• Learning Format Self-Paced Learning

Why Join this Program

Caltech Academic Excellence

Earn a program Certificate and up to 14 CEU credits from Caltech CTME

Gain Generative AI Mastery

Dedicated course on generative AI, prompt engineering, ChatGPT and more

IBM’s Industry Expertise

Obtain industry-recognized IBM certificates for IBM courses and get access to masterclasses by IBM

Caltech CTME

Earn a membership to Caltech CTME circle and an online convocation by the Caltech program director.


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Program Overview

This Caltech Post Graduate Program in Data Science leverages the superiority of Caltech's academic eminence covering critical data science topics like Python, R, machine learning, data visualization tools, generative AI, explainable AI, ChatGPT, and more through an interactive learning model with live sessions led by global practitioners.

Key Features

  • Earn a program certificate and up to 14 CEUs from Caltech CTME
  • Generative AI and prompt engineering: Explore a dedicated course with live sessions
  • Learn about ChatGPT, DALL-E, Midjourney & other prominent tools
  • Caltech CTME Circle Membership and online convocation by Caltech CTME Program Director
  • Access to hackathons and Ask Me Anything sessions from IBM
  • Masterclasses delivered by distinguished Caltech faculty and IBM experts
  • Industry-relevant capstone projects in 3 domains
  • Explore 25+ hands-on projects with seamless access to integrated labs

Post Graduate Program Advantage

This Data Science Post Graduate Program leverages Caltech's academic prowess and helps you pursue a successful career in data science.

  • Program Certificate

    Partnering with Caltech CTME

    • Masterclasses delivered by distinguished Caltech CTME instructors
    • Program completion certificate from Caltech CTME
    • Earn up to 14 CEUs to showcase your achievements
  • IBM Certificate

    Program in Collaboration with IBM

    • Industry-certified IBM certificates for IBM courses
    • Industry masterclasses delivered by IBM
    • Hackathons from IBM

Program Details

Embark on a meticulously crafted learning journey in generative AI, explainable AI, prompt engineering, ChatGPT, statistics, Python, data analysis, machine learning, data visualization, and more. Elevate your data science expertise and advance your career.

Learning Path

  • Welcome to this PG in Data Science, developed and presented with Caltech CTME and IBM, which will help you become an expert in the data science domain.

    • Develop a comprehensive understanding of coordinate geometry and linear algebra.
    • Comprehend the concepts of eigenvalues, eigenvectors, and eigendecomposition.
    • Build a strong foundation in calculus, including limits, derivatives, and integrals.
    • Distinguish between various types of statistics and recognize their applications 
    • Comprehend the differences between structured and unstructured data.
    • Describe mean absolute deviation (MAD), standard deviation, and variance.
    • Analyze the outcomes of hypothesis testing, including one-tail and two-tail test
    • Acquire proficiency in both procedural and object-oriented programming.
    • Recognize the advantages and benefits of using Python as a programming language.
    • Install Python and its integrated development environment.
    • Familiarize yourself with the Jupyter Notebook and its practical applications.
    • Implement Python identifiers, indentations, and comments effectively.
    • Understand Python's data types, operators, and string functions.
    • Learn about the various types of loops in Python.
    • Describe methods, attributes, and access modifiers in Python.
    • Attain a thorough grasp of databases and their interconnections.
    • Gain proficiency in utilizing popular query tools and handling SQL commands.
    • Achieve mastery in managing databases efficiently, covering transactions, table creation, and utilizing views.
    • Comprehend and successfully execute stored procedures for the execution of complex operations.
    • Explore a range of SQL functions, including those related to strings, mathematics, date and time, and pattern matching.
    • Grasp the functions related to user access control to ensure the security of databases.
    • Craft your inaugural Python script incorporating variables, strings, functions, loops, and conditional statements.

    • Gain a firm grasp of Python's fundamental principles, including lists, sets, dictionaries, conditional logic, branching, object-oriented programming, and classes.

    • Harness the power of the Pandas library to import, manipulate, save data, and manage file operations in Python.

    • Delve into the phases of data preparation, model construction, and assessment.
    • Develop a solid grasp of NumPy and its practical applications, encompassing array indexing and slicing techniques.
    • Elaborate on the null hypothesis and the alternative hypothesis within the context of hypothesis testing.
    • Examine various hypothesis tests, including the Z-test, T-test, and ANOVA.
    • Engage with pandas' core data structures, namely Series and DataFrame.
    • Leverage pandas for tasks.
    • Generate compelling visualizations using libraries like Matplotlib, Seaborn, Plotly, and Bokeh.
    • Scrutinize the machine learning pipeline and attain a thorough comprehension of the essential processes integral to machine learning operations
    • Investigate both supervised and unsupervised learning techniques.
    • Detect and generate correlation maps to explore the relationships between variables.
    • Enumerate various types of classification algorithms and grasp their specific practical applications.
    • Explore assorted ensemble modeling methods, such as bagging, boosting, and stacking.
    • Construct a recommendation system utilizing PyTorch.
    • Attain mastery in a range of visualization techniques,
    • Demonstrate adeptness in strategically using filters, parameters, and sets for effective data manipulation.
    • Become skilled in the utilization of special field types and Tableau-generated fields.
    • Acquire the knowledge and skills to craft diverse charts, interactive dashboards, and engaging story interfaces.
    • Develop proficiency in data blending, the creation of data extracts, and the efficient organization and formatting of data.
  • The Data Science Capstone project provides a valuable opportunity to apply the skills you have acquired during this Program. With the guidance of dedicated mentors, you will address a real-world data science problem aligned with industry standards. This comprehensive project covers data processing, model building, and reporting business results and insights. It is the final step in your learning journey and enables you to demonstrate your data science expertise to potential employers.

    • Acquire an understanding of mathematical concepts, variables, strings, vectors, factors, and operations involving vectors.
    • Develop a foundational grasp of arrays, matrices, lists, and data frames in R.
    • Gain insight into topics including conditions, loops, functions, objects, classes, and debugging within the R environment.
    • Learn how to effectively import and manipulate text, CSV, and Excel files, as well as write and store data objects in R.
    • Comprehend the manipulation of strings and date-related operations in R.
  • Attend an online interactive masterclass and get insights about advancements in technology/techniques in Data Science by Caltech

  • Attend this online interactive industry master class to gain insights about Data Science advancements

    • Comprehend the significance of business analytics and its role in the industry.
    • Gain a foundational understanding of Excel analytics functions and the utilization of conditional formatting.
    • Acquire the skills to analyze intricate data sets using pivot tables and slicers.
    • Apply statistical methodologies and principles, including moving averages, hypothesis testing, ANOVA, and regression, to data sets within Excel.
    • Depict your insights and results through the creation of charts and dashboards.
    • Acquire the knowledge and techniques to scrutinize data and derive valuable insights 
    • Craft interactive dashboards that facilitate data visualization
    • Enhance overall operational efficiency and effectiveness within your organization.
    • Learn the art of crafting dashboards from existing published reports.
    • Explore the utilization of Quick Insights to uncover valuable data patterns swiftly.
    • Acquire valuable troubleshooting techniques to address potential issues effectively.
    • Establish a foundational understanding of artificial intelligence and generative AI models.
    • Grasp the concept of explainable AI.
    • Implement effective prompt engineering methods to enhance performance.
    • Recognize and explore various applications and practical scenarios where ChatGPT can be employed.
    • Familiarize yourself with fine-tuning techniques to tailor and optimize ChatGPT models
    • Acknowledge the ethical dilemmas associated with generative AI models and ChatGPT.
    • Gain a perspective on the future of generative AI, including the challenges it may face as it evolves.

Skills Covered

  • Generative AI
  • Prompt Engineering
  • ChatGPT
  • Explainable AI
  • Conversational AI
  • Large Language Models
  • Exploratory Data Analysis
  • Descriptive Statistics
  • Inferential Statistics
  • Model Building and Fine Tuning
  • Supervised and Unsupervised Learning
  • Ensemble Learning
  • Data Visualization
  • Database management
  • Data Science

Tools Covered

ChatGPTDalle.2Mid-journeyNumPypandaspythonSciPyMatPlotlibMicrosoft ExcelMySQLFSDtableau

Industry Projects

  • Project 1

    Sales Analysis

    Utilize Python to analyze a clothing company’s sales data for Australia, and state by state help the company make data-driven decisions for the coming year.

  • Project 2

    Employee performance Analysis

    Create ML programs to understand different factors for employee turnover. Perform different machine learning techniques for performance, and suggest employee retention strategies.

  • Project 3

    Classification of Songs

    Perform exploratory data analysis and perform cluster analysis to create cohorts of songs. The classification would help to create an efficient recommendation system.

  • Project 4

    Creation of an Interactive Sales Dashboard

    Create an interactive Sales Dashboard for an apparel OEM in Tableau for the Sales department to use for ad-hoc analysis and reporting.

  • Project 5

    Crime Analysis with Tableau Dashboard

    Prepare a dashboard to keep the police department and the city updated on the statistics of crime events. You are required to create a dashboard/story using Tableau.

  • Project 6

    Marketing Strategies with Exploratory Data Analysis

    Perform exploratory data analysis and hypothesis testing. The goal is to gain a better understanding of the various factors that contribute to customer acquisition.

  • Project 7

    Develop Ecommerce app with Python

    Develop an e-commerce app on the Python platform that could categorize, add or remove items from the cart and support different payment options

  • Project 8

    Weather Prediction

    Create a classification model using ten years of rainfall data to predict the weather for different locations across Australia

  • Project 9

    Credit Card Fraud Analysis

    Utilize data science and machine learning methodologies. Identify fraudulent credit card transactions so that the end customers are not charged for items that they did not purchase

Disclaimer - The projects have been built leveraging real publicly available data-sets of the mentioned organizations.


Program Advisors

  • Rick Hefner

    Rick Hefner

    Caltech CTME, Program Director

    Dr. Rick Hefner serves as the Program Director for Caltech’s CTME, where he develops customized training programs for technology-driven organizations. He has over 40 years of experience in systems development and has served in academic, industrial, and research positions. 


Join the Data Science industry

Data Science and Artificial Intelligence are amongst the hottest fields of the 21st century that will impact all segments of daily life by 2025, from transport and logistics to healthcare and customer service.

Job Icon28%

Annual Job Growth By 2026

Source: Market Research Rpt
Job Icon11.5 M

Expected New Jobs For Data Science By 2026

Source: US bureau of Labor
Job Icon$86K - $157K

Average Annual Salary

Source: Glassdoor

Batch Profile

This Data Science program caters to working professionals across industries and backgrounds; the diversity of our students adds richness to class discussions and interactions.

  • The class consists of learners from excellent organizations and diverse industries
    Information Technology - 41%Consulting - 20%Pharma & Healthcare - 8%Manufacturing - 8%BFSI - 8%Others - 15%
    Tata Consultancy Services
    GE Aviation
    Ernst & Young

Admission Details

Application Process

The application process consists of three simple steps. An offer of admission will be made to the selected candidates and accepted by the candidates by paying the admission fee.


Submit Application

Tell us a bit about yourself and why you want to do this program


Application Review

An admission panel will shortlist candidates based on their application



Selected candidates can begin the program within 1-2 weeks

Eligibility Criteria

For admission to this Post Graduation in Data Science, candidates should have:

2+ years of work experience preferred
A bachelor's degree with an average of 50% or higher marks
Basic understanding of programming concepts and mathematics

Apply Now

Program Benefits

  • Program certificate & up to 14 CEUs from Caltech CTME
  • Exposure to ChatGPT, generative AI, Explainable AI and more
  • Masterclasses led by distinguished Caltech instructors
  • Gain access to Caltech CTME Circle Membership
  • Simplilearn’s Career Assistance post-program completion


  • What are the key learning outcomes of the course “Essentials of Generative AI, Prompt Engineering and ChatGPT”?

    This course will give you a holistic understanding of the essentials of generative AI and its landscape, prompt engineering, explainable AI, conversational AI, ChatGPT and other LLMs.

    The key learning objectives of this course are: 

    • Understand the fundamentals of generative AI models, including the working principles and various generative AI models.

    • Comprehend the concept of explainable AI, recognize its significance and identify different approaches to achieve explainability in AI systems.

    • Apply effective prompt engineering techniques to improve the performance and control the behavior of generative AI models.

    • Understand ChatGPT, including its working mechanisms, notable features and limitations.

    • Identify and explore diverse applications and use cases where ChatGPT can be leveraged.

    • Gain exposure to fine-tuning techniques to customize and optimize ChatGPT models 

    • Recognize the ethical challenges of generative AI models and ChatGPT to ensure responsible data usage, mitigate bias and prevent misuse. 

    • Understand the potential of generative AI to revolutionize industries and explore prominent generative AI tools. 

    • Gain insights into the future of generative AI, its challenges and the steps needed to unlock its full potential.

  • What are the key topics covered in the course “Essentials of Generative AI, Prompt Engineering and ChatGPT”?

    Some of the key topics covered in the course “Essentials of Generative AI, Prompt Engineering and ChatGPT” are: 

    • Generative AI and its Landscape 

    • Explainable AI 

    • Conversational AI 

    • Prompt Engineering 

    • Designing and Generating Effective Prompts 

    • Large Language Models 

    • ChatGPT and its Applications

    • Fine-tuning ChatGPT 

    • Ethical Considerations in Generative AI Models 

    • Responsible Data Usage and Privacy 

    • The Future of Generative AI 

    • AI Technologies for Innovation

  • What is Data Science?

    Data science is the domain that deals with vast volumes of data using modern tools and techniques to find unseen patterns, derive meaningful information, and make business decisions.

  • What are the top Data Science courses included in this program?

    This Data science program gives you a step-by-step guide to learning all the concepts from scratch. The entire syllabus is divided into the following courses:

    • R Programming for Data Science
    • Data Science with R
    • Python for Data Science
    • Data Science with Python
    • Machine Learning
    • Natural Language Processing
    • Tableau Training
    • Data Science Capstone

  • Disclaimer
  • PMP, PMI, PMBOK, CAPM, PgMP, PfMP, ACP, PBA, RMP, SP, and OPM3 are registered marks of the Project Management Institute, Inc.