Certified Data Science with Generative AI Professional

Master Data Science with Gen AI — Python • Machine Learning • Generative AI • LLMs • Predictive Analytics

Generative AI is transforming how data scientists explore datasets, develop machine learning models, generate code, and solve complex analytical problems. This program combines core data science concepts with Generative AI techniques to help learners build intelligent solutions, accelerate analytical workflows, and develop practical AI-powered applications.

Learn Python programming, statistics, machine learning, prompt engineering, and large language model fundamentals while exploring how Generative AI can support data preparation, code generation, model development, and insight generation. Gain practical experience through real-world datasets, machine learning exercises, and AI-focused projects.

  • Comprehensive Data Science and Generative AI Curriculum
  • Hands-on Training with Machine Learning and AI Applications
  • Practical Projects Using Python, LLMs, and Generative AI Tools
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5 months
Course Duration
Advanced
Course Level
Online
Course Delivery
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Program Highlights

The Data Science with Gen AI Certification Training combines programming, statistical analysis, machine learning, and Generative AI concepts. Learn to analyze data, develop predictive models, use AI assistants for coding, understand large language models, and explore intelligent application development.

💻 Instructor-Led Training and Practical Learning [Confirm Availability]
🎥 Recorded Video Sessions for Flexible Learning [Confirm Availability]
👨‍🏫 Industry Expert Guidance and Learner Support [Confirm Availability]
📄 Hands-on Labs with Python and Machine Learning
💳 Generative AI and Large Language Model Fundamentals
📞 Real-World Data Science and AI Projects
📄 Resume Preparation and Career Guidance [Confirm Availability]

Why Choose Data Science with Gen AI Certification Training?

Combine Data Science, Machine Learning, and Generative AI

Develop practical skills in data analysis, predictive modeling, and AI-assisted development. Learn how Generative AI can support code generation, data exploration, model experimentation, text analysis, and the development of intelligent applications.

Comprehensive Curriculum
Python, Statistics, Machine Learning, Generative AI, Prompt Engineering, Large Language Models, and AI Application Development.
AI-Assisted Learning
Explore how AI assistants can explain code, suggest analytical approaches, draft code, and support experimentation while learning to verify generated outputs.
Hands-on Labs & Projects
Practice data preparation, model development, AI-assisted analysis, text processing, and Generative AI application development.
Certification and Assessment
Build practical skills and prepare for the applicable course assessment or certification, subject to the selected program.
Enroll Now Course Curriculum
AI
Applied Learning
Data
Practical Projects
Data Science with Gen AI
Training Program
Practical Training
Data science neural network with generative AI

Data Science with Gen AI Certification Training

Build practical expertise in data science and Generative AI to explore predictive analytics, machine learning, language models, and AI-assisted development. Learn to work with datasets, develop models, experiment with LLMs, and build AI-powered prototypes through practical exercises and projects.

Program Level

What You Will Learn

1. Introduction to Data Science and Generative AI
  • Data science concepts and applications; artificial intelligence and machine learning fundamentals
  • Generative AI, large language models, and their applications in data science
  • Data science lifecycle, analytical workflows, and structured and unstructured data
  • Business problems and data-driven decision-making
2. Python Programming for Data Science
  • Python fundamentals, variables, data types, functions, and data structures
  • Writing reusable programs and reading, processing, and transforming datasets
  • Jupyter Notebook, NumPy, and Pandas for data science workflows
  • Using Generative AI assistants to draft and explain code; debugging and validating generated code
3. Statistics and Mathematics for AI
  • Descriptive statistics, probability, probability distributions, and sampling
  • Correlation, regression, hypothesis testing, and statistical interpretation
  • Linear algebra and mathematical foundations of machine learning
  • Analytical reasoning, evaluation metrics, and model performance
4. Data Analysis and Machine Learning
  • Data cleaning, preprocessing, exploratory analysis, feature engineering, and feature selection
  • Supervised and unsupervised learning, regression, decision trees, random forests, and clustering
  • Model training, testing, validation, evaluation, and performance improvement
  • Using AI assistants to support experimentation and code development
5. Generative AI and Prompt Engineering
  • Generative AI models, large language models, tokens, context windows, and model limitations
  • Prompt engineering fundamentals and structured prompts for data science tasks
  • AI-assisted code generation and explanation, documentation, and analytical summaries
  • Text and unstructured data; evaluating outputs and understanding hallucinations, bias, and responsible AI
6. Large Language Models and AI Application Development
  • LLM capabilities, text generation, summarization, embeddings, and semantic similarity
  • Vector representations, vector databases, and semantic search fundamentals
  • Model APIs where included in the curriculum; developing simple LLM-powered applications
  • Introduction to Retrieval-Augmented Generation (RAG), if included; testing application outputs and limitations
7. Generative AI for Data Science Workflows
  • AI-assisted exploratory analysis, code generation, and feature engineering
  • Supporting model experimentation, summarizing findings, and drafting technical documentation
  • Exploring AI-assisted visualization and appropriate task automation
  • Validating generated code, analysis, and conclusions; applying data privacy and responsible AI practices
8. Hands-on Projects and Career Preparation
  • Customer churn prediction, sales forecasting, and customer segmentation
  • AI-powered document summarization, text classification, and sentiment analysis
  • Basic LLM-powered question answering and document-based RAG exploration, if included
  • Portfolio development and interview preparation

Core Traits You’ll Build

Analytical ThinkingPython ProgrammingMachine LearningPrompt EngineeringCritical ThinkingAI LiteracyProblem-SolvingModel Evaluation

Your Analyst Style

Tools and Technologies Covered: Python, Jupyter Notebook, NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn, SQL, Generative AI assistants, prompt engineering, LLM APIs (if included), embeddings (if included), vector databases (if included), and RAG frameworks (if included).

Data Science with Gen AI Certification Training

Build practical skills in data science and Generative AI through machine learning workflows, LLM fundamentals, and applied projects. Certification and assessment details depend on the selected program.

Data Science Certificate

Build Data Science and Generative AI Skills

Develop programming, statistical analysis, machine learning, and Generative AI capabilities. Prepare for the applicable course assessment or certification, subject to the selected program.

  • Master Python programming for data science
  • Understand statistics and machine learning fundamentals
  • Perform data cleaning, exploratory analysis, and predictive modeling
  • Apply prompt engineering and understand LLM applications
  • Explore embeddings, semantic search, and RAG fundamentals
  • Build practical data science and AI projects while validating AI outputs

Tools & Technologies Covered

Microsoft Excel

Data cleaning, formulas, pivot tables, and reporting.

SQL & Databases

Query, filter, join, and analyze structured datasets.

Python

Automate analysis and explore data using libraries.

Power BI

Create interactive dashboards and business reports.

Tableau

Visualize trends and communicate analytical findings.

Pandas

Clean, manipulate, and analyze datasets with Python.

NumPy

Perform numerical calculations and array-based analysis.

Statistics

Apply descriptive and inferential statistics to identify patterns, test hypotheses, and draw reliable conclusions from data.

GGplot

GGplot is a system for declaratively creating graphics, based on The Grammar of Graphics.

Seaborn

Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for creating attractive and informative statistical graphics.

Pycharm

Pycharm is a Python IDE that provides a comprehensive set of tools for developing Python applications.

Matplotlib

Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python.

Data Science with Gen AI Certification Training

Data science and Generative AI are creating new ways to analyze information, develop intelligent applications, and automate parts of technical workflows. AI assistants can help explore code, understand datasets, generate documentation, and prototype applications while learners apply technical judgment to verify results.

Generative AI also enables text generation, summarization, semantic search, and question answering over documents. This program introduces data science and Generative AI through practical exercises, machine learning workflows, and applied projects.

Organizations are exploring ways to use data and AI to improve productivity, understand customers, automate processes, and develop intelligent products. Data science and Generative AI knowledge combine analytical and technical skills.

Work with datasets and case studies involving customer behavior, sales forecasting, text analysis, and business intelligence. Explore machine learning workflows, AI-assisted programming, language model applications, and document-based question answering where included.

Develop skills relevant to Junior Data Scientist, Machine Learning Analyst, AI Application Developer, Data Analyst, and Generative AI Associate roles, depending on experience, technical proficiency, and project portfolio.

Student Testimonials

Rahul Sharma

Lokesh Dandamudi

★★★★★

I had an excellent experience with Securium Academy! The courses are well-structured, up-to-date, and tailored to meet industry standards, especially in the field of cybersecurity. The instructors are highly knowledgeable and supportive, breaking down complex topics into easily understandable modules. What stood out the most was their hands-on approach with practical labs, real-world scenarios, and continuous mentoring. Whether you're a beginner or looking to advance your career, Securium Academy offers the right blend of theory and practice. Highly recommended for anyone serious about building a strong foundation in cybersecurity!

Lajin C P

★★★★★

Exam was very nice.. It was an excellent experience.. I highly recommend everyone who is highly passionate in cyber security field should attend this exam.

user

Malle Ganesh

★★★★★

My overall experience is soo Good with Securium Academy. They have excellent trainers with Good teaching Skills. Especially My trainer so good in CYBER SECURITY knowledge with excellent practical knowledge. They also providing Coupons for the certifications

user

Alka Sajeevan P

★★★★★

Happy to share that I have successfully earned the Certified Ethical Hacker (CEH) certification from EC-Council! This achievement has deepened my understanding of ethical hacking methodologies, penetration testing, and advanced cybersecurity concepts — empowering me to identify, analyze, and secure systems against evolving cyber threats.

user

Sreelakshmi Suresh

★★★★★

I recently completed the Certified Secure Computer User (CSCU) certification, and I must say it was an excellent experience! The CSCU program is well-structured and beginner-friendly, making it perfect for anyone looking to build a strong foundation in cybersecurity awareness, internet safety, and digital hygiene.

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Frequently Asked Questions

Data Science with Gen AI combines traditional data science techniques with Generative AI technologies. It covers data analysis, Python programming, machine learning, prompt engineering, and using large language models to support analytical tasks and build intelligent applications.

The course is suitable for students, graduates, working professionals, data analysts, Python developers, and IT professionals who want to develop data science skills and understand practical Generative AI applications.

The proposed curriculum may include Data Science fundamentals, Python programming, statistics and probability, data cleaning and exploratory analysis, machine learning and predictive modeling, Generative AI fundamentals, prompt engineering, large language models, embeddings and semantic search, AI-assisted workflows, LLM application development, and practical projects.

Prior experience depends on the course level. Beginners can start with Python programming and data science fundamentals before progressing to machine learning and Generative AI. Basic computer skills and an interest in analytical problem-solving are helpful.

A traditional Data Science course generally focuses on programming, statistics, data analysis, machine learning, and predictive modeling. Data Science with Gen AI adds Generative AI, prompt engineering, LLM applications, and AI-assisted development to those foundations.

Generative AI can help draft code, explain programming concepts, summarize data findings, generate documentation, process text, and prototype AI-powered applications. Data scientists still need to validate generated code, check analytical results, and assess output quality.

The proposed curriculum includes LLM fundamentals, prompt engineering, and possible applications such as summarization and question answering. Embeddings, APIs, and RAG should be included in the published course description only if they are part of the actual program.

Depending on the confirmed curriculum, tools may include Python, Jupyter Notebook, NumPy, Pandas, Scikit-learn, Generative AI assistants, and LLM APIs. Advanced components may include embeddings, vector databases, and RAG frameworks.

Proposed projects include customer churn prediction, sales forecasting, sentiment analysis, document summarization, and a basic LLM-powered question-answering application. Confirm the actual project list and practical training inclusions before enrollment.

Depending on your background and practical skills, you may explore opportunities in data analysis, junior data science, machine learning, AI application development, and Generative AI-related roles. Eligibility varies by employer, role, and experience.

The course can help working professionals develop data science and Generative AI skills for analytical workflows, programming, and AI application development. Confirm the schedule and training format before enrollment.

Duration and fee depend on the selected training package, learning format, course content, and certification inclusions. Contact Securium Academy for the confirmed duration, fee, and available payment options.

Certificate eligibility depends on the selected program. Confirm whether the course includes a completion certificate, an external certification assessment, or both.

Yes. Beginners can start with Python, basic statistics, and data analysis before moving to machine learning and Generative AI. Learning the fundamentals first helps learners understand AI-generated code and evaluate results effectively.

Advanced mathematics is not necessary to begin learning the fundamentals of Generative AI applications. A stronger understanding of statistics, linear algebra, probability, and machine learning is beneficial for deeper data science and model-development work.

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