Other Providers

Intro to Python for Data Science (Springboard)

Offered by Springboard,
Intro to Python for Data Science (Springboard)

Work with a real-world dataset and build foundational data science skills. Build foundational data science skills by working through a real-world case study using a real data set from Yelp. This self-paced course is designed for people with some experience programming in Python, but who want to learn more about using libraries such as pandas for data science work.

After installing a fully featured distribution of Python, you'll learn key aspects of Jupyter notebooks and pandas before tackling a realistic business problem using a Yelp data set. Upon completion, you will have learned how to:

  • Approach a data set with a business problem in mind
  • Use common data science tools
  • Load data and transform it for analysis
  • Explore data through use of plots and statistics
  • Present findings in the most relevant way

In short, you will have built the foundation of skills required by any professional data scientist.

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

Related Courses

Understanding China, 1700-2000: A Data Analytic Approach, Part 2 (Coursera) Coursera
The Hong Kong University of Science and Technology - HKUST

Understanding China, 1700-2000: A Data Analytic Approach, Part 2 (Coursera)

Statistics & Data Analysis Data Science

The purpose of this course is to summarize new directions in Chinese history and social science produced by the creation and analysis of big historical datasets based on newly opened Chinese archival holdings, and to organize this knowledge in a framework that encourages learning about China in comparative perspective. Our course demonstrates how a new scholarship of discovery is redefining what is singular about modern China and modern Chinese history.

Jun 8th 2026
4 Weeks
History China Data Science
Details
Regression Models (Coursera) Coursera
Johns Hopkins University

Regression Models (Coursera)

Statistics & Data Analysis Data Science

Linear models, as their name implies, relates an outcome to a set of predictors of interest using linear assumptions. Regression models, a subset of linear models, are the most important statistical analysis tool in a data scientist’s toolkit. This course covers regression analysis, least squares and inference using regression models.

Jun 8th 2026
4 Weeks
Statistics Regression Linear Regression
Details
Machine Learning Foundations: A Case Study Approach (Coursera) Coursera
University of Washington

Machine Learning Foundations: A Case Study Approach (Coursera)

Statistics & Data Analysis Data Science

Do you have data and wonder what it can tell you? Do you need a deeper understanding of the core ways in which machine learning can improve your business? Do you want to be able to converse with specialists about anything from regression and classification to deep learning and recommender systems? In this course, you will get hands-on experience with machine learning from a series of practical case-studies.

Jun 8th 2026
5-12 Weeks
Python Machine Learning Clustering
Details
The Data Scientist's Toolbox (Coursera) Coursera
Johns Hopkins University

The Data Scientist's Toolbox (Coursera)

Statistics & Data Analysis Data Science

In this course you will get an introduction to the main tools and ideas in the data scientist's toolbox. The course gives an overview of the data, questions, and tools that data analysts and data scientists work with. There are two components to this course. The first is a conceptual introduction to the ideas behind turning data into actionable knowledge. The second is a practical introduction to the tools that will be used in the program like version control, markdown, git, GitHub, R, and RStudio.

Jun 8th 2026
4 Weeks
Data Github Data Analysis
Details
Software Engineering Virtual Experience (JP Morgan) Other Providers
JP Morgan

Software Engineering Virtual Experience (JP Morgan)

CS: Software Engineering

JPMorgan Chase has traders in all the major financial centers and creates a marketplace for asset classes around the globe for our investor clients. You’re going to assist with some development of an alternative way to visualize and analyze share price data for our traders – this will alert them of potential trading opportunities.

Self Paced
Self-Paced
Programming Python Software Engineering
Details
Communicating Data Science Results (Coursera) Coursera
University of Washington

Communicating Data Science Results (Coursera)

Statistics & Data Analysis Data Science

Making predictions is not enough! Effective data scientists know how to explain and interpret their results, and communicate findings accurately to stakeholders to inform business decisions. Visualization is the field of research in computer science that studies effective communication of quantitative results by linking perception, cognition, and algorithms to exploit the enormous bandwidth of the human visual cortex. In this course you will learn to recognize, design, and use effective visualizations.

Jun 8th 2026
3 Weeks
Ethics Cloud Computing Privacy
Details
Statistical Inference (Coursera) Coursera
Johns Hopkins University

Statistical Inference (Coursera)

Statistics & Data Analysis Data Science

Statistical inference is the process of drawing conclusions about populations or scientific truths from data. There are many modes of performing inference including statistical modeling, data oriented strategies and explicit use of designs and randomization in analyses. Furthermore, there are broad theories (frequentists, Bayesian, likelihood, design based, …) and numerous complexities (missing data, observed and unobserved confounding, biases) for performing inference.

Jun 8th 2026
4 Weeks
Statistics Probability Data Analysis
Details
Practical Predictive Analytics: Models and Methods (Coursera) Coursera
University of Washington

Practical Predictive Analytics: Models and Methods (Coursera)

Statistics & Data Analysis Data Science

Statistical experiment design and analytics are at the heart of data science. In this course you will design statistical experiments and analyze the results using modern methods. You will also explore the common pitfalls in interpreting statistical arguments, especially those associated with big data. Collectively, this course will help you internalize a core set of practical and effective machine learning methods and concepts, and apply them to solve some real world problems.

Jun 8th 2026
4 Weeks
Machine Learning Models Methods
Details
Introduction to Machine Learning (Coursera) Coursera
Duke University

Introduction to Machine Learning (Coursera)

Data Science

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.

Jun 12th 2026
5-12 Weeks
ML NLP Machine Learning
Details
Predictive Modeling and Analytics (Coursera) Coursera
University of Colorado Boulder

Predictive Modeling and Analytics (Coursera)

Statistics & Data Analysis Data Science

Welcome to the second course in the Data Analytics for Business specialization! This course will introduce you to some of the most widely used predictive modeling techniques and their core principles. By taking this course, you will form a solid foundation of predictive analytics, which refers to tools and techniques for building statistical or machine learning models to make predictions based on data. You will learn how to carry out exploratory data analysis to gain insights and prepare data for predictive modeling, an essential skill valued in the business.

Jun 8th 2026
4 Weeks
Data Analysis Analytics Data Cleaning
Details