Hire the Best R Developers & Programmers
Athens, Georgia
Experienced business consultant with over a decade of helping small companies grow. I have 5 yrs + working with teams on data analysis/ML problems. Excellent analytical background paired with a deep business understanding to create solutions to problems instead of just using the latest tools for the sake of using them.
- R
- Python
- Statistical Analysis
- Data Analysis
- Analytical Presentation
- Machine Learning
- Deep Neural Network
- R Shiny
Knoxville, Tennessee
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
Istanbul, Turkey
As a highly skilled and experienced Python and R developer, bioinformatician, and data scientist, I am passionate about using technology to solve complex problems and uncover insights from large datasets. With a background in Bioengineering degree and data science projects, I have developed expertise in data analysis, visualization, and scripting with Python, R and Bash, and machine learning, including frameworks such as Keras, FastAI, Sklearn, Pytorch and AWS infrastructure. Whether you need help with python scripts, data analysis, bioinformatics, machine learning, or software development, I have the skills and experience to deliver high-quality work that meets your needs. I have a proven track record of delivering projects on time and within budget, and I am always looking for new challenges and opportunities to learn and grow. In addition to my technical skills, I am a clear and effective communicator who is committed to understanding your needs and delivering results that exceed your expectations. I am confident that I can help you achieve your goals and look forward to working with you on your next project.
- R
- Python
- Git
- pandas
- Docker
- Bioinformatics
- Data Analysis
- Genomics
- Data Visualization
- R Shiny
- Machine Learning
- Linux
- Scientific Writing
- Bash
- AWS CloudFormation
Kaisariani, Greece
I hold a PhD in Economics with expertise in empirical econometrics and five years of experience in economic analysis and data analytics. Additionally, I have extensive experience in time series forecasting and machine learning applications. 🛠 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 ggplot. ✅ 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. ✅ 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
Hyderabad, Pakistan
Are you looking for accurate data, automated workflows, or reliable web scraping solutions that save time and scale your business? I help businesses collect, organize, and automate data through a combination of Python development, web scraping, lead generation, and administrative support. With 4+ years of experience, I have worked with startups, agencies, e-commerce businesses, recruiters, and marketing teams to build custom data collection systems and automate repetitive processes. 🚀 What I Can Do For You 💠💠💠 Web Scraping & Data Extraction 💠💠💠 ✅ Google Maps Lead Extraction (Business Name, Address, Category, Email, Phone Number, Reviews, Ratings) ✅ E-commerce Data Scraping (Amazon, Shopify, eBay, Etsy, WooCommerce, and custom stores) ✅ Directory & Listing Website Scraping ✅ Review & Rating Extraction ✅ Web Crawling & Large-Scale Data Collection ✅ Dynamic & JavaScript Website Scraping ✅ Login-Protected Website Data Extraction ✅ Scheduled Scraping & Monitoring Solutions 💠💠💠 Lead Generation & Web Research 💠💠💠 ✅ B2B Lead Generation ✅ LinkedIn Research & Sales Navigator ✅ Contact List Building ✅ Email & Phone Number Collection ✅ Market Research & Competitor Analysis ✅ Data Enrichment & Validation 💠💠💠 Data Entry & Administrative Support 💠💠💠 ✅ Excel Data Entry & Data Cleaning ✅ PDF to Excel/Word Conversion ✅ CRM Data Management (HubSpot, Salesforce, Zoho) ✅ Product Uploading (Shopify & WordPress) ✅ Copy-Paste & Typing Tasks ✅ Form Filling & Survey Data Entry ✅ Data Organization & Management 💠💠💠 Automation & Workflow Solutions 💠💠💠 ✅ Python Automation Scripts ✅ Browser Automation ✅ Zapier Automation ✅ Make Workflows ✅ n8n Automation ✅ Pabbly Connect Integrations ✅ Automated Data Collection & Reporting ✅ Repetitive Task Automation 💠💠💠 Technical Skills 💠💠💠 • Python • Selenium • Scrapy • Playwright • BeautifulSoup • Requests • Pandas • Apify • Octoparse • ParseHub • PhantomBuster • Zapier • Make • n8n • Pabbly Connect • Airtable • Google Sheets 💠💠💠 Data Delivery Formats 💠💠💠 • Excel (XLSX) • CSV • JSON • SQL • Google Sheets • API-Ready Datasets All data is delivered clean, structured, and ready for immediate use. 💠💠💠 Industries I Support 💠💠💠 • Marketing & SEO Agencies • Lead Generation Companies • E-commerce Businesses • Real Estate Investors • Recruitment Agencies • Market Research Teams • SaaS Startups 🔒 Why Work With Me? ✔ 4+ Years of Experience ✔ Accurate & Detail-Oriented ✔ Fast Turnaround Times ✔ Scalable Automation Solutions ✔ Clear Communication ✔ Reliable & Deadline-Focused ✔ Long-Term Support Available Whether you need targeted leads, large-scale web scraping, workflow automation, or accurate data management, I can help you build efficient solutions that save time and improve results. Send me a message with your project requirements, website URL, or desired data fields, and I'll recommend the most efficient approach for your needs. I look forward to working with you.
- Automation
- Data Mining
- Data Extraction
- Data Scraping
- Web Crawling
- Web Scraping
- Python
- Data Entry
- Lead Generation
- CRM Software
- Microsoft Excel
- Google Sheets
- Company Research
- Product Listings
- WordPress
Eching, Germany
I work on genomics and biology projects, the kind where you have raw sequencing data, mass spec output, or protein sequences and you need someone to turn it into results you can publish or act on. I've done this across more than 67 projects on Upwork and through my PhD and postdoc, so I know what a clean analysis looks like and I know the shortcuts that cause problems later. Quick background: Actually postdoc in population genetics. PhD in bioinformatics and molecular evolution at Paris-Saclay (CNRS), where I worked on plant-bacteria interactions. Master's in bioinformatics from Sorbonne, and bachelor's in computer science from Descartes. I've been programming for over 10 years, mostly Python, R, and Bash, though I can work in Java, C, or JavaScript if a project calls for it. Eight years of building data pipelines specifically. What I do: Genomics and population genetics. GWAS, co-GWAS (PLINK2, Firth regression), population structure analysis (PCA, ADMIXTURE, pairwise FST), ancient DNA work (qpAdm, qpWave, ADMIXTOOLS2), variant calling and annotation. I've run these on human, plant, pathogen, and ancient samples. Genome assembly and annotation: De novo assembly from Nanopore or Illumina, gene prediction, functional annotation, genome comparison. Bacteria, plants, insects, human. I've assembled all of them at some point. RNA-seq: Differential expression from raw reads, co-expression networks, pathway enrichment. Full pipeline or just the analysis part, depending on what you need. Structural bioinformatics: AlphaFold2 batch runs, FoldSeek for structural comparisons, molecular docking with ClusPro, AutoDock Vina, or HADDOCK. I do the visualization too (PyMOL, ChimeraX), and create publication-ready figures, not just screenshots. Phylogenetics and molecular evolution: IQ-TREE2, RAxML, selection tests, gene family evolution, protein evolution. Proteomics and lipidomics: Mass spec data processing through to statistical analysis and biological interpretation. Pipelines: I build workflows, documented and tested with example data. Your team should be able to rerun them without me. Writing and code review: Methods sections, technical reports, manuscript figures. I also debug and review existing pipelines. If your code or script doesn't work, I'll fix it and tell you why it broke. Everything comes with commented code and R Markdown or Jupyter reports. Figures are made in ggplot2 or matplotlib and ready for submission. 100% Job Success score across 65+ projects. I reply the same day. "If you need a bioinformatics expert who's not only insanely skilled but also great to work with, Dr. Amira is the one. Would 100% work with her again!" (Client) "Amira delivered a thorough and well-structured technical review, including worked examples and code snippets that were very useful for demonstrating enablement. Her analysis was detailed, thoughtful, and delivered on time. I highly recommend her for bioinformatics and technical review projects." (Recent client) Drop me a message with your data and what you're trying to do. I'll get back to you with a plan and timeline.
- R
- Bioinformatics
- Genomics
- Genomic Data Analysis
- Biostatistics
- Python
- Structural Analysis
- Data Science
- Scientific Writing
- Bash Programming
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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.
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