Coursera - Meta Data Analyst Professional Certificate

Launch your career in data analytics. Build job-ready skills – and must-have AI skills – for an in-demand career. Earn a credential from Meta in 5 months or less. No degree or prior experience required.

What you'll learn
Collect, clean, sort, evaluate, and visualize data

Apply the OSEMN, framework to guide the data analysis process, ensuring a comprehensive and structured approach to deriving actionable insights

Use statistical analysis, including hypothesis testing, regression analysis, and more, to make data-driven decisions

Develop an understanding of the foundational principles of effective data management and usability of data assets within organizational context

Skills you'll gain
SQL
Pandas
Generative AI in Data Analytics
Data Analysis
Python Programming

Prepare for a career in the high-growth field of data analytics. In this program, you'll build in-demand technical skills like Python, Statistics, and SQL in spreadsheets to get job-ready in 5 months or less, no prior experience needed. You'll also have the option to learn how generative AI tools and techniques are used in data analytics.

Data analysis involves collecting, processing, and analyzing data to extract insights that can inform decision-making and strategy across an organization.

In this program, you'll learn basic data analysis principles, how data informs decisions, and how to apply the OSEMN framework to approach common analytics questions. You'll also learn how to use essential tools like SQL, Python, and Tableau to collect, connect, visualize, and analyze relevant data.

You'll learn how to apply common statistical methods to writing hypotheses through project scenarios to gain practical experience with designing experiments and analyzing results.

When you complete this full program, you'll have a portfolio of hands-on projects and a Professional Certificate from Meta to showcase your expertise.

Applied Learning Project

Throughout the program, you'll get to practice your new data analysis skills through hands-on projects including:

Identifying data sources

Using spreadsheets to clean and filter data

Using Python to sort and explore data

Using Tableau to visualize results

Using statistical analyses

By the end, you'll have a professional portfolio that you can show to prospective employers or utilize for your own business.

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