Certifications Guide: Working with Data

Learn about data science and machine learning, get hands-on experience with tools like SQL, Python, JupyterLab, and RStudio, for solving data problems.

Learn to analyze and interpret business data using Excel. Covers data cleaning, formulas, pivot tables, and basic visualizations to support decision-making and business insights.

Learn the basics of statistics and how to use R programming to explore, organize, and analyze data.

Who owns data? How do we value privacy? These and other important ethical considerations are at the core of this course. Explore the impact that data science has on modern society and examine the ethical and privacy implications of collecting and managing big data.

Learn how to use linear regression to analyze business data, interpret results, and make data-driven decisions, using Excel and real-world examples.

Develops core skills in business data analysis. Covers descriptive statistics, probability, hypothesis testing, and regression analysis, equipping students with practical tools for data-driven decision-making.

Get started with linear regression models with this Duke University course. Learn about the underlying theory, as well as how to fit, examine, and utilize regression models to analyze the relationship between multiple variables. Apply what you learn to data examples using R and RStudio.

Learn how to use Python for data science. This course will cover the basics of the Python programming environment, fundamental Python programming, and data manipulation and cleaning techniques in order to teach you how to use Python to clean tabular data, manipulate tabular data, and run basic inferential statistical analyses.

This beginner level course serves as an introduction to sampling and exploring data, basic probability theory, and Bayes rule. Explore various types of sampling methods and learn exploratory data analysis techniques such as numeric summary statistics and basic data visualization. In addition, you will also learn to use R and RStudio for lab exercises and a final project.

Learn how to use relational databases in business analysis with this introductory course. You will learn how relational databases work, how to use entity-relationship diagrams, and understand how to collect data in different business contexts. This course will ultimately equip you with the skills you need to properly collect data and gain insights into the best ways to improve business performance, as well provide you with hands on practice with real databases and a portfolio you can show future employers.

Learn to analyze data and build models using Python tools like pandas, Matplotlib, and scikit-learn, with hands-on projects.

Move beyond the basic of programming in Python and explore the built-in core data structures such as lists, dictionaries and tuples for data analysis. This is the follow up course to Programming for Everybody.

Learn how to harness data to drive success with this introduction to data visualization. The course will first ask you to consider what data is and what questions can be answered using data, and then moves into how to develop a research question describe variables and relationships between variables, basic statistics, and how to present results. Lastly you will learn how to use SAS or Python to manage and visualize your data.

This intensive certification program covers how create data visualizations in D3, the JSON APIs and Ajax challenges, and data visualization projects to help you practice your skills.

Explore how to use Python for statistics and learn about study design, data management, and data visualization. In this course you will learn to determine different types of data, how to visualize, analyze and interpret summaries for univariate and multivariate data, and probability and non-probability sampling. Apply what you learn in Python each week in hands-on, lab-based sessions.

A beginner-friendly program by Google covering data cleaning, analysis, and visualization using spreadsheets, SQL, R, and Tableau. Includes hands-on projects and a shareable certificate, preparing learners for entry-level data analyst roles.

Advance your Python skills and learn to apply them to data analysis. In this course you will learn how to install external packages for Python, acquire data sources, and clean, process, analyze, and visualize data.

Learn how to organize, study, and present data using tools like Excel, Python, and SQL, with step-by-step practice projects.

Learn to analyze and present data effectively. Covers key topics like data entry, formulas, charts, and basic automation.

Develop skills in analyzing business data, uncovering insights, and applying them to solve real business problems.