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Nata L, Data Analyst with BI and Data Engineering skills (Senior Certified Tableau Engineer)

Last Updated: 23 Apr 2024
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- Oriented Data and Business Intelligence Analysis engineer with Data Engineering skills. - 6+ years of experience with Tableau - Experience in Operations analysis, building charts & dashboards - 20+ years of experience in data mining, data analysis, and data processing. Unifying data from many sources to create interactive, immersive dashboards and reports that provide actionable insights and drive business results. - Adept with different SDLC methodologies - Waterfall, Agile SCRUM - Knowledge of performing data analysis, data modeling, data mapping, batch data processing, and capable of generating reports using reporting tools such as Power BI (advanced), Sisence(Periscope) (expert), Tableau (Advanced), Data Studio (Advanced) - Experience in writing SQL Queries, Big Query, Python, R, DAX to extract data and perform Data Analysis - AWS, Redshift - Combined expertise in data analysis with solid technical qualifications. - Advanced English, Intermediate German - Location: Germany

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R   2 yr.


Python   6 yr.


SQL   8 yr.

Microsoft Power BI

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Data Analysis Expressions (DAX)

Data Analysis Expressions (DAX)   4 yr.


Tableau   6 yr.

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Anton, Software Engineer

Last Updated: 23 Apr 2024

Software Engineer with a Computer Science and Software Engineering background and 5 years of experience specializing in data analysis, visualization, and backend systems across retail, mobile, and finance domains. Proven expertise in languages such as Python, SQL, and R, supported by strong knowledge of cloud services like AWS and GCP. Skilled in BI tools and data visualization with Tableau, Looker Studio, and programming libraries Matplotlib, Seaborn, and Folium. Experienced in database management with MySQL, PostgreSQL, and NoSQL databases like Redis and MongoDB. Proficient in Data Engineering practices using Apache Spark and ETL/ELT processes with Apache Airflow. Demonstrates advanced capabilities in Machine Learning and Data Science with extensive use of Pandas, NumPy, and Scikit-learn. Committed to DevOps with experience in Docker, Bash Scripting, and version control systems like Git.

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R   6 yr.





Google Charts

Google Charts



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Nadya, Data Analyst, Economic Processes Analysis & Modelling

Last Updated: 23 Apr 2024

- 10+ years in Forecasting, Analytics & Math Modelling - 8 years in Business Analytics and Economic Processes Modelling - 5 years in Financial Forecasting Systems - 5 years in Data Science - 3 years in Web Development - Master of Statistics and Probability Theory (diploma with honours), PhD (ABD) - BSc in Finance - Strong knowledge of Math & Statistics - Strong knowledge of R, Python, VBA - Strong knowledge of PostgreSQL and MS SQL Server - Knowledge of C# - Knowledge of .net web development technologies - Basic web technologies (JavaScript) - Self-motivated, conscientious, accountable, addicted to data processing, analysis & forecasting

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Data Analysis

Data Analysis   10 yr.



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Rolan A., ML/Computer Vision

Last Updated: 9 Aug 2023

- More than 10 years’ experience of software development - Data science skills. Computer Vision, multiple view geometry, camera calibration, LIDAR, object detection, semantic segmentation, instance segmentation, time series, dynamic programming - Software Engineering skills. Experience of IoT (Internet of Things) and Embedded development - Solution-oriented scientist focused on R&D and product delivery with 9 years of experience on the outsource domain - Accustomed to self-education and independent problem solution - My inspiration is exiting by challengeable and reasonable engineering tasks. Pitching skills from years of conferences attendance and strong understanding of business needs are my strengths - Intermediate English. - Availability starting from ASAP

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Alexey K., Data and Software Engineer

Kyiv, Ukraine
Last Updated: 4 Jul 2023

- 20+ years of software development - 5+ years of Python & Elixir, Go development, .Net development - Mathematical Science education (diploma with honours), R language for statistical computing and graphics - Strong knowledge in Python, Go, Redis, Elixir, ASP.NET MVC, Angular, DevExpress, C++, PHP, MS SQL Server, PostgreSQL, Oracle SQL, NodeJS, React, React Native, Azure, AWS, Apache Kafka - Strong knowledge of ERP Systems - Strong web technologies - Extensive knowledge of software development lifecycle - Available in approx 4 weeks

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Apache Kafka

Apache Kafka

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Table of Contents

TOP 10 Facts about R

  • R is a free and open-source programming language that is widely used for statistical computing and graphics.
  • R was created by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand in the early 1990s.
  • R is highly extensible and has a vast collection of packages that provide additional functionality for various data analysis and visualization tasks.
  • R has a strong focus on data manipulation and statistical modeling, making it a popular choice among data scientists and statisticians.
  • R has a rich set of graphical capabilities, allowing users to create high-quality visualizations for data exploration and presentation.
  • R is platform-independent and can run on various operating systems, including Windows, macOS, and Linux.
  • R has a large and active community of users and developers, which means there is extensive documentation, online resources, and support available.
  • R has integration capabilities with other programming languages such as C, C++, and Python, allowing users to leverage the strengths of different languages in their data analysis workflows.
  • R is widely used in academia, research, and industry for a wide range of applications, including data analysis, machine learning, bioinformatics, and finance.
  • R is continually evolving and improving, with regular updates and new packages being developed to address emerging data analysis challenges.

Soft skills of a R Developer

Soft skills are just as important as technical skills for a successful career as an R Developer. Here are the soft skills that are essential for R Developers at different levels:


  • Effective Communication: Ability to clearly communicate ideas and requirements with team members and stakeholders.
  • Problem-Solving: Aptitude for identifying and resolving issues in R code and data analysis processes.
  • Adaptability: Willingness to learn new techniques and adapt to changing project requirements.
  • Collaboration: Ability to work well within a team and contribute to group projects.
  • Time Management: Skill in managing time and meeting project deadlines.


  • Leadership: Capability to take ownership of projects and guide junior team members.
  • Critical Thinking: Proficiency in analyzing complex problems and developing innovative solutions.
  • Mentoring: Ability to mentor and train junior developers to enhance their R programming skills.
  • Attention to Detail: Strong focus on accuracy and precision in data analysis and programming tasks.
  • Client Management: Skill in managing client expectations and maintaining positive relationships.
  • Project Management: Competence in organizing and overseeing multiple projects simultaneously.
  • Teamwork: Collaboration with cross-functional teams to achieve project objectives.


  • Strategic Thinking: Ability to align R development efforts with overall business objectives.
  • Decision Making: Aptitude for making informed decisions based on data analysis and insights.
  • Innovation: Capacity to introduce new techniques and tools to improve R programming processes.
  • Conflict Resolution: Skill in resolving conflicts and managing disagreements within the team.
  • Client Engagement: Proficiency in engaging with clients to understand their needs and provide effective solutions.
  • Quality Assurance: Commitment to ensuring the quality and reliability of R code and data analysis.
  • Continuous Learning: Willingness to stay updated with the latest advancements in R programming.

Expert/Team Lead

  • Strategic Leadership: Ability to provide strategic direction and guidance to the R development team.
  • Team Management: Skill in managing and motivating a team of R Developers to achieve project success.
  • Business Acumen: Understanding of business processes and ability to align R development with organizational goals.
  • Risk Management: Proficiency in identifying and mitigating risks in R development projects.
  • Client Relationship Management: Capability to build and maintain strong relationships with clients.
  • Thought Leadership: Recognition as an industry expert in R programming and data analysis.
  • Project Planning: Competence in planning and executing complex R development projects.
  • Continuous Improvement: Commitment to driving continuous improvement in R programming practices.
  • Strategic Partnerships: Ability to establish strategic partnerships and collaborations for enhanced project outcomes.
  • Influence and Negotiation: Skill in influencing stakeholders and negotiating project requirements.
  • Technical Oversight: Capability to provide technical guidance and oversight to the R development team.

What are top R instruments and tools?

  • RStudio: RStudio is an integrated development environment (IDE) for R, which provides a user-friendly interface for writing, executing, and debugging R code. It was first released in 2011 and has since become one of the most popular tools for R programming. RStudio offers features such as syntax highlighting, code completion, and project management, making it a preferred choice for both beginners and experienced R users.
  • Shiny: Shiny is a web application framework for R that allows users to create interactive web applications directly from R code. It was introduced in 2012 and has gained significant popularity among data scientists and analysts. Shiny simplifies the process of building web-based dashboards and visualizations by providing a range of built-in widgets and reactive programming capabilities.
  • ggplot2: ggplot2 is a data visualization package for R that is based on the grammar of graphics. Developed by Hadley Wickham, ggplot2 offers a flexible and powerful system for creating high-quality visualizations. It allows users to easily customize plots by specifying different aesthetic mappings and layers. ggplot2 has become a staple tool for data visualization in R, known for its elegant and customizable graphics.
  • Caret: The caret package, short for Classification And Regression Training, is a comprehensive toolkit for machine learning in R. It provides a unified interface to various machine learning algorithms and facilitates the process of model training, tuning, and evaluation. Caret supports a wide range of classification, regression, and clustering techniques, making it a versatile tool for predictive modeling.
  • data.table: data.table is a high-performance data manipulation package for R. It offers a more efficient alternative to the base R data.frame, particularly for large datasets. data.table provides fast and flexible subsetting, aggregation, and joins operations, making it suitable for data preprocessing and analysis tasks. It was first introduced in 2006 and has gained popularity for its speed and memory efficiency.
  • TensorFlow: TensorFlow is an open-source machine learning framework developed by Google. While primarily associated with Python, TensorFlow also has an interface for R, allowing users to leverage its powerful deep learning capabilities. TensorFlow enables the construction and training of neural networks for various tasks such as image classification, natural language processing, and time series analysis.

TOP 10 R Related Technologies

  • R Programming Language

    R is a widely used programming language for statistical computing and graphics. It provides a vast collection of libraries and packages that make it easy to manipulate data, perform statistical analysis, and visualize results. R is known for its extensive data analysis capabilities, making it a popular choice for data scientists and statisticians.

  • Shiny Framework

    Shiny is an R package that allows you to create interactive web applications directly from R. It provides a simple and efficient way to build web-based dashboards, data visualizations, and interactive reports. With Shiny, you can easily share your R code and analysis results with others through a web browser interface.

  • ggplot2 Package

    ggplot2 is an R package for data visualization. It provides a powerful and flexible system for creating high-quality graphs and charts. ggplot2 follows the grammar of graphics concept, allowing you to build visualizations layer by layer. It offers a wide range of plot types and customization options, making it a favorite among data analysts and visualization experts.

  • dplyr Package

    dplyr is an R package that provides a set of functions for data manipulation and transformation. It offers a streamlined and intuitive syntax for performing common data manipulation tasks such as filtering, sorting, aggregating, and summarizing data. dplyr’s performance optimizations make it ideal for working with large datasets efficiently.

  • caret Package

    caret (Classification And REgression Training) is an R package for machine learning. It provides a unified interface for training and evaluating various machine learning algorithms, making it easier to compare different models and select the best one for your data. caret also includes functions for data preprocessing, feature selection, and model tuning.

  • RStudio IDE

    RStudio is a popular integrated development environment (IDE) for R. It provides a user-friendly interface for writing, debugging, and executing R code. RStudio offers many features that enhance productivity, such as code completion, project management tools, and integrated data viewers. It supports version control systems like Git and makes collaboration on R projects seamless.

  • TensorFlow for R

    TensorFlow is a widely adopted open-source machine learning framework developed by Google. TensorFlow for R allows you to leverage the power of TensorFlow within the R environment. It provides a high-level API for building and training deep learning models, making it easier to develop and deploy cutting-edge machine learning solutions.

How and where is R used?

Case NameCase Description
1. Data Analysis and VisualizationR is widely used for data analysis and visualization tasks. With its extensive library of packages such as ggplot2 and dplyr, R provides powerful tools for exploring, cleaning, and transforming data. It allows analysts to perform statistical analysis and generate visualizations to gain insights from data. For example, R can be used to analyze customer behavior data and create interactive visualizations to identify patterns and trends.
2. Machine Learning and Predictive AnalyticsR is a popular choice for developing machine learning models and conducting predictive analytics. The CRAN repository offers a wide range of machine learning packages such as caret and randomForest that enable users to build and evaluate models for various tasks like classification, regression, and clustering. R’s flexibility and rich set of algorithms make it suitable for solving complex problems, such as predicting stock market trends or diagnosing medical conditions based on patient data.
3. Statistical Modeling and SimulationR is known for its robust statistical modeling capabilities. It provides a comprehensive set of functions and packages for statistical analysis, hypothesis testing, and modeling. Researchers and statisticians often use R to develop models that explain relationships between variables, perform simulations, and make predictions. For instance, R can be utilized to simulate the spread of infectious diseases, analyze the impact of policy changes, or evaluate the effectiveness of marketing campaigns.
4. Web Scraping and Text MiningR offers powerful tools for web scraping and text mining, allowing users to extract data from websites and analyze textual data. Packages like rvest and tm enable developers to scrape web pages, extract information, and perform text mining tasks such as sentiment analysis, topic modeling, and natural language processing. This capability is valuable for tasks like monitoring online reviews, analyzing social media sentiment, or extracting data for research purposes.
5. Financial Analysis and Risk ModelingR is widely used in the finance industry for tasks such as financial analysis, risk modeling, and portfolio optimization. R’s packages like quantmod and PerformanceAnalytics provide functions to analyze financial data, calculate risk measures, and simulate investment strategies. This enables financial professionals to make informed decisions based on quantitative analysis, assess portfolio performance, and manage investment risks effectively.

Pros & cons of R

9 Pros of R

  • Powerful Data Analysis: R is a highly versatile and powerful language for statistical analysis and data visualization. It offers a wide range of statistical techniques and packages that allow users to manipulate and analyze data effectively.
  • Large Community and Support: R has a large and active community of users, which means that there is ample support available online. Users can find answers to their questions, share knowledge, and collaborate with other R users.
  • Open Source: R is an open-source language, which means that it is freely available and can be customized according to the user’s needs. This allows for continuous development and improvement of the language.
  • Integration with Other Languages: R can easily integrate with other programming languages like Python, Java, and C++. This flexibility allows users to leverage the strengths of different languages and libraries for their data analysis tasks.
  • Wide Range of Packages: R has a vast ecosystem of packages that provide additional functionality for various data analysis tasks. These packages cover a wide range of domains, including machine learning, data visualization, data manipulation, and more.
  • Reproducibility: R allows for easy reproducibility of analyses. Users can document their code and create reports that include both the code and the results. This makes it easier for others to understand and replicate the analysis.
  • Interactive Data Visualization: R offers several powerful packages, such as ggplot2 and plotly, that allow users to create interactive and visually appealing data visualizations. This makes it easier to explore and communicate insights from the data.
  • Statistical Modeling: R provides a comprehensive set of tools for statistical modeling. Users can easily perform linear regression, logistic regression, time series analysis, and other advanced statistical techniques.
  • Availability of Tutorials and Learning Resources: There are numerous online tutorials, books, and courses available to help users learn R. This wealth of learning resources makes it easier for beginners to get started with the language.

9 Cons of R

  • Steep Learning Curve: R has a steep learning curve, especially for users who have no prior programming experience. The syntax and concepts can be challenging to grasp initially.
  • Memory Management: R can be memory-intensive, especially when dealing with large datasets. Users need to be mindful of memory usage and optimize their code accordingly.
  • Performance: While R is a powerful language, it may not be the best choice for computationally intensive tasks. Other languages like Python or C++ may offer better performance for certain tasks.
  • Package Fragmentation: The vast number of packages available in R can lead to fragmentation. Users may find multiple packages offering similar functionality, making it difficult to choose the right one.
  • Data Size Limitations: R may have limitations when dealing with extremely large datasets. Users may need to resort to alternative tools or techniques when working with big data.
  • Debugging: Debugging can be challenging in R, especially for complex code. The error messages may not always be clear, making it harder to identify and fix issues in the code.
  • Interface Limitations: R’s command-line interface may not be as user-friendly as other environments like Python’s Jupyter Notebook. Users may find it less intuitive for exploratory data analysis.
  • Limited Support for Multithreading: R has limited support for multithreading, which can impact performance for parallel computing tasks.
  • Less Popular in Industry: While R is widely used in academia and research, it may not be as popular in industry settings compared to languages like Python. This could impact job opportunities for R users in certain industries.

Let’s consider Difference between Junior, Middle, Senior, Expert/Team Lead developer roles.

Seniority NameYears of experienceResponsibilities and activitiesAverage salary (USD/year)
Junior0-2 yearsAssisting senior developers in coding, testing, and debugging tasks. Learning and acquiring new skills. Working on smaller, less complex features or modules of a project under supervision.45,000 – 60,000
Middle2-5 yearsDeveloping software components or modules independently. Collaborating with team members to design and implement solutions. Participating in code reviews and providing constructive feedback. Assisting in mentoring junior developers.60,000 – 80,000
Senior5+ yearsLeading the design and architecture of complex software systems. Mentoring and guiding junior and middle developers. Taking ownership of major features or projects. Collaborating with stakeholders to understand business requirements and translate them into technical solutions.80,000 – 120,000
Expert/Team Lead8+ yearsProviding technical leadership and guidance to the entire development team. Defining and enforcing coding standards and best practices. Leading code reviews and ensuring high-quality code. Collaborating with cross-functional teams to align technical strategies with business goals.100,000 – 150,000+

Cases when R does not work

  1. R does not work well with large datasets: When dealing with big data, R may struggle due to its lack of efficient memory management. The memory limitations of R can lead to slow performance and even crashes when processing large datasets.
  2. R may not be the best choice for real-time data processing: If you require real-time analytics or need to process streaming data, R might not be the most suitable tool. Other programming languages like Python or Java are often preferred for real-time data processing due to their faster execution speed.
  3. R is not designed for heavy computational tasks: While R is excellent for statistical analysis and data manipulation, it is not optimized for heavy computational tasks such as complex simulations or intensive numerical computations. Other languages like C++ or Fortran are better suited for these types of tasks.
  4. R’s learning curve can be steep for beginners: R has a steeper learning curve compared to some other programming languages. Its syntax and data structures can be challenging for beginners with no prior programming experience. Individuals seeking a more beginner-friendly language may find Python to be a better option.
  5. R lacks strong support for multithreading: Multithreading allows programs to execute multiple threads simultaneously, improving performance for certain tasks. R does not have robust built-in support for multithreading, which can limit its ability to take advantage of modern multi-core processors.

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