Building Web Applications in R with Shiny Training Course

This instructor-led, live training in Kenya (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 Kenya (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 refers to technologies designed to store and process vast volumes of information. Originally developed by Google, these Big Data solutions have evolved and inspired numerous similar projects, many of which are available as open-source software. R is a widely used programming language within the financial sector.

This instructor-led live training in Kenya (online or onsite) targets beginner-level data analysts who wish to utilize R programming to manipulate data, conduct fundamental data analysis, and produce engaging 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.

This live, instructor-led training (Kenya), offered online or onsite, targets data analysts who wish to programme with R in 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 is an indispensable asset in the modern business landscape. This course is dedicated to cultivating practical, hands-on data analysis skills. Our objective is to empower participants to provide evidence-based responses to key business inquiries:

What has occurred?

• Processing and analyzing data
• Generating insightful data visualizations

What is likely to occur?

• Forecasting future performance
• Evaluating these forecasts

What should occur?

• Transforming data into evidence-based business decisions
• Optimizing business processes

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

Upon completing this training, participants will be able to:

• Grasp the fundamentals of Python, R, Power Query, and Power BI in the context of data analysis.
• Clean and organize datasets using Python and Power Query.
• Conduct statistical analysis and projections with R.
• Design professional dashboards and reports using Power BI.
• Effectively integrate and analyze data from various sources.

R is a widely popular, open-source environment designed for statistical computing, data analytics, and graphics. This course provides students with an introduction to the R programming language. It explores language fundamentals, key libraries, and advanced concepts, alongside practical applications of advanced data analytics and graphing using real-world datasets.

Audience

Developers and data analysts

Duration

3 days

Format

Lectures combined with hands-on practice

This instructor-led, live training in Kenya (online or on-site) is designed for intermediate-level data analysts and business professionals who aim to conduct time series forecasting and automate data analysis workflows using R.

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

• Comprehend the basics of forecasting techniques in R.
• Apply exponential smoothing and ARIMA models for time series analysis.
• Leverage the 'forecast' package to develop accurate forecasting models.
• Automate forecasting workflows for business and research applications.

This instructor-led, live training in Kenya (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 constitute over 90 percent of all data, predominantly in textual formats. This volume continues to expand through continuous contributions from blog posts, tweets, social media platforms, and other digital publications.

This instructor-led live course focuses on extracting valuable insights and meaning from such data. By leveraging the R Language and Natural Language Processing (NLP) libraries, we integrate concepts and techniques from computer science, artificial intelligence, and computational linguistics to algorithmically decode the significance within text data. Data samples can be provided in various languages to align with specific customer requirements.

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

Course Format

• A blend of lectures and discussions, with a strong emphasis on hands-on practice and occasional assessments to evaluate understanding

In this instructor-led, live training, participants will learn advanced techniques for Machine Learning with R as they step through the creation of a real-world application.

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

• Understand and implement unsupervised learning techniques
• Apply clustering and classification to make predictions based on real world data.
• Visualize data to quicly gain insights, make decisions and further refine analysis.
• Improve the performance of a machine learning model using hyper-parameter tuning.
• Put a model into production for use in a larger application.
• Apply advanced machine learning techniques to answer questions involving social network data, big data, and more.

R is a widely adopted programming language within the financial sector. It powers financial solutions ranging from core trading systems to sophisticated risk management tools.

Through this instructor-led live training, participants will acquire the skills to leverage R for building practical applications that address specific financial challenges.

Upon completing this training, participants will be able to:

• Grasp the fundamental concepts of the R programming language
• Choose and employ R packages and techniques to organize, visualize, and analyze financial data from diverse sources (CSV, Excel, databases, web, etc.)
• Develop applications that resolve issues related to asset allocation, risk analysis, investment performance, and more
• Troubleshoot, integrate, deploy, and optimize R applications

Audience

• Developers
• Analysts
• Quants

Format of the course

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

Note

• This training focuses on providing solutions for key challenges faced by finance professionals. If you have specific topics, tools, or techniques you wish to cover in greater detail or add to the curriculum, please contact us to arrange.

R Markdown serves as an authoring framework that integrates executable R code with narrative text to produce dynamic and reproducible documents.

This instructor-led live training (available online or on-site) is designed for beginner to intermediate R users who aim to generate automated, dynamic reports using R Markdown for internal documentation, research publications, or web publishing.

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

• Create R Markdown documents containing embedded R code chunks and formatted text.
• Generate outputs in various formats, including HTML, PDF, and Word.
• Utilize parameterized reports and conditional logic to produce dynamic content.
• Customize document aesthetics using themes, templates, and LaTeX for a professional presentation.

Format of the Course

• Interactive lecture and discussion.
• Extensive exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

This course is designed for data scientists and statisticians who already possess fundamental knowledge of R and C++ coding, as well as experience with R scripts, and now require advanced R programming skills.

The aim is to provide a practical, advanced R programming course tailored for participants who wish to apply these methods in their professional work.

Industry-specific examples are utilized to ensure the training is highly relevant to the audience's needs.