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Ryan T.

Knoxville, Tennessee

$65/hr
5.0
22 jobs

I help colleges, nonprofits, and data-heavy teams turn messy data into clear dashboards and research-backed insights. If you need someone who can build the data pipeline, run the analysis, and explain the results in plain language, I can help. I’m Ryan Tennis, an Institutional Research Analyst with over five years of experience in institutional research, data analytics, and reporting. At the University of Tennessee, I support strategic analysis through dashboard development, retention and graduation reporting, and improvements to data governance. I work with tools like Power BI, SQL, and R to build systems that help academic units and leadership teams make informed decisions. Previously, I led research efforts at Modesto Junior College and the University of the Pacific. My work there included: -Building predictive models to understand retention and completion -Automating reporting workflows so teams were not stuck in manual spreadsheets -Converting static reports into interactive dashboards for leadership and program review These projects helped teams monitor student outcomes, improve internal processes, and align their work with broader institutional goals. I have also trained staff on tools like Power BI, Tableau, Excel, and SQL so departments can maintain their own reports and dashboards. My focus is on solutions that are sustainable and practical, not one-off files that only I can fix. I’m currently pursuing a PhD in Evaluation, Statistics, and Methodology at the University of Tennessee. My research interests line up with the work I do every day, especially around program evaluation, applied statistical methods, and turning complex findings into straightforward recommendations. Outside of my full-time role, I run DataScienceHive.com, where I share examples of my work and offer consulting and training for organizations that want to make better use of their data. On Upwork, I can help with: -Cleaning and structuring data from spreadsheets or databases -Building or improving dashboards in Power BI, Tableau, or Google Sheets -Predictive modeling and outcome analysis in R or Python -Survey and assessment design, scoring, and analysis -Writing clear summaries and reports for non-technical stakeholders I can provide references from directors of institutional research who can speak to the quality and reliability of my work.

  • R
  • Data Analysis
  • Analytical Presentation
  • Microsoft Power BI
  • Microsoft Power BI Data Visualization
  • Tableau
  • Microsoft Excel
  • Python
  • SQL
  • Data Analytics
  • Statistical Analysis
  • IBM SPSS
  • Research & Development
  • Microsoft Excel PowerPivot
  • Power Query
Rachel D.

Durham, North Carolina

$370/hr
4.9
190 jobs

Top 1% Expert-Vetted Freelancer. I have a PhD in Computer Science (AI & Machine Learning) and I am a physician. I build custom AI systems with proprietary and public data, including creation of novel datasets and state-of-the-art models. * After a 30-minute to 1-hour initial meeting, I'll craft a clearly written report outlining a unique AI strategy for your project. * For long-term engagements, I implement AI solutions end-to-end, including data collection, data cleaning, novel model design, model implementation and training in Python, and iteration on the modeling and data strategy to achieve excellent performance. * One of my specialties is creating custom models for proprietary datasets, including images, videos, audio recordings, and domain-specific data. Book a 30-minute consultation with me to get immediate answers about your AI project: - How can you leverage AI in your organization? - What kind of AI do you need to solve a particular problem? - Should you use a model like ChatGPT/Claude or do you need a custom model? - What modeling strategy should you use? - How should you collect and annotate data? - How much data will you need? - How should you measure performance? After a consultation I’ll craft a clearly-written report outlining an AI strategy for your project. Specialties: Healthcare AI: I am an expert in healthcare AI, with a unique background that blends clinical understanding from my MD, deep technical expertise from my AI PhD, and business experience. I have completed 100+ engagements across medical specialties including radiology, dermatology, cardiology, mental health, family medicine, surgery, dentistry, physical therapy, women’s health, pediatrics, nutrition, and neurology. I have created datasets and custom models for diverse forms of biomedical data including x-rays, CTs, MRIs, clinical photographs, patient videos, medical audio, medical notes, EHR data, insurance claims, omics data, and more. Computer Vision: I create custom AI models for 2D images, 3D images, and videos on proprietary datasets. I am excited about projects from multiple industries including healthcare, manufacturing, automotive, and agriculture. I have extensive experience with classification, object detection, segmentation, and keypoint detection, to identify and localize abnormalities or features of interest. I developed the first machine learning model in the world to predict multiple abnormalities simultaneously from a CT scan. For a dermatology practice, I built the first CV model to predict Fitzpatrick skin type, pigmentation, redness, and wrinkle severity (mean accuracy 85%)—now a core model at Kesty AI. Artificial Intelligence R&D: I have led multiple AI research projects across industry and academia. I have developed novel AI methods, including HiResCAM, a convolutional neural network explanation method with mathematical guarantees. I have published original research across multiple areas of AI, including computer vision, natural language processing, explainable AI, expert systems, and applied AI. My research papers have been cited over 1,000 times. My healthcare AI blog Glass Box has over 700,000 readers from 185 countries. Natural Language Processing (NLP): I have a deep understanding of large language models (LLMs) like Claude, Gemini, ChatGPT, and Llama. I have leveraged Transformers and other NLP techniques for numerous applications, including customized chatbots, medical note generation, and structured information extraction. Advising Entrepreneurs: Before focusing on AI consulting, I spent seven years as the founder of a health AI startup. I led my previous company from concept to deployed B2B SaaS product serving medical practices. Our AI history-taking assistant and AI scribe saved clinicians 2+ hours daily. I managed engineering teams of 5-10 (60+ contributors), secured two U.S. patents, and raised competitive grant funding. I enjoy working with entrepreneurs and discussing pitch decks, fundraising, customer discovery, designing an MVP, and evaluating the ROI of an AI product. If you’d like to talk with me about your AI project, please feel free to send me a message or book a consultation using the link on my profile.

  • Natural Language Processing
  • PyTorch
  • Computer Vision
  • TensorFlow
  • Python
  • Machine Learning Model
  • Machine Learning
  • Neural Network
  • Convolutional Neural Network
  • Scientific Research
  • Scientific Writing
  • Artificial Intelligence
  • Medical Imaging
  • Machine Learning Framework
  • Research Methods
Tayyab R.

Sahiwal, Pakistan

$20/hr
4.7
13 jobs

Research Publications | AI Development | Data Analysis I'm a Data Scientist with 2+ years of experience in Data Scraping, Data Analysis, Data Visualization, Machine Learning, Deep Learning, Computer Vision, and NLP. I work primarily in Python and R/RStudio. What I Do: 📊 Statistical Analysis, Time Series Analysis, Quantitative & Predictive Analysis 🤖 Designing, developing, and fine-tuning ML/DL models that deliver real impact 🚀 Building models from scratch or optimizing existing ones Background: Currently pursuing a Master's in Data Science with hands-on experience across Machine Learning, Deep Learning, and Advanced Analytics. Technical Skills: Languages: Python 🐍, R 📈 Frameworks: TensorFlow, Keras, PyTorch, Scikit-learn, Hugging Face Transformers, XGBoost, LightGBM, CatBoost, OpenCV, FastAPI, Flask, Streamlit Libraries: NumPy, Pandas, Matplotlib, Seaborn, Plotly, Statsmodels, SciPy Data Tools: Selenium, Scrapy, SQL, Power BI, Tableau, AWS (S3, Glue, Redshift), GCP, Azure ML Others: MATLAB, Git, Docker, Google Earth Engine Project Experience: I've delivered projects across healthcare, finance, computer vision, and geospatial analytics. Some highlights: Built deep learning models for drought prediction using 20 years of satellite data Developed medical image classification systems for cervical cancer detection using DenseNet, ResNet, and EfficientNet architectures Designed sales forecasting pipelines and stock market analysis dashboards with interactive visualizations Created an AI voice assistant for paramedics Built a real-time multi-camera object tracking system Developed a student pressure dashboard powered by D3.js Explored XAI methods (GradCam, SHAP, LIME) for model interpretability Implemented models for churn prediction and match outcome forecasting These projects combine machine learning, deep learning, and data science to deliver practical, impactful solutions. What I Offer: ✅ Machine Learning Models: Build, fine-tune & deploy high-performance ML solutions ✅ Deep Learning Architectures: Custom DL models tailored to your needs ✅ Python Programming: Clean, optimized code for ML & data science tasks ✅ Data Analytics & Visualization: Insightful analysis and compelling visuals ✅ Data Scraping & Parsing: Data extraction using BeautifulSoup, Scrapy, and Selenium Why Work With Me: ✨ Strong expertise in both ML and DL ✨ Clear communication throughout projects ✨ Fast delivery with professional support ✨ Focus on quality and client satisfaction I take pride in delivering solutions that work. My approach is straightforward: build powerful models, provide actionable insights, and ensure clients are happy with the results. Let's Work Together: If you need someone who can take your machine learning projects to the next level, I'm here to help turn your ideas into reality. 🚀

  • R
  • Data Analysis
  • Data Visualization
  • Python
  • Computer Vision
  • Data Science
  • Deep Learning
  • Azure Machine Learning
  • Data Scraping
  • Data Mining
  • Quantitative Analysis
  • Statistical Analysis
  • Machine Learning
  • Natural Language Processing
  • YOLO
  • FastAPI
  • LangChain
Dimitris E.

Kaisariani, Greece

$30/hr
5.0
8 jobs

📊 I hold a PhD in Economics with expertise in empirical econometrics, complemented by five years of professional experience in economic analysis and data analytics. My background also includes extensive work in time series forecasting and machine learning applications. 📈 I have collaborated with professionals across the stock market and betting industries to deliver projects forecasting stock market indices and soccer outcomes. 🛠 Technical Skills & Expertise: ✅ Data Analysis: Proficient in data processing, cleaning, and exploratory data analysis using pandas and numpy to derive actionable insights. ✅ Data Visualization: Skilled in effectively communicating findings through tools such as matplotlib, seaborn, and interactive tools such as Streamlit. ✅ Machine Learning: Experienced in creating Machine Learning forecasts and reports using various packages, including scikit-Learn, keras, and statsmodels. ✅ Econometrics: Demonstrated academic success in empirical econometric projects, specializing in causal inference with panel data and employing models such as propensity score matching and difference-in-differences, as well as time series forecasting models (ARIMA/VAR). ✅ Research: Experienced in conducting rigorous empirical research in economics, applying statistical and econometric methods to analyze large datasets. Skilled in extracting meaningful insights and translating them into actionable economic conclusions. Published in peer-reviewed journals on firms' competitiveness. Also experienced in academic writing and research dissemination.

  • R
  • Stata
  • Big Data
  • Statistics
  • Python
  • Data Science
  • Machine Learning
  • Econometrics
  • LaTeX
  • Economics
Juan Manuel S.

Cordoba, Argentina

$34/hr
5.0
29 jobs

- I specialize in R and its scientific ecosystem, particularly the tidyverse. - I also have extensive experience with Fortran, especially for performance-intensive tasks. - With more than 12 years working with academic documents in LaTeX, I’ve contributed to theses, research papers, and scientific publications. - I’m skilled at optimizing scientific code, improving both execution time and memory usage through profiling and efficient algorithm design. - I believe that clear and consistent communication is key to delivering top-quality work—let’s keep the conversation going! - My combined expertise in R and Fortran allows me to develop robust, efficient solutions for applications ranging from statistical modeling to numerical simulations. -N8N: My combined experience in programming and editing allows me to build clear, efficient, and reliable automations. I create smart workflows using n8n, designed specifically to meet real business and project needs.

  • R
  • Astronomy
  • Python Script
  • Fortran
  • Data Analytics & Visualization Software
  • Scientific & Technical Services
  • Inkscape
  • LaTeX
  • Ubuntu
Yuldashev M.

Incheon, South Korea

$20/hr
5.0
54 jobs

33+ Successful Projects • 100% Job Success Top Rated 🎯 Computer Vision — YOLOv8/11, Faster R-CNN, U-Net, DeepLabV3+ 📍 Multi-Object Tracking — SORT, DeepSORT, ByteTrack 💬 LLM Applications — RAG, LangChain, Hugging Face, LoRA/QLoRA ⚡ Real-Time AI Deployment — OpenCV, PyTorch, TensorFlow 📈 Scalable Search — FAISS, vector DBs for face & document retrieval 🤖 LLM-Powered Telegram Bots — intelligent assistants & automation Why Work With Me 🟢 25+ successful projects with 100% Job Success (Top Rated) 🟢 Up to 90% automation through AI & workflow optimization 🟢 Scalable, production-grade systems (millions of records, real-time pipelines) 🟢 True end-to-end delivery — strategy → data → model → deployment 🟢 On-time delivery with measurable business impact 🟢 Clear, reliable communication throughout the project 📩 Message me for a free consultation — I’ll help you outline the best technical approach for your project.

  • Machine Learning
  • OpenCV
  • Deep Learning
  • PyTorch
  • TensorFlow
  • SQL
  • pandas
  • Matplotlib
  • Convolutional Neural Network
  • Neural Network
  • LLaMA
  • NLP Tokenization
  • OpenAI API
  • Vision Transformer
  • Hugging Face

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R vs. Java vs. Python: Which Is Right for Your Project?

When it comes to data science, there’s no one best programming language. There are a few standouts, however, each with its own specialties, as well as packages, libraries, and extensions that further enhance their capabilities.

In this article, we’re going to take a closer look at three of the most popular languages used by data scientists: Java, Python, and R. You’ll learn the basics of each, as well as how to tell which one is right for your data needs.

R: beloved by data scientists

Originally developed by statisticians as an open-source alternative to expensive suites of statistical software like SAS and MATLAB, R is one of the most popular languages for data analysis. It’s been likened to Excel on steroids, able to sift through reams of data, execute sophisticated analyses, and produce publication-quality graphs and tables. What makes R special? In short, it’s a tool built with data analysis in mind.

As data science has become critical to many businesses, R’s popularity has skyrocketed. Organizations as large and diverse as Google, Facebook, Microsoft, Bank of America, and the National Weather Service have all turned to R for reporting, analysis, and visualization.

A key component of R is that, unlike object-oriented programming languages like Java or Python, R is a procedural language, meaning it relies on a series of step-by-step subroutines to execute a programming task. The key difference here is that R uses procedures to operate on data, where object-oriented programming bundles procedures and data together as parts of objects. The advantage of procedural programming is that it gives clear visibility into complex operations with lots of dependencies, which can be important for many data analysis tasks. The tradeoff is that this often requires more lines of code than object-oriented languages.

Another benefit of R? It’s supported by a vibrant community of developers, especially academic statisticians and data scientists.

Java: speed at scale

Java is powerful, portable, and scalable, which makes the platform perfect for building enterprise-scale applications and supporting rapid growth. Java also includes many tools, collectively known as the Java Platform. This robust, open-source development environment includes libraries, frameworks, APIs, the Java Runtime Environment, Java plug-ins, and the Java Virtual Machine (JVM). Taken together, these tools simplify coding with Java and support development at every level, giving developers everything they need to build Java web systems and applications.

Java’s speed allows it to outperform other languages and frameworks, which is a big part of why it’s so well suited to large-scale applications. These performance gains are what prompted Twitter to shift its search engine to Java from Ruby on Rails and move more of its back-end stack to the Java Virtual Machine.

Another key component of Java is that it comes as close to being 100% object-oriented as you can get. With that comes all the benefits of object-oriented programming, from ease of development to modular software to flexibility and extensibility. As one of the most widely known programming languages, it’s easy to find and hire talented developers. What’s more, Java’s massive community of developers means that there’s lots of excellent documentation around.

Python: built for flexibility

Like Java, Python is built to handle high-traffic sites. It’s fast and efficient, with an emphasis on code readability. Python’s motto is “there should be one—and preferably only one—obvious way to do it.” That can mean there’s a bit of a learning curve as developers learn the ins and outs of Python syntax, but the upside is an ability to express concepts with fewer lines of code than would be possible in languages like C++ or Java.

Python’s other great strength is an extensive set of libraries that allow it to perform a wide array of tasks. In particular, the libraries NumPy and matplotlib enable Python to perform many of the analysis and plotting functionalities of MATLAB. These libraries have since been built upon by a number of other libraries that extend Python’s functionality even further.

In short, Python represents a compromise between R and Java, combining the sophistication of the former with the speed and scalability of the latter.

Which language is right for your data needs?

The short answer is that it depends on the kind of work you’re trying to do. A good rule of thumb might be if your work is closer to mathematics and statistics, R is probably your best bet. If your work is closer to programming, go with Python, and if you’re building enterprise-size products, take a look at Java. That said, many data scientists are increasingly turning to combinations of languages that allow them to take advantage of the individual strengths of each.

R

Great For:

  • In-Depth Statistical Analysis. Given that R was developed by and for statisticians, it’s no surprise that R is ideally suited to in-depth statistical analysis, whether you’re working with sensor data from an IOT device or elaborate financial models. What’s more, it’s very well supported by the statistics community through the CRAN repository, which contains literally thousands of packages that enable you to perform more elaborate analysis and visualization tasks.
  • High-Quality Reporting. Well-produced images convey more than numbers alone, and R places a great emphasis on easily producing high-quality graphs and charts. On top of that, its basic capabilities can be extended with a number of packages, including ggplot2, ggvis, googleVis, and rCharts. The Shiny framework also allows you to turn those visuals into interactive web applications.

Not Great For:

  • Performance. R was designed with data scientists in mind, not computers. As such, R is considerably slower than Python or Java.
  • Creating large-scale data products. In these instances, data scientists will often prototype in R and then switch to a more flexible language like Java or Python for actual product development.
  • Ease of Learning. If your background is in math or statistics, R’s array-oriented syntax can make implementation relatively straightforward. If you have programming experience, however, this approach is likely to seem counterintuitive.

Java

Great For:

  • Excellent Performance on Large-Scale Systems. Java’s speed makes it best for building large-scale systems. While Python is significantly faster than R, Java provides even greater performance than Python. Speed and scalability are why Twitter, LinkedIn, and Facebook rely on Java as the backbone of their data engineering efforts.
  • Faster Development Time. The Java Virtual Machine (JVM) is a great environment for developing custom tools quickly. The programming language Scala runs on JVM and is popular with data scientists for its combination of object-oriented and functional programming.

Not Great For:

Statistical modeling and visualization. Between these three languages, Java is definitely the least suited to hardcore analysis. Though packages do exist to add some of these functions, they’re neither as advanced nor as well supported as the ones you’ll find for Python and R.

Python

Great For:

  • Workflow Integration. Python’s flexibility makes it a popular choice for developers who need to apply statistical techniques or data analysis in their work, or for data scientists whose tasks need to be integrated with web apps or production environments. If you’re looking for a single tool to manage your entire data-related workflow, Python is a great option.
  • Machine Learning. The combination of specialized machine learning libraries (like scikit-learn, PyBrain, and TensorFlow) and general purpose flexibility makes Python uniquely suited to developing sophisticated models and prediction engines that plug directly into the production system.

Not Great For:

  • Highly specialized data tasks. Though the Python community is catching up, there are still hundreds of R packages that have no Python equivalents. If you’re looking for very specific capabilities, you might be better off with R.

Hiring a data scientist?

Now that you understand the differences between some of the major languages in data science, who do you need to set up and maintain your data infrastructure? Data scientists come from a variety of backgrounds. Some specialize more in performing statistical analysis, while some are more focused on building products that interface directly with production systems. Explore data scientists on Upwork.