What will you learn in this Generative AI for Data Analysts Specialization Course
Understand the fundamentals of generative AI and its applications in data analysis.
Gain knowledge of prompt engineering concepts, including zero-shot and few-shot techniques.
Identify and utilize popular generative AI models and tools such as GPT, DALL·E, IBM Watsonx, Prompt Lab, Spellbook, and Dust.
Apply generative AI tools to enhance data analytics workflows, including data generation, augmentation, and querying databases.
Examine real-world applications where generative AI can enhance data analytics workflows.
Program Overview
Course 1: Generative AI: Introduction and Applications
⏳ 7 hours
- Learn the basics of generative AI, its capabilities, and real-world use cases across various industries.
Course 2: Generative AI: Prompt Engineering Basics
⏳ 7 hours
- Delve into prompt engineering concepts, exploring techniques like zero-shot and few-shot prompting, and tools to create effective prompts.Coursera
Course 3: Generative AI: Enhance your Data Analytics Career
⏳ 14 hours
- Apply generative AI tools and techniques in data analytics processes such as data preparation, analysis, visualization, and storytelling.
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Job Outlook
Completing this specialization prepares you for roles such as Data Analyst, Business Intelligence Analyst, or AI Specialist.
The skills acquired are applicable across various industries that utilize data analytics and AI technologies.
Enhance your employability by gaining practical experience in applying generative AI to data analytics workflows.
Specification: Generative AI for Data Analysts Specialization
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FAQs
- No prior AI or ML experience is strictly required.
- Basic understanding of data analysis and spreadsheets helps.
- Python knowledge is beneficial but not mandatory.
- The course builds concepts progressively for beginners.
- Suitable for data analysts looking to apply AI in their workflow.
- Automates repetitive data preparation tasks.
- Generates insights and summaries from large datasets.
- Assists in creating visualizations and reports.
- Supports anomaly detection and predictive modeling.
- Speeds up decision-making and improves accuracy.
- Popular AI tools like OpenAI APIs or similar platforms.
- Data visualization tools integrated with AI-generated insights.
- Python libraries for AI-assisted analytics.
- Techniques to integrate AI into dashboards and reports.
- Methods for automating data workflows using AI.
- Yes, it’s valuable in finance, marketing, healthcare, and tech.
- Enhances analytics in startups and SMEs as well as large enterprises.
- Useful for generating business insights quickly across domains.
- Supports data-driven decision-making in diverse organizational contexts.
- Skills are transferable to roles like data analyst, BI specialist, and AI consultant.
- Includes exercises using AI to analyze real datasets.
- Projects demonstrate end-to-end workflow integration.
- Practical assignments reinforce AI-assisted insights generation.
- Encourages experimentation with generative AI models.
- Builds a portfolio of applied AI analytics projects.
