Course details of Generative AI for Data Analysts Specialization
About Generative AI For Data Analysts Specialization
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About Our Generative AI for Data Analysts Specialization Training

Our Generative AI for Data Analysts Specialization training in the USA is meticulously designed to help professionals harness AI's transformative power for data analysis. The training covers generative AI use cases, including automated content creation, personalized customer experiences, and big data processing. Participants will gain hands-on expertise with AI models and tools for text, code, image, audio, and video generation. Emphasizing ethical implications, considerations, and challenges, this course ensures you apply AI responsibly and innovatively in real-world scenarios.

Who can join this course

Data analysts, AI developers
Level- Intermediate

Prerequisites:

No formal prerequisites are required

Course duration

2 Months based on the mode of learning

Mode of training- Flexible

Online students- flexibility of self-paced learning, video content, and text content
Offline students- Instructor-led training, video-based content, and text-based content
Weekdays, Weekends, and Customized Days as per the request

Career opportunity

1. Generative AI Analyst

2. AI Data Analyst

3. Machine Learning Engineer

4. NLP Specialist

5. Big Data Specialist

Demand for Generative AI Career in 2024

According to Statista

· The market size in the Generative AI market is projected to reach US$11.66bn in 2024.

· The market size is expected to show an annual growth rate (CAGR 2024-2030) of 46.48%, resulting in a market volume of US$115.20bn by 2030.

· In global comparison, the largest market size will be in the United States (US$11.66bn in 2024).

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Key Highlights

Live or virtual instructor-led classes
Browse course details, syllabus and demo videos
Quality course material provided
Become a certified expert on the subject
Instant access to reference material
Get high-pay jobs offers post-training

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Certified educators for Generative AI For Data Analysts Specialization training programs

Educators for Generative AI For Data Analysts Specialization training programs

About Generative AI For Data Analysts Specialization training

Overview of Our Generative AI for Data Analysts Specialization Course USA
The Generative AI for Data Analysts Specialization course USA provides in-depth knowledge of generative AI techniques, including text generation with GPT-3, image generation with generative models, and prompt engineering concepts. Learners will explore types of generative models, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), while mastering critical AI applications like language modeling and sentiment analysis. With practical projects, participants will gain the skills needed to excel in AI-driven analytics.
Objective of Our Generative AI for Data Analysts Specialization Training and Course
The goal of our Generative AI for Data Analysts Specialization training is to prepare data analysts to utilize advanced AI tools and methodologies effectively. This course focuses on fine-tuning and customizing ChatGPT, implementing GDPR and data protection standards, and addressing bias and fairness in AI systems. By mastering big data analysis and integrating AI solutions, participants will enhance their ability to generate actionable insights and drive decision-making.
What You Will Learn in Generative AI for Data Analysts Specialization Course
1. Understand generative AI use cases, such as automated text, image, and video creation.
2. Master AI models and tools for versatile applications across industries.
3. Explore prompt engineering concepts for crafting optimal inputs.
4. Learn advanced generative AI techniques, including GANs and VAEs.
5. Gain knowledge of GDPR, data protection, and addressing ethical challenges.
6. Develop practical skills in fine-tuning ChatGPT and utilizing GPT-3.5 and GPT-4 models

Generative AI For Data Analysts Specialization syllabus

Module 1: Introduction to Generative AI and Data Analytics
Overview of Generative AI and its applications in data analytics
Fundamentals of data analysis and statistical methods
Generative AI use cases in industries like finance, healthcare, and marketing
Ethical considerations, bias, and fairness in AI
Module 2: Understanding Generative Models
Types of Generative Models: GANs, VAEs, and Diffusion Models
Fundamentals of Text, Image, and Video Generation
Applications of Generative Adversarial Networks (GANs)
Variational Autoencoders (VAEs) for data reconstruction
Module 3: Generative AI Tools and Frameworks
Exploring AI tools: TensorFlow, PyTorch, and Hugging Face
Fine-tuning pre-trained models like GPT-3.5 and GPT-4
Introduction to OpenAI, DALL-E, and other generative AI platforms
Tools for text, image, audio, and video synthesis
Module 4: Generative AI Techniques and Prompt Engineering
Techniques for optimizing generative models
Prompt engineering concepts and crafting effective prompts
Understanding context, tone, and specificity in AI responses
Practical exercises in prompt creation and refinement
Module 5: Advanced Natural Language Processing (NLP)
NLP basics: Language modeling and text generation
Applications of GPT-3 and GPT-4 in data analytics
Sentiment analysis and topic modeling using generative AI
Case studies on NLP in business analytics
Module 6: Generative AI for Big Data Analysis
AI-driven insights for large datasets
Data synthesis and augmentation with generative AI
Automating data cleaning and preprocessing tasks
Real-world applications of generative AI in big data analytics
Module 7: Implementation and Deployment
Building and deploying generative AI models
Integration of generative AI in business workflows
Ensuring compliance with GDPR and data protection laws
Monitoring AI performance and maintaining quality
Module 8: Capstone Project and Industry Applications
Designing a capstone project using generative AI tools
Industry-specific case studies: Healthcare, finance, marketing
Customizing ChatGPT for data analytics applications
Presentation of project outcomes and feedback from experts

FAQ's on Generative AI For Data Analysts Specialization training & certification

How do I become an AI data analyst?
To become an AI data analyst, you need a strong foundation in statistics, programming languages like Python or R, and data visualization tools such as Tableau or Power BI. Familiarity with machine learning concepts, databases (SQL), and AI frameworks is essential. Pursuing AI and data analytics certifications or enrolling in specialization courses can provide the skills needed to enter this field. Sulekha offers various IT courses and has tie-ups with reputed course providers to ensure high-quality training for aspiring AI professionals.
What is the salary of an AI data analyst?
The average salary of an AI data analyst varies based on experience, location, and industry. In the USA, entry-level roles typically start around $70,000 annually, while experienced professionals can earn over $120,000 per year.
What is the Generative AI Analyst salary in the USA?
Generative AI Analysts in the USA can earn significantly higher salaries due to the niche expertise required. Entry-level positions average around $100,000 annually, with senior-level roles exceeding $150,000, depending on experience and company.
How do I start an AI career with no experience?
Starting an AI career with no experience requires learning foundational skills like programming (Python, R), understanding data science principles, and exploring AI concepts through online courses or boot camps. Working on small projects, participating in AI competitions, and creating a portfolio showcasing your skills can help you land entry-level roles or internships.
Can a non-IT person learn AI?
A non-IT person can learn AI with dedication and the right resources. Many online courses and beginner-friendly programs are available to teach AI basics, programming, and data analysis from scratch. With consistent practice, anyone can acquire the skills needed to work in AI.
What does a Generative AI analyst do?
A Generative AI analyst specializes in using AI models to create synthetic data, generate content, and analyze patterns for predictive insights. They work on tasks like fine-tuning generative models, ensuring ethical AI implementation, and optimizing algorithms for text, image, and video generation applications.
Can AI do the work of a data analyst?
AI can assist with many data analyst tasks, such as automating data cleaning, identifying trends, and generating insights. However, human oversight is still required to interpret results, ensure data quality, and make strategic decisions based on context and domain expertise.
Can ChatGPT replace a data analyst?
ChatGPT can assist data analysts by generating insights, automating tasks, and interpreting data trends. However, it cannot fully replace a data analyst’s role, which requires critical thinking, contextual understanding, and the ability to make strategic decisions.
Which is better, AI or data analyst?
The choice depends on your interests and career goals. AI focuses on creating intelligent systems and developing algorithms, while a data analyst focuses on interpreting and analyzing data for insights. Both fields are lucrative, with AI offering more technical challenges and data analytics requiring strong domain knowledge.
Does a data analyst have a future?
Yes, data analysts have a promising future as businesses increasingly rely on data-driven decision-making. The demand spans industries like finance, healthcare, retail, and technology, where analyzing trends and customer behavior is essential. According to sources like the U.S. Bureau of Labor Statistics and LinkedIn's Jobs on the Rise Report, the data analyst role is growing rapidly, with companies prioritizing skilled professionals to interpret complex datasets and provide actionable insights.
Is Generative AI for data analysts a good career in 2025?
Yes, a career in Generative AI for data analysts is expected to be highly rewarding in 2025. Industries such as e-commerce, entertainment, finance, and marketing already leverage Generative AI for applications like content creation, predictive modeling, and customer personalization. According to Gartner and Forbes, the adoption of Generative AI tools is set to skyrocket, increasing the demand for analysts skilled in AI to develop innovative solutions and optimize workflows.

Generative AI For Data Analysts Specialization certification

Generative AI For Data Analysts Specialization training with certification with Sulekha's partnered course providers, you'll be ready to explore job opportunities with top IT companies in the USA or your local area. We focus on equipping you with the skills needed by leading organizations and helping you connect with career opportunities in industry giants like Amazon, Deloitte, NTT Data, and more.

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4. Generative AI for Business Intelligence (BI and AI)

5. Generative AI for Business Analysis

Generative AI For Data Analysts Specialization training and placement

Sulekha partnered with many recognized course providers. Our Generative AI For Data Analysts Specialization training providers offer online and offline classes and train them to get placed in reputed and top companies. We offer a blended and balanced learning experience, which helps participants gain knowledge and skills.

Generative AI For Data Analysts Specialization training offline

Face-to-face interactions can significantly enhance your education and career. That's why we offer instructor-led training sessions, providing opportunities for personal connections that can make a real difference in your learning experience.

Generative AI For Data Analysts Specialization training online

Our online students benefit from the flexibility of self-paced learning. At the end of most lessons, you'll find an online discussion with instructors and peers to accelerate your learning experience.

Notice how our courses include both text and video content? This approach is designed to keep your brain engaged and active, regardless of whether you’re an online or offline learner. While online students benefit from the flexibility of self-paced learning, offline students enjoy the added advantage of instructor-led training. However, both formats provide a rich mix of text and video content to ensure an effective learning experience.