Build job-relevant data analysis skills using Python, R, and Microsoft tools in this beginner-friendly specialization. You'll learn how to collect, clean, analyze, visualize, and communicate data insights using two of the most popular programming languages in analytics.
The program starts with Python programming fundamentals, then moves into data analysis, visualization, generative AI, and machine learning basics. You’ll also learn R programming, tidyverse workflows, data cleaning, and exploratory data analysis using real-world datasets.
Through guided labs and practical projects, you’ll use tools such as Jupyter Notebook, Visual Studio Code, GitHub Copilot, pandas, Matplotlib, ggplot2, Scikit-learn, and GitHub.
By the end of the specialization, you’ll be able to clean messy datasets, create dashboards and charts, automate analysis workflows, apply beginner machine learning models, and communicate findings clearly.
This specialization is ideal for aspiring data analysts, business professionals, career changers, and learners who want practical analytics skills without prior coding experience.
Applied Learning Project
Throughout the specialization, learners complete hands-on projects that simulate real workplace analytics tasks. Projects include cleaning raw datasets, building Python data workflows, creating charts and dashboards, reshaping data in R, analyzing healthcare and retail datasets, and building beginner machine learning models.
Learners also complete capstone-style projects using GitHub and Microsoft development tools to document work and showcase completed analysis. These projects help build portfolio-ready evidence of skills for interviews or career growth.
