#data analytics & ai

[victusinveritas]

[michaelmusselman]

[sssupplychain]

What is Supply Chain Optimization?

Supply chain optimization enhances performance and efficiency in a supply network using technology like AI, IoT, and blockchain. It is profitable for the companies and leads to supply chain sustainability.

Vital Phases of Supply Chain Optimization Process:

Vital phases that occur within a successful supply chain optimization process include:

Supply Chain Design - It states network design processes like the location of warehouse facilities and inventory flow between them. It also describes the planning and scheduling of manufacturing operations and demand forecasting.

Supply Chain Planning – It describes the deployment plan of a strategic supply chain, inventory planning, and assets coordination to optimize goods delivery.

Supply Chain Execution – It focuses on warehouse and inventory management, transportation management, global trade management, and other execution applications.

Importance of Supply Chain Optimization

An aptly designed supply chain optimization process manages the following issues:

Supply chain optimization through connected technology provides solutions for informational silos

It helps organizations meet and exceed customer expectations

Allows companies to build competitive advantage and protect the brand

Make supply chain agile and help companies remain competitive

Enable supply chain to have substantial environmental and social impacts  

[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.

[atozofsoftwareengineering]

Intelligent Automated Risk Management (IARM): Enhancing Risk Identification and Decision-Making

Unlock the future of risk management with Intelligent Automation: smarter, faster, and more proactive. #RiskManagement #AI #MachineLearning #Automation #BigData #Fintech #Healthcare #CyberSecurity

Introduction In an era where businesses face a myriad of risks—from financial uncertainties to cyber threats—traditional risk management approaches often struggle to keep up with the pace and complexity of emerging risks. Intelligent Automated Risk Management (IARM) offers a transformative approach by integrating cutting-edge technologies to enhance risk identification, assessment, and…

[careerlaunchpad]

Cognizance IIT Roorkee Internship and Training Program

Registration Link : https://forms.gle/E2cHdnjyzYytKxC39

[tshirtsbysdesignt]

what u think, to much colour, or less?

https://sdesignt.threadless.com/

[pandeypankaj]

How do I learn Python in depth?

Improving Your Python Skills

  Writing Python Programs Basics: Practice the basics solidly. 

  Syntax and Semantics: Make sure you are very strong in variables, data types, control flow, functions, and object-oriented programming. 

 Data Structures: Be able to work with lists, tuples, dictionaries, and sets, and know when to use which. 

 Modules and Packages: Study how to import and use built-in and third-party modules. 

Advanced Concepts

Generators and Iterators: Know how to develop efficient iterators and generators for memory-efficient code. 

Decorators: Learn how to dynamically alter functions using decorators. 

Metaclasses: Understand how classes are created and can be customized. 

Context Managers: Understand how contexts work with statements. 

Project Practice 

 Personal Projects: You will work on projects that you want to, whether building a web application, data analysis tool, or a game.

 Contributing to Open Source: Contribute to open-source projects in order to learn from senior developers. Get exposed to real-life code. 

 Online Challenges: Take part in coding challenges on HackerRank, LeetCode, or Project Euler. 

 Learn Various Libraries and Frameworks

 Scientific Computing: NumPy, SciPy, Pandas

 Data Visualization: Matplotlib, Seaborn

 Machine Learning: Scikit-learn, TensorFlow, PyTorch

 Web Development: Django, Flask

Data Analysis: Dask, Airflow

Read Pythonic Code

 Open Source Projects: Study the source code of a few popular Python projects. Go through their best practices and idiomatic Python. 

 Books and Tutorials: Read all the code examples in books and tutorials on Python. 

 Conferences and Workshops

  Attend conferences and workshops that will help you further your skills in Python. PyCon is an annual Python conference that includes talks, workshops, and even networking opportunities. Local meetups will let you connect with other Python developers in your area. 

Learn Continuously

 Follow Blogs and Podcasts: Keep reading blogs and listening to podcasts that will keep you updated with the latest trends and developments taking place within the Python community.

Online Courses: Advanced understanding in Python can be acquired by taking online courses on the subject.

 Try It Yourself: Trying new techniques and libraries expands one's knowledge.

Other Recommendations

 Readable-Clean Code: For code writing, it's essential to follow the style guide in Python, PEP 

Naming your variables and functions as close to their utilization as possible is also recommended.

 Test Your Code: Unit tests will help in establishing the correctness of your code.

 Coding with Others: Doing pair programming and code reviews would provide you with experience from other coders.

 You are not Afraid to Ask for Help: Never hesitate to ask for help when things are beyond your hand-on areas, be it online communities or mentors.

These steps, along with consistent practice, will help you become proficient in Python development and open a wide range of possibilities in your career.

[mvishnukumar]

Can statistics and data science methods make predicting a football game easier?

Hi,

Statistics and data science methods can significantly enhance the ability to predict the outcomes of football games, though they cannot guarantee results due to the inherent unpredictability of sports. Here’s how these methods contribute to improving predictions:

Data Collection and Analysis: 

Collecting and analyzing historical data on football games provides a basis for understanding patterns and trends. This data can include player statistics, team performance metrics, match outcomes, and more. Analyzing this data helps identify factors that influence game results and informs predictive models.

Feature Engineering:

 Feature engineering involves creating and selecting relevant features (variables) that contribute to the prediction of game outcomes. For football, features might include team statistics (e.g., goals scored, possession percentage), player metrics (e.g., player fitness, goals scored), and contextual factors (e.g., home/away games, weather conditions). Effective feature engineering enhances the model’s ability to capture important aspects of the game.

Predictive Modeling: 

Various predictive models can be used to forecast football game outcomes. Common models include:

Logistic Regression: This model estimates the probability of a binary outcome (e.g., win or lose) based on input features.

Random Forest: An ensemble method that builds multiple decision trees and aggregates their predictions. It can handle complex interactions between features and improve accuracy.

Support Vector Machines (SVM): A classification model that finds the optimal hyperplane to separate different classes (e.g., win or lose).

Poisson Regression: Specifically used for predicting the number of goals scored by teams, based on historical goal data.

Machine Learning Algorithms: 

Advanced machine learning algorithms, such as gradient boosting and neural networks, can be employed to enhance predictive accuracy. These algorithms can learn from complex patterns in the data and improve predictions over time.

Simulation and Monte Carlo Methods: 

Simulation techniques and Monte Carlo methods can be used to model the randomness and uncertainty inherent in football games. By simulating many possible outcomes based on historical data and statistical models, predictions can be made with an understanding of the variability in results.

Model Evaluation and Validation: 

Evaluating the performance of predictive models is crucial. Metrics such as accuracy, precision, recall, and F1 score can assess the model’s effectiveness. Cross-validation techniques ensure that the model generalizes well to new, unseen data and avoids overfitting.

Consideration of Uncertainty: 

Football games are influenced by numerous unpredictable factors, such as injuries, referee decisions, and player form. While statistical models can account for many variables, they cannot fully capture the uncertainty and randomness of the game.

Continuous Improvement: 

Predictive models can be continuously improved by incorporating new data, refining features, and adjusting algorithms. Regular updates and iterative improvements help maintain model relevance and accuracy.

In summary, statistics and data science methods can enhance the ability to predict football game outcomes by leveraging historical data, creating relevant features, applying predictive modeling techniques, and continuously refining models. While these methods improve the accuracy of predictions, they cannot eliminate the inherent unpredictability of sports. Combining statistical insights with domain knowledge and expert analysis provides the best approach for making informed predictions.

[pleasantinternetfest]

Data Fragmentation

This is the list of challenges that the businesses face due to data distribution across multiple systems in the digital lending industry

[emerging-jew]

Cloud modernization is out, generative AI is in

[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)

[digitalsoftware]

GeminAi Review 2024 - World's 1st True Google's Gemini Powered App

GeminAi Review 2024

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[butchgtow]

one of the most effective means of ending the digitization of socioeconomic systems of discrimination in the U.S. would have been to boycott Citigroup and its subsidiaries + Citadel LLC and its subsidiaries.

[newfangled-polusai]

Top 5 Benefits of Low-Code/No-Code BI Solutions

Low-code/no-code Business Intelligence (BI) solutions offer a paradigm shift in analytics, providing organizations with five key benefits. Firstly, rapid development and deployment empower businesses to swiftly adapt to changing needs. Secondly, these solutions enhance collaboration by enabling non-technical users to contribute to BI processes. Thirdly, cost-effectiveness arises from reduced reliance on IT resources and streamlined development cycles. Fourthly, accessibility improves as these platforms democratize data insights, making BI available to a broader audience. Lastly, agility is heightened, allowing organizations to respond promptly to market dynamics. Low-code/no-code BI solutions thus deliver efficiency, collaboration, cost savings, accessibility, and agility in the analytics landscape.