#DataScientist

[scientificworldresearch]

The Best Paper Award recognizes outstanding published research that demonstrates originality, impact, and scholarly excellence, celebrating contributions that advance knowledge and inspire innovation across academic and professional communities.  

Scientific World Research Awards    Visit our page : https://scientificworld.net/   Nominations page : https://scientificworld.net/award-nominati...   

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[d0nutzgg]

Predicting Alzheimer's With Machine Learning

Alzheimer's disease is a progressive neurodegenerative disorder that affects millions of people worldwide. Early diagnosis is crucial for managing the disease and potentially slowing its progression. My interest in this area is deeply personal. My great grandmother, Bonnie, passed away from Alzheimer's in 2000, and my grandmother, Jonette, who is Bonnie's daughter, is currently exhibiting symptoms of the disease. This personal connection has motivated me to apply my skills as a data scientist to contribute to the ongoing research in Alzheimer's disease.

Model Creation

The first step in creating the model was to identify relevant features that could potentially influence the onset of Alzheimer's disease. After careful consideration, I chose the following features: Mini-Mental State Examination (MMSE), Clinical Dementia Rating (CDR), Socioeconomic Status (SES), and Normalized Whole Brain Volume (nWBV).

MMSE: This is a commonly used test for cognitive function and mental status. Lower scores on the MMSE can indicate severe cognitive impairment, a common symptom of Alzheimer's.

CDR: This is a numeric scale used to quantify the severity of symptoms of dementia. A higher CDR score can indicate more severe dementia.

SES: Socioeconomic status has been found to influence health outcomes, including cognitive function and dementia.

nWBV: This represents the volume of the brain, adjusted for head size. A decrease in nWBV can be indicative of brain atrophy, a common symptom of Alzheimer's.

After selecting these features, I used a combination of Logistic Regression and Random Forest Classifier models in a Stacking Classifier to predict the onset of Alzheimer's disease. The model was trained on a dataset with these selected features and then tested on a separate dataset to evaluate its performance.

Model Performance

To validate the model's performance, I used a ROC curve plot (below), as well as a cross-validation accuracy scoring mechanism.

The ROC curve (Receiver Operating Characteristic curve) is a plot that illustrates the diagnostic ability of a model as its discrimination threshold is varied. It is great for visualizing the accuracy of binary classification models. The curve is created by plotting the true positive rate (TPR) against the false positive rate (FPR) at various threshold settings.

The area under the ROC curve, often referred to as the AUC (Area Under the Curve), provides a measure of the model's ability to distinguish between positive and negative classes. The AUC can be interpreted as the probability that the model will rank a randomly chosen positive instance higher than a randomly chosen negative one.

The AUC value ranges from 0 to 1. An AUC of 0.5 suggests no discrimination (i.e., the model has no ability to distinguish between positive and negative classes), 1 represents perfect discrimination (i.e., the model has perfect ability to distinguish between positive and negative classes), and 0 represents total misclassification.

The model's score of an AUC of 0.98 is excellent. It suggests that the model has a very high ability to distinguish between positive and negative classes.

The model also performed extremely well in another test, which showed the model has a final cross-validation score of 0.953. This high score indicates that the model was able to accurately predict the onset of Alzheimer's disease based on the selected features.

However, it's important to note that while this model can be a useful tool for predicting Alzheimer's disease, it should not be the sole basis for a diagnosis. Doctors should consider all aspects of diagnostic information when making a diagnosis.

Conclusion

The development and application of machine learning models like this one are revolutionizing the medical field. They offer the potential for early diagnosis of neurodegenerative diseases like Alzheimer's, which can significantly improve patient outcomes. However, these models are tools to assist healthcare professionals, not replace them. The human element in medicine, including a comprehensive understanding of the patient's health history and symptoms, remains crucial.

Despite the challenges, the potential of machine learning models in improving early diagnosis leaves me and my family hopeful. As we continue to advance in technology and research, we move closer to a world where diseases like Alzheimer's can be effectively managed, and hopefully, one day, cured.

[scopethings-blog]

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[fraoula1]

Python for Data Science: From Beginner to Expert – A Complete Guide!

Python has become the go-to language for data science, thanks to its flexibility, powerful libraries, and strong community support. In this video, we’ll explore why Python is the best choice for data scientists and how you can master it—from setting up your environment to advanced machine learning techniques.

🔹 What You'll Learn:

✅ Why Python is essential for data science

✅ Setting up Python and key libraries (NumPy, Pandas, Matplotlib) ✅ Data wrangling, visualization, and transformation

✅ Building machine learning models with Scikit-learn

✅ Best practices to enhance your data science workflow 🚀 Whether you're a beginner or looking to refine your skills, this guide will help you level up in data science with Python. 📌 Don’t forget to like, subscribe, and hit the notification bell for more data science and Python content!

[grey-space-computing]

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[cellbiologist]

Molecular Biology

"Molecular biologists unravel the complexities of life at the most fundamental level, studying the structure and function of molecules that make up cells. Their work advances our understanding of genetics, protein interactions, and cellular processes, driving breakthroughs in medicine, biotechnology, and environmental science. Through cutting-edge research, molecular biologists are at the forefront of discovering the molecular mechanisms that govern life, contributing to innovations that shape the future of science and healthcare."

[courseswebs]

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[excelworld]

[thebattlefordatasupremacy]

Data Engineer vs. Data Scientist The Battle for Data Supremacy

In the rapidly evolving landscape of technology, two professions have emerged as the architects of the data-driven world: Data Engineers and Data Scientists. In this comparative study, we will dive deep into the worlds of these two roles, exploring their unique responsibilities, salary prospects, and essential skills that make them indispensable in the realm of Big Data and Artificial Intelligence.

The world of data is boundless, and the roles of Data Engineers and Data Scientists are indispensable in harnessing its true potential. Whether you are a visionary Data Engineer or a curious Data Scientist, your journey into the realm of Big Data and AI is filled with infinite possibilities. Enroll in the School of Core AI’s Data Science course to day and embrace the future of technology with open arms.

[techblog-365]

WHAT IS THE PURPOSE OF DATA SCIENCE?

Data Science's main aim is to identify trends inside data. In order to Analyze and draw lessons from the results, it utilizes different statistical techniques. A Data Scientist should carefully scrutinize the information from data acquisition, wrangling and pre-processing. Then, from the details, he has the duty to make predictions. To read more visit: https://www.rangtech.com/blog/data-science/what-is-the-purpose-of-data-science

[scientificworldresearch]

Microbes Help Plants Beat the Shade

Shade avoidance responses are defined as the plastic responses of plants to neighboring shading signals through changes in the light spectrum, which limit planting density in modern agricultural practices.

Scientific World Research Awards Visit our page : https://scientificworld.net/ Nominations page : https://scientificworld.net/award-nomination/?ecategory=Awards&rcategory=Awardee

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[jnitupdates]

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[scopethings-blog]

Scope Computers

Unlock Your Future with Data Science!

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[job24by7]

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[metallurgicalengineering]

Prof. Dr. Qiangqiang Tan | Institute of Process Engineering，Chinese Academy of Sciences | China

"This award reflects the brilliance and passion you bring into everything you do."

"Congratulations on your well-deserved success!"

Metallurgical Engineering Awards -- The Metallurgical Engineering Awards are prestigious accolades that recognize excellence, innovation, and contributions in the field of metallurgy and materials science. These awards celebrate professionals, researchers, and innovators who have advanced the understanding, application, and sustainability of metallurgical practices. Categories may include achievements in extractive metallurgy, alloy development, materials characterization, and sustainable engineering

Get Connected

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[pangaeax]

Infographic - Freelance Data Analyst vs. Data Scientist Who Delivers Better ROI?

Thinking about hiring a freelance data expert?

Before you pick between a Data Analyst and a Data Scientist, you should really ask:

Who gives you more ROI for the project you’re working on?

Dashboards vs. predictions. Speed vs. complexity. $5K vs. $50K pipelines. Here’s a visual comparison to help you decide wisely 👇

TL;DR:

Analysts = great for fast, visual insights and tight budgets

Scientists = great for complex ML models and predictive workflows

Right choice = more ROI, fewer delays, and less burn

Best Use Cases:

Hire a Freelance Data Analyst if:

You need dashboards, reports, or cleaned-data insights

Your budget is under $5,000

You want something shipped this week

Hire a Data Scientist if:

You're building ML systems or predictive engines

You’ve already got structured data pipelines

You’re looking to automate, not just analyze

Need Help Finding the Right One?

Check out Pangaea X — the only platform just for Data Analytics & AI freelancers.