#jupyterlab

[nschool]

The Best Open-Source Tools for Data Science in 2025

Data science in 2025 is thriving, driven by a robust ecosystem of open-source tools that empower professionals to extract insights, build predictive models, and deploy data-driven solutions at scale. This year, the landscape is more dynamic than ever, with established favorites and emerging contenders shaping how data scientists work. Here’s an in-depth look at the best open-source tools that are defining data science in 2025.

1. Python: The Universal Language of Data Science

Python remains the cornerstone of data science. Its intuitive syntax, extensive libraries, and active community make it the go-to language for everything from data wrangling to deep learning. Libraries such as NumPy and Pandas streamline numerical computations and data manipulation, while scikit-learn is the gold standard for classical machine learning tasks.

NumPy: Efficient array operations and mathematical functions.

Pandas: Powerful data structures (DataFrames) for cleaning, transforming, and analyzing structured data.

scikit-learn: Comprehensive suite for classification, regression, clustering, and model evaluation.

Python’s popularity is reflected in the 2025 Stack Overflow Developer Survey, with 53% of developers using it for data projects.

2. R and RStudio: Statistical Powerhouses

R continues to shine in academia and industries where statistical rigor is paramount. The RStudio IDE enhances productivity with features for scripting, debugging, and visualization. R’s package ecosystem—especially tidyverse for data manipulation and ggplot2 for visualization—remains unmatched for statistical analysis and custom plotting.

Shiny: Build interactive web applications directly from R.

CRAN: Over 18,000 packages for every conceivable statistical need.

R is favored by 36% of users, especially for advanced analytics and research.

3. Jupyter Notebooks and JupyterLab: Interactive Exploration

Jupyter Notebooks are indispensable for prototyping, sharing, and documenting data science workflows. They support live code (Python, R, Julia, and more), visualizations, and narrative text in a single document. JupyterLab, the next-generation interface, offers enhanced collaboration and modularity.

Over 15 million notebooks hosted as of 2025, with 80% of data analysts using them regularly.

4. Apache Spark: Big Data at Lightning Speed

As data volumes grow, Apache Spark stands out for its ability to process massive datasets rapidly, both in batch and real-time. Spark’s distributed architecture, support for SQL, machine learning (MLlib), and compatibility with Python, R, Scala, and Java make it a staple for big data analytics.

65% increase in Spark adoption since 2023, reflecting its scalability and performance.

5. TensorFlow and PyTorch: Deep Learning Titans

For machine learning and AI, TensorFlow and PyTorch dominate. Both offer flexible APIs for building and training neural networks, with strong community support and integration with cloud platforms.

TensorFlow: Preferred for production-grade models and scalability; used by over 33% of ML professionals.

PyTorch: Valued for its dynamic computation graph and ease of experimentation, especially in research settings.

6. Data Visualization: Plotly, D3.js, and Apache Superset

Effective data storytelling relies on compelling visualizations:

Plotly: Python-based, supports interactive and publication-quality charts; easy for both static and dynamic visualizations.

D3.js: JavaScript library for highly customizable, web-based visualizations; ideal for specialists seeking full control.

Apache Superset: Open-source dashboarding platform for interactive, scalable visual analytics; increasingly adopted for enterprise BI.

Tableau Public, though not fully open-source, is also popular for sharing interactive visualizations with a broad audience.

7. Pandas: The Data Wrangling Workhorse

Pandas remains the backbone of data manipulation in Python, powering up to 90% of data wrangling tasks. Its DataFrame structure simplifies complex operations, making it essential for cleaning, transforming, and analyzing large datasets.

8. Scikit-learn: Machine Learning Made Simple

scikit-learn is the default choice for classical machine learning. Its consistent API, extensive documentation, and wide range of algorithms make it ideal for tasks such as classification, regression, clustering, and model validation.

9. Apache Airflow: Workflow Orchestration

As data pipelines become more complex, Apache Airflow has emerged as the go-to tool for workflow automation and orchestration. Its user-friendly interface and scalability have driven a 35% surge in adoption among data engineers in the past year.

10. MLflow: Model Management and Experiment Tracking

MLflow streamlines the machine learning lifecycle, offering tools for experiment tracking, model packaging, and deployment. Over 60% of ML engineers use MLflow for its integration capabilities and ease of use in production environments.

11. Docker and Kubernetes: Reproducibility and Scalability

Containerization with Docker and orchestration via Kubernetes ensure that data science applications run consistently across environments. These tools are now standard for deploying models and scaling data-driven services in production.

12. Emerging Contenders: Streamlit and More

Streamlit: Rapidly build and deploy interactive data apps with minimal code, gaining popularity for internal dashboards and quick prototypes.

Redash: SQL-based visualization and dashboarding tool, ideal for teams needing quick insights from databases.

Kibana: Real-time data exploration and monitoring, especially for log analytics and anomaly detection.

Conclusion: The Open-Source Advantage in 2025

Open-source tools continue to drive innovation in data science, making advanced analytics accessible, scalable, and collaborative. Mastery of these tools is not just a technical advantage—it’s essential for staying competitive in a rapidly evolving field. Whether you’re a beginner or a seasoned professional, leveraging this ecosystem will unlock new possibilities and accelerate your journey from raw data to actionable insight.

The future of data science is open, and in 2025, these tools are your ticket to building smarter, faster, and more impactful solutions.

[govindhtech]

Working With EMR Notebooks AWS Using Jupyter Notebook

Working with AWS EMR Notebooks

Amazon EMR Notebooks, renamed EMR Studio Workspaces, simplify data processing cluster interaction. They use the popular open-source Jupyter Notebook or JupyterLab editors and are available from Amazon EMR. This may be more efficient than EMR cluster notebooks. Users with suitable IAM rights can open the editor in the console.

Notebook statuses

When and how to communicate with EMR Notebooks requires knowing their status. The numerous states you may encounter are listed below:

The notebook is being produced and connected to the cluster. Launching, stopping, removing, or changing the editor's cluster is currently impossible. It starts rapidly but can take longer if a cluster forms.

You can access the fully prepared notebook in the notebook editor. Stop or remove the notebook in this state. Stop the notebook before altering the cluster. A Ready notebook will shut down after a long inactivity.

The notebook has been produced, however cluster integration may require resource provisioning or additional steps. In this case, you can launch the notebook editor in local mode, but cluster-dependent code will fail.

Stopping: Laptop or cluster shutdown. Like the ‘Starting’ state, the editor cannot be opened, stopped, deleted, or clusters altered while stopping.

The laptop shut down successfully. You can delete the laptop, swap clusters, or restart it on the same cluster (assuming the cluster is still operating).

Notebook is being removed from console list. Even after the notebook entry is erased, Amazon S3 will charge for the notebook file (NotebookName.ipynb). To retrieve the latest status, reload the console's notebook list.

Working in Notebook Editor

The notebook editor starts when the notebook is Ready or Pending. You choose Open in JupyterLab or Jupyter after choosing the notebook from the list. This opens a new browser tab with the editor. After opening, select your programming language's kernel from the Kernel menu.

The console-accessible editor's ability to limit EMR notebooks to one user is critical. Opening an already-used notebook will result in an error. Amazon EMR produces a unique pre-signed URL for each session that is only valid for a short time, displaying security.

This URL should not be shared since recipients could inherit your rights and be at risk. IAM permissions policies and granting EMR Notebooks service role access to the Amazon S3 location are two strategies to control access.

Preserving Work

While editing, your notebook cells and output are automatically and occasionally saved to the Amazon S3 notebook file. When there are no modifications since the last save, the editor displays “autosaved,” and otherwise, “unsaved.” You can manually save the notebook by pressing CTRL+S or choosing Save and Checkpoint from File. Manual saves create a checkpoint file (NotebookName.ipynb) in the notebook's principal Amazon S3 folder's checkpoints folder. This site stores only the latest checkpoint.

Attached Cluster Change

Switching the cluster to which an EMR notebook is linked without affecting its content is useful. Only Stopped notebooks can accomplish this. The approach involves selecting the paused notebook, viewing its data, selecting the Change cluster, and then choosing an existing Hadoop, Spark, and Livy cluster or creating a new one. Finally, select the security group and click Change cluster and start laptop to confirm.

Delete Notebooks and Files

The Amazon EMR interface lets you remove an EMR notebook from your list. Importantly, this approach does not delete Amazon S3 notebook files. These S3 data continue to accrue storage fees.

To remove the notebook entry and files, delete the notebook from the console and note its Amazon S3 location (in the notebook details). The AWS CLI or Amazon S3 interface must be used to manually remove the folder and its contents from the S3 location. An example CLI command removes the notebook directory and its contents.

Share and Use Notebook Files

Every EMR notebook has a NotebookName.ipynb file in Amazon S3. If it works with EMR Notebook Jupyter Notebook, you can open a notebook file as an EMR notebook. Saving the.ipynb file locally and uploading it to Jupyter or JupyterLab makes using a notebook file from another user straightforward. This method can recover a console-erased notebook or work with publicly published Jupyter notebooks if you have the file.

A new EMR notebook can be created by replacing the S3 notebook file. Stop all running EMR notebooks and close any open editor sessions.

Create a new EMR notebook with the precise name you want for the new file, record its S3 location and Notebook ID, stop it, and.Using the AWS CLI, copy and change the ipynb file at that S3 location, making sure the file name matches the notebook's name. This technique is shown using an AWS CLI command.

[aesthetic-uni]

The amount of times I have heard it’s alright to use ChatGPT at college is ridiculous. I would rather be peer pressured to do drugs

[gosoftwaremedia]

JupyterLab: Instalasi dan Latihan Pertama Python

Jupyter Jupyter adalah sebuah proyek open-source yang menyediakan antarmuka interaktif berbasis web untuk menulis dan menjalankan kode. Nama “Jupyter” berasal dari tiga bahasa pemrograman yang populer: Julia, Python, dan R. Salah satu produk utamanya adalah Jupyter Notebook, yang memungkinkan kita membuat dokumen dengan kode yang bisa dijalankan, visualisasi, dan teks naratif (markdown). Sangat…

[hawkstack]

Developing and Deploying AI/ML Applications on Red Hat OpenShift AI with Hawkstack

Artificial Intelligence (AI) and Machine Learning (ML) are driving innovation across industries—from predictive analytics in healthcare to real-time fraud detection in finance. But building, scaling, and maintaining production-grade AI/ML solutions remains a significant challenge. Enter Red Hat OpenShift AI, a powerful platform that brings together the flexibility of Kubernetes with enterprise-grade ML tooling. And when combined with Hawkstack, organizations can supercharge observability and performance tracking throughout their AI/ML lifecycle.

Why Red Hat OpenShift AI?

Red Hat OpenShift AI (formerly Red Hat OpenShift Data Science) is a robust enterprise platform designed to support the full AI/ML lifecycle—from development to deployment. Key benefits include:

Scalability: Native Kubernetes integration allows seamless scaling of ML workloads.

Security: Red Hat’s enterprise security practices ensure that ML pipelines are secure by design.

Flexibility: Supports a variety of tools and frameworks, including Jupyter Notebooks, TensorFlow, PyTorch, and more.

Collaboration: Built-in tools for team collaboration and continuous integration/continuous deployment (CI/CD).

Introducing Hawkstack: Observability for AI/ML Workloads

As you move from model training to production, observability becomes critical. Hawkstack, a lightweight and extensible observability framework, integrates seamlessly with Red Hat OpenShift AI to provide real-time insights into system performance, data drift, model accuracy, and infrastructure metrics.

Hawkstack + OpenShift AI: A Powerful Duo

By integrating Hawkstack with OpenShift AI, you can:

Monitor ML Pipelines: Track metrics across training, validation, and deployment stages.

Visualize Performance: Dashboards powered by Hawkstack allow teams to monitor GPU/CPU usage, memory footprint, and latency.

Enable Alerting: Proactively detect model degradation or anomalies in your inference services.

Optimize Resources: Fine-tune resource allocation based on telemetry data.

Workflow: Developing and Deploying ML Apps

Here’s a high-level overview of what a modern AI/ML workflow looks like on OpenShift AI with Hawkstack:

1. Model Development

Data scientists use tools like JupyterLab or VS Code on OpenShift AI to build and train models. Libraries such as scikit-learn, XGBoost, and Hugging Face Transformers are pre-integrated.

2. Pipeline Automation

Using Red Hat OpenShift Pipelines (Tekton), you can automate training and evaluation pipelines. Integrate CI/CD practices to ensure robust and repeatable workflows.

3. Model Deployment

Leverage OpenShift AI’s serving layer to deploy models using Seldon Core, KServe, or OpenVINO Model Server—all containerized and scalable.

4. Monitoring and Feedback with Hawkstack

Once deployed, Hawkstack takes over to monitor inference latency, throughput, and model accuracy in real-time. Anomalies can be fed back into the training pipeline, enabling continuous learning and adaptation.

Real-World Use Case

A leading financial services firm recently implemented OpenShift AI and Hawkstack to power their loan approval engine. Using Hawkstack, they detected a model drift issue caused by seasonal changes in application data. Alerts enabled retraining to be triggered automatically, ensuring their decisions stayed fair and accurate.

Conclusion

Deploying AI/ML applications in production doesn’t have to be daunting. With Red Hat OpenShift AI, you get a secure, scalable, and enterprise-ready foundation. And with Hawkstack, you add observability and performance intelligence to every stage of your ML lifecycle.

Together, they empower organizations to bring AI/ML innovations to market faster—without compromising on reliability or visibility.

For more details www.hawkstack.com 

[tpointtechedu]

Data Science Tutorial for 2025: Tools, Trends, and Techniques

Data science continues to be one of the most dynamic and high-impact fields in technology, with new tools and methodologies evolving rapidly. As we enter 2025, data science is more than just crunching numbers—it's about building intelligent systems, automating decision-making, and unlocking insights from complex data at scale.

Whether you're a beginner or a working professional looking to sharpen your skills, this tutorial will guide you through the essential tools, the latest trends, and the most effective techniques shaping data science in 2025.

What is Data Science?

At its core, data science is the interdisciplinary field that combines statistics, computer science, and domain expertise to extract meaningful insights from structured and unstructured data. It involves collecting data, cleaning and processing it, analyzing patterns, and building predictive or explanatory models.

Data scientists are problem-solvers, storytellers, and innovators. Their work influences business strategies, public policy, healthcare solutions, and even climate models.

Essential Tools for Data Science in 2025

The data science toolkit has matured significantly, with tools becoming more powerful, user-friendly, and integrated with AI. Here are the must-know tools for 2025:

1. Python 3.12+

Python remains the most widely used language in data science due to its simplicity and vast ecosystem. In 2025, the latest Python versions offer faster performance and better support for concurrency—making large-scale data operations smoother.

Popular Libraries:

Pandas: For data manipulation

NumPy: For numerical computing

Matplotlib / Seaborn / Plotly: For data visualization

Scikit-learn: For traditional machine learning

XGBoost / LightGBM: For gradient boosting models

2. JupyterLab

The evolution of the classic Jupyter Notebook, JupyterLab, is now the default environment for exploratory data analysis, allowing a modular, tabbed interface with support for terminals, text editors, and rich output.

3. Apache Spark with PySpark

Handling massive datasets? PySpark—Python’s interface to Apache Spark—is ideal for distributed data processing across clusters, now deeply integrated with cloud platforms like Databricks and Snowflake.

4. Cloud Platforms (AWS, Azure, Google Cloud)

In 2025, most data science workloads run on the cloud. Services like Amazon SageMaker, Azure Machine Learning, and Google Vertex AI simplify model training, deployment, and monitoring.

5. AutoML & No-Code Tools

Tools like DataRobot, Google AutoML, and H2O.ai now offer drag-and-drop model building and optimization. These are powerful for non-coders and help accelerate workflows for pros.

Top Data Science Trends in 2025

1. Generative AI for Data Science

With the rise of large language models (LLMs), generative AI now assists data scientists in code generation, data exploration, and feature engineering. Tools like OpenAI's ChatGPT for Code and GitHub Copilot help automate repetitive tasks.

2. Data-Centric AI

Rather than obsessing over model architecture, 2025’s best practices focus on improving the quality of data—through labeling, augmentation, and domain understanding. Clean data beats complex models.

3. MLOps Maturity

MLOps—machine learning operations—is no longer optional. In 2025, companies treat ML models like software, with versioning, monitoring, CI/CD pipelines, and reproducibility built-in from the start.

4. Explainable AI (XAI)

As AI impacts sensitive areas like finance and healthcare, transparency is crucial. Tools like SHAP, LIME, and InterpretML help data scientists explain model predictions to stakeholders and regulators.

5. Edge Data Science

With IoT devices and on-device AI becoming the norm, edge computing allows models to run in real-time on smartphones, sensors, and drones—opening new use cases from agriculture to autonomous vehicles.

Core Techniques Every Data Scientist Should Know in 2025

Whether you’re starting out or upskilling, mastering these foundational techniques is critical:

1. Data Wrangling

Before any analysis begins, data must be cleaned and reshaped. Techniques include:

Handling missing values

Normalization and standardization

Encoding categorical variables

Time series transformation

2. Exploratory Data Analysis (EDA)

EDA is about understanding your dataset through visualization and summary statistics. Use histograms, scatter plots, correlation heatmaps, and boxplots to uncover trends and outliers.

3. Machine Learning Basics

Classification (e.g., predicting if a customer will churn)

Regression (e.g., predicting house prices)

Clustering (e.g., customer segmentation)

Dimensionality Reduction (e.g., PCA, t-SNE for visualization)

4. Deep Learning (Optional but Useful)

If you're working with images, text, or audio, deep learning with TensorFlow, PyTorch, or Keras can be invaluable. Hugging Face’s transformers make it easier than ever to work with large models.

5. Model Evaluation

Learn how to assess model performance with:

Accuracy, Precision, Recall, F1 Score

ROC-AUC Curve

Cross-validation

Confusion Matrix

Final Thoughts

As we move deeper into 2025, data science tutorial continues to be an exciting blend of math, coding, and real-world impact. Whether you're analyzing customer behavior, improving healthcare diagnostics, or predicting financial markets, your toolkit and mindset will be your most valuable assets.

Start by learning the fundamentals, keep experimenting with new tools, and stay updated with emerging trends. The best data scientists aren’t just great with code—they’re lifelong learners who turn data into decisions.

[davesanalytics]

Data Cleaning with ChatGPT

[learning-code-ficusoft]

Exploring Jupyter Notebooks: The Perfect Tool for Data Science

Exploring Jupyter Notebooks: The Perfect Tool for Data Science Jupyter Notebooks have become an essential tool for data scientists and analysts, offering a robust and flexible platform for interactive computing.

 Let’s explore what makes Jupyter Notebooks an indispensable part of the data science ecosystem. 

What Are Jupyter Notebooks? 

Jupyter Notebooks are an open-source, web-based application that allows users to create and share documents containing live code, equations, visualizations, and narrative text. 

They support multiple programming languages, including Python, R, and Julia, making them versatile for a variety of data science tasks. 

Key Features of Jupyter Notebooks Interactive Coding Jupyter’s cell-based structure lets users write and execute code in small chunks, enabling immediate feedback and interactive debugging. 

This iterative approach is ideal for data exploration and model development. 

Rich Text and Visualizations Beyond code, Jupyter supports Markdown and LaTeX for documentation, enabling clear explanations of your workflow. 

It also integrates seamlessly with libraries like Matplotlib, Seaborn, and Plotly to create interactive and static visualizations.

 Language Flexibility With Jupyter’s support for over 40 programming languages, users can switch kernels to leverage the best tools for their specific task. 

Python, being the most popular choice, often integrates well with other libraries like Pandas, NumPy, and Scikit-learn. 

 Extensibility Through Extensions Jupyter’s ecosystem includes numerous extensions, such as JupyterLab, nbconvert, and nbextensions, which add functionality like exporting notebooks to different formats or improving UI capabilities. 

Collaborative Potential Jupyter Notebooks are easily shareable via GitHub, cloud platforms, or even as static HTML files, making them an excellent choice for team collaboration and presentations. 

Why Are Jupyter Notebooks Perfect for Data Science? Data Exploration and Cleaning Jupyter is ideal for exploring datasets interactively. 

You can clean, preprocess, and visualize data step-by-step, ensuring a transparent and repeatable workflow. 

Machine Learning Its integration with machine learning libraries like TensorFlow, PyTorch, and XGBoost makes it a go-to platform for building and testing predictive models. 

Reproducible Research The combination of narrative text, code, and results in one document enhances reproducibility and transparency, critical in scientific research. 

Ease of Learning and Use The intuitive interface and immediate feedback make Jupyter a favorite among beginners and experienced professionals alike. 

Challenges and Limitations While Jupyter Notebooks are powerful, they come with some challenges: 

Version Control Complexity: 

Tracking changes in notebooks can be tricky compared to plain-text scripts. 

Code Modularity: 

Managing large projects in a notebook can lead to clutter. 

Execution Order Issues:

 Out-of-order execution can cause confusion, especially for newcomers. 

Conclusion 

Jupyter Notebooks revolutionize how data scientists interact with data, offering a seamless blend of code, visualization, and narrative. 

Whether you’re prototyping a machine learning model, teaching a class, or presenting findings to stakeholders, Jupyter Notebooks provide a dynamic and interactive platform that fosters creativity and productivity.

[hackernewsrobot]

Zasper: A Modern and Efficient Alternative to JupyterLab, Built in Go

https://github.com/zasper-io/zasper

[datasciencewithgenerativeai]

Data Science With Generative Ai | Data Science With Generative Ai Online Training

Top Tools and Techniques for Integrating Generative AI in Data Science

Introduction

Data Science with Generative Ai the integration of generative AI in data science has revolutionized the way insights are derived and predictions are made. Combining creativity and computational power, generative AI enables advanced modeling, automation, and innovation in various domains. With the rise of data science with generative AI, businesses and researchers are leveraging these technologies to develop sophisticated systems that solve complex problems efficiently. This article explores the top tools and techniques for integrating generative AI in data science, offering insights into their benefits, practical applications, and best practices for implementation.

Key Tools for Generative AI in Data Science

TensorFlow

Overview: An open-source library by Google, TensorFlow is widely used for machine learning and deep learning projects.

Applications: Supports tasks like image generation, natural language processing, and recommendation systems.

Tips: Leverage TensorFlow’s pre-trained models like GPT-3 or StyleGAN to kickstart generative AI projects.

PyTorch

Overview: Developed by Facebook, PyTorch is known for its dynamic computation graph and flexibility.

Applications: Ideal for research-driven projects requiring custom generative AI models.

Tips: Use PyTorch’s TorchServe for deploying generative AI models in production environments efficiently.

Hugging Face

Overview: A hub for natural language processing (NLP) models, Hugging Face is a go-to tool for text-based generative AI.

Applications: Chatbots, text summarization, and translation tools.

Tips: Take advantage of Hugging Face’s Model Hub to access and fine-tune pre-trained models.

Jupyter Notebooks

Overview: A staple in data science workflows, Jupyter Notebooks support experimentation and visualization.

Applications: Model training, evaluation, and interactive demonstrations.

Tips: Use extensions like JupyterLab for a more robust development environment.

OpenAI API

Overview: Provides access to cutting-edge generative AI models such as GPT-4 and Codex. Data Science with Generative Ai Online Training

Applications: Automating content creation, coding assistance, and creative writing.

Tips: Use API rate limits judiciously and optimize calls to minimize costs.

Techniques for Integrating Generative AI in Data Science

Data Preprocessing

Importance: Clean and structured data are essential for accurate AI modeling.

Techniques:

Data augmentation for diversifying training datasets.

Normalization and scaling for numerical stability.

Transfer Learning

Overview: Reusing pre-trained models for new tasks saves time and resources.

Applications: Adapting a generative AI model trained on large datasets to a niche domain.

Tips: Fine-tune models rather than training them from scratch for better efficiency.

Generative Adversarial Networks (GANs)

Overview: A two-part system where a generator and a discriminator compete to create realistic data.

Applications: Image synthesis, data augmentation, and anomaly detection.

Tips: Balance the generator and discriminator’s learning rates to ensure stable training.

Natural Language Processing (NLP)

Overview: NLP techniques power text-based generative AI systems.

Applications: Sentiment analysis, summarization, and language translation.

Tips: Tokenize data effectively and use attention mechanisms like transformers for better results.

Reinforcement Learning

Overview: A technique where models learn by interacting with their environment to achieve goals.

Applications: Automated decision-making and dynamic systems optimization.

Tips: Define reward functions clearly to avoid unintended behaviors.

Best Practices for Integrating Generative AI in Data Science

Define Objectives Clearly

Understand the problem statement and define measurable outcomes.

Use Scalable Infrastructure

Deploy tools on platforms like AWS, Azure, or Google Cloud to ensure scalability and reliability.

Ensure Ethical AI Use

Avoid biases in data and adhere to guidelines for responsible AI deployment.

Monitor Performance

Use tools like Tensor Board or MLflow for real-time monitoring of models in production. Data Science with Generative Ai Training

Collaborate with Interdisciplinary Teams

Work with domain experts, data scientists, and engineers for comprehensive solutions.

Applications of Data Science with Generative AI

Healthcare

Drug discovery and personalized medicine using AI-generated molecular structures.

Finance

Fraud detection and automated trading algorithms driven by generative models.

Marketing

Content personalization and predictive customer analytics.

Gaming

Procedural content generation and virtual reality enhancements.

Challenges and Solutions

Data Availability

Challenge: Scarcity of high-quality labeled data.

Solution: Use synthetic data generation techniques like GANs.

Model Complexity

Challenge: High computational requirements.

Solution: Optimize models using pruning and quantization techniques.

Ethical Concerns

Challenge: Bias and misuse of generative AI.

Solution: Implement strict auditing and transparency practices.

Conclusion

The integration of data science with generative AI has unlocked a world of possibilities, reshaped industries and driving innovation. By leveraging advanced tools like TensorFlow, PyTorch, and Hugging Face, along with techniques such as GANs and transfer learning, data scientists can achieve remarkable outcomes. However, success lies in adhering to ethical practices, ensuring scalable implementations, and fostering collaboration across teams. As generative AI continues to evolve, its role in data science will only grow, making it essential for professionals to stay updated with the latest trends and advancements.

Visualpath Advance your career with Data Science with Generative Ai. Gain hands-on training, real-world skills, and certification. Enroll today for the best Data Science with Generative Ai Online Training. We provide to individuals globally in the USA, UK, etc.

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Data Science, Programming Skills, Statistics and Mathematics, Data Analysis, Data Visualization, Machine Learning, Big Data Handling, SQL, Deep Learning and AI

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Visit us: https://www.visualpath.in/online-data-science-with-generative-ai-course.html 

[ericvanderburg]

Unsecured JupyterLab and Jupyter Notebooks servers abused for illegal streaming of Sports events

http://i.securitythinkingcap.com/TGH7dJ

[govindhtech]

Vertex AI Workbench Pricing, Advantages And Features

Get Vertex AI Workbench price clarity. Take use of its pay-as-you-go notebook instances to scale AI/ML applications on Google Cloud.

Secret Vertex AI Workbench

Google Cloud is expanding Vertex AI's Confidential Computing capabilities. Confidential Computing, in preview, helps Vertex AI Workbench customers meet data privacy regulations. This connection increases privacy and anonymity with a few clicks.

Vertex AI Notebooks

You can use Vertex AI Workbench or Colab Enterprise. Use Vertex AI Platform for data science initiatives from discovery to prototype to production.

Advantages

BigQuery, Dataproc, Spark, and Vertex AI integration simplifies data access and machine learning in-notebook.

Rapid prototyping and model development: Vertex AI Training delivers data to training at scale using infinite computing for exploration and prototyping.

Vertex AI Workbench or Colab Enterprise lets you run training and deployment procedures on Vertex AI from one place.

Important traits

Colab Enterprise blends Google Cloud enterprise-level security and compliance with Google Research's notebook, used by over 7 million data scientists. Launch a collaborative, serverless, zero-config environment quickly.

AI-powered code aid features like code completion and code generation make Python AI/ML model building easier so you can focus on data and models.

Vertex AI Workbench offers JupyterLab and advanced customisation.

Fully controlled compute: Vertex AI laptops provide enterprise-ready, scalable, user management, and security features.

Explore data and train machine learning models with Google Cloud's big data offerings.

End-to-end ML training portal: Implement AI solutions on Vertex AI with minimal transition.

Extensions will simplify data access to BigQuery, Data Lake, Dataproc, and Spark. Easily scale up or out for AI and analytics.

Research data sources with a catalogue: Write SQL and Spark queries in a notebook cell with auto-complete and syntax awareness.

Integrated, sophisticated visualisation tools make data insights easy.

Hands-off, cost-effective infrastructure: Computing is handled everywhere. Auto shutdown and idle timeout maximise TCO.

Unusual Google Cloud security for simplified enterprise security. Simple authentication and single sign-on for various Google Cloud services.

Vertex AI Workbench runs TensorFlow, PyTorch, and Spark.

MLOps, training, and Deep Git integration: Just a few clicks connect laptops to established operational workflows. Notebooks are useful for hyper-parameter optimisation, scheduled or triggered continuous training, and distributed training. Deep interface with Vertex AI services allows the notebook to implement MLOps without additional processes or code rewrite.

Smooth CI/CD: Notebooks are a reliable Kubeflow Pipelines deployment target.

Notebook viewer: Share output from regularly updated notebook cells for reporting and bookkeeping.

Pricing for Vertex AI Workbench

The VM configurations you choose determine Vertex AI Workbench pricing. The price is the sum of the virtual machine costs. To calculate accelerator costs, multiply accelerator pricing by machine hours when utilising Compute Engine machine types and adding accelerators.

Your Vertex AI Workbench instance is charged based on its status.

CPU and accelerator usage is paid during STARTING, PROVISIONING, ACTIVE, UPGRADING, ROLLBACKING, RESTORING, STOPPING, and SUSPENDING.

The sources also say that managed and user-managed laptop pricing data is available separately, although the extracts do not provide details.

Other Google Cloud resources (managed or user-controlled notebooks) used with Vertex AI Workbench may cost you. Running SQL queries on a notebook may incur BigQuery expenses. Customer-managed encryption keys incur Cloud Key Management Service key operations fees. Like compute Engine and Cloud Storage, Deep Learning Containers, Deep Learning VM Images, and AI Platform Pipelines are compensated for the computing and storage resources they use in machine learning processes.

[nandithamn]

Data Visualization in Python From Matplotlib to Seaborn

Data visualization is an Important  aspect of data analysis and machine learning.You can give key insights into your data through different graphical representations. It helps in understanding the data, uncovering patterns, and communicating insights effectively. Python provides several powerful libraries for data visualization, graphing libraries, namely Matplotlib, Seaborn, Plotly, and Bokeh.

Data visualization is an easier way of presenting the data.It may sometimes seem easier to go through of data points and build insights but usually this process many not yield good result. Additionally, most of the data sets used in real life are too big to do any analysis manually.There could be a lot of things left undiscovered as a result of this process.. This is essentially where data visualization steps in.

However complex it is, to analyze trends and relationships amongst variables with the help of pictorial representation.

The Data Visualization advantages are as follows

Identifies data patterns even for larger data points

Highlights good and bad performing areas

Explores relationship between data points

Easier representation of compels data

Python Libraries

There are lot of Python librariers which could be used to build visualization like vispy,bokeh , matplotlib plotly seaborn cufflinks folium,pygal and networkx. On this many Matplotlib and seaborn very widely used for basic to intermediate level of visualization

Matplotlib is a library in Python being two of the most widely used Data visualization is a crucial part of data analysis and machine learning . That enables users to generate visualizations like scatter plots, histograms, pie charts, bar charts, and much more. It helps in understanding the data, uncovering patterns,and communicating insights effectively. Seaborn is a visualization that built on top of Matplotlib. It provides data visualizations that are more typically statistically and aesthetic sophisticated.

Matplotlib;- Matplotlib is a comprehensive library for creating animated, static, , and interactive visualizations in Python. It provides a lot of flexibility and control over the appearance of plots but can sometimes require a lot of code for simple tasks. Matplotlib makes easy things easy and hard things possible.

 Basic Example with Matplotlib

Use a rich array of third-party packages build on Matplotli

Export to many file formats

Make interactive figures that can pan,zoom, update.

Embed in Graphical and  jupyterLab User Interfaces

Crete public quality plots.

Seaborn;-Seaborn is a python data visualization built on top of Matplotlib . It provides a high-level interface for drawing attractive and informative statistical graphics. It is particularly well-suited for visualizing data from Pandas data frames

Basic Example with Seaborn

Advanced Visualizations

Plots for categorical data

Pairplot for Multivariate Analysis

Combining Matplotlib and Seaborn

Distributional representations

Both Matplotlib and Seaborn are powerful tools for data visualization in Python. Matplotlib provides fine-grained control over plot appearance, while Seaborn offers high-level functions for statistical plots and works seamlessly with Pandas data frames. Understanding how to use both libraries effectively can greatly enhance your ability to analyze and present data.

Can I use Matplotlib and seaborn together?

You can definitely use Matplotlib and Seaborn together in your data visualizations. Since Seaborn Provides an API on top of Matplotlib, you can combine the functionality of both libraries to create more complex and customized plots. Here’s how you can integrate Matplotlib with Seaborn to take advantage of both libraries' strengths.

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PyCharm vs. VS Code: A Comprehensive Comparison of Leading Python IDEs

Introduction: Navigating the Python IDE Landscape in 2024

As Python continues to dominate the programming world, the choice of an Integrated Development Environment (IDE) holds significant importance for developers. In 2024, the market is flooded with a plethora of IDE options, each offering unique features and capabilities tailored to diverse coding needs. Considering the kind support of Learn Python Course in Hyderabad, Whatever your level of experience or reason for switching from another programming language, learning Python gets much more fun.

This guide dives into the leading Python IDEs of 2024, showcasing their standout attributes to help you find the perfect fit for your coding endeavors.

1. PyCharm: Unleashing the Power of JetBrains' Premier IDE

PyCharm remains a cornerstone in the Python development realm, renowned for its robust feature set and seamless integration with various Python frameworks. With intelligent code completion, advanced code analysis, and built-in version control, PyCharm streamlines the development process for both novices and seasoned professionals. Its extensive support for popular Python frameworks like Django and Flask makes it an indispensable tool for web development projects.

2. Visual Studio Code (VS Code): Microsoft's Versatile Coding Companion

Visual Studio Code has emerged as a formidable player in the Python IDE landscape, boasting a lightweight yet feature-rich design. Armed with a vast array of extensions, including Python-specific ones, VS Code empowers developers to tailor their coding environment to their liking. Offering features such as debugging, syntax highlighting, and seamless Git integration, VS Code delivers a seamless coding experience for Python developers across all proficiency levels.

3. JupyterLab: Revolutionizing Data Science with Interactive Exploration

For data scientists and researchers, JupyterLab remains a staple choice for interactive computing and data analysis. Its support for Jupyter notebooks enables users to blend code, visualizations, and explanatory text seamlessly, facilitating reproducible research and collaborative work. Equipped with interactive widgets and compatibility with various data science libraries, JupyterLab serves as an indispensable tool for exploring complex datasets and conducting in-depth analyses. Enrolling in the Best Python Certification Online can help people realise Python's full potential and gain a deeper understanding of its complexities.

4. Spyder: A Dedicated IDE for Scientific Computing and Analysis

Catering specifically to the needs of scientific computing, Spyder provides a user-friendly interface and a comprehensive suite of tools for machine learning, numerical simulations, and statistical analysis. With features like variable exploration, profiling, and an integrated IPython console, Spyder enhances productivity and efficiency for developers working in scientific domains.

5. Sublime Text: Speed, Simplicity, and Customization

Renowned for its speed and simplicity, Sublime Text offers a minimalistic coding environment with powerful customization options. Despite its lightweight design, Sublime Text packs a punch with an extensive package ecosystem and adaptable interface. With support for multiple programming languages and a responsive developer community, Sublime Text remains a top choice for developers seeking a streamlined coding experience.

Conclusion: Choosing Your Path to Python IDE Excellence

In conclusion, the world of Python IDEs in 2024 offers a myriad of options tailored to suit every developer's needs and preferences. Whether you're a web developer, data scientist, or scientific researcher, there's an IDE designed to enhance your coding journey and boost productivity. By exploring the standout features and functionalities of each IDE, you can make an informed decision and embark on a path towards coding excellence in Python.

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Embarking on Your Data Science Journey: A Beginner's Guide

Are you intrigued by the world of data, eager to uncover insights hidden within vast datasets? If so, welcome to the exciting realm of data science! At the heart of this field lies Python programming, a versatile and powerful tool that enables you to manipulate, analyze, and visualize data. Whether you're a complete beginner or someone looking to expand their skill set, this guide will walk you through the basics of Python programming for data science in simple, easy-to-understand terms.

1. Understanding Data Science and Python

Before we delve into the specifics, let's clarify what data science is all about. Data science involves extracting meaningful information and knowledge from large, complex datasets. This information can then be used to make informed decisions, predict trends, and gain valuable insights.

Python, a popular programming language, has become the go-to choice for data scientists due to its simplicity, readability, and extensive libraries tailored for data manipulation and analysis.

2. Installing Python

The first step in your data science journey is to install Python on your computer. Fortunately, Python is free and can be easily downloaded from the official website, python.org. Choose the version compatible with your operating system (Windows, macOS, or Linux) and follow the installation instructions.

3. Introduction to Jupyter Notebooks

While Python can be run from the command line, using Jupyter Notebooks is highly recommended for data science projects. Jupyter Notebooks provide an interactive environment where you can write and execute Python code in a more user-friendly manner. To install Jupyter Notebooks, use the command pip install jupyterlab in your terminal or command prompt.

4. Your First Python Program

Let's create your very first Python program! Open a new Jupyter Notebook and type the following code:

python

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print("Hello, Data Science!")

To execute the code, press Shift + Enter. You should see the phrase "Hello, Data Science!" printed below the code cell. Congratulations! You've just run your first Python program.

5. Variables and Data Types

In Python, variables are used to store data. Here are some basic data types you'll encounter:

Integers: Whole numbers, such as 1, 10, or -5.

Floats: Numbers with decimals, like 3.14 or -0.001.

Strings: Text enclosed in single or double quotes, such as "Hello" or 'Python'.

Booleans: True or False values.

To create a variable, simply assign a value to a name. For example:

python

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age = 25

name = "Alice"

is_student = True

6. Working with Lists and Dictionaries

Lists and dictionaries are essential data structures in Python. A list is an ordered collection of items, while a dictionary is a collection of key-value pairs.

Lists:

python

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fruits = ["apple", "banana", "cherry"]

print(fruits[0]) # Accessing the first item

fruits.append("orange") # Adding a new item

Dictionaries:

python

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person = {"name": "John", "age": 30, "is_student": False}

print(person["name"]) # Accessing value by key

person["city"] = "New York" # Adding a new key-value pair

7. Basic Data Analysis with Pandas

Pandas is a powerful library for data manipulation and analysis in Python. Let's say you have a dataset in a CSV file called data.csv. You can load and explore this data using Pandas:

python

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import pandas as pd

# Load the data into a DataFrame

df = pd.read_csv("data.csv")

# Display the first few rows of the DataFrame

print(df.head())

8. Visualizing Data with Matplotlib

Matplotlib is a versatile library for creating various types of plots and visualizations. Here's an example of creating a simple line plot:

python

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import matplotlib.pyplot as plt

# Data for plotting

x = [1, 2, 3, 4, 5]

y = [2, 4, 6, 8, 10]

# Create a line plot

plt.plot(x, y)

plt.xlabel('X-axis')

plt.ylabel('Y-axis')

plt.title('Simple Line Plot')

plt.show()

9. Further Learning and Resources

As you continue your data science journey, there are countless resources available to deepen your understanding of Python and its applications in data analysis. Here are a few recommendations:

Online Courses: Platforms like Coursera, Udemy, and DataCamp offer beginner-friendly courses on Python for data science.

Books: "Python for Data Analysis" by Wes McKinney and "Automate the Boring Stuff with Python" by Al Sweigart are highly recommended.

Practice: The best way to solidify your skills is to practice regularly. Try working on small projects or participating in Kaggle competitions.

Conclusion

Embarking on a journey into data science with Python is an exciting and rewarding endeavor. By mastering the basics covered in this guide, you've laid a strong foundation for exploring the vast landscape of data analysis, visualization, and machine learning. Remember, patience and persistence are key as you navigate through datasets and algorithms. Happy coding, and may your data science adventures be fruitful!

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Developing and Deploying AI/ML Applications on Red Hat OpenShift AI (AI268)

As artificial intelligence (AI) and machine learning (ML) become central to enterprise innovation, organizations are seeking platforms and tools that streamline the development, deployment, and management of intelligent applications. Red Hat OpenShift AI (formerly known as Red Hat OpenShift Data Science) provides a robust, scalable, and secure foundation for building intelligent applications — and the AI268 course is your gateway to mastering this powerful ecosystem.

In this blog post, we'll explore what the AI268 – Developing and Deploying AI/ML Applications on Red Hat OpenShift AI course offers, who it’s for, and why it’s crucial for modern data scientists, ML engineers, and developers working in hybrid cloud environments.

What is Red Hat OpenShift AI?

Red Hat OpenShift AI is an enterprise-ready platform that brings together tools for the entire AI/ML lifecycle — from model development to training, deployment, monitoring, and retraining. Built on OpenShift, Red Hat’s industry-leading Kubernetes platform, OpenShift AI integrates open source AI frameworks, Jupyter notebooks, model serving frameworks, and MLOps tools like KServe and Kubeflow Pipelines.

It’s designed to:

Accelerate AI/ML development with pre-integrated tools.

Enable collaboration between data scientists and developers.

Simplify deployment of models to production environments.

Ensure compliance, scalability, and lifecycle management.

About the AI268 Course

Course Name: Developing and Deploying AI/ML Applications on Red Hat OpenShift AI Course Code: AI268 Delivery: Classroom, Virtual, or Self-paced (via Red Hat Learning Subscription) Duration: 4 days (may vary based on delivery mode) Skill Level: Intermediate to Advanced

What You’ll Learn

AI268 is a hands-on course that covers the entire journey of AI/ML application development within the OpenShift AI platform. Participants will learn how to:

Use JupyterLab for exploratory data analysis and model development.

Leverage OpenShift AI components like Pipelines, Workbenches, and Model Serving.

Train, deploy, and monitor models in a containerized, Kubernetes-native environment.

Implement MLOps practices for versioning, automation, and reproducibility.

Work collaboratively across roles — from data science to operations.

Key Topics Covered

Introduction to OpenShift AI and its architecture

Building models using Jupyter notebooks and popular ML libraries (e.g., scikit-learn, PyTorch)

Automating training workflows with Kubeflow Pipelines and OpenShift Pipelines

Model serving using KServe

Version control and experiment tracking with MLflow

Securing and scaling AI/ML workloads in hybrid cloud environments

Who Should Take This Course?

This course is ideal for:

Data Scientists looking to transition from local development to scalable, production-grade platforms.

Machine Learning Engineers who want to operationalize ML pipelines.

DevOps and Platform Engineers supporting AI workloads on Kubernetes.

IT Architects interested in building secure and scalable AI/ML platforms.

Prerequisites include a solid understanding of data science fundamentals, Python, and container concepts. Familiarity with Kubernetes or OpenShift is recommended but not mandatory.

Why Choose Red Hat OpenShift AI for Your AI/ML Journey?

Red Hat OpenShift AI enables teams to bring AI/ML applications from research to production with consistency and reliability. Whether you're building predictive analytics tools, real-time inference engines, or large-scale ML platforms, OpenShift AI gives you the tools to innovate without compromising security or compliance.

AI268 equips you with the skills to thrive in this environment — by aligning data science workflows with enterprise IT standards.

Take the Next Step

Ready to accelerate your career in AI/ML and bring real business value to your organization? The AI268 course will help you:

✅ Develop AI/ML applications faster ✅ Deploy models at scale with confidence ✅ Implement MLOps best practices in OpenShift ✅ Prepare for Red Hat certification paths in AI/ML

Explore Red Hat’s Learning Subscription to access this course and others, or reach out to us at HawkStack Technologies — a Red Hat Training Partner — to enroll in the next batch.

🚀 Empower Your AI/ML Teams with Red Hat OpenShift AI

Whether you're starting your AI/ML journey or scaling up existing models, AI268 helps bridge the gap between innovation and implementation. Let Red Hat OpenShift AI be your platform for intelligent enterprise applications.

For more details www.hawkstack.com