Shiny, R and HTML: Merging Data Science and Web Development Training Course

This instructor-led live training in Turkey (online or onsite) is designed for intermediate-level R users who aim to use R to build faster workflows, improve code quality, and handle more complex analysis tasks.

By the end of this training, participants will be able to create reusable functions, enhance data workflows, debug and optimize code, and produce reproducible reports.

This instructor-led, live training in Turkey (online or onsite) is aimed at business analysts who wish to automate trade with algorithmic trading, Python, and R.

By the end of this training, participants will be able to:

• Employ algorithms to buy and sell securities at specialized increments rapidly.
• Reduce costs associated with trade using algorithmic trading.
• Automatically monitor stock prices and place trades.

Big Data encompasses solutions designed for the storage and processing of massive datasets. Originally developed by Google, these Big Data frameworks have evolved and inspired numerous similar initiatives, many of which are available as open-source software. R is widely recognized as a leading programming language within the financial sector.

This instructor-led live training, delivered Turkey (online or onsite), is tailored for beginner-level data analysts who intend to use R programming for data manipulation, basic data analysis, and creating impactful visualizations to uncover insights.

Upon completion of this training, participants will be able to:

• Grasp the core principles of R Programming.
• Implement fundamental data science processes.
• Generate visual representations of data.

R is a free, open-source programming language designed for statistical computing, data analysis, and graphics. It is increasingly utilized by managers and data analysts within both corporate and academic sectors. R has also gained popularity among statisticians, engineers, and scientists without prior coding experience due to its user-friendly nature. Its widespread adoption stems from the growing need for data mining to achieve objectives such as optimizing pricing strategies, accelerating drug discovery, and refining financial models. R offers an extensive range of packages tailored for data mining.

This instructor-led, live training in Turkey (online or on-site) is designed for data analysts who want to use R within SAS for cluster analysis.

By the end of this training, participants will be able to:

• Use cluster analysis for data mining
• Master R syntax for clustering solutions.
• Implement hierarchical and non-hierarchical clustering.
• Make data-driven decisions to help to improve business operations.

Data analytics serves as a pivotal asset in contemporary business environments. Our primary focus throughout this course is to cultivate practical, hands-on skills in data analysis. The objective is to empower participants to formulate evidence-based responses to key questions:

What has happened?

• processing and analyzing data
• producing informative data visualizations

What will happen?

• forecasting future performance
• evaluating forecasts

What should happen?

• turning data into evidence-based business decisions
• optimizing processes

This instructor-led, live training in Turkey (online or onsite) is aimed at beginner-level professionals who wish to clean and analyze data, make statistical projections, and create insightful visualizations using these tools.

By the end of this training, participants will be able to:

• Understand the basics of Python, R, Power Query, and Power BI for data analysis.
• Clean and organize datasets using Python and Power Query.
• Perform statistical analysis and projections with R.
• Create professional dashboards and reports with Power BI.
• Integrate and analyze data from multiple sources effectively.

R is a widely used, open-source environment designed for statistical computing, data analytics, and graphics. This course provides students with an introduction to the R programming language, covering its fundamentals, libraries, and advanced concepts. Participants will learn how to perform advanced data analytics and create graphics using real-world datasets.

Target Audience

Developers and data analytics professionals

Duration

3 days

Format

Lectures and Hands-on

This instructor-led, live training in Turkey (online or onsite) is designed for beginner-level professionals seeking to master the fundamentals of R and learn how to effectively work with data.

Upon completing this training, participants will be able to:

• Navigate the R programming environment and RStudio interface.
• Import, manipulate, and explore datasets using R commands and packages.
• Conduct basic statistical analysis and summarize data.
• Create visualizations using both base R and ggplot2.
• Efficiently manage workspaces, scripts, and packages.

This instructor-led, live training in Turkey (online or onsite) is aimed at intermediate-level data analysts and business professionals who wish to perform time series forecasting and automate data analysis workflows using R.

By the end of this training, participants will be able to:

• Understand the fundamentals of forecasting techniques in R.
• Apply exponential smoothing and ARIMA models for time series analysis.
• Utilize the ‘forecast’ package to generate accurate forecasting models.
• Automate forecasting workflows for business and research applications.

R is a freely available, open-source programming language designed for statistical computing, data analysis, and data visualization. Its popularity is rapidly growing among corporate managers and data analysts across both industry and academia. R boasts a comprehensive ecosystem of packages specifically tailored for data mining.

This instructor-led, live training in Turkey (online or onsite) is aimed at data scientists who wish to program in Python and R for KNIME.

By the end of this training, participants will be able to:

• Plan, build, and deploy machine learning models in KNIME.
• Make data driven decisions for operations.
• Implement end to end data science projects.

Unstructured data is estimated to comprise over 90% of all data, with text forming a significant portion of this volume. Sources such as blog posts, tweets, social media platforms, and other digital publications continuously contribute to this expanding dataset.

This instructor-led, live course focuses on extracting insights and meaning from such data. By leveraging the R programming language and Natural Language Processing (NLP) libraries, the course integrates principles from computer science, artificial intelligence, and computational linguistics to algorithmically decipher the context within text data. Sample datasets are provided in various languages, tailored to customer needs.

Upon completion of this training, participants will be equipped to prepare datasets of varying sizes from diverse sources and apply appropriate algorithms to analyze their significance and generate reports.

Course Format

• A blend of lectures and discussions, with a strong emphasis on practical, hands-on exercises and periodic assessments to measure comprehension.

Audience

Course Format

Upon completing this training, participants will be able to:

In this instructor-led live session, attendees will learn how to manipulate and visualize data using the tools within the Tidyverse ecosystem.

The Tidyverse comprises a suite of powerful R packages designed for cleaning, processing, modeling, and visualizing data. Key packages include: ggplot2, dplyr, tidyr, readr, purrr, and tibble.

• Individuals new to the R programming language
• Beginners in data analysis and data visualization

• A blend of lectures, discussions, exercises, and extensive hands-on practice

• Conduct data analysis and generate compelling visualizations
• Extract meaningful insights from various sample datasets
• Filter, sort, and summarize data to address exploratory questions
• Transform processed data into informative charts, including line plots, bar plots, and histograms
• Import and filter data from diverse sources such as Excel, CSV, and SPSS files