Generative AI: Elevate Your Data Science Career (Coursera)

Offered by IBM,
Generative AI: Elevate Your Data Science Career (Coursera)

Generative AI is now mainstream. Boost your career with a course that features leading-edge, in-demand, generative AI skills tuned to the needs of data scientists. This course is suitable for existing and aspiring data scientists, data professionals, analysts, and engineers. The course addresses real-world data science problems data scientists encounter—across multiple industries— with data generation, data augmentation, and feature engineering. Gain skills you can immediately put to use implementing generative AI models and techniques that address these real-world issues.

Class Deals by MOOC List - Click here and see Coursera's Active Discounts, Deals, and Promo Codes.

Then, learn how to use generative AI to speed data visualizations, build models and to produce data insights. You’ll also learn about key ethics considerations around generative AI and data, key concerns for executives across industries.
Demonstrate your new generative AI skills in a hands-on data augmentation and feature engineering project that you can apply in your real-life profession.
Then complete your final quiz to earn your certificate. You can share both your project and certificate with your current or prospective employers.
This course is part of the Generative AI for Data Scientists Specialization.

What you'll learn

  • Leverage generative AI tools, like GPT 3.5, ChatCSV, and tomat.ai, available to Data Scientists for querying and preparing data
  • Examine real-world scenarios where generative AI can enhance data science workflows
  • Practice generative AI skills in hand-on labs and projects by generating and augmenting datasets for specific use cases
  • Apply generative AI techniques in the development and refinement of machine learning models

Syllabus

Data Science and Generative AI
In this module, you will explore the role of generative AI in data science. Lesson 1 introduces you to generative AI and how it can serve various purposes in the hands of data scientists. You will learn about the four common types of generative AI models and their impact and applications across diverse industries. Lesson 2 will cover how data scientists can leverage generative AI in the data science lifecycle. You will learn how data scientists can effectively use generative AI to perform data generation, data preparation, data querying, and data augmentation. You will also learn about data preparation and querying challenges and how generative AI models can help tackle these challenges.

Use of Generative AI for Data Science
In this module, you will explore the role of generative AI in data science. Lesson 1 will cover generative AI for understanding data and model building. You will learn how data scientists can use generative AI to visualize, develop, and build models. Lesson 2 will cover the use of generative AI for data science regarding tools and techniques to help in exploratory data analysis (EDA) and develop a predictive model. You will learn about the industry-specific considerations while using generative AI and the challenges data scientists face. You will also learn about the skills data scientists require to succeed in their field and how generative AI can help them hone those skills in today’s world.

Final Project and Exam
Enhance your data science with generative AI and complete the guided project and evaluation.

Go to Class
MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Related Courses

Principles of fMRI 2 (Coursera) Coursera
Johns Hopkins University,University of Colorado Boulder

Principles of fMRI 2 (Coursera)

Functional Magnetic Resonance Imaging (fMRI) is the most widely used technique for investigating the living, functioning human brain as people perform tasks and experience mental states. It is a convergence point for multidisciplinary work from many disciplines. Psychologists, statisticians, physicists, computer scientists, neuroscientists, medical researchers, behavioral scientists, engineers, public health researchers, biologists, and others are coming together to advance our understanding of the human mind and brain. This course covers the analysis of Functional Magnetic Resonance Imaging (fMRI) data.

Nov 16th 2026
4 Weeks
Assessing Health Program Delivery (Coursera) Coursera
Johns Hopkins University

Assessing Health Program Delivery (Coursera)

This course provides in-depth knowledge about implementation strength, quality of care, and service utilization, which are essential components of health program delivery. This course is primarily aimed at implementers, managers, funders, and evaluators of health programs in low- and middle-income settings (LMISs) targeting women and children, and undergraduate and graduate students in health-related fields.

Nov 16th 2026
5-12 Weeks
Introduction to Machine Learning (Coursera) Coursera
Duke University

Introduction to Machine Learning (Coursera)

This course will provide you a foundational understanding of machine learning models (logistic regression, multilayer perceptrons, convolutional neural networks, natural language processing, etc.) as well as demonstrate how these models can solve complex problems in a variety of industries, from medical diagnostics to image recognition to text prediction.

Nov 16th 2026
5-12 Weeks
Advanced Linear Models for Data Science 1: Least Squares (Coursera) Coursera
Johns Hopkins University

Advanced Linear Models for Data Science 1: Least Squares (Coursera)

Welcome to the Advanced Linear Models for Data Science Class 1: Least Squares. This class is an introduction to least squares from a linear algebraic and mathematical perspective. Before beginning the class make sure that you have the following: a basic understanding of linear algebra and multivariate calculus; a basic understanding of statistics and regression models; at least a little familiarity with proof based mathematics; basic knowledge of the R programming language.

Nov 16th 2026
5-12 Weeks
Data Science for Business Innovation (Coursera) Coursera
Politecnico di Milano,EIT Digital

Data Science for Business Innovation (Coursera)

The course is a compendium of the must-have expertise in data science for executive and middle-management to foster data-driven innovation. It consists of introductory lectures spanning big data, machine learning, data valorization and communication. Topics cover the essential concepts and intuitions on data needs, data analysis, machine learning methods, respective pros and cons, and practical applicability issues.

Nov 16th 2026
4 Weeks
Bioinformatic Methods I (Coursera) Coursera
University of Toronto

Bioinformatic Methods I (Coursera)

Large-scale biology projects such as the sequencing of the human genome and gene expression surveys using RNA-seq, microarrays and other technologies have created a wealth of data for biologists. However, the challenge facing scientists is analyzing and even accessing these data to extract useful information pertaining to the system being studied. This course focuses on employing existing bioinformatic resources – mainly web-based programs and databases – to access the wealth of data to answer questions relevant to the average biologist, and is highly hands-on.

Nov 16th 2026
5-12 Weeks
Case studies in business analytics with ACCENTURE (Coursera) Coursera
ESSEC Business School

Case studies in business analytics with ACCENTURE (Coursera)

This course is RESTRICTED TO LEARNERS ENROLLED IN Strategic Business Analytics SPECIALIZATION as a preparation to the capstone project. During the first two MOOCs, we focused on specific techniques for specific applications. Instead, with this third MOOC, we provide you with different examples to open your mind to different applications from different industries and sectors. The objective is to give you an helicopter overview on what's happening in this field. You will see how the tools presented in the two previous courses of the Specialization are used in real life projects.

Nov 16th 2026
3 Weeks
A Scientific Approach to Innovation Management (Coursera) Coursera
Università Bocconi

A Scientific Approach to Innovation Management (Coursera)

How can innovators understand if their idea is worth developing and pursuing? In this course, we lay out a systematic process to make strategic decisions about innovative product or services that will help entrepreneurs, managers and innovators to avoid common pitfalls. We teach students to assess the feasibility of an innovative idea through problem-framing techniques and rigorous data analysis labelled ‘a scientific approach’.

Nov 16th 2026
5-12 Weeks
AI-Driven Attribution Testing (Coursera) Coursera
Board Infinity

AI-Driven Attribution Testing (Coursera)

Welcome to AI-Driven Attribution Testing course an engaging and comprehensive course designed to guide you through the fundamental concepts and practical applications of attribution testing powered by artificial intelligence. This course is most suitable for marketers, data analysts, data scientists, and business leaders who aim to leverage data-driven insights for decision-making. It's also beneficial for students and professionals with a keen interest in the convergence of AI, data analysis, and marketing.

Nov 16th 2026
2 Weeks
ChatGPT Prompt Engineering for Developers (Coursera) Coursera
DeepLearning.AI

ChatGPT Prompt Engineering for Developers (Coursera)

Go beyond the chat box. Use API access to leverage LLMs into your own applications, and learn to build a custom chatbot. In ChatGPT Prompt Engineering for Developers, you will learn how to use a large language model (LLM) to quickly build new and powerful applications. Using the OpenAI API, you’ll be able to quickly build capabilities that learn to innovate and create value in ways that were cost-prohibitive, highly technical, or simply impossible before now.

Nov 16th 2026
1 Week
Precision Medicine (Coursera) Coursera
University of Geneva

Precision Medicine (Coursera)

This course will provide you with the key knowledge and tools to understand the fundamentals and practical implications of precision medicine, its opportunities and challenges. It will address precision-medicine era diagnostics, treatment selection, genetic counseling, public health interventions, and biomedical research. It will also deal with data science and ethical issues. From genomic analysis and genetic counseling to cancer biomarkers, from risk assessment of chronic diseases to the understanding of gene-environment interactions, from pharmacogenomics to multi-omics data integration, experts will walk you through the many aspects of precision medicine, from the bench to the bedside and to population health.

Nov 16th 2026
5-12 Weeks