Data analysis using AI

Course ID : AIM-007

Duration In-class (в days) : 4 hours

Duration Online : 4 hours

Сurriculum : in-class, Virtual Instructor-Led Training - ONLINE

Overview

Using AI for data analysis allows you to process large volumes of information at high speed. This course will introduce you to the basic data processing tools and help you choose the best ones.

Neural networks allow you to automatically collect data from various sources, check it for accuracy and completeness, visualize it, and generate reports. Data analysis using generative AI allows you to discover hidden patterns, make forecasts, and generate recommendations.

This course will teach you how to analyze data using AI: from simple summarization to identifying hidden patterns in tables and text.

You’ll learn which tasks are best delegated to neural networks and which require human oversight. You’ll learn how to formulate prompts to achieve the optimal balance between response accuracy and depth of detail.

As a result, you’ll be able to analyze massive amounts of data in minutes instead of hours, leaving you time for strategic decisions.

Audience for this course

Executives, managers, marketers, developers, data analytics and applied statistics specialists

The course is also suitable for those who want to use AI technologies and data analysis in their daily work but don’t know where to start.

Objective

Learn to analyze, interpret, and visualize data using AI, as well as consciously select tools for specific business tasks.

Prerequisites for this course

Confidently build prompts in generative AI systems from various manufacturers.

Outcomes

After completing this course, students will gain the skills to:

    • Correctly formulate queries (prompts) for data analysis, adapting queries to different types of information.
    • Apply AI tools to summarize, interpret, and identify patterns in large volumes of data.
    • Work with tabular and text data, using neural networks for data analysis as a daily practice.
    • Draw informed conclusions based on data, distinguishing reliable results from model “hallucinations.”
    • Understand the limitations and risks of using generative AI in solving analytical problems.

Outline

  1. Introduction to data analysis using generative AI.
    • What is data analysis, the main stages (collection, processing, interpretation).
      The role of generative AI in working with data.
    • Typical tasks: summarization, structuring, finding patterns, explaining data.
    • Overview of Russian and international data analysis services.
    • Features of working with different types of data.
    • How AI for analytics works and how it differs from classic BI tools.
  1. Basics of prompting for data analysis.
    • Characteristics of setting data analysis tasks for AI.
    • Formulating queries to obtain correct results.
    • Refining and iterating with model responses.
    • Decomposing data analysis tasks.
    • Constructing complex and structured queries.
    • Combining queries to solve complex problems.
    • Using AI to generate hypotheses.
    • Forming text interpretations and descriptions of results.
    • Typical errors and how to avoid them.
  1. Practical block: data analysis using AI
    • Solving applied problems using the provided test cases accounts.
    • Analysis of case studies on descriptive analytics and advanced data analysis.
    • Comparison of results from different approaches to query formulation (simple and structured prompts) and analysis of the impact of the prompt on the accuracy and completeness of the findings.

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