Professional Certificate in Data Science and Generative AI (In collaboration with Purdue University and IBM)

Professional Certificate in Data Science and Generative AI

In collaboration with Purdue University and IBM

• 6 months length program (Live class & eLearning)
• Live classroom (5-8hrs / Week weekend classes)
• Ask us for the next cohort and schedule details!

The Professional Certificate in Data Science and Generative AI, offered by Purdue University Online, provides a comprehensive curriculum for mastering data science, machine learning, and Generative AI. Combining theoretical learning with practical application, participants gain expertise in programming, data management, applied data science with Python, machine learning, deep learning, and generative AI techniques.

Through hands-on projects, labs, and mentorship sessions, learners acquire experience with industry-standard tools and real-world applications, preparing them for careers in data science, machine learning, and Generative AI. The program suits both recent graduates and experienced professionals, offering a blend of self-paced videos, live virtual classes, and high-engagement practical exercises.

Key Features

• Course and material are in English
• in collaboration with Purdue University Online
• Beginner to advanced level
• 6 months of live classroom by Purdue faculty (5-8 hours/week weekend classes)
• 250+ hours of live classes and mentor-led project support
• 25+ hours eLearning video content
• 300+ hours of study time and practice recommended
• Flexible learning with session recordings and 24/7 access
• Gain exposure to ChatGPT, Gemini, Keras, TensorFlow and other prominent tools
• Interactive sessions on the latest AI trends, such as GenAI, prompt engineering, LLMs, and more
• 3 capstones and 25+ hands-on projects from various industry domains
• Networking benefits via Purdue’s Alumni Association
• Program completion certificate from Purdue University Online.
• Industry-recognized IBM certificates for IBM courses

Engaging Learning Experience

• Peer Interaction
• Enjoy a true classroom-like environment by connecting with fellow learners and engaging with mentors in real time through Slack.
• Flexible Learning
• Never fall behind—access recorded sessions anytime to catch up and stay aligned with your cohort.
• Mentorship Sessions
• Receive expert support from mentors to resolve doubts, get project guidance, and enhance your learning journey.
• Dedicated Support
• Benefit from a Cohort Manager who provides personalized assistance and ensures you stay on track toward success.

About Purdue University

Purdue University is a leading public research university known for creating practical solutions to some of today’s most pressing problems. Recognized by U.S. News & World Report as one of the top 10 Most Innovative Universities in the U.S. for four consecutive years, Purdue is at the forefront of groundbreaking research and innovation.

Learning Objective

• Understand core statistical and SQL concepts.
• Grasp AI fundamentals, applications, and business impact.
• Perform distributed and parallel computing on GPUs.
• Master mathematical and scientific computing using NumPy, SciPy, and Scikit-Learn.
• Execute data science workflows: wrangling, exploration, visualization, hypothesis testing.
• Learn NLP fundamentals with Python’s NLTK, including text understanding and generation.
• Build deep learning models using Keras, TensorFlow, and cloud platforms like AWS SageMaker.
• Explore advanced AI topics: Generative AI, GPT, explainable AI, and GANs.
• Develop computer vision applications and speech recognition models.
• Apply supervised and unsupervised learning, recommendation engines, and time series modeling.
• Deploy deep learning models using Flask/Django, Docker, Kubernetes, and serverless environments.
• Implement neural networks and advanced deep learning techniques such as object detection.
• Gain proficiency in Power BI for data storytelling, creating interactive dashboards and reports.
• Understand Generative AI landscape and practical applications for business and research.
• Learn to validate machine learning models using accuracy metrics and performance evaluation.

18+ Skills Covered

• Data Analysis & Statistics: Exploratory Data Analysis, Descriptive & Inferential Statistics
• Machine Learning: Model Building, Supervised & Unsupervised Learning, Ensemble Learning, Model Training, Optimization, Evaluation, and Validation
• Deep Learning & AI: Deep Learning, Generative AI, Reinforcement Learning, Natural Language Processing, Computer Vision, Speech Recognition
• AI Techniques & Tools: Prompt Engineering, Explainable AI, Data Visualization, Machine Learning Algorithms, Fine-Tuning

Target Audience:

This program is designed for working professionals across industries, bringing diverse perspectives that enrich class interactions. It is well-suited for both new and experienced professionals aiming to build careers in data science and generative AI. Ideal candidates include those with an analytical mindset from any educational background, such as:

• IT Professionals
• Software Developers
• Analytics Professionals
• Product Managers
• Business Analysts
• Technology Consultants

Prerequisites:

• High School Diploma or Bachelor’s degree (or equivalent)
• Basic knowledge of programming and mathematics
• Preferably 2+ years of professional experience (not mandatory)

Learning Path

1. Program Induction
2. Programming Refresher
3. Data Management using SQL
4. Python for Data Science (IBM)
5. Applied Data Science with Python
6. Machine Learning
7. Deep Learning Specialization
8. Essentials of Generative AI, Prompt Engineering & ChatGPT
9. Generative AI for Data Professionals
10. Generative AI Skills for Data Scientist (IBM)
11. Capstone Project

Electives

• Academic Masterclass by Purdue University Online
• Industry Masterclass by IBM
• R Programming for Data Science (IBM)
• Data Visualization using PowerBI
• Advanced Deep Learning & Computer Vision
• Natural Language Processing
• Data Ethics

COURSE CONTENT DETAILS

Course 1: Program Induction

Begin your journey with Purdue University Online in this program, exploring the fundamentals of data science. Start with preparatory courses in Statistics and Programming to build a strong foundation for the rest of the curriculum.

Course 2: Programming Refresher

This course builds essential Python skills to prepare you for the program

Learning Outcomes

• Master procedural and object-oriented programming
• Understand loops, variable scope, and data types
• Install Python and use Jupyter Notebook for practical applications
• Learn methods, attributes, access modifiers, and proper coding conventions
• Gain knowledge of operators, string functions, and multi-threading

Course curriculum

• Fundamentals of Programming & Python Functions
• Introduction to Python Programming
• Object-Oriented Programming Concepts
• Threading
• Python Data Types and Operators
• Conditional Statements and Loops

Course 3: Data Management Using SQL

This course equips you with essential SQL skills to manage databases efficiently and support scalable applications

Learning Outcomes

• Develop a thorough understanding of databases and their relationships
• Execute stored procedures for complex operations
• Use SQL commands and common query tools effectively
• Apply string, mathematical, date/time, and pattern-matching functions
• Master transactions, table creation, and views for efficient database management
• Implement user access control for database security
• Gain expertise in filtering, ordering, aliasing, aggregates, grouping, joins, subqueries, views, and indexing

Topics Covered

• SQL Statements
• Aggregate Commands
• Restore and Backup
• Group By Commands
• Selection Commands – Filtering
• Conditional Statements & Ordering
• Joins
• Date and Time Functions
• String Functions
• Pattern (String) Matching
• Mathematical Functions
• User Access Control Functions

Course 4: Python for Data Science (IBM)

This course equips participants with the skills to use Python for data science, enabling them to write Python scripts and perform hands-on data analysis in a Jupyter lab environment.

Learning Outcomes

• Write Python scripts using variables, strings, functions, loops, and conditions.
• Apply Python concepts such as lists, sets, dictionaries, objects, classes, and branching.
• Use Pandas for data loading, manipulation, saving, and file operations.

Topics Covered

• Python Basics
• Python Data Structures
• Python Programming Fundamentals
• Working with Data in Python
• Working with NumPy Arrays

Course 5: Applied Data Science with Python

This course provides a comprehensive foundation in data science using Python, covering data preparation, modeling, evaluation, and visualization. Participants will develop practical skills in Python programming, statistics, and data analysis.

Learning Outcomes

• Understand data science fundamentals and practical applications.
• Perform hypothesis testing (Z-test, T-test, ANOVA) and interpret results.
• Manipulate and analyze data using Pandas (loading, indexing, merging).
• Prepare, normalize, and standardize data using techniques like binning.
• Apply NumPy for array operations, indexing, and slicing.
• Develop and evaluate data models effectively.
• Create compelling visualizations with Matplotlib, Seaborn, Plotly, and Bokeh.
• Apply Python concepts (strings, Lambda functions, lists) in data science tasks.
• Understand statistical measures including skewness, covariance, correlation, central tendency, and dispersion.
• Use linear algebra principles in data analysis, including applications in calculus.

Topics Covered

• Introduction to Data Science
• Essentials of Python Programming
• NumPy for numerical computing
• Linear Algebra for data analysis
• Statistics Fundamentals & Advanced Statistics
• Data Manipulation with Pandas
• Data Analysis and Wrangling
• Data Visualization (Matplotlib, Seaborn, Plotly, Bokeh)
• Probability Distributions and End-to-End Statistics
• Practical Applications of Data Science in Python

Course 6: Machine Learning

This course provides an in-depth exploration of machine learning, covering supervised learning (regression and classification), unsupervised learning (clustering), and ensemble modeling. Participants gain hands-on experience building a recommendation engine with PyTorch and learn to evaluate frameworks like TensorFlow and Keras, while understanding the complete machine learning pipeline.

Learning Outcomes

• Understand different types of machine learning and their applications
• Explore supervised learning: regression, classification, and correlation analysis
• Learn to detect and prevent overfitting and underfitting
• Examine unsupervised learning: clustering techniques and their use cases
• Master ensemble modeling: bagging, boosting, stacking
• Analyze the machine learning pipeline and MLOps operations
• Build a recommendation engine using PyTorch
• Evaluate and compare frameworks like TensorFlow and Keras

Topics Covered

• Machine Learning Fundamentals
• Supervised Learning
• Unsupervised Learning
• Regression and Its Applications
• Classification and Its Applications
• Ensemble Learning
• Recommendation Systems

Course 7: Deep Learning Specialization

This course equips you with the skills to deploy deep learning models using AI/ML frameworks. You’ll explore core concepts and practical applications, understand the differences between deep learning and machine learning, and gain hands-on experience with neural networks, forward/backward propagation, TensorFlow 2, Keras, PyTorch, CNNs, RNNs, autoencoders, object detection, transfer learning, performance optimization, and model interpretability. By the end, you’ll be able to build and optimize deep learning models effectively.

Learning Outcomes

• Understand various types of neural networks and their applications
• Master forward and backward propagation in deep neural networks (DNNs)
• Differentiate between deep learning and traditional machine learning
• Build and optimize models using TensorFlow 2, Keras, and PyTorch
• Apply performance improvement techniques like dropout and early stopping
• Gain expertise in convolutional neural networks (CNNs) and object detection
• Learn recurrent neural networks (RNNs) and their use cases
• Understand autoencoders and transfer learning
• Perform hyperparameter tuning and enhance model interpretability
  
  

Topics Covered

• Introduction to Deep Learning
• Artificial Neural Networks (ANN)
• Deep Neural Networks (DNN)
• Convolutional Neural Networks (CNN)
• Recurrent Neural Networks (RNN)
• PyTorch
• TensorFlow
• Transfer Learning
• Autoencoders
• Object Detection
• Model Optimization and Performance Improvement
• Transformer Models for Natural Language Processing (NLP)

Course 8: Essentials of Generative AI, Prompt Engineering & ChatGPT

This course provides an in-depth study of generative AI, with a focus on ChatGPT and large language models. You’ll learn core principles of GenAI, explainable AI, and conversational AI, along with prompt engineering, fine-tuning, and responsible AI usage. Participants gain hands-on knowledge to apply ChatGPT and other generative AI tools creatively across industries while understanding ethical considerations, model limitations, and strategies to maximize transparency and performance.

Learning Outcomes

• Grasp the fundamentals of AI and generative AI models.
• Understand explainable AI and its importance, exploring different approaches.
• Learn how ChatGPT works, including its features and limitations.
• Explore diverse real-world applications and use cases of ChatGPT.
• Gain hands-on exposure to fine-tuning techniques for model optimization.
• Recognize ethical challenges and responsible AI usage for generative models.
• Understand future trends and challenges in generative AI.

Topics Covered

• Introduction to Generative AI and its landscape
• Ethical considerations: responsible data usage and privacy
• Explainable AI: ensuring transparency
• Future trends and innovations in Generative AI
• Leveraging AI technologies for business and creative applications
• Designing effective prompts for AI models
• In-depth analysis of ChatGPT and its applications
• Conversational AI: applications and advancements
• Large language models: exploration and understanding
• Fine-tuning techniques for model personalization

Course 9: Generative AI for Data Professionals

Explore the role of generative AI in data science, from data generation and preparation to querying. Learn to overcome lifecycle challenges, leverage AI for data understanding, model building, and predictive analysis, and master tools for exploratory data analysis to thrive in data-driven roles.

Learning Outcomes

• Identify the four main types of generative AI models and their industry applications.
• Apply generative AI techniques for data generation, preparation, querying, and augmentation.
• Use generative AI tools for exploratory data analysis (EDA) and predictive modeling.
• Visualize data and build models leveraging generative AI.
• Address industry-specific considerations and challenges in implementing generative AI.
• Develop predictive models and enhance data science skills with generative AI.

Topics Covered

• Introduction to Generative AI and its role in data science
• Common types of generative AI models and applications across industries
• Using generative AI throughout the data science lifecycle: generation, preparation, querying, augmentation
• Overcoming data preparation and querying challenges with generative AI
• Industry-specific considerations and challenges for data scientists
• Data visualization and model building with generative AI
• Enhancing core data science skills through generative AI techniques

Course 10: Generative AI Skills for Data Scientists (IBM)

This course trains data scientists, analysts, and engineers to harness generative AI for solving data challenges and optimizing workflows. Participants gain hands-on experience applying generative AI tools to real-world scenarios.

Learning Outcomes

• Explore generative AI models such as GANs and VAEs for data preparation, augmentation, and querying across industries.
• Utilize generative AI for exploratory data analysis (EDA), predictive modeling, and solving industry-specific challenges.
• Apply generative AI techniques to real-world problems through a guided project and final assessment.
• Develop core data science competencies with a focus on real-world applications and the ethical use of generative AI.

Topics Covered

• Generative AI Tools and Models
• Applications in Data Preparation and Augmentation
• Exploratory Data Analysis with Generative AI
• Predictive Modeling and Industry-Specific Challenges
• Real-World Project and Final Assessment

Course 11: Capstone Project

The Data Science, Machine Learning, and Generative AI Capstone Project lets you apply your skills to real-world challenges under expert mentorship. Using Python or SAS, you'll implement regression, decision trees, and AI algorithms, and apply techniques like k-fold cross-validation to ensure robust models. This project consolidates your learning, showcasing your expertise in data science and AI workflows and preparing you to impress potential employers with practical, industry-ready skills.

Industry Case Studies and Projects

• Project 1: Build a conversational Virtual Assistant with Generative AI for dialogues, Q&A, recommendations, and task assistance.
• Project 2: Develop a Python-based e-commerce app with item management and multiple payment options.
• Project 3: Create an online car rental platform with scheduling, billing, and object-oriented programming.
• Project 4: Apply time series forecasting to predict restaurant item demand for the food industry.
• Project 5: Use exploratory data analysis and hypothesis testing to optimize marketing strategies and customer acquisition.
• Project 6: Perform cluster analysis to create personalized song playlists based on user behavior.
• Project 7: Build a machine learning model to predict employee attrition rates using behavioral patterns.
• Project 8: Automate ship detection using CNN-based deep learning to prevent human-error incidents.
• Project 9: Develop deep learning models to predict house loan repayment using historical data.
• Project 10: Implement facial recognition with deep learning for diagnosing genetic disorders in healthcare systems.
• Project 11: Detect diabetic retinopathy using CNNs and deploy models with TensorFlow Serving.
• Project 12: Map employee performance and generate appraisal reports using SQL databases.
• Project 13: Perform air cargo data analysis using SQL to improve services and customer experience.
• Project 14: Create a Tableau dashboard for crime analysis to keep police and city authorities informed.
• Project 15: Build an interactive Tableau sales dashboard for an apparel OEM to support ad-hoc reporting and analysis.

Elective Courses:

Elective 1: Academic Masterclass offered by Purdue University Online

Provides an interactive online session where participants gain insights into the latest technological advancements and methodologies in Data Science, AI, and Machine Learning.

Elective 2: Industry Masterclass by IBM

Join this interactive online industry masterclass to explore the latest developments in Data Science and AI methodologies.

Elective 3: R Programming for Data Science course by IBM

Covers fundamental R concepts such as mathematical operations, variables, strings, vectors, and factors. Learners gain hands-on experience with arrays, matrices, lists, and data frames, as well as with conditions, loops, functions, objects, classes, and debugging. The curriculum focuses on managing text, CSV, and Excel files, reading and writing data, handling strings and dates, and developing essential skills for data manipulation and analysis in R.

Elective 4: Data Visualization using Power BI

Teaches participants how to harness Power BI for data analysis and uncover meaningful business insights. Learners gain skills in creating interactive dashboards, developing reports, and using Quick Insights to quickly identify data patterns. The course emphasizes practical techniques for efficient Power BI usage, enabling participants to maximize the tool’s capabilities for informed decision-making and improved operational efficiency.

Elective 5: Advanced Deep Learning & Computer Vision

This course equips participants with advanced skills in deep learning and computer vision to tackle complex challenges. It covers image formation, processing, and techniques such as CNNs, object detection, and segmentation. Learners also explore generative models, OCR, distributed and parallel computing, and Explainable AI (XAI), focusing on mastering advanced methods and effectively deploying models to solve real-world computer vision problems.

Elective 6: Natural Language Processing

Participants in this course learn to apply machine learning to natural language data, emphasizing both understanding and generating language. The curriculum covers feature engineering for NLP, automated speech recognition, speech-to-text and text-to-speech conversion, and building voice assistants and Alexa skills, providing advanced expertise in NLP and speech applications.

Elective 7: Data Ethics

This module provides a thorough understanding of data ethics, including legal frameworks, privacy, and security considerations. Participants learn to address biases, make ethical decisions, and visualize data responsibly, gaining insights into the societal impact of analytics and developing the ability to navigate ethical challenges professionally.

FREQUENTLY ASKED QUESTIONS

How is the program delivered?

The course is delivered entirely online through live virtual classes, offering an 80:20 blend of experiential training and theoretical learning. You'll engage in hands-on projects, case studies, and interactive sessions led by industry experts.

How is the class schedule looks like? Is there recordings?

The course typically spans about 6 months with an estimated 5–8 hours of weekly weekend live sessions with a variety of schedules. In between courses, there will be a lot of hands-on project to complete. Please email us to get the details of the schedule of the program. If you miss a class, you can always watch the recording.

NOTE:

Attendance cannot be marked by simply watching the session recordings. Attendance is recorded only when a learner joins the live session. Since these are university-affiliated programs, the criteria are more stringent, as they are set by the universities themselves. However recordings will be available . Learners can view the specific certificate criteria for each course directly on their LMS

Can I work full-time while enrolled in this program?

Yes, you can! The program schedule is designed to help busy professionals with full-time work. You can attend live instructor-led sessions which are mostly held on weekends at the designated time according to your schedule and then complete assignments/projects during your free time.

What does a Data Science professional do?

A Data Science professional identifies key business problems and develops algorithms to enable faster, more accurate, and large-scale data analysis using tools like Python, Tableau, Hive, and others. They collect, integrate, and analyze data to extract actionable insights, resolve data issues, and build predictive models and strategies. Data Science applications are widespread, impacting industries such as technology, finance, and healthcare.

What are the benefits of enrolling in the Data Science Certificate Program?

The Data Science Certificate Program, offered in collaboration with Purdue University, is a highly regarded certification designed by academic leaders and industry experts. It helps participants differentiate themselves and attract the attention of top data science employers. The curriculum combines theoretical knowledge with hands-on experience and project-based learning using real-world datasets from companies like Amazon, Uber, and Comcast, equipping learners with the skills needed to launch a successful career in data science.

Who are the instructors for this Data Science Certificate Program, and how are they selected?

The program features highly qualified instructors chosen through a rigorous selection process that includes profile screening, technical evaluations, and training demonstrations. Only trainers with consistently high alumni ratings are retained, ensuring a top-quality learning experience.

Can I apply without a technical background?

Yes, you can enroll even without prior technical experience. However, having a basic understanding of programming and mathematics will be beneficial.

Do Data Science professionals need coding skills?

Yes, coding is a fundamental part of data science. Scripting and mathematical skills are essential for executing data projects. Knowledge of programming languages like Python and R is highly recommended, and this program is designed to help you build that foundation effectively.

What career opportunities are available after completing the program?

Completing this Professional Certificate in Data Science and Generative AI opens doors to roles such as Data Scientist, Machine Learning Engineer, Data Analyst, and Business Intelligence Analyst. The program equips you with expertise in data manipulation, statistical analysis, and machine learning, making you valuable across industries like technology, finance, healthcare, and e-commerce. With experience and ongoing learning, you can progress to senior positions such as Data Science Manager, Lead Data Scientist, Chief Data Officer, or advanced roles in AI and Deep Learning.

What will I receive upon completing the program?

After successfully finishing the program, you will be awarded a completion certificate from Purdue University Online. Additionally, you will gain 12 months of access to Purdue’s Alumni Association membership, which can be renewed annually for a nominal fee payable to Purdue University Online.