In Descriptive Statistics and Data Visualization, you’ll learn to explore datasets with code and turn findings into targeted visuals that drive clear decisions. This is a skill-based path organized around real analyst job tasks. You’ll start with a quick check of what you already know, then focus on the skills you want to strengthen. Skip topics you’ve mastered and dive deeper where you need practice. Each lesson is curated from expert instructors so every step builds a concrete workplace skill.
Using Python (pandas, seaborn/matplotlib), you’ll perform exploratory data analysis (EDA), compute and interpret measures of central tendency and dispersion, summarize categorical variables with frequency analysis, and create exploratory charts. In Tableau, you’ll identify the right chart for a question and build comparison visuals that stack KPIs against targets and across business segments. By the end, you can explore data reproducibly, summarize results clearly, and design targeted visuals including skills that map to responsibilities in roles like Data Analyst, Business Intelligence Analyst, Reporting Analyst, and Operations Analyst.
Some points apply to every course of this kind; see how we rank.
Coursera is in Tier 4: commercial training companies and platform-made courses of our institution ranking (56/100). Made by the platform, often short guided projects.
MIT 18.650 Statistics for Applications, Fall 2016 View the complete course: http://ocw.mit.edu/18-650F16 Instructor: Philippe Rigollet This course offers an in-depth the theoretical foundations for statistical methods that are useful in many applications. The goal is to understand the role of mathe…
Harvard University · edX
Focusing on the basics of machine learning and embedded systems, such as smartphones, this course will introduce you to the “language” of TinyML.
Harvard University · edX
Learn the concepts and techniques that make up the foundation of data science and machine learning.
This is an introductory-level course in supervised learning, with a focus on regression and classification methods. The syllabus includes: linear and polynomial regression, logistic regression and linear discriminant analysis; cross-validation and the bootstrap, model selection and regularization m…
This is an introductory-level course in supervised learning, with a focus on regression and classification methods. The syllabus includes: linear and polynomial regression, logistic regression and linear discriminant analysis; cross-validation and the bootstrap, model selection and regularization m…
Harvard University · edX
An introduction to the intellectual enterprises of computer science and the art of programming.
