- Apache Iceberg Wins: https://lnkd.in/eRXEbHA8

- Nessie Ecosystem: https://lnkd.in/e-SW9_3m

- Deep dive on Dremio Reflections: https://lnkd.in/ej7cHJsj

- Evolution of Iceberg Catalogs: https://lnkd.in/ekrdkqRy

—-Tutorials—-

- Intro to iceberg exercise: https://lnkd.in/eeq7DZiG

- Iceberg with git for data & dbt: https://lnkd.in/eR8mKvkk

#DataEngineering #ApacheIceberg #BigData #DataLakehouse