This practical course introduces participants to the use of artificial intelligence in data analysis. It covers data preparation, AI-assisted analysis, visualization, interpretation, and the responsible communication of insights to support informed decision-making.
This practical course introduces participants to the use of artificial intelligence in data analysis. It covers data preparation, AI-assisted analysis, visualization, interpretation, and the responsible communication of insights to support informed decision-making.
By the end of the course, participants will be able to:
The course combines concise theoretical instruction with demonstrations, guided exercises, practical activities, and a final applied project. Assessment includes pre- and post-course tests, practical tasks, and evaluation of the final project.
Target Audience
Professionals, managers, analysts, administrators, and learners who wish to develop practical AI-supported data analysis skills.
Prerequisites
Basic computer literacy and familiarity with spreadsheets. No previous programming or advanced data analysis experience is required.
في خانة Program Content انسخ النص التالي:
Module 1: Foundations of Data Analysis and AI
Introduction to the data analysis lifecycle, types of data, AI capabilities, limitations, and responsible use.
Module 2: Data Collection and Preparation
Importing datasets, identifying data-quality issues, handling missing values, removing duplicates, and preparing structured data.
Module 3: Exploratory Data Analysis
Using descriptive statistics and AI-assisted techniques to identify patterns, trends, relationships, and anomalies.
Module 4: AI-Assisted Analysis
Writing effective prompts, generating analytical queries, validating AI outputs, and reducing errors and bias.
Module 5: Data Visualisation and Dashboards
Selecting appropriate charts, designing dashboards, presenting key performance indicators, and communicating findings clearly.
Module 6: Applied Project
Completing an end-to-end analysis of a practical dataset and presenting evidence-based conclusions and recommendations.
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