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

This instructor-led, live training in Uzbekistan (online or onsite) is aimed at intermediate-level advanced R users who wish 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, improve data workflows, debug and optimize code, and produce reproducible reports.

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

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

• Use algorithms to buy and sell securities rapidly at specialized increments.
• Reduce trading-related costs through the use of algorithmic trading.
• Automatically monitor stock prices and execute trades.

The term 'Big Data' refers to solutions designed for the storage and processing of extensive data sets. Initially developed by Google, these Big Data solutions have evolved and inspired numerous similar initiatives, many of which are available as open-source projects. R is a widely adopted programming language within the financial industry.

This instructor-led live training in Uzbekistan (online or onsite) is aimed at beginner-level data analysts who wish to use R programming to manipulate data, perform basic data analysis, and create compelling visualizations for insights.

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

• Understand the basics of R Programming.
• Apply fundamental data science processes.
• Create visual representations of data.

R is an open-source, free programming language designed for statistical computing, data analysis, and graphical visualization. An increasing number of managers and data analysts across corporations and academic institutions rely on R. It has also gained popularity among statisticians, engineers, and scientists who lack formal programming skills but find the language intuitive and easy to use. R's growing adoption is driven by the rising demand for data mining to achieve diverse objectives, such as optimizing advertising prices, accelerating drug discovery, and refining financial models. The language offers a vast ecosystem of packages tailored for data mining tasks.

This instructor-led, live training in Uzbekistan (online or onsite) is designed for data analysts who wish to program using R within SAS to perform cluster analysis.

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

• Apply cluster analysis for data mining purposes.
• Master R syntax for developing clustering solutions.
• Implement both hierarchical and non-hierarchical clustering techniques.
• Make data-driven decisions to enhance business operations.

In today's business landscape, data analytics is an indispensable tool. This course prioritizes the development of practical, hands-on skills for effective data analysis. Its primary goal is to empower participants to provide evidence-based answers to critical 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 Uzbekistan (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 adopted, open-source environment for statistical computing, data analytics, and graphics. This course introduces students to the R programming language, covering its fundamentals, essential libraries, and advanced concepts. Participants will explore advanced data analytics and visualization techniques using real-world datasets.

Target Audience

Developers and data analysts

Duration

3 days

Format

Lectures combined with hands-on practical sessions

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

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

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

This instructor-led, live training in Uzbekistan (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 an open-source, free programming language designed for statistical computing, data analysis, and data visualization. It is increasingly adopted by corporate managers and data analysts in both business and academic settings. R offers a comprehensive ecosystem of packages tailored for data mining and analysis.

This instructor-led, live training in Uzbekistan (online or onsite) is tailored for 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.

It is estimated that unstructured data accounts for more than 90 percent of all data, much of it in the form of text. Blog posts, tweets, social media, and other digital publications continuously add to this growing body of data.

This instructor-led, live course centers around extracting insights and meaning from this data. Utilizing the R Language and Natural Language Processing (NLP) libraries, we combine concepts and techniques from computer science, artificial intelligence, and computational linguistics to algorithmically understand the meaning behind text data. Data samples are available in various languages per customer requirements.

By the end of this training participants will be able to prepare data sets (large and small) from disparate sources, then apply the right algorithms to analyze and report on its significance.

Format of the Course

• Part lecture, part discussion, heavy hands-on practice, occasional tests to gauge understanding

Target Audience

Course Format

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

This instructor-led, live training guides participants through the process of manipulating and visualizing data using the tools provided by the Tidyverse.

The Tidyverse is a comprehensive collection of powerful R packages designed for cleaning, processing, modeling, and visualizing data. Key packages included in the Tidyverse are: ggplot2, dplyr, tidyr, readr, purrr, and tibble.

• Beginners to the R language
• Beginners to data analysis and data visualization

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

• Perform data analysis and create compelling visualizations
• Draw actionable insights from various sample datasets
• Filter, sort, and summarize data to answer exploratory questions
• Convert processed data into informative line plots, bar plots, and histograms
• Import and filter data from diverse sources, including Excel, CSV, and SPSS files