Professional Certificate in Data Analytics and Generative AI (in collaboration with Purdue University)

Professional Certificate in Data Analytics and Generative AI

In collaboration with Purdue University and IBM 

• 8 months length program 

• Live classroom (5-8hrs / Week weekend classes)

• Ask us for the next cohort and schedule details!

Advance your career with the Professional Certificate in Data Analytics & Generative AI, offered in collaboration with Purdue University Online and IBM. This program blends theoretical knowledge, real-world case studies, and hands-on practice, providing a comprehensive learning experience.

Designed for learners from both technical and non-technical backgrounds, the program delivers an in-depth education in data analytics and the growing field of generative AI. Participants gain access to self-paced online videos, live virtual classes, practical projects, labs, and personalized mentorship sessions.

The program’s blended learning approach ensures practical exposure to essential tools and programming languages, including Excel, SQL, Python, Tableau, and Power BI, while integrating the latest generative AI applications and concepts.

Key Features

• Course and material are in English
• in collaboration with Purdue University Online
• Beginner to advanced level
• 8 months of live classroom by industry experts (5-8 hours/week weekend classes) 
• 170+ hours of live classes and mentor-led project support
• 300+ hours of study time and practice recommended
• 1 year platform access & class recordings
• Capstone from 3 domains and 14+ Data Analytics Projects with Industry datasets
• Hands-on projects across the data analytics lifecycle, plus generative AI applications
• 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.

What added value does Purdue University contribute to the program?

The program curriculum is designed and reviewed with the assistance of the university, which gives the program quality legitimacy and a co-branded certificate of completion. Please be aware that the live classes are not held by actual University faculty staff but by many experienced Industry experts who are suitable for each topic.

Learning Objective

• Understand fundamental concepts of data analytics and generative AI
• Utilize Excel, SQL, and ETL processes to extract, transform, load, and analyze various data sets
• Master Python libraries such as NumPy, Pandas, SciPy, and scikit-learn for statistics, data wrangling, and visualizations
• Apply machine learning methods and generative AI algorithms for data-driven insights and AI solutions
• Create interactive Power BI dashboards and prepare for the PL-300 certification exam
• Implement advanced data visualization techniques in Tableau, including heat maps, treemaps, and waterfalls
• Design end-to-end analytics solutions, covering data ingestion, modeling, and forecasting
• Integrate data ethics principles to ensure privacy, fairness, and security
• Solve real-world problems through a capstone project, demonstrating full proficiency in data analytics and generative AI

11+ Skills Covered

• Data Analytics
• Generative AI
• Data Storytelling
• Data Ethics
• SQL
• Python
• ETL
• Statistical Analysis using Excel
• Data Analytics using Python
• Data Visualization with Tableau
• Data Visualization with Power BI

Target Audience:

• IT professionals upskilling in data analytics and AI
• Banking & finance professionals leveraging generative AI
• Marketing managers optimizing campaigns with data
• Supply chain managers improving logistics through analytics
• Undergraduate and postgraduate students
• Anyone interested in data analytics and generative AI, with or without programming experience

Prerequisites:

• High School Diploma or Bachelor’s degree (or equivalent)
• Applicants from both programming and non-programming backgrounds are welcome
• Prior work experience is not required

Learning Path

1. Program Induction
2. Business Analytics with Excel
3. Data Acquisition and Manipulation Using SQL
4. Extract, Transform, and Load (ETL)
5. PL-300 Microsoft Power BI Certification
6. Fundamentals of Python Programming
7. Data Analytics With Python
8. Generative AI Literacy
9. Applications of Generative AI in Data Analytics
10. Generative AI for Business and Professionals (IBM)
11. Capstone Project

Electives

• Academic Masterclass by Purdue University Online
• Industry Masterclass – Data Analytics
• Data Visualization using Tableau
• Prompt Engineering Essentials (IBM)
• Data Ethics
• Responsible and Ethical Generative AI (IBM)

COURSE CONTENT DETAILS

Course 1: Program Induction

Kick off your Data Analytics journey with this program in collaboration with Purdue University Online. Begin with foundational courses in Statistics, an Introduction to Data Analytics, and SQL training to build a strong base for the certification.

Course 2: Business Analytics With Excel

Gain an understanding of business analytics and its industry relevance. Use Microsoft Excel to perform analytics tasks and create visual insights with charts and dashboards. Build a foundation in statistics and analytical techniques as the first step in the data analytics program.

Learning Outcomes

• Understand the importance of business analytics in a professional context
• Utilize statistical techniques in Excel, including moving averages, hypothesis testing, ANOVA, and regression
• Master Excel functions and conditional formatting for data analysis
• Analyze complex datasets using pivot tables and slicers
• Create visual insights through charts and dashboards

Course curriculum

• Introduction to Business Analytics
• Excel for Business Analytics
• Conditional Formatting and Key Functions
• Statistical Data Analysis
• Analysis Using Pivot Tables
• Dashboard Creation

Course 3: Data Acquisition and Manipulation Using SQL

Develop foundational skills for working with SQL databases. Explore core SQL concepts such as statements, commands, conditional queries, joins, subqueries, and a variety of functions essential for efficient database management and scalable data operations.

Learning Outcomes

• Gain a clear understanding of database structures and their relationships
• Learn to manage transactions, create tables, and work with views
• Execute and understand stored procedures
• Use common query tools effectively

Topics Covered

• Core SQL statements and commands
• Database restoration and backup techniques
• Filtering, ordering, and applying selection commands
• Alias usage and aggregate command applications
• Group By command implementation and conditional statements
• Understanding and executing joins and subqueries
• Working with views and indexes
• Implementing string, mathematical, date, and time functions
• Pattern matching in strings and user access control functions

Course 4: Extract, Transform, and Load (ETL)

Begin a thorough study of ETL fundamentals essential for effective data analysis. Understand how to systematically extract structured and unstructured data, create rules, and use ETL tools such as Nifi and Talend. Build skills in loading data into repositories, differentiate between batch and real-time ETL processes, and manage ETL workflows efficiently to support smooth data analysis.

Learning Outcomes

• Understand the role and importance of ETL in data management and integration
• Identify and connect to various data sources, including databases and APIs
• Extract structured and unstructured data using full and incremental methods
• Cleanse, validate, normalize, and aggregate data to address quality issues
• Create transformation rules, manage schema changes, and map diverse data types
• Work with ETL tools like Apache NiFi and Talend using platform-specific features
• Load transformed data efficiently into target repositories for optimal performance
• Distinguish between batch and real-time ETL and implement real-time processing
• Develop strategies for maintaining data quality, with logging and monitoring mechanisms
• Optimize ETL workflows for speed and efficiency, including parallel processing techniques
• Automate ETL job scheduling and monitoring for seamless execution
• Implement systems for monitoring ETL process health and follow best practices for maintenance and version control

Topics Covered

• ETL Fundamentals
• Data Extraction
• Data Transformation
• Data Mapping and Conversion
• ETL Tools and Technologies
• Data Loading
• Batch and Real-Time ETL
• Error Handling and Logging
• Performance Optimization
• Automation
• Monitoring and Maintenance

Course 5: Microsoft Power BI Certification

This Power BI course teaches you how to perform insightful data analysis and create interactive dashboards while preparing for the Microsoft PL-300 Power BI Data Analyst exam. The program covers leveraging Power BI to solve business challenges and improve operational efficiency. You will learn to build dashboards from published reports, extract insights using Quick Insights, apply a range of Power BI functions for data collection and analysis, and gain practical strategies to troubleshoot common Power BI issues.

Learning Outcomes

• Design interactive dashboards from reports to enhance visualization and user engagement
• Create visuals and dashboards using Quick Insights to uncover actionable data insights
• Use natural language Q&A to generate meaningful visuals
• Set up and manage data alerts for timely information updates
• Apply best practices in report layout and data visualization for maximum impact
• Choose and optimize charts based on context and narrative needs
• Incorporate shapes to emphasize key points and improve storytelling in reports
• Share reports and dashboards efficiently across teams or stakeholders
• Execute complete Power BI projects for end-to-end data analysis and visualization
• Integrate custom visuals tailored to specific business or project requirements

Topics Covered

• Techniques for efficient data retrieval and preparation
• Effective data management strategies
• Creation of interactive reports and dashboards
• Power BI tips and tricks to enhance efficiency

Course 6: Fundamentals of Python Programming

Gain foundational knowledge of Python programming and its applications in data analytics. Understand key concepts such as variables, data types, and functions, and apply these skills to analyze data. Reinforce your learning by working on a hands-on project that tackles a real-world business scenario.

Learning Outcomes

• Understand the advantages and use cases of Python
• Learn about Python data types, operators, and string functions
• Set up Python and Jupyter Notebook and understand their applications
• Explore different loop types and variable scope within Python functions

Topics Covered

• Programming Fundamentals
• Python Data Types and Operators
• Conditional Statements and Loops in Python
• Introduction to Python
• Python Functions

Course 7: Data Analytics with Python

Explore key Python packages for data analytics, with a focus on NumPy and Pandas. Understand the role of statistics in analyzing data, and master essential concepts such as data categorization, cleaning, and visualization to generate actionable insights.

Learning Outcomes

• Collect, process, analyze, and visualize data using Python libraries for actionable insights
• Apply statistical methods for data analysis and interpretation
• Perform advanced statistical analyses for comprehensive data understanding
• Conduct hypothesis testing to interpret research findings
• Use Python visualization libraries to create clear and informative data representations
• Apply data optimization techniques to improve data quality and integrity
• Design and interpret ANOVA tests for group comparisons
• Understand scalars and vectors, including linear independence

Topics Covered

• Python Packages for Data Analytics
• Working with Categorical Data and Text Data
• Hypothesis Testing Mechanisms
• Statistical Functions
• Exploratory Data Analysis (EDA)
• Data Visualization

Course 8: Generative AI Literacy

Establish a solid foundation in generative AI and machine learning by understanding core principles, essential algorithms, and practical applications. Explore deep learning, large language models, and AI-driven tools to gain hands-on experience in deploying effective AI solutions.

Learning Outcomes

• Distinguish between deep learning, machine learning, AI, and generative AI.
• Understand the differences and applications of supervised, unsupervised, and reinforcement learning.
• Explore generative AI algorithms, including neural networks, GANs, and transformers.
• Learn how large language models (LLMs) power chatbots.
• Investigate AI models such as ChatGPT, Gemini, Claude, and Falcon.
• Understand image generation techniques using GANs, diffusion models, and VAEs.
• Gain hands-on experience with AI image generation tools like DALL·E 2, Stable Diffusion, and MidJourney.
• Work with video generation tools such as Runway ML, Synthesia, and Gen-2 by Runway.
• Discover open-source AI repositories like Hugging Face and their applications.
• Explore AI marketplaces and prompt marketplaces like PromptBase to track emerging tools.
• Build expertise in prompt engineering for chatbots and AI search.
• Experiment with OpenAI Playground settings, including temperature and sampling techniques.

Topics Covered

• Introduction to Machine Learning and Generative AI
• Types of Machine Learning: Supervised, Unsupervised, and Reinforcement Learning
• Generative AI Algorithms: Neural Networks, GANs, Transformers (GPT and others)
• Large Language Models (LLMs) and Chatbots (ChatGPT, Gemini, Claude, Falcon, etc.)
• Image Generation Techniques: GANs, Diffusion Models, VAEs, and hands-on tools (DALL·E 2, Stable Diffusion, MidJourney)
• Video Generation: Architectures (GANs, Diffusion Models, Transformers) and practical tools
• Open-Source AI Model Ecosystem: Hugging Face and AI marketplaces
• Prompt Engineering: Chatbot prompts, OpenAI Playground settings, and image generation prompts

Course 9: Applications of Generative AI in Data Analytics

Explore how generative AI transforms the data analytics workflow. Learn its applications in the ETL process, create augmented data with ChatGPT, and generate synthetic data using AI tools. Understand its role in data visualization, modeling, forecasting, and risk analysis, while addressing challenges and ethical considerations for responsible AI use.

Learning Outcomes

• Understand the core concepts and significance of generative AI in modern data analytics.
• Apply generative AI techniques to tackle data scarcity, generate synthetic data, and support privacy-preserving analysis and data augmentation.
• Use generative AI tools to create interactive, customized, and accessible data visualizations.
• Automate ETL processes using generative AI for improved efficiency.
• Recognize challenges and ethical considerations when integrating generative AI into data projects.
• Explore real-world scenarios where generative AI is applied for data augmentation, anomaly detection, and exploratory data analysis.

Topics Covered

• Overview of Generative AI in Data Analytics
• Data Augmentation Using Generative AI
• Generative AI for Tailored Data Visualization
• Generative AI in Data Exploration
• AI-Optimized ETL Processes
• Generative AI in Data Modeling and Forecasting
• Challenges in Integrating Generative AI
• Future Directions in Generative AI

Course 10: Generative AI for Business and Professionals - IBM

The Generative AI for Business and Professionals course by IBM provides essential knowledge on leveraging generative AI to drive business growth, uncover new market opportunities, and enhance career prospects. Participants will learn how to adapt their skills to the evolving job market through expert guidance and practical experience. 

The program explores the transformative impact of generative AI across industries, demonstrating how businesses can optimize processes and identify new opportunities. It also offers insights into career pathways and ways generative AI can enhance current roles and skills. Industry experts share current trends, future perspectives, and practical applications, while hands-on labs and projects allow learners to gain real-world experience and showcase their proficiency in generative AI.

Learning Outcomes

• Understand the transformative impact of generative AI on businesses and careers
• Identify and leverage new business opportunities using generative AI
• Prepare organizations for successful generative AI adoption
• Explore career opportunities driven by generative AI across various industries
• Enhance professional skills with generative AI tools
• Gain hands-on experience through practical labs and projects

Topics Covered

• Generative AI Trends and Implementation
• Business Applications of Generative AI
• Career Growth through Generative AI
• Custom GPTs and Personalized AI
• Generative AI for Content Creators, IT Professionals, and Executives

Course 11: Capstone Project

The capstone project serves as the culmination of your comprehensive learning experience, allowing you to apply your skills in business analytics, data manipulation, visualization, and programming to real-world scenarios. Using tools like Excel, Python, SQL, and Tableau, you will tackle practical data challenges, demonstrating your proficiency in ETL processes, visualization techniques, and ethical data practices. This final project provides an opportunity to showcase your expertise, creativity, and strategic thinking while solving complex problems in data analytics.

Industry Case Studies and Projects

• Project 1 – Rating Prediction for Apps on Google Play Store: Build a model to predict app ratings using app-related information to enhance visibility.
  
  
• Project 2 – Demand Forecast for Walmart: Forecast store sales and demand for Walmart across the U.S., considering economic conditions.
  
  
• Project 3 – Designing a Sales Dashboard in Excel: Analyze sales across different product categories and visualize insights through an Excel dashboard.
  
  
• Project 4 – Online Car Rental Platform: Develop a platform where customers can view and rent available cars based on categories.
  
  
• Project 5 – Comparison of Regions Based on Sales: Create a dashboard to visualize region-wise sales performance and suggest improvements.
  
  
• Project 6 – Healthcare Analytics: Identify factors impacting cardiovascular health and develop a predictive system for heart attacks using relevant datasets.

Elective Courses:

Elective 1: Academic Masterclass offered by Purdue University Online

offers an engaging online session where participants gain valuable insights into the latest advancements in data analytics and the generative AI domain.

Elective 2: Industry Masterclass – Data Analytics

Participate in this interactive online industry masterclass to explore the latest advancements and techniques in data analytics.

Elective 3: Data Visualization using Tableau

This Tableau course offers a complete guide to creating impactful visualizations, organizing data efficiently, and designing informative charts and dashboards to support better business decisions. Participants will explore core data visualization concepts, master different chart types, and build interactive dashboards using filters, parameters, and sets. The course also covers advanced visualization techniques, including heat maps, treemaps, and Pareto charts.

Elective 4: Data Ethics

This course offers an in-depth exploration of ethical considerations and responsibilities in data analytics. Participants will examine the broad landscape of data ethics, including its importance, legal frameworks, privacy and security concerns, biases, ethical decision-making, responsible visualization practices, and the societal impacts of data-driven insights.

Elective 5: Prompt Engineering Essentials - IBM

This course teaches effective prompting strategies to optimize generative AI tools such as ChatGPT. Participants will explore techniques including zero-shot, few-shot, Interview Pattern, Chain-of-Thought, and Tree-of-Thought prompting. Hands-on labs with tools like IBM watsonx Prompt Lab, Spellbook, and Dust provide practical experience. The program is designed for professionals, executives, developers, students, and AI enthusiasts.

Elective 6: Responsible and Ethical Generative AI - IBM

This course examines the ethical challenges, limitations, and societal impacts of generative AI, covering topics such as data privacy, security, workforce implications, deepfakes, biases, and broader socioeconomic effects. Participants learn responsible AI practices, analyze real-world case studies, and gain practitioner insights to understand transparency, accountability, and the economic and social considerations of generative AI through interactive modules and hands-on projects.

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 8 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 is a Data Analytics Certificate?

A Data Analytics Certificate is an accredited credential that demonstrates your expertise as a data analyst. It encompasses key skills such as data collection, cleaning, analysis, visualization, and interpretation. The program is structured to benefit both beginners and experienced professionals, covering foundational topics like data preparation as well as advanced areas in analysis, visualization, and insights generation. Earning this certificate serves as a strong validation of your capabilities and knowledge in the data analytics domain.

What does a Data Analyst do?

In today’s data-driven world, everything from market research and sales to expenses and logistics generates vast amounts of information. Data analysts play a crucial role in making sense of this data. They collect, clean, and interpret large, complex datasets to extract meaningful insights that help answer business questions or solve problems. To do this effectively, analysts need a solid understanding of core concepts such as statistics, data mining, mathematics, and machine learning models. This Data Analytics Certificate program equips learners with the skills to navigate and leverage this data, transforming it into actionable insights for informed decision-making.

Benefits of Enrolling in the Data Analytics Certificate Program

This Data Analytics Certificate Program is designed for both beginners and experienced professionals. Beginners will gain the foundational knowledge needed to secure entry-level roles, while experienced learners can enhance and sharpen their existing skills through advanced modules. Key benefits of the program include:

• A joint certification from Purdue University Online 
• Access to exclusive IBM courses with individual certificates
• Live sessions covering trending topics such as Generative AI, Prompt Engineering, and Explainable AI
• Hands-on experience through real-world projects, including a capstone spanning three domains and over 14 data analytics projects using datasets from Google Play Store, Lyft, and the World Bank

Who are the instructors for the program?

The instructors for the Data Analytics Certificate Program are carefully chosen based on their expertise in data analytics and proven teaching experience. The selection process ensures they have both the theoretical knowledge and practical skills required to effectively deliver the program content.

Can I enroll without any prior knowledge of data analysis?

Yes, you can enroll in the Data Analytics Certificate Program even without any prior knowledge. The course covers all essential skills, tools, and concepts—from basic to advanced—allowing you to build a solid foundation in data analytics.

What are the different roles in data analytics?

The field of data analytics offers a variety of specialized roles as organizations increasingly rely on data for decision-making. Key roles include business intelligence analyst, data analyst, data scientist, data engineer, data visualizer, quantitative analyst, operations analyst, and data analytics consultant.

What are the top responsibilities of a data analytics expert?

Data analytics professionals play a crucial role in helping organizations make informed, data-driven decisions. Their main responsibilities include extracting data from databases, APIs, or cloud platforms; preprocessing and normalizing data for analysis; performing statistical analyses and building predictive models; and designing impactful data visualizations using tools such as Tableau or Power BI.

What will I earn after completing the program?

After successfully completing the program, you will be awarded a program completion certificate from Purdue University Online. Additionally, you will gain 12 months of access to Purdue’s Alumni Association membership, with the option to renew annually for a nominal fee.