Introduction to Data Analytics (Coursera)

Offered by Meta,
Introduction to Data Analytics (Coursera)

This course provides a practical understanding and framework for basic analytics tasks, including data extraction, cleaning, manipulation, and analysis. It introduces the OSEMN cycle for managing analytics projects and you'll examine real-world examples of how companies use data insights to improve decision-making.

Class Deals by MOOC List - Click here and see Coursera's Active Discounts, Deals, and Promo Codes.

By the end of this course you will be able to:
• Formulate business goals, KPIs and associated metrics
• Apply a data analysis process using the OSEMN framework
• Identify and define the relevant data to be collected for marketing
• Compare and contrast various data formats and their applications across different scenarios
• Identify data gaps and articulate the strengths and weaknesses of collected data
You don't need marketing or data analysis experience, but should have basic internet navigation skills and be eager to participate. Ideally you have already completed course 1: Marketing Analytics Foundationin this program.
This course is part of the Meta Marketing Analytics Professional Certificate.

What You Will Learn

  • How to apply the data analysis process OSEMN to marketing data

Syllabus

Working with Data
This week, you will learn what data analytics are and what a data analyst does. You’ll be introduced to the OSEMN framework as well as important business metrics, KPIs and their value to a business.

Obtaining and Scrubbing Data
In the second week you will learn how to discover different sources of data and how to evaluate their validity. You will also explore different data formats. You’ll begin to apply the OSEMN framework by learning the steps in the data cleaning process as well as how to handle missing or incorrect data in your datasets.

Exploring and Modeling Data
This week moves onto the Exploring and Modeling phases of OSEMN. You will learn how to inspect and summarize your data as well as evaluate data relationships. You will discover the purpose of data modeling and common types of data models and data visualizations.

Interpreting Data
This week you will learn how to interpret the data you have working with and relate the results of your analysis back to a specific business goal. You will also learn how to create a story for a presentation of your data in order to explain and engage an audience.

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

Related Courses

Machine Learning: Concepts and Applications (Coursera) Coursera
University of Chicago

Machine Learning: Concepts and Applications (Coursera)

This course gives you a comprehensive introduction to both the theory and practice of machine learning. You will learn to use Python along with industry-standard libraries and tools, including Pandas, Scikit-learn, and Tensorflow, to ingest, explore, and prepare data for modeling and then train and evaluate models using a wide variety of techniques. Those techniques include linear regression with ordinary least squares, logistic regression, support vector machines, decision trees and ensembles, clustering, principal component analysis, hidden Markov models, and deep learning.

Oct 19th 2026
5-12 Weeks
Problem Solving, Python Programming, and Video Games (Coursera) Coursera
University of Alberta

Problem Solving, Python Programming, and Video Games (Coursera)

This course is an introduction to computer science and programming in Python. Important computer science concepts such as problem solving (computational thinking), problem decomposition, algorithms, abstraction, and software quality are emphasized throughout. The Python programming language and video games are used to demonstrate computer science concepts in a concrete and fun manner. However, a learner can take the knowledge and skills from this course and apply them to non-game problems, other programming languages, and other computer science courses.

Oct 19th 2026
5-12 Weeks
Empathy, Data, and Risk (Coursera) Coursera
University of Illinois at Urbana-Champaign

Empathy, Data, and Risk (Coursera)

Risk Management and Innovation develops your ability to conduct empathy-driven and data-driven analysis in the domain of risk management. This course introduces empathy as a professional competency. It explains the psychological processes that inhibit empathy-building and the processes that determine how organizational stakeholders respond to risk.

Oct 26th 2026
4 Weeks
Using Databases with Python (Coursera) Coursera
University of Michigan

Using Databases with Python (Coursera)

This course will introduce students to the basics of the Structured Query Language (SQL) as well as basic database design for storing data as part of a multi-step data gathering, analysis, and processing effort. The course will use SQLite3 as its database. We will also build web crawlers and multi-step data gathering and visualization processes. We will use the D3.js library to do basic data visualization.

Oct 19th 2026
5-12 Weeks
Machine Learning for Accounting with Python (Coursera) Coursera
University of Illinois at Urbana-Champaign

Machine Learning for Accounting with Python (Coursera)

This course, Machine Learning for Accounting with Python, introduces machine learning algorithms (models) and their applications in accounting problems. It covers classification, regression, clustering, text analysis, time series analysis. It also discusses model evaluation and model optimization. This course provides an entry point for students to be able to apply proper machine learning models on business related datasets with Python to solve various problems.

Oct 19th 2026
5-12 Weeks
Applied Social Network Analysis in Python (Coursera) Coursera
University of Michigan

Applied Social Network Analysis in Python (Coursera)

This course will introduce the learner to network analysis through the NetworkX library. The course begins with an understanding of what network analysis is and motivations for why we might model phenomena as networks. The second week introduces the concept of connectivity and network robustness.. The third week will explore ways of measuring the importance or centrality of a node in a network. The final week will explore the evolution of networks over time and cover models of network generation and the link prediction problem.

Oct 26th 2026
4 Weeks
Laboratório de Programação Orientada a Objetos - Parte 1 (Coursera) Coursera
Universidade de São Paulo, Brasil

Laboratório de Programação Orientada a Objetos - Parte 1 (Coursera)

Este curso apresenta os conceitos mais importantes em torno do paradigma de desenvolvimento mais comum da indústria de software hoje: a Programação Orientação a Objetos (POO). Oferecido pelo Departamento de Ciência da Computação do Instituto de Matemática e Estatística da USP, o curso é voltado para quem já conhece os conceitos básicos de POO e quer se aprofundar no assunto, tornando-se um excelente programador. Ele funciona bem como uma sequência natural aos 2 cursos anteriores do Prof. Fabio Kon do IME-USP no coursera: Introdução à Ciência da Computação com Python.

Oct 19th 2026
5-12 Weeks
Learn SQL with Databricks (Coursera) Coursera
Edureka

Learn SQL with Databricks (Coursera)

Welcome to the Learn SQL with Databricks course, where you'll embark on a journey to acquire essential skills in database management, data analysis, and advanced data manipulation techniques. This course is meticulously designed to guide you through the intricacies of SQL, leveraging the powerful and versatile Databricks platform.

Oct 26th 2026
5-12 Weeks
Analyzing and Visualizing Data in Looker (Coursera) Coursera
Google Cloud

Analyzing and Visualizing Data in Looker (Coursera)

In this course, you learn how to do the kind of data exploration and analysis in Looker that would formerly be done primarily by SQL developers or analysts. Upon completion of this course, you will be able to leverage Looker's modern analytics platform to find and explore relevant content in your organization’s Looker instance, ask questions of your data, create new metrics as needed, and build and share visualizations and dashboards to facilitate data-driven decision making.

Oct 26th 2026
2 Weeks