Building Web Applications in R with Shiny Training Course

This instructor-led, live training in Botswana (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 Botswana (online or on-site) is intended for business analysts who wish to automate trading using algorithmic methods, Python, and R.

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

• Utilise algorithms to buy and sell securities rapidly at specialised intervals.
• Reduce costs associated with trading through the use of algorithmic trading.
• Monitor stock prices automatically and place trades.

The term 'Big Data' refers to solutions designed for storing and processing large data sets. Originally developed by Google, these Big Data solutions have evolved and inspired other similar projects, many of which are now available as open-source software. R is a widely used programming language in the financial industry.

This instructor-led, live training in Botswana (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.

This instructor-led live training in Botswana (online or onsite) is designed for 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 plays an essential role in modern business. This course concentrates on building practical, hands-on skills for data analysis. Our objective is to empower participants with the ability to provide evidence-based answers 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 Botswana (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 acclaimed, open-source environment designed for statistical computing, data analytics, and graphics. This course introduces learners to the R programming language, covering foundational language elements, essential libraries, and advanced concepts. Participants will engage in advanced data analytics and graphical representation using real-world datasets.

Target Audience

Software Developers and Data Analytics Professionals

Duration

3 days

Delivery Format

Lecture-based instruction combined with practical, hands-on exercises

This instructor-led, live training in Botswana (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.

This instructor-led, live training in Botswana (online or onsite) is designed for data scientists who wish to programme in Python and R for KNIME.

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

• Plan, construct, and deploy machine learning models using KNIME.
• Make data-driven decisions to optimise operations.
• Execute 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 centres 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 analyse and report on its significance.

Format of the Course

• Part lecture, part discussion, heavy hands-on practice, occasional tests to gauge 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 quickly 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 supports a broad spectrum of financial applications, from core trading systems to sophisticated risk management platforms.

Through this instructor-led live training, participants will acquire the skills necessary to leverage R for developing practical solutions to specific finance-related challenges.

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

• Grasp the core fundamentals of the R programming language
• Select and employ R packages and techniques to organise, visualise, and analyse financial data sourced from various channels (CSV, Excel, databases, web APIs, etc.)
• Construct applications that address issues such as asset allocation, risk analysis, investment performance evaluation, and more
• Troubleshoot, integrate, deploy, and optimise R applications

Audience

• Developers
• Analysts
• Quants

Format of the course

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

Note

• This training is designed to provide solutions for key problems encountered by finance professionals. However, if there is a specific topic, tool, or technique you wish to include or explore in greater depth, please do contact us to arrange accordingly.

R Markdown is an authoring framework that combines executable R code with narrative text to create dynamic and reproducible documents.

This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level R users who wish 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 with embedded R code chunks and formatted text.
• Generate outputs in multiple formats including HTML, PDF, and Word.
• Use parameterized reports and conditional logic to generate dynamic content.
• Customize document appearance with themes, templates, and LaTeX for professional presentation.

Format of the Course

• Interactive lecture and discussion.
• Lots of 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 programme is designed for data scientists and statisticians who possess foundational knowledge of R and C++ coding and require advanced R skills to enhance their capabilities.

The primary objective is to deliver a practical, advanced R programming course tailored for participants keen on applying these techniques in their professional environments.

Industry-specific examples are integrated to ensure the training is directly relevant to the audience's work context.