Analysing Financial Data in Excel 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.

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 onsite) is designed for individuals seeking to learn and master the fundamentals of econometric analysis and modeling.

Upon completing this training, participants will be able to:

• Acquire and comprehend the core principles of econometrics.
• Effectively utilize Eviews and risk simulators.

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.

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

In this instructor-led, live training, participants will master advanced techniques for Machine Learning with R as they build a real-world application step by step.

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

• Understand and implement unsupervised learning techniques
• Apply clustering and classification methods to generate predictions based on real-world data.
• Visualize data to quickly gain insights, support decision-making, and further refine analysis.
• Enhance the performance of a machine learning model through hyper-parameter tuning.
• Deploy a model into production for integration within a larger application.
• Apply advanced machine learning techniques to address challenges involving social network data, big data, and more.

R is a widely adopted programming language within the financial sector. It powers a broad spectrum of financial applications, from core trading systems to risk management platforms.

Through this instructor-led live training, participants will learn how to leverage R to create 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
• Select and employ R packages and techniques to organize, visualize, and analyze financial data from diverse sources (such as CSV, Excel, databases, and web APIs)
• Develop applications that solve problems related to asset allocation, risk analysis, investment performance, and more
• Troubleshoot, integrate, deploy, and optimize R applications

Audience

• Developers
• Analysts
• Quantitative Analysts (Quants)

Course Format

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

Note

• This training is designed to provide solutions for some of the key issues faced by finance professionals. However, if there is a specific topic, tool, or technique you wish to add or expand upon, please contact us to make arrangements.